#1 Localization of a Product

Foreign businesses cannot make the mistake of assuming that because they have experienced success in other countries that the same approach will automatically work in China. Creating a local version of a product often involves many distinct steps, such as translating the language on the product’s labeling and considering the various markets within China.

It is important to start closer to a beginner mindset when emerging into the Chinese market and not make assumptions about previous use by consumers in other regions.

You may have to consider how the local Chinese market may respond to your product or service and what objections they might have to it. Additionally, you may need to consider the different geographic regions in China and whether there are certain areas that will be more responsive to your product or service than others.

You may also need to consider if the timing of your entry into the Chinese market is optimal. You might be too early or too late into the market. Consider using effective market research offered by Horizons to test your product early and affordably.

You must learn about problems consumers in China are having and develop solutions to address them. Creating customized protypes will show that you value the experience of the Chinese user. Talking to potential customers and learning from their experience can give you great insight into the market.

#2 Marketing Strategy for China

Once you have conducted some market research and determined that your product might fit the local market, you should then begin adapting your market strategy to the Chinese. You will need to consider the significant cultural differences in China in comparison to the other cultures where you have previously launched your business. Avoid having a minimal return on investment by not staying loyal to a proven marketing strategy that worked in the west.

You will need to adapt your social media strategy to local channels and make key changes in your marketing plan so that you bring in the Chinese market and do not alienate potential customers. For example, since Facebook doesn’t work in China, you may need to spend more time on WeChat, a social media platform and corporate marketing base.

Companies may garner more success when they publish product catalogues, share interesting content and promote events on this platform. Having a strong social presence and being actively engaged is expected in the Chinese market.

You may need to learn about how to use the Weibo wholesale platform to connect with local customers.Another effective strategy is to observe your competitors’ marketing strategies and see how they are successful. You can also test different marketing strategies by focusing on certain channels and then measure their return.

It is also important to market your product in a way that demonstrates cultural awareness. Collectivism is usually more important in China and eastern cultures than individualism. Some activities may be more socially inherent to these consumers, such as shopping online.

Your marketing strategy should be adapted to resonate with the local market in China. We can provide strategic consulting that is based on our intimate knowledge of the Chinese market and culture to help you better match your product or service to your eastern companies.

#3 Technical Issues

Western cultures that are accustomed to Googling everything and having easy access to information may be surprised to know that China’s Great Firewall actively blocks a number of important websites. This site provides a list of blocked websites in China, including the following:

Google

Gmail

Facebook

Twitter

Dropbox

Slideshare

Google Drive

iStockPhoto

New York Times

Bloomberg

YouTube

WordPress.com

Google Wallet

Google Chrome

Microsoft OneDrive

Google Translate

Therefore, if you come to China and plan on doing work here, you should be prepared to use different programs and not to have access to some of the tools you might take for granted.

Additionally, you may experience problems when attempting to use APIs, SDKs or other plug-in services from abroad. Push messaging services, map services and other standard tools might also create problems.

It is not uncommon in China for businesses who have went through the process of hosting their site on a whitelisted IP to experience unexpected slow down, finding their site is inaccessible or their system becoming unreliable.Additionally, it may be difficult to obtain the type of license you need.

Foreign businesses can avoid some of these problems by hosting their server in China and building global and local solutions into their technological infrastructure.

#4 Local Platforms

Many businesses have apps or platforms that they are used to using to sell their product or service. However, operating in China may require you to adapt to local platforms instead, even if you are not familiar with them. You will want to connect with customers on their level and on the platforms that they are used to using.

For this reason, online sellers might want to sell off popular channels like Taobao, TMall or Alibaba stores. These are the channels that locals are most familiar with, so it will be much easier and cheaper to try to appeal to them on these channels rather than luring them to your individual website.

Similarly, businesses that use mobile apps may need to create a WeChat application to reach their customer base and use this as their primary way to engage with potential customers. Many international mobile applications are not widely used by the Chinese market and many of these are quickly abandoned.

Chinese customers often prefer using WeChat for many of their service purchases and using payment solutions through this platform.

#5 Local Partners

Many foreign businesses decide to work with a local partner to have better access to local networks and connections, as well as a better understanding of the language and culture. They may create full joint ventures with these domestic companies. This arrangement can help you have more success and have a trusted partner who can assist you with the common challenges associated with launching a business in this massive market. It could also allow you to have access to more equitable options.

Working with a local partner can also help you use an ICP license, which is very restricted. This allows you greater access where you need it most.

#6 Local Employees

Not all business entities in China are allowed to hire local employees. Working with a local partner can give you access to hiring from the local talent pool.

However, if you do not want to have a formal arrangement with a domestic corporation in China, another option is to use the PEO services of a recognized employer-of-record like Horizons.

We have access to the greatest talent throughout the world and can help you find the perfect members to add to your team, including operational employees, management and others. We can also help you navigate the immigration system and assist with visa processing so that you can complete this process seamlessly.

Once your employees are in place, we serve as the employer-of-record and are responsible for all compliance measures and reporting to local and national government.

#7 Business Cycles

It is also important to learn how to measure the success of your business in shorter cycles.

China is a vast country that prides itself on its ability to quickly innovate and be successful. Therefore, many businesses must enter the market at lightning speed. It is also important to gain the first-to-market advantage since knockoffs are quite common and competition will quickly emerge.

These factors will require you to continuously evaluate your product or service to determine if it is still a good fit for the market. If it is not, you will need to be able to quickly pivot and make necessary changes to remain relevant.

Contact Horizons

Horizons can assist you with every aspect of launching your business in China, from conducting market research, providing strategic consulting, helping with business formation, visa assistance, legal assistance and handling all HR and payroll responsibilities.

Contact us today to find out more about how we can be your strategic partner in your expansion.

Measure

Measure

Key Takeaways

1. China has become a leading country globally in business processing, manufacturing, and Information Technology (IT) outsourcing opportunities for foreign companies. 

2. Education is highly valued in Chinese culture — this means as an outsourcing destination, foreign companies will have access to an immense pool of highly educated individuals with specialized skills to meet current and future market demands.  

3. One of the top benefits of outsourcing to China is cost saving, while key disadvantages can be related to quality control and Intellectual Property (IP) concerns.

4. There are a several issues related to compliance that need to be overcome when outsourcing to China – A Global PEO can help mitigate any compliance challenges, ensuring a smooth transition into the Chinese market.

China has advanced to be one of the world’s leading outsourcing nations — expanding as more than a manufacturing hub to catering in outsourcing opportunities across other capacities – such as business process and IT outsourcing.

As the most populated country in the world, China presents a unique opportunity to foreign companies to increase their access to a vast pool of talent, skills, services, and entry into neighbouring markets. Not only can this cut costs and maximise profits but improve the overall efficiency in business operations – ultimately setting them apart within their internal markets.

This article will highlight China as an outsourcing destination and discuss the top benefits and disadvantages companies need to consider when choosing to outsource to China in any capacity.

What is the definition of outsourcing?

In business terms, the practice of outsourcing is when a company looks to hire a third-party to perform duties, create goods, support operations, or provide a service to the company. Some examples of a third-party source is an individual with specialised skills (think consulting), an entire department (IT, HR, legal or accounting), a software solution (SaaS or PaaS), or a specific service (data entry, content creation, marketing etc.). Companies choose to outsource to lower costs and improve efficiencies and gain a strategic competitive advantage. A company can outsource onshore, offshore or to neighbouring countries.

What types of business activity can be outsourced to China?

China is constantly expanding its aptitude for outsourcing opportunities in business processing, manufacturing, and IT outsourcing. Below will be a brief rundown for each of these categories and highlight some common business activities associated with each that can be outsourced to China.

Business process outsourcing to China

In recent years, the Chinese market share for providing business process outsourcing (BPO) services to foreign companies has seen exponential growth. BPO services that are commonly outsourced to China include both front and back-office processes. The top types of BPO services include accounting processes such as payroll solutions; administrative tasks such as data entry; customer experience through contact centres; procurement services; digital marketing; content creation and travel reservations and itinerary bookings..

Manufacturing outsourcing to China

China is the world’s largest manufacturing nation, cultivating it’s leading position since the 1950’s with major industrial reforms. This makes China a goldmine to foreign companies if seeking to outsource all or part of their manufacturing needs. Even some of the biggest consumer-oriented multinational companies such as Apple and Tesla have chosen to expand or completely base their manufacturing facilities in China. The most common products that can be outsourced to China include electronics, clothing and textiles, shoe manufacturing, furniture, and plastic products.

IT outsourcing to China

The drive towards digital transformation globally has enabled China to create a solid foundation to build and provide IT outsourcing opportunities. In-house software development is renowned for being a costly undertaking for any business, so being able to acquire high-quality solutions at a fraction of the cost makes China a lucrative choice with foreign companies. As with its ever-growing market of  innovative local software solutions, companies can also outsource database development, web development, virtual helpdesks, application support and management services across all these avenues.  

What are the benefits of outsourcing to China?

Below will be a brief synopsis of the top three benefits of outsourcing to China.

Cost Saving

The undisputable principal reason and benefit for companies to outsource to China is that its cost effective across several avenues. China presents numerous opportunities to save cash and maximise profits, while presenting ample access to an abundance of local talent across many industries. There is also the benefit of lower salary caps for basic or specialised skillsets and cheap access to raw materials and manufactured goods.

Additionally, many mundane and repetitive business processes such as employee onboarding or purchase order processing – once tasks that could only be completed through manual labour – have been transformed into autonomous and effective technological solutions. China presents copious opportunities to adopt locally developed software in an increasing array of business processing tasks. This presents companies with a unique opportunity to eliminate all associated human labour costs either partly or in their entirety.

Access to a massive, growing consumer market

As the most populated country in the world, choosing to outsource to China instantly presents companies access to one of the fastest growing and largest consumer markets globally. Building on this benefit pinpoints to China’s convenient geographic location, producing further market entry access advantages if looking to introduce products into other Asian and European markets.

Highly educated and skilled potential workforce

Prior to the COVID-19 pandemic, the Chinese economy was already undergoing rapid changes that focused on launching initiatives towards education, training, and its supporting infrastructure. These initiatives also surround the development of technical skills that support technological innovation and adoption.

Additionally, education is highly valued in China and considered a great source of pride linked with enhancing a person’s worth and career. The key benefit of this makes China home to an immense pool of highly educated and rising talent that can meet current and future market demands.

What are the disadvantages of outsourcing to China?

It’s important to recognise and understand what the disadvantages are with any new business endeavour. Here are the top three disadvantages that should be considered when choosing to outsource to China.

Compliance

Companies looking to hire foreign talent in China need to take a few things into consideration. Chinese employment and labor law are relatively complex and are subject to round the clock audits and enforcement by the Chinese Government. There is also a competing tension between policies for accelerating and controlling innovation – emphasised by the creation of the Chinese Social Credit System, a system to control and determine a company’s “trustworthiness” in their business operations. It is important to understand these regulatory systems and the potential consequences of non-compliance when outsourcing to China.

Control

For businesses that like to have control over their business processes, this can sometimes get lost if outsourcing to external agencies. Outsourcing to China can run the risk of facing quality control issues, intellectual property (IP) violations and differing standards for monitoring of task and employee performance.

A key consideration if a company is looking to outsource its product manufacturing surrounds the fact that quality control practices in China are still underdeveloped across many industries. Quality control issues can mean your product does not meet the local standards as set out in your local jurisdiction. 

China also has had instances in the past where foreign Intellectual Property (IP) rights and international copyright standards have not been respected. Highly problematic if you’re looking at creating or have an existing cutting-edge product on the market.

Both these points can have a significantly negative impact for a company, both financially and reputationally if not taken into consideration.

Language and Cultural differences

Finally, as a consideration when looking to outsource to any country, language and cultural barriers can pose a considerable challenge. Although language barriers are easy to overcome, cultural ones can prove more difficult and result in a misalignment in business vision moving forward.  

In a recent Deloitte survey, 27 percent of respondents identified as a key learning from prior outsourcing, the importance of a third-party strategic advisor. If you have no local connections in China, you might consider hiring a Global Professional Employment Organization (a ‘Global PEO‘) to facilitate your entrance into the Chinese market. Global PEOs can also help companies with an existing presence in China, but who are concerned they are not meeting their compliance requirements.  

These third-party companies take over and maintain compliance through rigorous contracts. This ensures a business is and can remain compliant in their new market. Although considered a cost-effective measure to get the ball rolling, it can take considerable time researching to find an appropriate Global PEO that is good value for money and can cater to all required business needs.  

Conclusion

If your company is seriously looking at outsourcing to China but is not sure where to even begin – then Horizons can provide the needed support in getting the process started. For example,  Horizons are leading experts in providing China Payroll solutions, acting as the employer of record within China, saving on costs and the need to establish a legal entity in China. This means you will be able to begin quickly and compliantly trading in China. Check out how Horizons can help your business expand globally.

Measure

Measure

Most marketers want to answer one key question: Did my ads actually cause new-customer behavior, or would the people who saw my ads have converted anyway? 

Most marketers want to answer one key question: Did my ads actually cause new-customer behavior, or would the people who saw my ads have converted anyway?

Even the most advanced attribution models don’t resolve this question. Sure, they can help you understand how many of the people you reached went on to make a conversion. This can lead to helpful insights about your audience, including where to find them and how they are interact with your brand. But at the end of the day, many marketers use last-touch attribution, which only shows a correlation between an ad exposure and a conversion; it doesn’t measure causation or incremental lift. In other words, it doesn’t tell you if seeing your ad actually caused a person to convert.

This is where Conversion Lift comes in. By using Conversion Lift experiments to measure campaigns on The Trade Desk, you can understand how your ads are driving incremental results. You can answer questions like:

• What is the impact of my media spend on The Trade Desk in driving incremental conversions?

• Which of these three Connected TV (CTV) creative assets drove the most incremental conversions?

• What audience strategy drove the most incremental conversions?

How does Conversion Lift work?

Conversion Lift uses test and control groups to compare the behavior of people who were exposed to your ads to other people from your target audience who were not exposed to your ads.

We’re essentially running an experiment to see if the variable of serving an ad to someone causes them to take a specific action — in this case, engaging with your brand in some way, whether that’s going to your website, visiting a store, or buying a product.

With some advertising platforms, lift experiments require a certain level of investment in public service announcement (PSA) ads. This is not the case with our solution. We use a process sometimes known as ghost bidding. Once a user is eligible to be bid on by a client’s ad media, they are randomly assigned to either a test or control group. If a user is assigned to the test group, we place a bid to show the user the ad; otherwise, we mark that we would have bid and they’re put into the control group (aka holdout). This means you don’t have to set aside any of your media budget for PSA ads, as there’s no additional cost to run Conversion Lift.

So how do we run Conversion Lift experiments on The Trade Desk?

1. First, you need to define the conversion event you want to measure for the campaign. Ideally the tracking tags are set up and collect data for at least two weeks (ideally a full month) before the experiment begins.

2. You work with your rep at The Trade Desk to set up the campaign, define audience targeting, and adjust the relevant cross-device settings.

3. Our platform then creates randomized statistically sound test and control groups based on your target audience, using our cross-device graph to ensure that all devices for a single user are assigned to the same group. We bid on the test group only, and the control group comprises a similar set of users we would have bid on but don’t.

4. We track conversions and compare the differences in conversion behavior between the test and control groups (which are essentially the same) to analyze, understand, and report on incremental lift in conversion events driven by ad exposure.

The next lift phase: single-cell vs. multicell experiments

The standard Conversion Lift experiment is called single cell and it helps you understand if your campaign spend is working to drive incremental conversions.

But things really start getting interesting when you consider multicell experiments. These give you the ability to conduct true hypothesis testing in our platform, with experiments that aim to answer the question “What is the best way for my campaign to drive incremental conversions?”

With multicell experiments, you can test a variety of variables:

• Creatives. Compare different creative variations (such as alternate copy or visuals).

• Media channels. Compare different channels and media-mix strategies (such as video versus display versus both).

• Frequency. Test multiple frequency caps to uncover optimal ad exposure.

• Recency windows. Discover the best way to retarget by comparing recency windows

Conversion Lift best practices

While Conversion Lift may sound like the perfect solution, it does not always make sense for every advertiser or campaign. For example, if you’re running a mass-reach campaign outside of The Trade Desk, let’s say on linear TV, then it’s likely that the control group will be contaminated due to ad exposure from non-The Trade Desk media. In this case, Conversion Lift may not be the right fit.

Once you’ve determined that it is the right fit, there are some nuances to setting up experiments and interpreting results. Here are some best practices that we recommend:

• Feasibility:

• Channels: Are you running on channels that are supported? Currently we support display, video, CTV, audio, and native.

• Conversion types: All online conversions and in-app events are eligible for Conversion Lift experiments. We also support several offline conversion events.

• You can measure lift in foot traffic from several location partners, including PlaceIQ, Foursquare (including Factual), and Adsquare.

• You can also measure other offline conversion events, such as in-store sales from a weekly data feed, from partners like LiveRamp.

• Tracking-tag (pixel) placement: Tracking tags must be set up and collecting data for at least two weeks (ideally a full month) before the experiment begins. This enables us to confirm that test and control users had similar conversion behaviors before the test group was exposed to ads.

• Conversion Lift experiments can only run on decisioned media. This is not a good solution for programmatic guaranteed campaigns, since we’re not able to withhold ads at a user level.

• Single-cell experiments

• If you’re trying to understand the overall impact of an advertiser’s media within our platform (on top of any external media), the experiment should be done at the advertiser level so that the control group remains consistent across all campaigns and is not exposed to any of the advertiser’s media from our platform. This is recommended, especially if you’re using the product for the first time, as it will lead to the highest levels of lift.

• If you’re trying to understand the impact of a subset of campaigns/ad groups on top of all other media running on (and outside) our platform, the experiment should contain this subset such that the control group does not receive ads from the chosen campaign but does from all other campaigns running for the advertiser.

• Multicell experiments

• We recommend testing a maximum of four cells for a single experiment to get statistically significant results without impacting scale.

• Holdouts: You can choose anywhere from 5 percent to 50 percent for your holdout group, but we typically recommend around 20 percent. The larger the holdout, the greater the probability you will detect lift (if it exists), but the more it can impact your campaign’s ability to scale.

• Cross-device: You should enable the appropriate cross-device targeting settings for all audiences in your experiment to ensure that all devices from a person or household are assigned to the same group (test versus control).

• Results:

• Results can vary greatly depending on the the brand, campaign, target audience, and conversion events

  being tested. This means we cannot provide benchmarks for Conversion Lift results.

• Generally, we recommend taking an iterative test-and-learn approach, trying out different variables to gather actionable insights.

Whether you’re running single- or multicell experiments, we always recommend implementing a learning agenda by thinking through the hypotheses you want to test with Conversion Lift. Testing different variables can help you understand how your media investment is driving incremental conversions so you can try to achieve more efficient performance and make better investment decisions.

Measure

Measure

As marketers brave the new world of increasingly complex targeting, cookieless attribution, and analysis strategies, the need for an all-encompassing measurement method is increasingly apparent. 

This is where omnichannel measurement comes in. 

Unlike traditional siloed measurement methods, which offer visibility over only one marketing channel at a time, omnichannel measurement provides a holistic view of an organization’s entire marketing strategy at once. Modern marketers are now able to make more accurate and informed decisions about not only their next marketing tactic but their entire marketing strategy as a whole with granular prescriptive insights.

The Growing Use of Omnichannel Measurement & Challenges Ahead

This past January, 90% of consumers surveyed demanded an omnichannel experience from the organizations they buy from. This is both good and bad news for forward-thinking marketers aiming to create a state-of-the-art marketing strategy. 

On the one hand, this metric reflects the surging popularity of all-encompassing brand experiences spanning not only platforms but also channels and devices. 

On the other hand, it signals an era in which it is no longer acceptable to analyze and measure results from each marketing campaign channel by channel. In short, omnichannel measurement is no longer a trendy “optional” technique – it’s a strategy that is rapidly becoming indispensable to all organizations. 

This creates its own challenges, as any marketer already using an omnichannel measurement strategy already knows. While an efficient analysis strategy may be relatively simple to set up with the right omnichannel marketing tools on hand, the question of effectiveness remains. Many marketers face overwhelm when digging into their data and find themselves unsure of how to use the insights it provides effectively. Others struggle to bring together analysis results from multiple sources due to an ineffective omnichannel measurement platform. As we dig into some of the most important keys to effectively measuring omnichannel performance, we’ll discover how the right measurement platform can solve all these challenges and more, even before they appear.

4 Critical Keys to Measuring Omnichannel Performance

Every omnichannel measurement strategy needs to start off on the right foot using a few essential techniques. The 4 keys we’ll explore below not only optimize the effectiveness of every step in the marketing funnel but also ensure data from all channels is accurately collected, measured, and analyzed. 

1. Run Test Measurement Experiments

Don’t just guess at which marketing tactics will serve you best – experiment with them painlessly and risk-free. 

One of the key techniques marketers can use to ensure their entire marketing strategy is working cohesively and deploying optimally is running test marketing scenarios using Marketing Evolution’s Scenario Planner. This innovative approach allows marketers to take a peek into the possible future of their marketing tactics and choose the best ones to combine into a holistic marketing strategy.

2. Look Closely at Attribution Models

While every marketer is likely aware of the critical role accurate attribution plays in developing an effective marketing strategy, many are still using outdated approaches such as last-touch attribution. Also, the focus is on ‘attribution’ when in fact, ‘contribution’ takes into account the lag effects advertising exposure has on consumer choice.

By examining an organization’s attribution models and updating to more effective methods, marketers help ensure the accuracy of their marketing data by acknowledging the role every customer interaction plays in conversion.

3. Add Omnichannel Metrics to Routine Analytics Reporting

Don’t let all that valuable omnichannel data go underutilized! When building out your analytics reporting metrics, be sure to incorporate the specific KPIs you’ll be tracking in your omnichannel measurement strategy. This may sound like a no-brainer, but a simple mismatch in labeling or definitions when measuring marketing data can create an apples-to-oranges comparison scenario in which vital pieces of information get lost, which is the opposite effect you want your omnichannel strategy to have.

4. Analyze Overall Performance Using Aggregate Data

This may be the most important omnichannel measurement key of all: using aggregate data to analyze overall performance is possible at the person level with AI/machine learning-based techniques. 

There is no scenario in omnichannel measurement in which each marketing channel should be analyzed independently. Instead, every step in the funnel and every customer interaction should be aggregated and analyzed together. 

Only by ensuring that every data point is combined effectively at the end of the omnichannel measurement process can a truly holistic overview of a marketing strategy take shape. 

Proven advances in AI/machine learning for marketing are available to ensure a holistic understanding of the synergistic effects of advertising exposure across media channels on consumer and customer choice.

9 KPIs to Use When Measuring Omnichannel Performance

While each industry demands its own unique set of data points to create a holistic omnichannel measurement strategy, there are a few unifying data points that every optimized marketing plan includes. 

Effectively analyzing these 9 KPIs will ensure your marketing strategy is optimally efficient from the first touch to ultimate conversion.

1. Membership Enrollments

2. Social Media Engagement

3. Website Engagement

4. Content Page Engagement

5. Customer Conversion

6. New Customer Registration

7. Customer Satisfaction Score (CSAT)

8. Net Promotor Score (NPS)

9. Purchase Consideration

How to Organize Omnichannel Performance Measurement

Now that you know what to measure and how to analyze the data you glean, how do you get started? 

One of the most important things to remember when starting up or running an effective omnichannel performance measurement strategy is to break down silos around data channels. This means bringing channel managers and department heads together to aggregate every bit of relevant information in one place before starting analysis or decisioning. 

Without this key step, marketers are likely to end up with an incomplete and inaccurate picture of their marketing strategy. But when done correctly, eliminating silos creates a holistic and actionable view of an organization’s marketing landscape, creating a foundation from which to build successful future marketing campaigns with more conversions at lower costs. 

How Marketing Evolution Can Help with Omnichannel Performance Measurement

A great omnichannel performance measurement strategy starts with a great measurement and analysis tool. When your data is accurate, relevant, and aggregated across all your marketing channels, you can be confident that every marketing decision you make is not only correctly prioritized but also perfectly timed. 

Marketing Evolution has the solution. From aggregate data that fuels your cookieless marketing strategy to state-of-the-art attribution models to omnichannel measurement solutions, Marketing Evolution has your back. Check out a free demo of our flagship solution, Mevo, and discover how easy and effective omnichannel measurement can be today. 

Measure

Measure

Omnichannel marketing is the integration and cooperation of the various channels organizations use to interact with consumers, with the goal of creating a consistent brand experience. This includes physical (e.g. stores) and digital channels (e.g. websites). The goal of an omnichannel marketing strategy is to create a convenient, seamless user experience for consumers that offers many opportunities for fulfillment.  An omnichannel strategy may give consumers the chance to find and purchase online, in-store, or a combination thereof – such as “buy online and pick up in-store”. Today, organizations across industries are leveraging omnichannel strategies, including healthcare, retail, finance, technology, and more.

Thanks to online channels, modern consumers have more options than ever and expect information in real-time. Omnichannel marketing enables them to engage with brands on their own terms, leading to a better customer experience overall.

What is Omnichannel Marketing?

Omnichannel marketing is the seamless integration of branding, messaging, and online and offline touchpoints as consumers move down the sales funnel, enabling a more impactful customer experience. 

Omnichannel marketing takes a consumer-centric view of marketing tactics. Consumers can now interact with brands on innumerable channels, from social media to customer service hotlines. An omnichannel approach ensures that the consumer has a positive, consistent experience on each channel, by offering a few key elements:

• Consistent, identifiable brand tone and vision

• Personalized messaging based on specific interests 

• Content that is informed by past interactions and current stage of the buyer’s journey 

An identifiable brand simplifies brand recognition, while personalization based on interests and shopping history makes consumers more likely to interact with branded content across channels.  

What’s the Difference Between Multichannel vs. Omnichannel?

While omnichannel and multi-channel are both concepts based on the idea of engaging consumers across multiple platforms, they are not interchangeable. Multichannel looks at the specific channel and how the transaction will be completed there. Alternatively, omnichannel takes into account that the customer journey may span multiple channels – and looks at how to create the best experience as consumers move between them. Each interaction is a touchpoint on a path, leading to a conversion. Let’s take a deeper look at the differences between the two:

Multichannel

Multichannel is much simpler in its intention, which is to distribute content and advertisements across various channels. A multichannel strategy makes an organization available to consumers online, in print, in-store, etc. The consumer can choose where they want to interact with the brand, however, content and engagements within these various channels are often very siloed. With this in mind, multichannel is more reflective of operations, reaching as many channels as appropriate, while omnichannel is more reflective of the overall customer experience. 

Omnichannel

Omnichannel also makes brands accessible across online and offline channels, however, it goes a step further to ensure an integrated, seamless experience across each one. As consumers move across devices and online and offline platforms, transitions are seamless and messages are informed by prior encounters. An omnichannel approach enables organizations to truly take a consumer-centric approach that keeps the comprehensive customer journey top of mind.

The Benefits of Using an Omnichannel Approach 

Today, most brands will agree that an omnichannel approach can yield the best results.  While implementing an omnichannel approach is far from simple, when done properly it offers a host of benefits. Today’s consumers are accustomed to being bombarded with messaging from various brands, and as a result, they have become increasingly selective of which brands they choose to engage with. Creating omnichannel customer engagements can act as a brand differentiator, bringing the following benefits: 

• A Better User Experience – Since omnichannel focuses on the individual experience across devices instead of the channel, the customer experience (CX) is better. By focusing on the customer instead of the platform, companies can drive more sales and better retention rates.

• Cohesive Brand Strategy & Identity – Creating a seamless strategy across channels means building an easily identifiable brand image and tone. Organizations should base this image on core audience needs and values. By focusing on the overall experience and working within your brand guidelines to target each channel, you will have a more comprehensive brand strategy that will translate into increased loyalty and more targeted messaging. 

• Increased Revenue – An omnichannel approach encourages customers to engage with a brand across multiple touchpoints and channels. These increased, diverse engagements at each stage of the buyer’s journey can help increase revenue, as research shows that customers that engage with multiple touchpoints tend to be 30 percent more valuable. This more targeted messaging also builds loyalty, making it more likely a customer will purchase from your brand again. Repeat customers on average contribute to 40 percent of revenue, despite being a smaller portion of your consumer base. 

• Better Attribution Data – Going truly omnichannel should not just extend to a user’s experience with your brand, but with your data analytics as well. By tracking engagements across channels, brands get a better understanding of what the customer journey looks like, when and where consumers prefer to engage, and which campaigns have created the most value. All of this data can be put back into your strategy to build more targeted campaigns and optimize media spend. 

What is Omnichannel Attribution?

In a world where there are now multiple touchpoints across channels, which should get credit for the conversion? That can be difficult for marketers to answer, without the appropriate attribution model in place. Marketers often rely on multi-touch attribution and media mix modeling (MMM) to understand what led to a conversion, however, these models are not perfect.

• MMM: Media Mix Modeling only looks at long-term aggregate data, rather than person-level insights. While this allows marketers to see the impact a campaign had on conversions, as well as historical trends, such as times of year when shoppers increase or decrease engagements, it does not provide insight into individual preferences. MMM also uses several year’s worth of data, meaning teams cannot use this model to optimize campaigns in real-time.

• MTA: Multi-touch attribution offers granular, person-level data in real-time across each touchpoint. When analyzed, teams can use this data to make changes to campaigns as they run, to better cater to consumer needs. The challenge with multi-touch attribution is that it is difficult to determine how much credit each touchpoint should be given for a conversion. For example, was the webinar or the email campaign more influential in moving the consumer toward conversion?

Attribution models no longer have to rely on outdated practices and can now give a more holistic view of the marketing funnel and the buyer’s journey. Just as omnichannel tactics combine online and offline channels, omnichannel attribution removes silos between campaign measurements to understand the role each touchpoint played in the journey. 

Leveraging omnichannel attribution offers a host of benefits to brands, allowing them to correlate online and offline measurements, and gain visibility into both person-level insights and aggregate, historical shopper trends. 

Steps for Leveraging Omnichannel Marketing

As noted, creating an omnichannel experience needs to take into account how the individual interacts with your brand. It focuses not on the channel, but the experience as a whole. With this in mind, there are a few essentials when it comes to creating an omnichannel experience:

1. Data Collection

Collecting accurate, timely data about your consumers is essential to the implementation of an omnichannel strategy. This data will allow you to understand when your target audience prefers to interact with brands and on what devices, which type of messaging they are more likely to engage with, what products and features they are looking for, etc. This data will be the driving force behind an omnichannel strategy.. Brands need to make sure they have the tools in place to effectively collect this data across online and offline channels. A smart way to do this is with Unified Marketing Measurement (UMM), an attribution model that combines the person-level metrics of multi-touch attribution, with the historic, aggregate measurements of media mix modeling. This way, touchpoints can be informed by individual preferences as well as historical trends such as regional or seasonal elements that affect engagements / conversions.

2. Data Analysis

Data collection is only the first step. Without a team and platform that can translate all of this big data into actionable insights, it is useless. Brands need to deploy an analytics platform that can distill all of this data in near real-time so that teams can course-correct while campaigns run, to meet consumer needs in the moment.

3. Customer Journey Mapping

Before launching an omnichannel campaign, organizations should be sure to create customer journey maps for each of their audience segments. The customer journey map evaluates the steps taken between the customer discovering the brand and purchasing from the brand. Outlining these maps allows brands to create more targeted campaigns by considering individual interests, the user experience and interface, and factors outside of the brand’s control that may impact the path to purchase, such as economic factors. 

4. Brand Guidelines

It’s important for organizations to develop a brand identity with clear guidelines for messaging and creative. These guidelines should be adhered to across each channels to help facilitate brand awareness and recognition through a cohesive message. Another way that organizations can help facilitate an omnichannel experience is by leveraging brand tracking tools that can help measure and predict their brand’s health in the mind of the consumer

5. Testing / Optimization

One of the most important components of an omnichannel marketing strategy is to continuously test the efficacy of your omnichannel approach. This enables the marketing team to determine ways to optimize campaign spend, messaging, creative, and more. Today’s organizations should utilize media planning tools that can run “what if” scenarios that take budget, target audience, multiple KPIs and media mix into consideration and in turn provide a highly granular media plan that can maximize ROI and inform future decision-making.

Examples of Omnichannel Marketing

When building an omnichannel strategy, take a look at these brands who have done so successfully: 

1. Starbucks

Through its mobile rewards app, Starbucks is able to better integrate the mobile experience with the in-store one to put consumer convenience first. Users can reload their cards from their phone or desktop computer. By using the app to pay, they are rewarded with points that can be applied to a free coffee. Additionally, they can skip the morning line by ordering in advance. 

2. Walgreens

Walgreens created a custom mobile app that makes it easier for customers to refill prescriptions, which they can then pickup in store. Their app also showcases store specific inventory making it easier for customers making a trip to decide which location they should visit.

3. Timberland

Timberland is combining the convenience of online with the experience of the in-person customer experience through the installation of near field communication (NFC) technology. Timberland created Touchwalls in their store, which leads to further information on their shoes. Customers can then add these to their online shopping list or purchase in-store. In addition, Timberland utilizes a product recommendations engine to gain exposure to lesser-known products based on user preferences.

Industries Applying Omnichannel Tactics

Omnichannel approaches have become popular across industries as consumers become more empowered, however, they are particularly prominent in these verticals: 

• Retail: Retail in particular has faced drastic changes in today’s omnichannel environment. With the ability to buy in-store or online and the emergence of social media and review sites, retail marketers need to centralize how consumers are interacting with their brand across a multitude of channels to ensure a positive outcome.

• Healthcare: Healthcare customers generally interact with many touchpoints across various providers, from hospitals, to primary care, to pharmaceuticals. By analyzing data around the customer journey and engagements, healthcare providers can better cater to individuals, providing them with data that matters most to them, while mitigating potential health risks.

• Automotive: Since cars are a long term investment, keeping top of mind and driving customer loyalty are big priorities for car dealerships and manufacturers. Today’s advertisements may not yield the desired effects immediately, but if they engage current customers and interest prospects, they will impact sales down the line.  However, the buying journey, even in automotive has changed with 80 percent of shoppers researching cars online first. Additionally, it is estimated that 4.5 million cars could be sold online only in 2020. Having an encompassing advertising strategy that engages with buyers across all touchpoints has become more vital than ever.

• Financial Services: The banking and financial services industry is shifting from a product-obsessed mindset to a more customer-centric view. As they do so, organizations must consider how they can deliver personalized experiences that can gain insight into which of the various services and products would be the best fit for each user based on their personal preferences, wants, and needs.

Trends in Omnichannel

As omnichannel becomes more popular, several trends have emerged that can help make these efforts more effective to improve consumer satisfaction and maximize marketing ROI. These include:

• Integration of In-Store and Online – Many consumers are shopping online, only to pick up their purchases in-store. This could be to avoid searching for items in-store or to avoid delivery fees. Today’s shoppers are expecting the ease of their online experience to be integrated with the in-store experience. Almost 70 percent of US shoppers expect a notification that their order is ready within 2 hours of ordering it online. When Destination XL realized this trend, they combined customer location data with inventory to help customers find what they were looking for online to pick it up in-store. Additionally, stores like Kohl’s have created parking spots designed for shoppers who are picking up orders they made online.

• Focus on the Brand, Not the Channel – As the Forrester Report: “Retailers are Starting to Reap the Rewards of Omnichannel Commerce” notes, “Customers believe they are engaging with one unified brand or organization, regardless of the various touchpoints that they use. This means retailers must ensure the continuity of information and resources across digital and store touchpoints — or risk losing customers to competitors that do.” Brands need to provide a consistent identity across channels with messages that resonate with the customer, regardless of platform.

• More Devices for One Purchase – Customers are frequently beginning their journey on one device and making a purchase on another. However, many retailers are struggling to address this element of the customer journey, as it can be difficult to account for all cross-device interactions. Failing to account for this shift in trends could drastically impact your bottom line and media spend optimization efforts.

• Multiple Channels Mean Better Customers – When tracked correctly, customers who visit your site across multiple devices tend to be better customers and spend an average of three to four times more than customers who only interact with a single channel.

Key Takeaways

An omnichannel marketing strategy allows teams to meet their consumers where they are, with the right message at the right time. Through omnichannel marketing, organizations can deliver a unified customer experience that acknowledges the previous touchpoints along the customer journey. This not only fosters brand awareness in the mind of the consumer, but also leads to improved engagement, increased ROI and sales, and enhanced customer retention and loyalty.

Today, organizations can more easily enable an omnichannel experience for consumers through the help of advanced marketing performance measurement platforms that can offer reliable, person-level insights to identify the optimal media mix, targeting, and more. By analyzing the customer journey at every step, organizations can make more informed decisions about how to optimize campaigns and reduce wasted ad spend.

Measure

Measure

The Role of Speed to Insight in Marketing

Marketing has evolved dramatically with the advent of data analytics and technology. Today, marketers have access to vast amounts of data that can provide valuable insights into customer behavior, which is crucial in creating a data-driven marketing strategy. However, without the ability to analyze and interpret this data in a timely manner, these opportunities can quickly slip away.

Speed to insight allows marketers to spot trends, identify emerging opportunities, and address issues in real time. It is the key to staying agile and responsive in an ever-evolving marketplace. In this blog, we will explore why speed to insight is crucial for a data-driven marketing strategy and how it can give businesses a competitive edge with the help of Salesforce Marketing Cloud and Mevo. 

What are the Problems of Traditional Data Analysis?

Traditional data analysis faces several challenges in today’s fast-paced digital age. Firstly, traditional methods often rely on manual processes and are time-consuming. With the ever-increasing volume of data generated, it becomes difficult to analyze and derive insights efficiently using traditional techniques. 

It is also limited in handling complex and unstructured data types such as text, images, and videos. This restricts the ability to gain comprehensive insights from diverse data sources. Lastly, traditional approaches may not be equipped to handle real-time data analysis, which is crucial for making timely and informed business decisions.

The Marketer’s Challenge: Speed to Insight for Better Marketing Strategy

Automation and standard procedures are crucial for marketers as they strive for speed in their decision-making processes. According to Salesforce Research, many marketers are unable to access insights quickly enough to make impactful decisions. Waiting for 6-12 months for traditional mix modeling and attribution implementations is not feasible for marketers who need fast and data-driven decisions. Legacy Solutions’ slow speed-to-value track record serves as a warning for buyers, highlighting the need for a solution to address the critical need for speed in marketing decision-making.

The challenge lies in receiving these data insights as quickly as possible. With the rapidly-changing market landscape and consumer behavior, marketers must stay agile and adapt their strategies accordingly. By having access to real-time data, marketers can make timely adjustments, optimize their campaigns, and ensure they effectively reach and engage theirtarget audience.

The Solution: Getting Actionable Insights Efficiently with Salesforce Marketing Cloud and Mevo

Manual processes and a lack of automation have long hindered legacy marketing mix and media attribution. This has led to inefficiencies, errors, and the need for constant rework. The Salesforce Marketing Cloud and Mevo combination aims to address these issues by leveraging end-to-end technology. 

The Mevo App automates the ingestion of various data feeds from Salesforce Marketing Cloud Intelligence, utilizes GenAI for automated modeling, and presents the data in an easy-to-use  UI. Additionally, it provides a comprehensive consumer-level outcome data set for further analysis and mining. By streamlining and automating the marketing mix and media attribution process, this combination offers improved speed and accuracy in delivering results.

Recent Salesforce customers have experienced remarkable results in terms of throughput due to the implementation of this data mastery solution. In collaboration with the Mevo App and Salesforce Marketing Cloud, a significant agency complex updated its Marketing Intelligence base data on April 8th. Two days after, the UI started displaying modeled media optimization and reallocation recommendations that produced a return on investment. 

This remarkable speed sets a new record in the Martech sector, enabling marketers to swiftly adjust and act across the entire media mix in near real time. Gone are the days of waiting for months to obtain actionable insights; now, marketers can make informed decisions promptly.

What are the Best Practices for Implementing Speed to Insight in Data-Driven Marketing Strategy?

Quickly gathering and analyzing data allows marketers to make informed decisions and adapt their strategies in real time. However, achieving speed to insight requires following best practices and implementing effective strategies.

Here are some of the best practices to consider for your business:

Utilize Real-time Data Analysis

Implementing speed to insight in a data-driven marketing plan requires real-time data analysis. This involves utilizing tools and technologies like Salesforce Marketing Cloud and Mevo that can process and analyze data quickly, allowing marketers to make informed decisions and take immediate actions based on the insights gained from the data.

Automate Data Collection and Integration

Automating the collection and integration of various data sources is crucial to achieving speed to insight. This includes integrating data from different channels, such as social media, website analytics, customer relationship management (CRM) systems, and third-party data providers. By automating this process, marketers can save time and ensure they have access to up-to-date and comprehensive data for analysis.

Implement Agile Marketing Practices

Adopting agile marketing practices can greatly enhance the speed to insight with data-driven marketing strategies. This involves breaking down marketing campaigns into smaller, manageable tasks and continually testing and iterating based on insights gained from data analysis. 

Foster a Data-Driven Culture

Establishing a data-driven culture within the marketing team is essential for implementing speed to insight. Hence, it involves investing in the right tools, promoting the use of data in decision-making, encouraging collaboration between data analysts and marketers, and providing training and resources to enhance data literacy among team members.

A Single Customer View (SCV): Everything You Need to Know

By Samuel Kellett

2023/11/24

The term single customer view (SCV) has been bandied about in marketing circles for over a decade. It’s something that is often referenced and is vital for a business’ marketing and sales efforts.

But mentioning a term isn’t the same as understanding it fully, which many have trouble doing with a SCV. Obtaining a complete view of the customer calls for an immense amount of data collected and organized in the right way, which isn’t an easy thing to do. And with a host of variables that can go into the process (how the data is sourced, identity resolution, the speed of profile updates, etc.) it’s hard to lock down a universal definition that sums it up in a short, simple way.

That’s why we’re diving deep into what a unified customer view is, how your business can use it, and why it is so important for ecommerce businesses to use to optimize the customer journey. Keep reading to learn everything there is to know.

Key Takeaways

A single customer view (SCV) can change the way your marketing team connects with customers.

1. An SCV is a database with customer profiles (containing accurate data points like their purchase history, site activity, product recommendations, etc.) for every individual person who interacts with your business.

2. Single customer view data is used to manage customer segmentation and marketing automation campaigns.

3. Your SCV allows you to optimize the customer journey and understand how to personalize marketing communications for customers.

What Is a Single Customer View?

In the age of digital commerce, there’s never been more information for marketers to use to create effective campaigns. But there’s also never been more important data points to keep track of. 

With customers shopping and purchasing from multiple different devices and the universal move toward omnichannel communication, a method of cataloging all that data is required for ecommerce businesses. 

At its simplest, a single customer view solves this problem — a single customer view is a database with customer profiles (containing accurate data points like their purchase history, site activity, product recommendations, etc.) for every individual person who interacts with your business.

But not all single customer views are created equal. A true single customer view is more than just a customer database that can store customer data. It needs to be scalable, flexible, and updated in real time so your marketing team can offer the best possible customer experience.

That last point is a crucial difference. SCV data is used to enable segmentation and marketing automation — if the system is out of date, your customers could be seeing the wrong messages. Despite this, many companies that claim to offer a single customer view still manage their data with a rigid framework and time-delayed updates, which can be detrimental to marketing efforts.

To fully understand the important need for a unified source of data, let’s break down the different types of data needed to make SCVs work. Then we’ll walk through the history of customer data management that led to the single customer view, the biggest benefits that businesses can get from a holistic view of their customer journey, and actionable examples of a true single customer view in use.

Collecting Customer Data for a Unified Customer View

To create a holistic, unified view of your customers, you need to be able to collect data from a range of sources, including:

Behavioral data: All the relevant interactions like clicks, preferences and filters picked, and time spent on page that a customer has with your brand. You need data on the categories and products they favor, the products they’ve added to a basket, and the searches they’ve abandoned. 

Customer relationship and offline data sources: This includes all the pertinent personal information, including any data that you might need to complete a purchase or reach your customer through marketing channels. This includes data such as a postal address, telephone number, email address, social media channels, etc.

Transactional data: This encompasses all the purchases that your customer has made with your business, like products purchased, the volume of each purchase, order values, product returns, and so on.

Data on GDPR consent acquisitions: Your business is responsible for keeping track of consent management for each customer, ensuring your brand is GDPR-compliant.

Optimizing the Customer Journey With a Single Customer View

With so many different data sets and customer profiles to manage, maintaining a single customer view can be a real challenge for businesses. But there are several benefits to collecting data in a SCV. 

A Centralized Place for All Your Data 

First, a unified customer view helps to ensure data quality by providing a centralized data repository. Each customer’s data points are directed towards a single, interconnected profile, which makes sure you don’t have siloed data getting lost in the cracks between different marketing channels, touchpoints, and customer interactions.

This can be helpful for businesses that rely on data from multiple sources, especially with multiple channels as well as online and offline data to organize. 

A Holistic View of Every Customer

A SCV offers a complete, 360-degree view of your relationship with your customers. This can help inform marketing decisions by providing a complete picture of who they are, what they want, and how they’re connections with your brand can grow with future interactions. 

For modern businesses looking to personalize their marketing for their unique audience, a single customer view is crucial. It provides context and insights that can help businesses tailor marketing messages to meet the exact needs of their customers. Without a SCV, truly personalized marketing is next to impossible.

Improved Customer Service

Finally, a SCV can help improve customer service by providing a more complete view of the customer’s interaction with the company. This can help businesses identify opportunities to improve the shopping experience and customer satisfaction.

Overall, collecting data in a single customer view can be beneficial for businesses in terms of data quality, helping brands make more informed marketing decisions and offer prospective customers and loyal patrons the best customer service.

Why Real-Time Data Is Vital for a Single Customer View

To understand the need for a unified customer view and its vital relationship with up-to-the-minute data, let’s take a look at the history of data management and how the digital landscape evolved into the data-rich era we live in now.

The History of Database Management Systems

In the 1970s, when the internet was in its infancy, companies began storing their customer data using relational database management  (RDBM) systems. These data warehouse systems allowed companies to store data as individual pieces of information in different fields (first name, last name, customer ID, etc.), and then access that data through SQL queries. As the popularity of computers continued to climb, this method of managing customer information became the standard and fueled better and more targeted marketing campaigns.

The internet continued to grow, and companies continued to invest in their RDBM systems. These systems could still handle the customer information being gathered, and most companies saw no need to change their methods.

That was the case until about 2008, when big data started hitting hard. Suddenly, data was the most important resource for businesses and marketers, and the sheer amount of customer information that could be gathered increased exponentially. The formerly superior RDBM systems could no longer efficiently handle all the details.

The Need for a Single Customer View

The idea of a unified customer view was born then out of necessity. Customer data was far richer and more detailed than ever before. Customers were starting to make purchases from all different directions: in-store sales transactions, purchases on their phones, buying on tablets and PCs. All that customer data was going to different places, often managed by different departments, and even using different software.

There was more and more danger of data duplication and inaccurate data being assigned to customer identities because there was no way to effectively track the customer throughout their lifecycle and communicate with them in a relevant way.

Introduction of NoSQL Databases

Non-relational (NoSQL) databases began to be seen as the best solution to the siloed customer data problem. NoSQL is built to handle large amounts of unstructured data. It’s more flexible, scalable, and faster than SQL when dealing with something like big data.

Unlike SQL, NoSQL systems can track any piece of data at any time, with no need to prepare the structure for it. New data sources can be tracked without the need to set anything up, and the system is much less likely to produce duplicate data. 

In short, NoSQL was better for using the data these companies now had access to.

Unfortunately, decades had been spent building relational databases— countless personnel hours and piles of cash — and this widespread system was now showing its limitations.In addition to the previously mentioned issues of collecting more detailed data and customers that connect through multiple devices, all methods for the company to interact with customers were disconnected as well.Customer relationship management (CRM) systems were in one data silo, email management was in another, analytics in a third, and on and on.

The legacy companies that had invested early on in relational database management (Oracle, IBM, Emarsys) were now at a disadvantage. With all the time and money spent on their now out-of-date RDBM systems, they couldn’t just start over from scratch.

Instead, they tried to convert their relational databases into non-relational databases (NoSQL). This required pushing together a number of different data silos and pointing them all in the direction of the customer, hardcoding something that looks like a single customer view but doesn’t function with the same flexibility or speed.

How Did Bloomreach Solve the Single Customer View?

Bloomreach acquired Exponea in 2021 and now offers the world’s #1 Commerce Experience Cloud. 

Rather than altering existing tech to face a new problem, Bloomreach had the opportunity to look at the problem first, then create the tech around it. As soon as a customer interacts with your company, you need to qualify them and take action in that exact moment. 

This approach allowed Bloomreach to create a truly customer-centric system: an all-in-one customer data platform built around NoSQL, rather than one adapted to work around the limitations of relational datadabes.

Bloomreach Engagement allows businesses to collect all customer data relevant for activation in a single place so you can power personalization efforts across the entire customer journey. 

No matter what data point you gain — whether it’s on-site data, back-end data, offline data, calculated metrics, or predicted future data — your single customer view reflects customer behavior with your brand. It’s the key to utilizing customer insights, segments, and attributes in real time and creating personalized experiences at scale.

Bloomreach Engagement brings the essential marketing tools together with this unique 360-degree customer view. You get a CRM system, email management platform, campaign building and automation capabilities, real-time predictions, analytics, and more, all available within one dashboard.

Plus, the same data that powers all your efforts is constantly updating — so fast that you can actually watch a customer profile update itself as the customer clicks around.

But it’s not just about speed. The flexibility of this customer-centric system, built around NoSQL and using an in-memory framework, creates new opportunities for communicating with customers. With a system parsing each individual action that every customer takes in real time, Bloomreach has developed powerful customer recommendations that adapt and interact with the customer, even as they browse the site.

Picture it like this: with legacy systems, you get dropped in the ocean on a scraped-together raft made from what’s floating around you. It keeps you from drowning for now, but it’s not ideal.

With Bloomreach Engagement, you’re on a boat designed for the ocean. Not only does it keep you afloat, it’s the optimal method for navigating the seas.

Finally, another advantage of an all-in-one platform is how quickly the software can be implemented. Bloomreach’s basic software can be set up and running within days. The most barebones version can be installed in minutes, as it only requires a single code integration.

Compare that to the weeks or months required to integrate all the disparate parts of a legacy company and it’s easy to see the full benefits of Bloomreach Engagement.

Understanding a Single Customer View

In the simplest sense, a single customer view is a database of customer profiles (one for every user) composed of purchase history, site activity, product recommendations, etc.

A true single customer view is a valuable resource, letting businesses utilize real-time data to enable personalized customer journeys, detailed segmentation, advanced predictive analytics, and more.

NoSQL (non-relational) databases are superior to relational databases when dealing with large sets of detailed data (i.e., big data).

Not all single customer views are equal — most legacy companies run their SCVs on converted relational databases, creating a slower, less-flexible single customer view than one built around a NoSQL database from the beginning.

Bloomreach Engagement‘s single customer view is built around NoSQL. It’s flexible, updates in real time, and combines CRM, email management, campaign building and automation, real-time predictions, analytics, and more into one main dashboard. It will allow you to personalize customer experiences for your valued consumers. 

With the power of a single customer view, your marketing efforts can offer the one-to-one personalization that today’s customers crave from their favorite businesses. It’s an essential tool for any digital marketer looking to get ahead in the modern digital landscape. 

Measure

Measure

For ages, achieving optimal model accuracy has been revered as the ultimate goal, with marketers relying on model fit as the benchmark for discovering the ideal equation that harmonizes with their data. However, what if there’s a deeper layer to model accuracy beyond mere numerical alignment? What if we challenge conventional wisdom and explore alternative dimensions of accuracy that transcend traditional models?

In this blog post, we will embark on a journey to reimagine model accuracy, pushing the boundaries to truly grasp the essence of marketing insights. Come along as we venture into unexplored territory, uncovering a novel perspective on accuracy that promises to transform our approach to data-driven marketing decision-making.

Key Takeaways Covered in this Post: 

• The Importance of Parameter Alignment in Maximizing Media Mix Effectiveness

• The Importance of Testing at Scale in Marketing

• The Profit-Driven Nature of Publishers and Its Implications

• Overcoming External Factors: Using Marketing Response Models

• Exploring the Connection Between Generative AI and Model Accuracy

• The Search for Accurate Testing: Where to Go?

• Unlock the Power of Accurate Testing and Optimization with Marketing Evolution

The Importance of Parameter Alignment in Maximizing Media Mix Effectiveness

In the process of optimizing the media mix, achieving proper parameter alignment is critical for obtaining accurate test outcomes. Parameter alignment entails the degree to which selected parameters align with the goals and objectives of media campaigns. By meticulously selecting and refining appropriate parameters, marketers can fine-tune their media mix, facilitating closed-loop marketing adjustments. This involves establishing parameters that accurately capture the target audience, reflect key performance indicators (KPIs), and are in line with the overarching marketing strategy. When parameters align effectively, test results become dependable and actionable, empowering marketers to make data-driven decisions and enhance the effectiveness of their media mix.

The Importance of Testing at Scale in Marketing

Conducting large-scale tests aids marketers in collecting dependable data and making well-informed decisions regarding their campaigns. Nonetheless, a hurdle marketers often encounter is attaining statistically significant results during campaign testing. Limited resources and time constraints pose challenges in testing multiple variables and thoroughly analyzing their impact. A potential remedy to this issue is A/B testing, enabling marketers to compare two campaign versions and identify the superior performer. However, A/B testing has its constraints as it only evaluates two variations at a time and may overlook the intricate nuances of consumer behavior. Consequently, conducting tests at scale becomes imperative to ensure comprehensive and precise insights for optimizing marketing strategies.

The Profit-Driven Nature of Publishers and Its Implications

Publishers typically maximize their financial returns, prioritizing revenue generation over rigorous testing and evaluation of marketing metrics. Rather than thoroughly testing and refining their marketing endeavors, publishers might lean towards strategies that yield immediate profits. However, this approach could overlook opportunities for more effective or innovative marketing tactics. Additionally, the profit-driven focus of publishers can result in unpredictable fluctuations in testing campaigns. They may hesitate to allocate resources to experimentation and swiftly abandon campaigns that do not yield immediate positive outcomes. Consequently, this hampers long-term growth potential and prevents potentially successful strategies from being uncovered.

Overcoming External Factors: Using Marketing Response Models

Marketing response models, such as generative attribution, aid marketers in gauging the influence of diverse marketing tactics and external variables on consumer behavior and response. Consequently, marketers can evaluate the efficacy of their marketing campaigns and implement necessary adjustments for enhanced outcomes. These models also facilitate an understanding of how external factors, such as economic shifts or shifts in consumer preferences, impact consumer responses.

The benefits and challenges lie in integrating non-media and environmental factors, thereby providing a comprehensive understanding alongside traditional factors like media, individual, geographical, and temporal details simultaneously.

Moreover, marketing response models assist marketers in identifying and mitigating the impact of external variables on testing campaigns. By integrating these models into their testing methodologies, marketers can gain deeper insights into how external factors shape consumer behavior and responses. This comprehension empowers them to make well-informed decisions and devise strategies to navigate external fluctuations.

Exploring the Connection Between Generative AI and Model Accuracy

Traditional models often require assistance in accurately attributing the impact of various marketing touchpoints, leading to incomplete insights and less-than-optimal decision-making. As we strive to expand the horizons of model accuracy, it becomes crucial to explore emerging technologies like generative attribution, which have the potential to enrich our understanding of marketing insights. Among these technologies, generative AI emerges as a particularly promising candidate. By harnessing the power of machine learning, generative AI can forge new and innovative marketing strategies, uncovering previously obscured patterns and insights.

But how does generative AI intersect with the concept of model accuracy? The answer lies in generative attribution. 

Generative AI has the potential to revolutionize marketing attribution by analyzing vast amounts of data and identifying the genuine drivers of success. Envision a scenario where every marketing effort is precisely attributed, enabling you to optimize your strategies with unparalleled accuracy. Generative AI opens up new horizons for marketers, empowering them to unveil hidden patterns, identify unexplored opportunities, and make data-driven decisions that truly make a difference.

The Search for Accurate Testing: Where to Go?

It’s imperative for marketers to prioritize investments in precise measurement and analysis, yet discovering dependable testing methodologies can pose a challenge. Marketing response models could be one option for a viable solution. These models take into account consumer behavior, market trends, and external influences, resulting in more dependable outcomes. By leveraging such models, marketers can mitigate external variables and gain precise insights to steer marketing strategies and enhance performance.

A modern and scalable approach is to implement advanced analytics and machine learning algorithms that have the capability to sift through vast datasets, identifying patterns and correlations that may elude traditional testing methods. These tools empower marketers to unearth insights and make informed, data-driven decisions to optimize their strategies. However, it’s crucial to underscore the importance of using predictive analytics in conjunction with marketing response models to ensure accuracy.

Measure

Measure

explored the origins and convergence of Data warehouses and Customer Data Platforms (CDPs) and how their integration has become the cornerstone of modern enterprises’ growth strategies. As we continue the series, we’ll focus on building a cross-functional team to maximize the potential of data warehouses and CDPs working in tandem. We’ll identify critical stakeholders from engineering, data, and marketing and discuss strategies for uniting them to address use cases and drive innovation in customer engagement and retention.

Part 2: The Significance of a Cross-Functional Team:

Organizations must foster collaboration across various departments to fully leverage the combined power of data warehouses and CDPs. If we break down the acronym CDP, we should always remember that the goal of working together is to focus on the Customer first. This requires fostering a customer-driven culture that leads by listening to the voice of the customer in the Data and building a robust Platform for collecting and activating their preferences so we can adapt to the customer’s needs as we engage with them.   

A cross-functional team made up of members from engineering, data, and marketing is not only beneficial but crucial for ensuring the seamless integration and utilization of these technologies. Organizations can develop a holistic approach to data management and customer engagement by bringing together diverse skill sets and perspectives.

Key Stakeholders:

• Engineering: The engineering team is crucial in implementing and maintaining the technical infrastructure required for data warehouses and Customer Data Platforms (CDPs). Engineering teams are also vital in implementing event capture libraries across applications, websites, and mobile platforms. These libraries standardize the capture of behavioral events on the front end before they are sent downstream to various destinations. Engineers work closely with data and marketing teams to understand the specific behavioral events these teams need to capture to meet their requirements.

• Data: Data scientists and analysts form the core of data-driven organizations, tasked not only with extracting insights from extensive data in warehouses and captured by CDPs but also with empowering business users. Their job involves providing the necessary data to make informed decisions for analytics while ensuring a unified definition of the customer across operational systems such as help desks, marketing tools, and sales platforms. By employing advanced analytics like machine learning and predictive modeling, data teams reveal patterns and trends in customer behavior, informing marketing strategies and personalizing experiences. However, synchronizing all systems to operate off a single customer record definition presents a significant challenge.

• Marketing: The marketing team is a key player in customer engagement and retention, leveraging insights from data warehouses and CDPs to tailor campaigns, personalize content, and enhance the customer journey across various platforms. However, when marketing tools are isolated from the data stored in warehouses, marketers face significant challenges in accessing and utilizing this data directly for complex targeting efforts. Typically, they must coordinate with data teams to extract sophisticated audience lists, a process that can slow down campaign execution and reduce agility. While CDPs assist in forming customer profiles, they often fall short in enabling more intricate targeting without further engineering support. This necessitates a collaborative effort with data teams to devise and implement data-driven strategies that effectively resonate with segmented customer groups.

Uniting Stakeholders to Address Use Cases

Organizations must foster collaboration and communication among key stakeholders to leverage data warehouses and CDPs effectively. Here are some strategies for uniting these teams

Establish clear goals and objectives:

This is a critical first step in leveraging the integration of data warehouses and Customer Data Platforms (CDPs) to their fullest potential. This involves clearly defining the specific use cases and desired outcomes that the organization aims to achieve through this integration. It’s essential to articulate what success looks like, whether it’s improved customer insights, enhanced marketing campaigns, or more personalized customer experiences.

To ensure a cohesive effort toward these goals, it’s vital that all stakeholders—from engineering and data teams to marketing and executive leadership—are not just aligned, but they are the driving force behind the objectives. This alignment includes: 

1. A thorough understanding of each party’s role in the integration process 

2. How each group will contribute to achieving the established goals.

Setting clear goals and objectives aids in the evaluation of the integration’s effectiveness over time. By having predefined metrics for success, organizations can measure progress, identify areas for improvement, and make informed decisions about future data strategies. This strategic approach maximizes the benefits of data warehouse and CDP integration and facilitates a culture of data-driven decision-making within the organization.

Encourage cross-functional collaboration:

Once clear goals and objectives are established, creating opportunities for engineering, data, and marketing teams to collaborate closely on projects and initiatives is crucial. By fostering a culture of data-driven decision-making, organizations can encourage all stakeholders to utilize insights from data warehouses and Customer Data Platforms (CDPs) in their everyday processes. This mindset shift promotes a more strategic approach towards leveraging data, resulting in projects and initiatives that are deeply informed by actionable insights.

To ensure these cross-functional meetings are outcome-focused and adept at overcoming blockers, organizations must prioritize setting clear objectives. Design these sessions to drive collaboration and innovation by enabling team members with varied backgrounds to contribute their unique insights and skills towards achieving specific goals and resolving any challenges that may arise.

By setting up cross-functional teams and defining specific use cases that require input from engineering, data, and marketing, companies can ensure that these teams have a focused direction to work towards together. Creating shared workspaces, either physical or digital, can further enhance this collaboration, providing a platform for continuous communication and idea exchange. This collaborative environment not only accelerates project development but also breeds innovative solutions that may not have been discovered in siloed teams.

Conclusion:

Building a cross-functional dream team is not just essential for maximizing the potential of data warehouses and CDPs in driving customer engagement and retention – it’s a gateway to a world of possibilities. It’s about maximizing the potential of data warehouses and CDPs in driving customer engagement and retention. By bringing together key stakeholders from engineering, data, and marketing, organizations cannot only develop a holistic approach to data management and customer-centricity but also pave the way for innovation and growth. Through collaboration, training, and continuous iteration, businesses can stay ahead of the curve in delivering personalized experiences and fostering long-term customer loyalty, and that’s where the real excitement lies.

In our next post, we’ll explore real-world examples of how leading companies are leveraging data warehouses and CDPs to drive growth and innovation. Stay tuned for insights and inspiration on applying these strategies to your organization.

You’ve heard the buzz about customer data platforms. You’ve heard the letters CDP bandied about. Maybe your boss asked you if your company needs a CDP. Maybe you’re a boss wondering the same thing.

Whatever questions you have about CDPs, you’ve come to the right place. We’re here to give you all the info you need on this crucial marketing technology.

Key Takeaways

Ecommerce professionals can simplify their tech stacks and easily personalize customer experiences with a customer data platform (CDP).

1. A CDP is a smart, user-friendly software that confidently consolidates and manages all customer data, creating a unified, enduring record of each customer’s attributes.

2. Unlike some other database software programs, a CDP is a tool built mainly for marketers. But having access to technical support will be essential for integration and operation concerns.

3. While a CDP is similar to customer relationship management (CRM) software, it is also distinctly different and a CDP with marketing automation capabilities gives marketers additional options to power ecommerce personalization.

What Is a Customer Data Platform (CDP)?

A Customer Data Platform (CDP) is a type of marketing technology software. Specifically, it’s a kind of unified customer database software: one that creates persistent, consolidated records of all your customers, their attributes, and their data. A good CDP should easily integrate with your existing data and allow for easy retrieval of the data it stores.

A CDP builds a complete picture of your customers on an individual level. It collects first-party customer data (transactional, demographic, and behavioral data) from a multitude of sources and systems, and links that information to the customer that created it. 

This creates a 360-degree customer profile, also called a single customer view, which can then be used by third-party tools or built-in marketing automation toolstoexecute marketing campaigns and analyze their performance.

How to Build a Customer Data Platform

So, how do you build a CDP? For any customer data platform to function, there are three main steps involved:

Integration

First and foremost, compiling and assembling all relevant data into a single database is the primary task of any CDP. It works to solve the problem of disconnected data sets by linking all your sources and systems together in one place.

Organization

Once your data is integrated, a CDP needs quality control protocols. It needs to identify and address any missing information, remove duplicate data sets, and cross-check for accuracy so that segments and audiences can be identified.

Identity Resolution

After connecting all the dots, merging data from multiple sources and attributing it to specific customer profiles is where a customer data platform really shines. This is called data unification and it lets you build complete profiles of every individual customer, where you can build and expand on insights as they interact with your business.

What are the Values and CDP Characteristics

There are a variety of businesses offering various CDP offerings, but the best of the best provide a few essential characteristics that every marketer should look for:

• Ready‑to‑Use Solution

All customer data is neatly organized and available for immediate use. Some technical resources are required to set up and maintain the CDP, but it does not require a high level of technical skill compared to a traditional data warehouse.

• Single Customer View

Customer data collected and organized with a CDP is visualized through individual data profiles for each user. This 360-degree view of the customer is possible due to the fact that all customer data is located in one central location.

• Customer Data Unification

Inconsistent data from multiple online and offline sources is combined to create a unified single customer view.

• Accessible Data for Third Parties

Data contained within a CDP is ready for use in third-party systems focused on adtech and campaign delivery.

Who Needs a CDP

Unlike some other database software programs, a CDP is a tool built mainly for marketers. That doesn’t necessarily mean that a CDP can be operated without any technical support. To get the most out of a CDP, an organization will typically need these three roles:

• Marketer: a person who understands the market and can suggest business-tailored use cases for the CDP.

• IT Person: someone to help support the marketer during the implementation phase of the CDP, and can help manage tasks like using webhooks, deploying recommendations on the web, or helping with integrations. Knowledge of HTML, CSS, and Javascript is also helpful for building powerful weblayers.

• Analytical Person: someone that knows how to work with data and what to track in custom dashboards, how to analyze A/B tests, and can report results to the marketing team.

These don’t have to be three separate people, but for maximum value from a CDP you’ll need all those skills.

What Is the History of Customer Data Platforms?

Managing customer data is nothing new. From handwritten filing cards and massive independent mainframes to modern cloud-based solutions, the search for the best tool has been going strong for decades. Modern computing power has significantly increased the pace of progress, allowing for more and more useful tools. 

Online customer relationship management (CRM) software was introduced in the 90s and allowed companies to manage their interactions with both current and potential customers. These customer relationship management platforms could also perform customer data analysis that could help drive retention and sales. While useful, these tools had some limitations: They only managed data for registered clients and only used predefined first-party data. 

Things changed in the 2000s with the rise of data management platforms (DMPs). These were aimed towards advertisers and helped with the planning and execution of media campaigns. Unlike CRMs, DMPs worked with second- and third-party data, and could segment anonymous IDs.

The customer data platform (CDP) was introduced a few years back as a reaction to the demand for an improved customer experience and omnichannel marketing initiatives. Older tools, while useful for their purposes, had created data silos. CRM data was one thing, DMP data was another — and marketers weren’t able to productively use all the data they had access to. 

CDPs solved this problem by offering a unified customer view that gathers a company’s first-party data (and to some extent, second- and third-party data) into a single, comprehensive platform. A major advantage of CDPs is their ability to store extremely granular first-party data, such as events on a website.

Bloomreach Engagement: A CDP Since 2012

Bloomreach acquired Exponea in early 2021, a SaaS company that built its CDP architecture from the ground up starting in 2012. This has allowed Bloomreach to refine and improve its customer data platform, and build powerful tools on top of it to help modern businesses manage customer data and harness its utmost potential.

Thanks to years of hard work and growth, Bloomreach has an industry-leading CDP, made even more powerful by user-centric analytics, predictions, recommendations, and marketing automation layers. We call it Bloomreach Engagement. 

Why Is Customer Data Important?

Today’s customers expect a lot from companies. They’ve experienced good personalized service, and if you want to keep their business, you need to provide that elevated standard. A consistent customer experience across channels, appropriate recommendations, tailored communications — for today’s customers, these are necessary. 

Not many companies can actually deliver these personalized experiences. But if you can’t meet customers heightened expectations, you have a problem. If customers think you don’t care about them, they’ll take their business somewhere else — and they won’t be coming back. The fight to win those customers back will be much more difficult than getting their business in the first place.

This is why it’s so crucial to have well-maintained, accessible, and insightful customer data. And now, a good CDP makes that possible. It’s only a matter of getting the right data.

What Kind of Customer Data Does a CDP Work With?

The sheer volume and speed of digital data is hard to comprehend, and overwhelms traditional database software. A CDP, however, is purpose-built to manage this flow of data. 

The most reliable way for a CDP to collect this type of data is via their own SDK, but most CDPs can also ingest data from other systems via JSON or batch ETL transfers. 

The types of data a CDP can work with include: 

• Events: behavioral data that arises from a user’s actions in a session on a website, in an app, or on a mobile browser.

• Customer Attributes: this includes names, addresses, contact details, birthdays, etc. Advanced CDPs can also store machine learning-powered predictions, such as likelihood to purchase.

• Transactional Data: purchases, returns, and other info from ecommerce or POS systems.

• Campaign Metrics: engagement, reach, impressions, and other metrics from campaigns.

• Customer Service Data: live chat data, number and length of interactions, frequency, NPS scores, and other data from CRM systems.

What Makes Customer Data Platforms Different From DMP and CRM?

When comparing data gathering software, it’s easy to get overwhelmed. There’s a sea of similar acronyms, product descriptions that look almost the same, and lots of claims about which program best suits your needs.

You might have come across customer relationship software (CRM), CDPs, and data management platforms (DMP). While their capabilities might sound similar, it’s important to understand the distinctions between them so you can evaluate vendors and choose the right product for your business needs.

CDP vs. DMP vs. CRM: Table Explained

• Holistic Customer Data: Does the platform manage customer data from all available sources (behavioral, demographic, personal, transactional, device, etc.)?

• Lasting Customer Profiles: Does the platform retain data for a long period of time?

• Packaged System: Can the platform exist as a ready-to-use piece of software?

• Real-time Capability: Does the platform update data in real time, allowing for quick reactions to changes?

• Open Platform: Is it simple to get data into the platform? Is it easy to share data from the platform with other services?

• Cross-channel Personalization: Does the platform allow for the personalization of messages across different customer touchpoints?

• Only Anonymized Data: A data management platform by design works with anonymized customer data. CRMs and CDPs work with identified customers, and allow for granular views of individual customers.

• Identity Resolution: Does the platform allow you to connect the customer behavior of anonymous visitors with known customers after they have given their consent? Does the platform recognize customers across devices?

• First-party Data Priority: Does the platform primarily deal with data from first-party sources?

• Third-party Data Priority: Does the platform primarily deal with data from third-party sources?

• Requires IT Support: Does day-to-day operation of the software require support from IT?

Finding the right platform is no easy task. But understanding what you can expect your CDP to do for you on a daily basis helps. 

In our knowledge card, you’ll get essential know-how on CDPs and learn more about the features that your company should be looking for in its CDP. 

Types of Customer Data Platforms

The customer data platform market has matured, leading to a number of different providers. These providers are differentiated based on their target market and their intended use cases. Let’s take a look at some of the differences.

A Standalone CDP vs. CDP + Marketing Automation

A key distinction among CDP vendors is whether they provide a product which is only a CDP, or a CDP plus other capabilities. It’s crucial to understand what your vendor is providing, because this distinction can cause large differences in how your business uses the CDP.

A Standalone CDP

A standalone CDP is exactly what it sounds like: a customer data platform without extra capabilities. It ingests all of a company’s first-party data and builds complete pictures of all of your customers (a single customer view). Usually, a standalone CDP will offer analytics capabilities, allowing for granular segmentations of your audience. 

This data is accessible for use by other systems, but the standalone CDP cannot execute campaigns. It needs dedicated tools that can make use of the comprehensive data it collects.

For companies that already have campaign execution tools, a standalone CDP might make sense. But companies that lack those capabilities should consider a CDP + marketing automation platform.

CDP + Marketing Automation

A customer data platform coupled with marketing automation is the next generation of the CDP. It combines all the benefits of a standalone CDP with marketing campaign tools, creating a single, powerful, customer-centric marketing platform.

This gives marketers the complete toolset they need for creating incredible customer experiences by bringing together AI-driven marketing capabilities, real-time analytics, and UX optimization with a CDP.

A CDP combined with marketing automation simplifies workflows and increases productivity by collecting frequently used tools into one integrated interface. But it is also flexible and can fit into your existing tech stack — it molds around what you already have and fills gaps.

Key Benefits:

• Provides a foundation for a 360-degree customer view

• Makes customer loyalty-driven decision making possible

• Facilitates more precise targeting and higher-quality interactions with customers

• Allows for meaningful analysis of marketing initiatives across different channels

• Enables agile responses to changes in the market or customer preferences

Bloomreach Engagement: The Most Versatile Platform on the Market

Bloomreach offers you the flexibility to pick and choose which features you want to use; it’s not an “all-or-nothing” solution. Although Bloomreach is a CDP + marketing automation, it can act as a standalone CDP to provide a unified source of customer data to an existing technology stack, or it can be used to handle all marketing activities using the additional layers of campaign execution and analytics.

If you already have a CDP, Bloomreach Engagement’s customer data engine can help you fully activate your data and maximize the ROI of your ecommerce marketing efforts. Our customer data engine is what makes our platform truly stand out with capabilities that go beyond the standard scope of marketing toolsets. Our powerful data core combines CDP capabilities and advanced analytics to help marketers understand the customer journey in real time and create omnichannel campaigns that drive results.

The Difference Between Enterprise-grade CDPs and Small Business CDPs

There are multiple CDP providers out there, each with differing purposes and capabilities.

A key consideration when choosing a CDP is the intended scale of the software. Is it built for small businesses? Or is it a full-fledged enterprise solution? There are some key points to remember when answering these questions:

Scalability. Enterprise-level companies need to work with massive amounts of data. That data can change quickly, and for a CDP to be useful, it needs to respond to those changes swiftly and accurately. This means that CDP architecture needs to be built for scale from the very beginning.

Flexibility. No two companies are the same. For enterprise-level companies, a plug-and-play solution will almost never be suitable for the unique needs of a company — therefore flexibility in a CDP is a must-have. A customer data platform must be able to ingest a company’s data from all its unique sources, as well as interface successfully with the platforms the company uses to function.

Integrity. A CDP needs to be trusted with the sensitive data it uses, and that can mean data for millions of customers. This requires rigorous security protocols and a dedication to privacy. These need to be core values of the CDP provider if they are to be trusted with customer data.

Bloomreach Engagement: An Enterprise-grade CDP

Bloomreach Engagement was built from the ground up as an enterprise-grade CDP. Thoughtful product planning and experience with world-class clients has made Bloomreach an industry leader in customer data platforms for the most demanding of applications.

Scalability: Bloomreach’s agile in-memory framework is scalable by design and is ready to handle massive volumes of rapidly changing data at the speeds necessary for business success.

Flexibility: Bloomreach easily adapts to the needs of enterprise-class businesses. A quick onboarding process integrates Bloomreach with existing data. A rich API makes third-party integrations smooth and painless. And native integrations with best-in-class tools means Bloomreach works with the tools you already use.

Security:Privacy and security have been core values of Bloomreach from the very beginning. Bloomreach undergoes regular audits to maintain our status as a leader in this area.

The Benefits of a Customer Data Platform (Key CDP Use Cases)

There’s a mutlitude of benefits to using a CDP, but most the types of advantages you get from a platform really boils down to the way your business wants to employ it.

And just as there are numerous benefits, there is the large number of CDP vendors on the market, which can be overwhelming. When choosing a vendor, it’s helpful to consider the use cases you hope to accomplish with a help of CDP.

While it’s important to have high-level goals (improve the customer experience, foster loyalty, etc.), you also need to know how a CDP can help you achieve those goals through lower-level use cases.

We’ve collected what we believe to be some of the most important use cases, and benefits, below.

CDP Use Cases:

1. Online to Offline Connection

Merge online and offline activities to create an accurate customer profile. Identify customers from online activities when they enter a brick and mortar store.

2. Customer Segmentation and Personalization

Segment customers according to their behavior (RFM, LTV prediction) to deliver a personalized, omnichannel experience throughout the entire customer lifecycle.

Read This Next: Ecommerce Personalization: Your Complete Guide

https://www.bloomreach.com/en/blog/2017/ecommerce-personalization

3. Predictive Customer Scoring

Enrich your customer profiles with predictive data (probability of purchase, churn, visit, email open rates).

4. Smart Behavioral Retargeting and Lookalike Advertising

Integration with Facebook Ads, Google Ads, Google Analytics, and Doubleclick enables you to leverage insights and run powerful acquisition and retention (lookalike) campaigns outside of your website.

Read This Next: Weird Fish Increases Facebook Ads Revenue by 82% With Bloomreach

https://www.bloomreach.com/en/case-studies/weird-fish-increases-facebook-ads-revenue-by-82-percent-with-facebook-conversions-api

5. Product Recommendations

Create different recommendation models such as “similar products” or “customers also bought” and deliver the best shopping experience to drive engagement, increase brand loyalty, and sell, up-sell, or cross-sell your products or services.

Read This Next: Why Product Recommendations Are Key to Winning With Ecommerce

https://www.bloomreach.com/en/blog/2022/why-product-recommendations-are-key-to-winning-with-e-commerce-personalization

6. Conversion Rate Optimization and A/B Testing

Quickly transform the appearance of your pages. Use our smart website overlays (pop-ups) or send cart abandonment emails to increase your ROI. Create different designs and determine which variant performs better with the automatic A/B testing feature.

7. Omnichannel Automation

Guide your customers through their entire lifecycle with personalized messages sent to their preferred channel, significantly enhancing your opportunities to both acquire and keep a loyal customer.

Read This Next: What Is Omnichannel Commerce? Definition, Benefits, and Trends

https://www.bloomreach.com/en/blog/2019/omnichannel-commerce-for-business

8. Email Deliverability Enhancement

Increase email open rates. Thanks to an AI-powered algorithm, you can determine the ideal distribution time for each user based on their email opening habits and reach them at this optimal hour.

9. Reviews Optimization

Get better and more frequent online reviews from your customers through personalized omnichannel communication and NPS survey analysis.

How Can a CDP Improve Customer Lifetime Value and Foster Customer Loyalty?

The most effective way to foster customer loyalty is to give your customers exactly what they’re looking for: a consistent, high-quality, and personalized experience. Customer data platforms make it possible to deliver these experiences at scale, personalizing the journey of each customer.

CDPs enable loyalty-building strategies by solving the problem of fragmented data silos. They arrange customer data in a way that makes personalization at scale possible (though personalization tools themselves are not always part of a CDP). 

If your data is siloed, you can’t create a consistent experience for your customers. Without that central data hub, you can’t provide the omnichannel experience customers expect, which is receiving up-to-date interactions regardless of which channel the customer communicates through.

Read This Next: 3 CDP Personalization Tactics to Fuel Your Marketing

How Long Does It Take to Implement a Customer Data Platform?

The short answer? It depends. A very rough estimate would be 4-12 weeks.

The long answer? Without knowing the details of your organization and business needs, there’s no one-size-fits-all answer. There are a few things you’ll need to take into consideration:

• Integration complexity — how many tools will you need to integrate with? 

• CDP output requirements —what will you need from the CDP?

• Current state of your data — data cleansing can lead to a longer implementation

• Unique business rules — are there business-specific stipulations to consider?

• Identity merging needs — siloed data can lead to a single customer having multiple profiles across different platforms, and merging these profiles takes time

• Level of detail in data attributes

Every business that wants the benefits of a CDP will have different requirements and goals, making it impossible to give a precise answer to how long the implementation process will take.

Nevertheless, most businesses can expect to go through a similar set of steps when implementing a CDP.

Let’s walk through the typical steps in the process of implementing a CDP.

We’ll also look at the differences between implementing a standalone CDP and CDP with built-in campaign execution and analytics capabilities.

The 3 Necessary Stages To Implementing a CDP

1. Planning Phase

All the necessary groundwork for integrating a CDP needs to be taken care of before any technical work can begin. Some necessary parts of this stage include:

Project Scope Creation: describe business goals, use cases, step-by-step integration and implementation.

Tracking Document Creation: describe customer attributes, consents, and custom events to be tracked.

2. Integration Phase 

Once ouy lay the groundwork, it’s time for the technical integrations to begin. Most of the integration steps will be standard for any data collection tool, but this process will vary slightly depending on what type of CDP you choose. Let’s go over the routine integration steps first:

CDP Initialization: This is the process of connecting the CDP to your online & offline data sources, allowing you to identify your customers and analyze their actions. With Bloomreach, this is very simple: just paste a snippet of code into the header of your website. Other solutions might look quite different.

Customer IDs and Attributes Tracking: After initializing the CDP, set up customer IDs and attributes tracking for the information you’ve decided to collect. This data is helpful for segmenting your audience, triggering campaigns, sending personalized information, and more.

Events Tracking: Follow and get insight into customer behavior by tracking purchases, clicks, returns, browsing behavior, and more. Connect this to a customer’s unique identifier to build complete pictures of each customer.

Data Imports: Connect all your existing data (customer data, event data, product catalogs) to your new platform. 

The steps in this importing process depend entirely on your CDP. 

If you are using a Standalone CDP, you will want to integrate it with your other tools and platforms so you can make the most of its capabilities. Consider which of the below platforms you want to use. They will each need to be integrated with the CDP.

• ESP Integration

• Business Intelligence Platform

• Web Optimization Platform

• Recommendation Platform

• Predictive Analytics Platform

• Advertising Platform

• Mobile Marketing Platform

These integrations are unnecessary with a CDP + marketing automation, since analytics and automation abilities are built in. 

If you are using a CDP + Marketing Automation, there’s no further integration process needed. A CDP + marketing automation platform doesn’t require any integration with analytics and execution tools, since those capabilities are native. As soon as the platform is integrated, you can begin analyzing data and executing automated marketing campaigns.

Note: If you want to keep some of your existing third-party tools, you can integrate them with Bloomreach Engagement, just like a standalone CDP.

3. Execution Phase

You’ve finished initializing your CDP, you’ve set up customer identifiers and event tracking, you’ve integrated all your tools and platforms — now you can start using your platform to power insightful analytics and marketing automation.

But again, this process will look different depending on what type of CDP you employ.

Because a standalone CDP was not built together with your analytics and execution platforms, you can expect the following:

• Many User Interfaces

• Different Technologies

• Unidirectional Data Flow

• Difficult Omnichannel Orchestration

• Delays in Response

A CDP + marketing automation offers some advantages for execution. Thanks to an all-in-one solution, marketers can expect:

• One User Interface

• Unified Technology

• Bidirectional Data Flow

• Easy Omnichannel Orchestration

• Real-time Response

How to Choose the Right CDP for Your Company

After you’ve decided that a CDP is the right tool for your business, you’ve got to decide which vendor to choose. The number of possible vendors might make the choice seem overwhelming, so it’s important to have a plan for your buying process.

Each company will have different requirements and use cases, but some parts of the buying process should look the same for most businesses.

First, you need to define your use cases. How do you plan to use a CDP? Do you want a CDP with execution layers and ecommerce personalization capabilities? Or do you just need identity resolution and customer segmentation (standalone CDP)? Answering this question will help you better understand your requirements.

Once you’ve done that, you can start to match your requirements to potential vendors. Can they handle the use cases that you require? This allows you to create a short list of candidates.

Next, evaluate the vendors you’ve selected. Ask them to demonstrate their platform executing a use case that you require, instead of relying on a canned demo that only showcases the best that platform has to offer. This will show you if a potential solution is right for you or not.

Finally you can make your decision. This might involve an RFP or a pilot project to make sure that the solution you’ve chosen actually meets your needs. If it has, congratulations! You’re ready to start taking advantage of all a CDP has to offer.

If you dream of achieving better business results with less effort, allow us to introduce you to marketing automation.

Because it streamlines and optimizes digital marketing efforts, marketing automation helps businesses increase revenue and elevate the customer experience. Just ask the 77% of marketing automation users who report an increase in conversions thanks to these tools. 

However, implementing marketing automation can be confusing and overwhelming, especially for anyone who isn’t already familiar with the concept. 

This guide will demystify marketing automation for you. Keep reading to learn the ins and outs of marketing automation, from the basics of what it is and how it works to best practices for implementing a successful marketing automation strategy. 

Marketing Automation: A Definition

What is automated marketing? Automated marketing refers to the use of marketing software or other tools to automate repetitive tasks, streamline workflows, and manage campaigns across multiple channels, including email, social media, digital ads, and websites. 

With marketing automation, ecommerce brands increase efficiency and enjoy a host of other benefits, including better customer engagement and revenue growth. 

What Are the Benefits of Marketing Automation? 

Automated marketing provides businesses with several advantages that help them gain a deeper understanding of their customers. With automated marketing in place, ecommerce businesses are more efficient, improve lead management, boost customer engagement, and more:

Free up Time and Lower Costs

Automated marketing reduces costs and increases efficiency by automating repetitive tasks. This gives marketers more time to focus on high-level tasks, like creating strategies and improving the customer experience. This increased efficiency also leads to cost savings, as automated marketing reduces labor costs and reduces the likelihood of human error. 

Personalize the Customer Experience 

Marketing automation places your brand in the right place at the right time, so you can personalize the customer experience like never before. Target customers with personalized messages after they make a purchase, browse specific products, or abandon a shopping cart. Stay consistent with customization and you’ll increase customer engagement and loyalty over time.

Monitor Target Audiences

Automated audience monitoring segments customers by demographics, behavior, interests, and brand interactions. Assign lead scores to promising prospects so you can target them more effectively. Identify trends in customer data points and use this intel to make informed decisions about which touchpoints to deploy and when.

Improve Marketing ROI

Increased conversions go hand-in-hand with a marketing automation process. This is because automated marketing makes it easier for ecommerce businesses to send targeted content to customers at various stages of the buyer’s journey. When customers receive marketing messages that they like and respond to, they view your brand as trustworthy and credible, and are more likely to shop with you. 

How Does Automated Marketing Work?

While marketing automation may seem like magic, there are logical processes behind the scenes that make it work. 

It begins with workflows. Workflows are a series of automated steps that a contact goes through based on their behavior, preferences, and demographics. Workflows are triggered by the actions your customer takes, like a form submission or website visit. 

Messages are the pieces of content you send along to your customer via SMS, push notifications, emails, and social media posts. To be successful, your message should always include personalization and a call-to-action that’s relevant to that subscriber’s unique position along the buyer’s journey. 

If you’ve ever received a welcome email after signing up for a newsletter, you’ve seen workflows and messaging in action. The automatic response is a type of workflow. The email you received with that 10% discount code is the marketing message. 

Marketing Automation Tools and Platforms

Marketing automation is powerful and can sometimes intimidate marketers who are unsure of where to start with marketing automation tools. Fortunately, there are many platforms available to help marketing teams automate tasks, analyze customer data, and deliver a personalized omnichannel experience. 

Here are a few popular marketing automation solutions known to help ecommerce brands elevate their customer experience and drive growth: 

• Email marketing. In the digital marketing world, email still reigns supreme. An email marketing automation tool can build your send list for you, segment your customers based on demographics and preferences, and enhance deliverability metrics so your emails stay out of the spam folder. 

• SMS, MMS, and WhatsApp messaging. 74% of consumers have a positive impression of brands that send them text messages. With this type of marketing automation software, you can contact customers with the right message at the right time. 

• Mobile app marketing. Custom notifications, unique messaging, and more are at your fingertips with mobile app marketing. Recommend relevant products your customers are likely to buy and solicit helpful feedback right in the app. Gather data about how your shoppers respond and use it to zero in on marketing messages that’ll convert. 

• Web personalization. Your website is ground zero for your ecommerce business. Ensure that your web experience is top-notch with a platform that helps you deliver real-time product recommendations, choose a banner most likely to convert, and more. You can also gather zero-party data to refine your automation over time.

Bloomreach Marketing Automation Platform

Instead of setting up and trying to connect different pieces of marketing automation software, you can use a tool like Bloomreach Engagement to bring the best digital marketing automation tools together to work perfectly in sync. Bloomreach Engagement makes it easy to personalize messaging across multiple channels, track customer behavior, and analyze data to inform marketing strategies. Engagement is also scalable and user-friendly, making it a clear choice for businesses of all sizes.

Marketing Automation Process and Best Practices

By now, it’s clear that marketing automation is a game-changing way to drive revenue by automating email sends and text messaging, while also uncovering data-rich insights that sharpen your efforts over time. 

But, what does a successful marketing automation process look like, and which best practices should you follow to make the biggest impact on your goals? 

In this section, we’ll outline the marketing automation process and share a few tips that will take your automated marketing to the next level. 

What Is the Marketing Automation Process?

Marketing automation stacks include a combination of software platforms and processes that, when used together, streamline and automate repetitive marketing tasks. The typical marketing automation process will involve: 

• Collecting data about your prospects and current customers. Data is often gleaned from a variety of sources, like website sessions, form fills, and email campaigns.

• Segmenting your target audience after data collection so that similar customers are grouped alongside others. By grouping customers by their identifying factors, you can distribute marketing messages that are most likely to resonate and convert. 

• Creating campaigns across multiple channels that present a personalized offer, based on segmentation-sourced data. 

• Automating delivery schedules, social media marketing posts, and other campaigns based on preestablished triggers, so your marketing team can focus on more important tasks.

• Analyzing the results of your automated efforts and tweaking the approach as necessary. 

Know Your Audience

Automating your marketing may feel impersonal, but if you want to be successful at it, you have to make sure that every automated touchpoint is designed with your audience’s needs in mind. Knowing your audience allows you to segment them properly, so that they receive only the most relevant messages possible and, in turn, convert at higher rates. 

But it can be challenging to make sense of all the customer data that’s available to your business and activate it in meaningful ways. With multiple channels and a myriad of data sources that need to be consolidated and organized, you need a tool that can help you make sense of all the information you’ve gained. 

That’s why a customer data platform (CDP) is so essential for any automated marketing strategy. A CDP is a marketing technology that collects data from all your customer interactions and combines them into a single customer view. This view provides a unified customer profile for each individual, giving you insights into their behavior that can be easily activated with marketing campaigns.

A CDP and marketing automation is one of the most valuable technology pairings available to marketers. With a unified data source fueling your automated marketing strategy, you can send effective, personalized messaging that feels relevant to each and every customer.

Read This Next: Why You Need a CDP With Marketing Automation

Work With the Customer Journey in Mind

You’ll also want to know the unique journey your customer is taking with your brand. What buttons are they clicking? Which channels do they use to interact with your brand? What questions or concerns do they have about your product? When you have that information, you can strategize how to give them exactly what they’re looking for at every turn in the customer lifecycle. 

Plan Your Automated Flow

You can’t measure what you can’t map out. So, when implementing automated marketing, be sure to clearly strategize how your process will handle lead generation and provide all the right touchpoints to nudge your customer toward a desired action. Remember, it’s okay to tweak your approach later on. 

Don’t Neglect Email Deliverability

You put a lot of insightful content in your emails, so the last thing you want is to have your messages end up in the spam folder. To avoid this, consider email deliverability alongside marketing automation. Here are a few tips: 

• Avoid sending messages that lack personalization. 

• Honor communications preferences. Don’t send more emails than your customer has agreed to receive. 

• Provide an opt-out button. While it stings to lose a subscriber, uninterested contacts weigh down your metrics and may mark your email as spam.

Pro tip: Email inbox placement is even more important than email deliverability, and is something that top brands will focus on. See how some of Bloomreach’s customers have seen success as part of the 99% Inbox Placement Club. 

How To Create a Marketing Automation Strategy

Creating a successful marketing automation strategy involves a series of critical steps.

First, define the objectives of the marketing automation strategy. Decide as a team what your top priorities are. This could be increasing customer interactions, earning more qualified leads, or boosting revenue by a certain percentage.

Once your goals are clearly defined, create buyer personas. Buyer personas bring your audience to life and help you better understand their needs, challenges, and interests, so you can position your marketing automation strategy accordingly. 

Now, it’s time to create your workflows and campaigns. This is where you’ll rely upon marketing automation platforms to establish the various messages and triggers that carry your customer through the workflow and, ultimately, drive them to a desired action. 

Once your campaigns are deployed, it’s time to start analyzing your results, optimizing along the way as you see fit. For example, you might notice that a certain WhatsApp message isn’t drawing any clicks, so you rephrase it to see how it performs for the duration of the campaign. This small tweak could be the deciding factor between the success or failure of your campaign.

Marketing Automation Examples

Unsure what a marketing automation process looks like in the wild? Reading through a few marketing automation examples can be helpful in understanding how real businesses like yours are using automation to make more money and operate more efficiently. 

Let’s meet a few Bloomreach clients who have found success in an effective marketing automation strategy. 

Whisker Boosts Conversion Rates

Whisker, a leader in connected pet care, recognized that its email messaging lacked consistency and could yield better conversion rates. The problem was that until the company implemented Bloomreach Engagement, its customer data was siloed and difficult to translate into effective email messaging. 

Whisker saw a significant improvement in its email-generated sales by automating its email messages and tracking the type of messaging that compelled its users to take action. A/B and multivariate testing further enhanced this process, helping Whisker increase conversion rates by 107%. This success has prompted Whisker to replicate the process across other marketing channels, making it an excellent example of the power of marketing automation.

Vivamix Automates Back-in-Stock Messaging

Like many ecommerce companies, Vivamix, a Polish distributor and servicer of KitchenAid products, struggled to keep up with customer inquiries about product availability. Rather than losing customers due to unavailable products, Vivamix aimed to enhance customer service by automating the process of notifying customers when a product came back in stock.

Using Bloomreach Engagement, Vivamix successfully automated its product availability notifications. Now, customers can sign up for back-in-stock notifications, and the system will automatically alert customers when new inventory is in stock. Thanks to marketing automation, Vivamix achieved a 65% open rate and a 40% click-through rate on back-in-stock emails. This process has led to increased customer trust and loyalty, as shoppers can now rely on Vivamix to provide timely and accurate availability notifications.

Benefit Cosmetics Launches Category-Defining Blushes

Benefit Cosmetics, the number one prestige blush brand in the United Kingdom, aimed to introduce a new line of blushes while upholding its exceptional level of customer service. Bloomreach Engagement brought Benefit’s launch to life with an omnichannel campaign that leveraged email marketing, lead generation, and weblayers.

With special attention given to prelaunch, launch, and post-launch stages of the campaign, Benefit was able to capture audiences at multiple points along the customer journey. As a result, Benefit’s countdown-to-launch emails enjoyed a remarkable 10.10% click-through rate, and the entire campaign yielded 40% more revenue than similar ones sent in recent months.

Bloomreach Engagement Equips You With Powerful Marketing Automation

Marketing automation is a transformative tool for your digital marketing efforts. But to market effectively across all your channels, you’ll need an all-in-one marketing automation solution like Bloomreach Engagement. Make marketing automation work for your brand by seamlessly connecting your customer insights with every channel your customers are using — this way, you can personalize the experience no matter where they are. 

Want to get even more ideas for your own marketing efforts? Check out our guide on top-performing D2C use cases and campaigns to see how else you can use marketing automation to boost revenue and conversions.

Every marketer understands the importance of collecting and utilizing customer data to streamline your customer relationship management, your marketing efforts, and to increase conversions. But before you can put all of your customer data to work, you need to choose the right tools for the job. 

Because it can be challenging to make sense of all the customer data collected and activate it in meaningful ways. You likely have multiple channels and a myriad of data sources that need to be consolidated and organized into understandable unified customer profiles. And that’s just the beginning — leveraging your customer data to execute marketing campaigns that drive increased customer lifetime value is just as important.

That’s why a customer data platform (CDP) and marketing automation are such vital tools for your marketing tech stack. And to make your efforts shine across every stage of the customer journey, you need a solution that brings both of these tools together.

Let’s see what a CDP and marketing automation tool can do for your business, and dig into why it’s important to get a solution that combines the best of both worlds.

What Is a Customer Data Platform?

A customer data platform is a marketing technology that collects first-party customer data from various sources, such as online and offline interactions, and combines them into a single customer view. This view provides a unified customer profile, and gives you insights into customer behavior that were previously unattainable.

Consolidating all of your customer data is a CDP’s primary function. A CDP’s ability to manage customer data will ultimately give your marketing team key insights and data points needed to execute personalized marketing campaigns that drive revenue.

Once your CDP completes its data management responsibilities, it integrates all your customer information into a single unified database, it  builds a complete picture of your customers on an individual level.  A CDP makes all your data make sense, allowing you to harness a complete view of your customers to build effective and personalized marketing campaigns.

With a CDP, marketers can collect, segment, and activate their customer data, which allows for more accurate targeting, improved engagement, and increased revenue.

The Benefits of a Customer Data Platform

Some of the key benefits of incorporating  a CDP into your marketing strategy include:

Unified Data

A CDP creates a comprehensive view of the customer, which allows marketers to know what customers are doing across all marketing channels. This means that marketers can align their messaging with specific touchpoints in the customer journey, ensuring a more targeted approach for meaningful engagement.

Improved Customer Experience

A CDP helps marketers create relevant and personalized experiences at scale for their customers during  every stage of their buying journey. This includes personalized messaging, content, and recommendations that are tailored to the customer’s interests and preferences.

Improved ROI

A CDP can help increase ROI by providing marketers with a more accurate and in-depth understanding of their customers. With this valuable information, marketers can optimize their campaigns and focus on the channels and strategies that drive the most conversions.

What Is Marketing Automation?

Marketing automation systems allow marketers to replace manual marketing tasks with automation software that can both manage and monitor routine workflows. 

These workflows might span multiple channels, such as mobile, web, and email marketing, and may include tasks such as lead management, email nurturing, remarketing ads, personalizing content, and retargeting messages.

Put simply, marketing automation can put your most straightforward marketing efforts on autopilot, so you don’t get bogged down in mundane tasks. It’s a technology designed to help marketing teams create predefined customer journeys using real-time triggers, built-out scenarios, and predicted behaviors. 

With the right marketing automation platform, you can create targeted, personalized campaigns that are tailored to the customer’s needs and preferences — without the hassle of building them over and over.

Read This Next: Ecommerce Marketing Automation and Its Benefits

https://www.bloomreach.com/en/blog/2022/e-commerce-marketing-automation-and-its-benefits

The Benefits of Marketing Automation

Some of the key benefits of incorporating  marketing automation into your ecommerce strategy include:

Increased Productivity

Marketing automation enables teams to automate repetitive tasks, such as email campaigns, social media campaigns, and lead nurturing. This frees up time for marketers to focus on tasks that need a more human touch, like creating more engaging and personalized content for their customers.

Improved Lead Quality

Marketing automation helps marketers capture, nurture, and score leads more accurately. This means that marketers can target  the leads that are most likely to convert, which can lead to increased conversions and revenue.

Better Data Insights

Marketing automation platforms provide marketers with detailed analytics and reporting, which can be used to optimize campaigns and improve ROI. With a deeper understanding of each customer based on their habits and preferences, you can make data-driven decisions about nearly every aspect of your communications and get more from your campaigns.

Why You Need a CDP + Marketing Automation 

Now that we know how important a CDP and marketing automation software are for your business, it’s time to consider how these tools work within your marketing tech stack — because close collaboration is absolutely vital for all your platforms.

Building out your marketing technology toolkit is all about making sure your platforms are working in sync. If you have a system of separate solutions, they need to seamlessly collaborate with each other to make your marketing efforts run smoothly. 

Standalone customer data platforms can’t execute campaigns, and marketing automation tools can’t ingest and build out customer profiles from all the data you have available, so integration between these two platforms  needs to be absolutely perfect. 

That’s why a CDP with native marketing automation is such a valuable tool for marketers. With a platform that incorporates both solutions, you are guaranteed to have your customer data and marketing automation working hand in hand.

What You Get With a Standalone CDP 

With a standalone CDP, you get exactly what you expect: a customer data platform without extra capabilities. It ingests all of a company’s first-party data and builds complete pictures of all of your customers. That’s it.

This data is accessible for use by other systems, like email marketing platforms, campaign management tools, and various online channels, but a standalone CDP cannot execute campaigns. It needs dedicated tools that can make use of the comprehensive data it collects, and a lackadaisical add-on won’t always do the trick.

What You Get With a CDP With Marketing Automation

A customer data platform coupled with marketing automation is the next evolution of a CDP. It combines all the benefits of a standalone CDP with marketing campaign tools, creating a single, powerful, and customer-centric marketing platform.

This gives marketers the complete toolset they need for creating incredible customer experiences by bringing together AI-driven marketing capabilities, real-time analytics, and UX optimization — all natively powered by a CDP.

A CDP combined with marketing automation simplifies workflows and increases productivity since it merges  frequently used tools into one integrated interface. Now you don’t have to connect your platforms and pray that your data is syncing in real time with your automation efforts.  Instead, you can rest assured that it’s all working seamlessly in one platform, with a main hub for you to orchestrate truly unique and highly effective campaigns.

Ecommerce Personalization Powered By a CDP

A CDP plus marketing automation tool can also put your company ahead of the game when it comes to powering ecommerce personalization efforts.

With a CDP, you can confidently map out your customer’s journey, understanding their likes, dislikes, and shopping habits. After you have collected customer data to gain this understanding of your customers, you can begin to personalize content and shopping journeys for them to ensure they can go from browsing to purchase with your brand.

Want to make more sales? You’ll need to gather lots and lots of customer data. 

Customer data is the secret sauce behind marketing campaigns that convert and sow the seeds of customer loyalty. 86% of medium- and large-sized companies consider first-party customer data the most important aspect of their communications strategy. 

Customer data platforms (CDPs) are customer databases that house unified records of your customers, their preferences, and other relevant data. Because they organize all of the customer data you need to hit your business goals, CDPs are indispensable for organizations looking to deliver personalized experiences that drive revenue. 

When it comes to CDP installation, you have two options: building or buying your customer data platform. Before you decide whether to build or buy a customer data platform, let’s take a look at some important factors for your consideration.

Key takeaways:

• Building a CDP from scratch can be pricey. Prebuilt solutions will save you time and money on development and ongoing maintenance.

• Bringing your own CDP to market can take years, while an off-the-shelf CDP can go live in as little as six weeks. 

• Trying to scale a DIY solution can lead to resourcing challenges and compatibility issues with other tools, while out-of-the-box CDPs can seamlessly handle big increases in data volume. 

• Using a single vendor for your CDP means less complexity and more streamlined operations. You’ll also get standardized training and 24/7 support instead of doing it all in house. 

Key Considerations for Your Customer Data Platform

Cost-Efficiency

If you’re looking for an effective customer data platform that doesn’t break the bank, weighing cost-efficiency is crucial when deciding whether to build or buy your CDP.

Building a CDP from the ground up can be expensive. You not only have to invest significant capital upfront, but you’ll also need to update and maintain your CDP over time to keep it running effectively. 

A prebuilt solution allows you to avoid hefty development costs and ongoing maintenance expenses. While you’ll still need to pay for the initial setup and deployment of the customer data platform, prebuilt systems aren’t as expensive in the long run. 

Time-to-Market

You know what they say: time is money. This is especially true when you want to deploy a customer data platform. 

Designing your own platform is an attractive part of DIY CDPs. However, when you have so many decisions to make, you can easily spend years developing and deploying your CDP. When it takes too long to bring your CDP to market, it’s easy to go over your budget and cut costs elsewhere to fund the project.

Buying an off-the-shelf customer data platform saves you an incredible amount of time. For example, Bloomreach Engagement can have you up and running in just six weeks, so your team can spend less time building your CDP and more time actually using it. 

Scalability

Scaling up is a goal for most brands. Expanding your business indicates that your brand is successful and attractive to both customers and investors. When your business scales, you’ll need to make sure that your tools can scale with it. 

A do-it-yourself CDP can be hard to scale, especially when you mix and match several different tools to create one master CDP. This is because some vendors may not accommodate scalability to the extent of others, and if your platforms can’t play nice together, your business suffers.

An out-of-the-box CDP like Bloomreach Engagement is designed to seamlessly expand with your business, ensuring uninterrupted operations even during high-traffic events like Black Friday. In fact, during Cyber Week 2022, Bloomreach handled approximately 1.2 billion user events per day, while consistently maintaining 100% uptime. When you use a CDP like Bloomreach, you can provide exceptional customer experiences year-round.

Customization

Every business is different, so it only makes sense to pursue a CDP that can be customized to fit your needs.

In fact, customization is what drives many teams to build their own CDP. Creating a CDP from scratch is an appealing idea to those who believe they can only get what they need from a CDP when they build it themselves. 

However, that’s not the case. Expert CDP providers also prioritize data flexibility and customization. With Bloomreach Engagement, you can configure capabilities, like custom analytics, reporting, segmentation, and AI prediction models, so that they align with your unique needs.

Single Vendor Advantage

Managing multiple suppliers can be a logistical nightmare. Dealing with several different vendors and systems can lead to problems like inefficiency and disorganized data management.

Unfortunately, that’s what many business owners face when building their own customer data platform. Because building a CDP necessitates that you work with countless vendors to build your own tech stack, you don’t get the luxury of having a solid relationship with one vendor. 

When you buy an out-of-the-box CDP, you don’t need to deal with multiple service providers. With only one company handling your CDP and its upkeep, you can simplify contract management, reduce complexity, and streamline your operations, ultimately leading to cost savings.

System Integrations

System integrations should be top of mind when considering building or buying a CDP. Why? Because if you can’t plug in all of your tools, you can’t use your CDP. 

When building a CDP, dealing with system integrations can become complicated and expensive, causing delays in your implementation. You’ll need to collaborate with each vendor to ensure you understand how to connect their product to others. This process can drag on, especially if your CDP tech stack includes numerous solutions.

Working with one supplier who has already done the legwork in securing integrations saves you from having to do this yourself. For example, Bloomreach offers an impressive 139 integrations with some of the most common tools on the market, so you can streamline your integration process and reduce costs.

Standardized Training

Customer data can take your brand’s success to new heights. Once your CDP is implemented, you’ll need to make sure that everyone on your team can use it properly so you can put that data to good use. 

This can be a challenge for a DIY customer data platform, which will include several different parts and protocols. When you build your CDP, your tech stack isn’t unified and will therefore lack standardized certifications and training materials. This makes it harder to teach your team how to use your new CDP effectively. 

When you buy your CDP, you’re purchasing a single solution that is powered by a single vendor and has its own training system. This empowers you to train your team in one standardized way, which reduces confusion and speeds up adoption. 

24/7 Expert Support

As an ecommerce pro, you understand that Murphy’s law can strike at any time. When that happens, you’ll need round-the-clock support to help you get back to work. 

When you create your own CDP, you are in charge of fixing issues and offering support during platform downtime. While you can seek assistance from individual vendors who can help with specific parts of your CDP, you won’t have a single reliable source for resolving conflicts. This could lead to a costly and time-consuming process to ensure your CDP operates smoothly.

When you buy a prebuilt CDP, you can rely on 24/7 expert support. Trusted vendors provide personalized assistance through global teams, available whenever you need help. This means you can get back to work quicker without the hassle and cost of troubleshooting your platform during downtime.

The marketing industry constantly changes, and new techniques and technologies are being developed daily. One promising development is the growth of generative AI in marketing. Traditionally, marketing relies on machine analysis and human prediction. However, generative AI allows for greater creativity and effectiveness by leveraging the speed of a machine with how about saying ‘super-human’ instead of ‘human-like’ predictive abilities. This rapidly growing technology can automate content generation, design, and strategy with algorithms and machine learning, providing a window into the evolving future of marketing.

With technologies like generative AI and generative attribution reshaping marketing aspects ranging from personalized content production to predictive analytics, staying on top of the latest advances is critical to keep your brand competitive. This blog will help to do that by exploring the impact of generative AI on the marketing industry and the future of marketing.

What is Generative AI?

Generative AI focuses on producing unique and fresh content. It consists of a generator and a discriminator that work together to create realistic and distinctive outputs. The discriminator evaluates the generated samples and distinguishes them from accurate data, while the generator produces new pieces like images, sounds, or texts. This process is repeated until the generator can produce outputs indistinguishable from real data. Generative AI has various applications, including text creation, virtual characters, images, and videos. It can revolutionize entertainment, design, and marketing by offering original and innovative ideas.

How Does Generative AI Work?

Generative AI has been in existence since the 1960s. While many people associate AI with GPT-based models, it goes beyond that and focuses on using computers to create new and unique pieces of data autonomously. It can include sentences, images, consumer touchpoint journeys, and more. The discussion around generative AI today tends to focus on GPT-based applications, but it is important to recognize this technology’s broader scope and potential.

What are the Uses Cases of Generative AI?

Generative AI has a wide range of applications across various industries. Some of the most prominent applications of generative AI include:

Efficient Data Analysis and Predictive Modeling

Generative AI algorithms can process and analyze these extensive datasets much faster and more accurately than traditional methods, enabling marketers to uncover patterns, trends, and correlations that would be otherwise difficult, if not impossible, to identify. Moreover, it can help in predictive modeling, using huge datasets, including historical data, to anticipate future trends and outcomes, enabling organizations to address potential challenges and seize opportunities proactively.

Recovered Consumer Journeys

Generative AI has revolutionized data analysis, enabling the recovery of consumer journeys that were previously unavailable due to missing data. These customer journeys provide valuable insights into consumer behavior and preferences. It uncovers hidden patterns and trends in consumer journeys that were previously inaccessible.

Personalized Content Creation

With generative AI, marketers can effortlessly create content tailored to individual preferences, interests, and needs. Whether it is personalized product recommendations, customized emails, or targeted advertisements, it helps them deliver content that resonates with their audience on a personal level. It not only enhances user engagement but also increases conversions and brand loyalty.

Improved Customer Experience Through Chatbots

Generative AI-powered chatbots enhance customer experience by providing instant and personalized assistance. These bots understand natural language and context, engaging in human-like conversations and offering accurate responses. Marketers can use it to provide 24/7 customer support, efficiently resolve queries, and ensure seamless interactions.

Targeted Advertising and Recommendation Systems

Generative AI algorithms enable advertisers to analyze vast amounts of data quickly. This analysis helps them understand consumer preferences, interests, and behavior patterns. As a result, they can create tailored advertisements that resonate with their target audience. It leads to higher conversion rates and better marketing ROI. Generative AI also powers recommendation systems that analyze user data and provide personalized suggestions based on preferences and past behavior. It enhances the user experience, increases engagement, and drives sales.

Democratized Data

A lack of technical expertise can significantly challenge marketing teams in handling and analyzing data quality. However, generative AI has eliminated this barrier. Marketing teams no longer need data scientists or IT professionals to access and interpret data. Generative AI tools empower marketers to quickly understand and use data for informed decision-making. This data democratization improves marketing campaign efficiency and effectiveness, allowing teams to target the right audience, optimize content, and personalize customer experiences.

What are the Benefits of Generative AI in Marketing?

In the field of marketing, generative AI offers several advantages that can greatly enhance advertising and promotional efforts. Some advantages of Generative AI in marketing include the following:

Increased Efficiency and Productivity

Marketers utilize generative AI to automate tedious tasks and procedures, enabling them to concentrate on strategic and creative aspects of their business while eliminating human error that impacts data quality. This technology simplifies and enhances the creation of tailored and targeted content on a large scale. With generative AI, companies can optimize their marketing efforts, improve campaigns, and successfully reach their intended audience efficiently.

Enhanced Customer Engagement and Satisfaction

Generative AI can analyze data to understand customer behavior and preferences better. It allows marketers to tailor their strategies accordingly. The technology enables personalized recommendations, targeted advertising, and interactive experiences, which resonate with customers on a deeper level.

Cost-effective Marketing Strategies

By utilizing generative AI technologies, marketers can automate and optimize various aspects of their campaigns, reducing the need for manual labor and streamlining processes. This automation saves time and reduces costs associated with traditional marketing methods. It analyzes data, identifies patterns, and generates personalized marketing content for more targeted and effective marketing campaigns. Due to this cost-effectiveness, marketers can allocate resources efficiently and achieve better returns on marketing investments.

Ability to Learn Constantly

With access to more data over time, generative AI algorithms improve their understanding and ability to cater to a customer’s specific needs. As they learn from user interactions and feedback, these AI systems continuously enhance their capabilities, allowing them to provide more accurate and personalized solutions.

What are the Best Strategies for Implementing Generative AI for Marketing?

Implementing generative AI in marketing requires careful planning and consideration of the best strategies. Let’s look at the best strategies for implementing generative AI for marketing.

Clearly Define Marketing Goals

Defining your marketing goals includes identifying specific objectives, like increasing brand awareness, improving customer engagement, or driving sales. Having clear goals helps align generative AI strategies with your overall data-driven marketing strategy. This clarity guides implementation and ensures the effective use of AI technology to meet marketing objectives.

Identify Relevant Data Sources

Identifying relevant data sources involves determining the types of data that will be most useful. These data sources could include customer demographics, purchasing behavior, social media interactions, website analytics, and more. Marketers should utilize these relevant data sources to train the generative AI on accurate and valuable information, ensuring more effective marketing initiatives.

Train and Fine-tune Generative AI Model Using Collected Data

One effective strategy is to train and fine-tune it using collected data, which includes gathering relevant data from customer interactions, market trends, and historical marketing campaigns. Marketers can create more personalized and effective marketing materials by continuously refining the model through iterations. The trained model can generate content such as ads, social media posts, and email campaigns that resonate with the target audience.

Evaluate and Optimize Generated Marketing Content

Generative AI can benefit marketers by automating marketing content creation. This can be done by analyzing engagement, click-through rates, and conversion rates. Ongoing evaluation and optimization maximize the impact of generative AI in marketing campaigns.

Find the Ideal Generative AI Tool

New technologies are often adopted in the marketing industry without assessing if they genuinely address a particular problem. Marketers must exercise caution when embracing it to follow trends. Instead, they should evaluate whether this technology aligns with their goals and enhances their product or campaign.

Prioritize Security Measures

Marketers must guarantee that the AI tool has the necessary security measures to protect sensitive data. For example, allowing anyone to upload data to platforms like ChatGPT or other open-source tools can potentially expose confidential information. Therefore, it is crucial to thoroughly evaluate the security protocols of the AI tool to ensure the secure handling of proprietary data.

What are the Challenges and Potential Risks of Generative AI?

As AI systems become more advanced and capable of generating increasingly realistic content, challenges arise regarding the potential misuse of this technology for other purposes. Some of the challenges and risks associated with generative AI are as follows:

Representative Data is Used for Model Training

One of the challenges is the reliance on representative data for model training. Just like humans, AI models learn from their environment, and if they are trained on biased or limited data, they will reflect those biases and limitations in their output. If the training data does not accurately represent the real world, the AI model cannot build an accurate real-world model, which can lead to skewed or inaccurate results. An AI model possesses limited knowledge and understands only what it has been programmed to learn.

Organizational Readiness

Although some applications of Gen AI are relatively simple and require low levels of readiness, more complex problems demand careful consideration of various factors. Privacy, legal, regulatory, and ethical concerns all come into play when dealing with these advanced applications.

Generative AI has gained significant attention for its ability to mimic human intelligence and generate seemingly independent responses. However, it is important to understand that despite its impressive capabilities, it functions more like a highly skilled parrot. It reads the context and responds accordingly, but it is not truly independently intelligent. Instead of replacing humans, it serves as a tool to amplify human abilities.

Privacy and Data Security Concerns

As generative AI models learn from large datasets, there is a concern in data privacy that these models may inadvertently incorporate sensitive or private information into the generated content. This could potentially lead to the exposure of personal data or the creation of misleading or harmful content.

Legal and Regulatory Challenges

Many companies operating in highly regulated industries are hesitant to fully embrace AI due to concerns about the lack of established legislation and regulations governing its use. These companies are adopting a cautious approach, waiting for clear guidelines to be established before fully implementing Generative AI technologies.

Ethical Considerations

One of the main challenges is the potential for misuse or malicious intent. Since Generative AI can create realistic and convincing content, there is a risk of using it to spread misinformation, generate fake news, or even create fake videos that can be used to deceive or manipulate people. There are also concerns about copyright infringement when using it to create content that may be similar to existing works.

What is the Future of Marketing with Generative AI?

As AI marketing continues to advance, marketers can build and recover models more effectively. They can understand consumer journeys with greater accuracy and recover valuable insights from them. By utilizing generative AI, marketers can close the loop more efficiently, taking faster and more informed actions based on each consumer’s specific interests and actions. This goes beyond traditional targeting and retargeting methods, as it allows for creating custom content tailored to each client’s unique preferences and probabilities.

Moreover, it holds immense potential for marketers with access to large CRM datasets. Marketers can go beyond traditional segmentation and personalize promotions and pricing for each individual consumer. This level of customization allows marketers to target their audience more effectively and deliver personalized experiences that resonate with consumers.

Commonly Asked Questions about Generative AI

What is generative AI vs. AI?

Generative AI refers to artificial intelligence systems that can generate new and original recommendations based on complex analysis, like media plans and content, such as images, music, or text, based on patterns and data it has learned. On the other hand, AI generally refers to the broader field of computer systems that can perform tasks or make decisions that typically require human intelligence.

What is generative AI good for?

Generative AI is good for generating new and unique content, such as artwork, music, and writing. It can also be used for data augmentation and for creating realistic simulations for forecasting or scenario building.

How does generative AI affect digital marketing?

Generative AI significantly impacts digital marketing by automating and enhancing various tasks such as complex marketing analysis like media mix modeling, attribution, media scenario planning, content creation, personalized advertising, and customer segmentation. It enables marketers to access powerful, robust marketing insights for decision-making and deliver more targeted and relevant messages to their audience, improving engagement and conversion rates.

How does generative AI affect marketing analytics?

Generative AI greatly enhances marketing analytics by generating more reliable, representative unbiased insights and predictions based on large amounts of data. It automates the analysis process, improves data quality and representativeness, identifies patterns and trends, and provides valuable recommendations for marketing strategies.