Are your traditional marketing strategies not producing the expected outcomes? Assuming the answer is yes, we always strive for better outcomes. In 2024, it is mission-critical that you adopt a more personalized and data-driven marketing strategy.
Traditionally, decisions are still being guided by assumptions and generalized marketing data. A modern approach needs to recognize the diverse responses of individuals and the influence of their unique characteristics, attitudes, and behaviors.
Today, with the advancement of generative attribution technology coupled with access to detailed data, marketers can optimize their efforts by understanding how individuals respond across various platforms and optimizing and personalizing messages accordingly. It is time to move away from outdated methodologies and embrace a more targeted approach to marketing.
Key Takeaways Covered in this Post:
Media Models Assume Everyone Reacts Uniformly to Marketing Efforts
Reconstructing Privacy-Compliant Customer Journeys Is Possible
Old Marketing Strategies: Automated Data to Overcome Outdated Methods
The Advantage of Granular Insights in Today’s Media Environment
Unlocking the Power of Generative Attribution in Modern Marketing
Media Models Assume Everyone Reacts Uniformly to Marketing Efforts
Traditional marketing models assume that all individuals respond uniformly to marketing efforts. This isn’t true, as each person possesses distinct preferences, needs, and behaviors that significantly shape their reaction to marketing messages.
It is imperative for marketers to recognize and comprehensively understand these individual variances to tailor their traditional marketing strategies and optimize their impact adeptly. Today’s tools need to support real market conditions. Numerous factors, encompassing demographic characteristics, personal experiences, cultural influences, and psychological elements, can profoundly sway an individual’s perception and deliberation to engage or abstain from a marketing message. By duly acknowledging and catering to these dynamic individual disparities, marketers can proficiently craft a more customized and influential brand campaign that deeply resonates with their intended target audience. Newer tools are now available to marketers to understand all of these factors along the buyer’s path to conversion.
Reconstructing Privacy-Compliant Customer Journeys Is Possible
Marketers want to customize their strategies to specific audiences and customer segments because it maximizes marketing ROI and effectiveness. However, privacy concerns and data challenges are becoming more challenging every day, rendering the assembly of full customer journeys very hard, if not impossible. Many marketers have had to revert to outdated marketing mix methods that rely only on aggregated data and broad assumptions to drive their marketing strategies. But with advancements in technology, like machine learning and generative attribution, marketers can intelligently reconstruct full customer journeys using multiple available data sources without sacrificing the important ability to generate detailed and personalized marketing allocation recommendations.
Old Marketing Strategies: Automated Data to Overcome Outdated Methods
Outdated mix methodologies in marketing were once the only thing marketers had but have become obsolete due to advancements in data and the generative attribution approach. These old methods were devised to overcome limitations like the slow arrival of data or expensive technology. With the pace of digital marketing advancement, these outdated methods have lost their efficacy.
Marketers must base their decisions on accurate and current data quality in our contemporary, fast-paced, and data-centric world. Marketers can obtain valuable insights into consumer behavior, preferences, and market trends through automated data analysis. This allows them to recast their old marketing strategies and yield superior outcomes affordably. The leap resulting to data-driven decision-making helps marketers avoid missed opportunities and squandering of marketing resources.
The Advantage of Granular Insights in Today’s Media Environment
There is an increasing need for detailed insights in today’s media environment. Traditional generalized data methods are limited as they only offer a surface-level understanding of audience behavior and preferences, hindering marketers from making deeply informed decisions. However, advancements in AI and generative attribution have made obtaining detailed insights more accessible. This enables marketers to delve into specific data points and comprehensively understand their target audience.
Furthermore, the greater speed and lower cost of data processing are crucial factors in facilitating optimization across this deep detail. Analyzing and interpreting data quickly and cost-effectively allows marketers to pinpoint their actions and make real-time adjustments based on audience engagement and preferences.
Unlocking the Power of Generative Attribution in Modern Marketing
Traditional marketing strategies must be improved by capturing the complex consumer journey. With so many channels for brands to connect with their audiences and countless creative options, coupled with data privacy regulations, an experienced media planner can’t do it alone. They need the assistance of advanced measurement tools for structured data to handle this complexity, making it easier and faster to optimize their customer engagement.
Traditional marketing measurement tools, like multi-touch attribution and media mix modeling, provide a limited and biased view of communication influence. Marketers need a system that focuses on the customer when measuring results, and AI offers an opportunity to transform how data is used for planning, measuring, and optimizing campaigns.
With AI’s predictive capabilities, marketers can understand consumer journeys in detail and create campaigns that better align with their goals. Generative AI enhances and produces novel data to empower marketers to dynamically and effectively develop campaigns. In the end, this generative attribution approach allows marketers to unlock the full potential of their marketing strategies.
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.
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, 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.
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.
Programmatic advertising refers to the practice of automating media buying and creating digital ads with the use of marketing technology. For an effective programmatic advertising strategy, use an automated workflow to effectively deliver ads to your audience.
What is programmatic advertising?
Programmatic advertising is the use of advertising technology to buy and sell digital ads. Programmatic advertising serves up relevant ad impressions to audiences through automated steps, in less than a second.
Programmatic advertising uses an automated process, within advertiser-defined parameters, to purchase digital ad inventory across the web, mobile, apps, video, and social media. Programmatic advertising uses workflow automation and machine learning algorithms to deliver the most effective ads to audiences based on a variety of signals, like shopping patterns.
Programmatic ad buying takes place when consumers click on a publisher’s website, and the publisher puts the ad impression up for auction through header bidding and one or more SSPs. Then, the DSP bids on behalf of the advertiser for that impression based on campaign’s strategies, budget, creative sizes, and other factors. The publisher automatically assigns impressions to the winning bidder—the advertiser/DSP offering the highest CPM (cost per mille, or the cost per one thousand advertising impressions). The ad is instantly served on the website.
What is programmatic media buying?
Programmatic media buying uses an automated process to buy digital space for ads. Programmatic media buying also cuts down on wasted ad impressions by serving ads to relevant audiences and minimizing ad fraud risk—making it cost-efficient, as well.
Why is programmatic advertising important?
Programmatic advertising is important because it can save time in the creation of your ads and campaigns. Not only does it automate the process, it analyzes your campaign performance to help you optimize for success.
What is the difference between programmatic advertising and display ads?
Display ads could be a type of programmatic ad. While display ads are the ads themselves, programmatic advertising is the process of distributing these ads. Display advertising is also a type of digital advertising, but it does not necessarily need to be programmatic.
What are the benefits of programmatic advertising?
Traditional advertising requires time to develop requests for proposals (RFPs) and quotes, conduct negotiations, and create insertion orders. With programmatic, the process is more streamlined. Advertisers can buy and place ads quickly through Real-Time Bidding (more on that later).
2. Reach
Programmatic allows advertisers to reach audiences based on different marketing signals, such as shopping and browsing activity across devices.
3. Transparency
With traditional media buying, ads are purchased in bulk and advertisers have little control over the inventory and placement. With programmatic advertising, advertisers know where their ads will appear and can have greater confidence that they’ll show up in relevant brand environments.
4. Measurement
Programmatic offers real-time measurement and optimization to drive maximum results.
What are the challenges of programmatic advertising?
Potential challenges with programmatic advertisng include commoditization, transparency, and a steep learning curve. Despite the potential challenges, brands that don’t adopt to programmatic advertising strategies may be missing out on the benefits of efficiency, reach, transparency, and accountability.
1. Commoditization
As with other forms of media, programmatic is commoditized. Everyone is playing in the same space and some types of ad supply may be scarce. With the growing popularity of Programmatic Guaranteed, an increasing amount of non-commoditized inventory is becoming available to advertisers programmatically.
2. Transparency
While programmatic provides advertisers with greater control over viewability and where their ads appear, brand safety and transparency remain top-of-mind for advertisers. Amazon Ads and many third-party solutions address these concerns to help preserve brand trust.
3. Learning curve
Programmatic requires a learning curve that may initially feel overwhelming. Working with partners, agencies, or Amazon Ads directly can help advertisers to ease their foray into programmatic.
What is a demand-side platform?
A demand-side platform (DSP) is programmatic software for advertisers. A DSP helps facilitate media buying from numerous publishers through SSPs, ad exchanges, ad networks, and direct integrations. DSPs help brands and agencies (the demand side) determine which impressions to buy and at what price (the supply side). Advertisers can select audiences based on demographics, shopping patterns, browsing behavior, and many other signals.
What is a supply-side platform?
A supply-side platform (SSP) is programmatic software for publishers to facilitate sales of advertising impressions via ad exchanges. By connecting publishers with multiple ad exchanges, demand-side platforms, and networks at once, SSPs let suppliers sell impressions to a greater pool of potential buyers, and allows suppliers to set the bidding range to maximize their revenue.
What is Real Time Bidding (RTB)?
Real-time bidding (RTB) is a way to buy ads programmatically. With RTB, advertisers can participate in an auction when an impression becomes available. If their bid wins the auction, their ad is displayed instantly on the publisher’s site. RTB is not only efficient, but it helps advertisers focus on the most relevant inventory.
What is header bidding?
Header bidding is a technology that allows publishers to simultaneously request bids from multiple demand sources and send the bids to their ad server to conduct an auction. The ad server determines the winning bid and renders the ad on the site. Allowing multiple bidders to bid on the same inventory at the same time increases competition, but offers advertisers the opportunity to access premium inventory with these publishers.
What is an ad exchange?
In programmatic advertising, an ad exchange is an online marketplace where advertisers, agencies, demand-side platforms, publishers, and supply-side platforms can bid on advertising inventory from various publishers using RTB. Advertisers determine the price by participating in the bidding process. Additionally, with an ad exchange, advertisers gain visibility regarding where their ads will appear.
How do I run programmatic ads?
Once you’ve established your campaign goals, such as driving new product discovery or increasing sales; defined your ad types, such as display, video ads, or in-app ads; and have set up the DSP, you’re ready to get started.
To maximize your impact at the lowest cost, think about the various components of your campaign setup, such as duration and audience size, as this will help determine the CPMs you need to win your bid. At this stage, you can estimate the budget you need for the campaign and develop a bidding strategy.
CPM bid levels vary by media types and creative units. Generally, display demands the lowest CPMs whereas video demands the highest CPMs.
With programmatic advertising, you have the opportunity to evaluate campaign and creative effectiveness mid-campaign through reporting on metrics such as CTR, CPC, overall spend, and conversions. You can then optimize your campaign based on these insights.
How much does programmatic advertising cost?
The cost of programmatic advertising varies and is generally based on CPM. CPMs range in price based on who you’re trying to reach, supply settings, advertising budget, and the amount of time the campaign has to run. Adjusting these factors will change the end price for the campaign.
For example, holding everything else constant, when you’re reaching a broad audience, the CPM is typically less than when you are trying to reach a more niche audience. Targeting and ad group variables impact the CPMs required to secure inventory. With programmatic, advertisers are charged prices through RTB.
How do I measure programmatic advertising?
Impressions, clicks, and actions are three main ways to measure your programmatic advertising campaign, but they are just starting points. You can also measure key performance indicators (KPIs) that map back to your business objectives. Below is a helpful guide; utilize the metrics and OKRs most relevant to your objective:
Goal
KPIs
Branding and awareness – What is x?
Reach/unique reach, average frequency, share of voice, brand lift, views
Interest and consideration – I want to know more about x.
Open marketplaces are also known as RTB. This is a type of programmatic advertising where bids are placed for ad space and impressions.
2. Private marketplaces
Private marketplaces are by invitation only. Advertisers can place their ads after receiving placement options from publishers.
3. Preferred deals
Preferred deals are given priority placement. This type of advertising is a joint collaboration between ad sellers and publishers on the placement or impressions.
4. Programmatic guaranteed
Programmatic guaranteed advertising promises a certain amount of impressions for your ads. This is decided manually, without bidding.
Bajaj Finserv and their media agency, Arm Worldwide, worked with Amazon Ads to identify relevant audiences for their Insta EMI card. Once they were ready, they created a campaign with display ads from Amazon DSP, which would direct consumers to an Insta EMI application webpage where they could learn more.
Lenovo worked with Amazon Ads on strategic campaigns and high-impact placements to help them connect with the growing gaming community in the UAE. They used Amazon DSP video, display banners, and more to showcase their cutting-edge laptop devices.
Groupe SEB, a manufacturer of domestic appliances and cookware, used Amazon DSP campaigns across both Amazon-owned and third-party supply networks. This helped them to engage audiences and bring them relevant products, while also driving ROAS.
Reckitt, a producer of health, hygiene, and home products, worked with Amazon Ads and their local agency Cadastra to create campaigns for in-market and lifestyle audiences. By using Amazon DSP, they were able to remarket to prior customers.
How do effective marketers predict the impact of their strategies with precision?
There’s a science to this craft. Beyond the realm of simple A/B testing lies the multi-dimensional response curve, a sophisticated model rooted in statistical analysis and predictive algorithms. Here, where the interplay between variables is carefully deconstructed, we discern complex consumer behaviors, thus enabling targeted strategies that resonate with nuanced market segments.
Key Takeaways Covered in this Post:
Decoding Multi-Dimensional Response Curves
The Limitations of Traditional Models in Marketing
The Importance of Multi-Dimensional Response Curves in Marketing
Analyzing Response Curves Data for Decision-Making
The Future of Multi-Dimensional Response Curve in Marketing
The Rise of Generative AI and Machine Learning
Decoding Multi-Dimensional Response Curves
A multi-dimensional response curve is a complex framework designed to model consumer interactions. This response curve considers demographics and temporal dynamics to create a detailed picture of market responses. Marketers use multiple axes to plot variables, allowing them to understand the cause-and-effect relationships within their data more comprehensively and non-linearly.
Sophisticated models, like generative attribution, help decision-makers navigate marketing’s complex landscape. Once seen in isolation, variables reveal their interdependencies through multi-dimensional analysis. This allows for strategic alignments that more clearly and predictably forecast consumer engagement.
For example, marketers can identify target audience segments by analyzing the response curve for a new product launch based on variables like age, income level, and time of day. This information enables them to tailor marketing campaigns and messaging to effectively engage these segments, leading to data-driven decisions that optimize marketing strategies and maximize consumer engagement.
The Limitations of Traditional Models in Marketing
Traditional models overlook the fact that various factors influence consumer behavior. Due to historical data, technology constraints, and the simple fact that factors can differ from person to person, traditional models lack accuracy when predicting consumer response. Traditional models also neglect to consider the multi-dimensional nature of consumer behavior. They only consider the relationship between media exposure and consumer response, ignoring other influential factors like XXXX, XXXXX, and XXXX.
Ultimately, this ‘lack of accuracy’ and ‘neglection’ results in
The Importance of Multi-Dimensional Response Curves in Marketing
Embracing multi-dimensional response curves is important to overcoming traditional media measurement limitations. These curves provide a better understanding of how different factors affect consumer response. By considering factors like demographics, locations, attitudes, time, and media exposures, marketers can gain a more comprehensive picture of how exposure to advertising impacts buying decisions. This produces more insightful data for effective brand campaign decision-making.
Demographic factors like age, gender, income, and education greatly affect consumer response to advertising. Household mix and cultural influences also play a role in shaping consumer behavior and response patterns. By understanding how these factors interact with media exposure, marketers can customize their campaigns for specific target audiences.
In marketing analysis, three important factors greatly influence consumer behavior and shape response curves – Education, Household Mix, and Culture. It is essential for marketers to understand these factors to accurately predict outcomes, optimize strategies, and allocate resources efficiently. Let’s examine each factor in depth.
Education
The level of education an individual possesses can impact their decision-making process, comprehension of marketing messages, and perception of value. Marketers must consider the educational background of their target audience to customize their messaging and communication strategies accordingly. By understanding their audience’s educational preferences and requirements, marketers can develop more relevant and impactful campaigns that resonate with consumers and generate higher response rates.
Household Mix
Family size, age, and roles influence purchasing decisions and response rates. The composition and dynamics of a household greatly impact response curves.
For example, households with children may have different priorities than older adults. Marketers must consider household mix when designing marketing strategies to target the right audience effectively. Marketers can customize their offerings by understanding the unique needs of different household compositions to maximize response rates and engage customers.
Culture
Various cultures have different values, beliefs, and norms that impact how people interpret and react to marketing messages. Marketers must be mindful of cultural nuances and adjust their strategies accordingly. By grasping the cultural context of their target market, marketers can create campaigns that deeply resonate with consumers, fostering trust and forging strong connections. Considering cultural factors when analyzing response curves enables marketers to optimize their strategies and ensure their messages effectively reach the target audience.
The chart above shows the following curves – Frequency, Spend, and Reach. The optimization chart projects onto a response surface in a multi-dimensional space. It considers market orientation, media exposure level, and consumer characteristics. This differs from a typical curve in 2D space because it captures more information. The optimization engine determines the best allocation of spending. The curves have equal slopes, and the budget is fully utilized.
Analyzing Response Curves Data for Decision-Making
Various factors influence a person’s response rate in media and advertising. These curves show how consumers react to pricing, promotion, and distribution changes. By understanding these interactions, marketers can increase their accuracy of predicted outcomes, optimize tactics, and allocate resources accordingly. Interpreting curve data strategically guides brands to make optimal decisions that align with market dynamics and their consumer preferences.
The Future of Multi-Dimensional Response Curves in Marketing
Today, generative AI and machine learning provide multi-dimensional response curves, offering more profound insights into complex consumer landscapes.
AI-drivenpredictive analytics is becoming more advanced as technology enables faster, more affordable processing of huge datasets. Advancements in big data processing facilitate granular analysis down to the individual media impression level.
Marketing response curves can now change from static models to dynamic, adaptive frameworks adapting to variables in a fluid marketplace and more accurately predicting consumer actions.
Generative attribution enables a new era of hyper-personalized marketing. These approaches adjust in near real-time to optimize and personalize marketing strategies based on predicted changes in consumer behavior.
Advancements in big data processing are leading to a paradigm shift in maximizing marketing efficiency. These advancements enable granular analysis down to the individual media impression level. This unlocks the potential for marketers to fine-tune campaigns with unparalleled precision.
The Rise of Generative AI and Machine Learning
AI significantly impacts marketing by using iterative learning and pattern recognition to predict future behaviors. Traditional linear, two-dimensional analysis, which is widely used for developing response curves, has become obsolete.
Generative AI and machine learning together transform marketing dynamics. They anticipate consumer trends to inform decision-making processes. This intelligence-driven marketing landscape empowers marketers to navigate consumer engagement confidently with minimal risk because many more influences on response are analyzed.
Generative AI and Machine Learning insights allow the creation of adaptive response curves that react to market changes instantly. Here’s how:
Generative AI and machine learning enable a new analytical paradigm where response curves are dynamic roadmaps based on media exposure impact across different types of people.
Machine learning algorithms analyze enormous datasets to uncover complex advertising exposure and consumer response patterns.
This capability ensures that marketing strategies remain agile and data-driven, providing a competitive advantage in the market.
You need to get started using programmatic buying tools, but you’ve never done it before, and you don’t know where to begin. Join the club. We get it. It’s the same reason we haven’t learned to cook for ourselves yet.
Here’s the good news: We’ve got experts at Basis Technologies who know the ins and outs of programmatic advertising. All you have to do is ask the right questions. Lucky for you, we’ve asked the basic questions and we’ve come equipped with answers (and, lucky for us, everyone assures us there is no such thing as a dumb question).
Understanding Programmatic Ad Buying
To start: Programmatic is a very broad term. Simply put, it’s technology that automates digital media buying. This can include automating anything from rate negotiation and campaign set up to optimizations and actualizations. One of the primary buying tools you have at your disposal is a DSP.
If there really are no dumb questions, then can I ask: What is a DSP?
A demand side platform (DSP) is an automated ad buying platform, where advertisers and agencies go to purchase digital ad inventory. Examples of ad inventory include banner ads on websites, mobile ads on apps and the mobile web, and in-stream video. DSPs are integrated into multiple ad exchanges.
I’ve heard of a SSP. Is that the same acronym and I’ve been typing it wrong into Google this whole time?
Nope, it’s not the same thing, but it is similar in concept. Supply-side platforms, or sell-side platforms (SSPs), facilitate the sale of publisher inventory through an ad exchange. SSPs offer services such as minimum bid requirements in order for the publisher to maximize how much their ad space sells for. The difference is that DSPs are for marketers and SSPs are for publishers. SSPs (like DSPs) are plugged into multiple ad exchanges.
You keep mentioning ad exchanges. What are those? And why do DSPs and SSPs both need to be connected to ad exchanges?
Think of the ad exchange as the “go-between” in the automated buying world. An ad exchange is a digital marketplace that enables advertisers and publishers to buy and sell advertising space via real-time bidding (RTB). Meaning the ad exchange announces each impression—with the inventory flowing through DSPs and SSPs—in real time and asks buyers if they are interested in buying said impression and at which price.
All of this makes sense now, but I still don’t understand why I should use a DSP!
In order to understand why DSPs matter, it’s important to remember where the need came from and how the ad industry operated before automated buying. Traditionally, if you were a media buyer at an ad agency, the buying process was facilitated through human beings—it was you (the advertisers), the publishers (website where ad will appear), an audience (the viewer of the ad), and a bunch of spreadsheets and emails going back and forth negotiating prices. This process was complicated, time-consuming, and often error-prone. DSPs allow advertisers and agencies to buy across a lot of sites at the same time—and all of this is done instantly and efficiently, usually before the webpage loads.
How do I know which Demand side platform is right for me?
There are many DSPs in the programmatic world to choose from. Choosing the right DSP for you depends on a number of factors, like what type of data you need (first-party or third-party) and how many ad exchanges the DSP is plugged into, because that can affect reach. DSPs like Basis DSP give you access to over 40 billion daily impressions across all devices and channels. Other things to consider include cost, how much training and hands-on support you prefer, and ease of use—many DSPs have multiple, clunky or confusing user interfaces, which require a lot of education.
Speaking of training, I’m glad you mentioned that. What if I sign up for a Demand Side Platform and find out I have no idea what I’m doing?
Some DSPs come with a full team of experts, offering you everything from full-service to self-service and everything in between. With Basis DSP, you’ll start with a three-month platform training program, offering you an overview of programmatic, a walk-through of the interface, and best practices for campaign creation and optimization. Ongoing support is available in the form of a customer success manager and resources to keep you informed—like new feature webinars, best practice guides, and new quarterly business reviews.
Customer data platforms (CDP) and data management platforms (DMP) are marketing and advertising tools (in that order). They have similar sounding acronyms and, in some ways, work in the same way. For example, they both capture and organize data, use existing data, generate analysis and reports, and help to create a single customer view. With a CDP and a DMP, digital marketers can personalize their marketing campaigns, see how effective those campaigns were, and drive leads.
But to maximize your marketing results, there are distinct differences between the two platforms that you need to understand.
How to use a customer data platform and a data management platform
Data management platform
A data management platform collects, segments, analyzes, and stores anonymous customer data from various sources. Advertisers (mainly) use this unified, segmented data to effectively target (and retarget) advertising campaigns to their intended audience. Designed primarily as an adtech tool, a DMP can also drive product recommendations on your website for each unique visitor.
Examples of how to use a DMP effectively include:
Leveraging audience data to identify any new customer segments and reach those target audiences through various paid media channels
Using this audience data to personalize interactions
Learn more about DMPs
Customer data platforms
A customer data platform is a marketing solution that collects data from your existing customer database, website, mobile app, and CRM to customize marketing and content for current customers. It’s an ideal solution for any/all remarketing efforts.
Both platforms handle first-party data (direct from the customer, CRM and/or marketing automation database, or purchase transactions), second-party data (data provided from other companies, such as partners, resellers, etc.), and third-party data (data from multiple sources).
Both CDPs and DMPs collect the same types of data, but what they target differs. DMPs primarily pursue third-party data (cookies and segmented customer IDs) and then store that data for a short time. CDPs focus on structured, semistructured, and unstructured PII first-party data.
A CDP stores this data over long periods of time so marketers can build in-depth, accurate customer profiles and nurture customer relationships. And a CDP can share and draw data with any system (CRM or DMP) that needs it (and has it) to influence all types of marketing.
CDP vs. DMP—User profiles, data selection, and data capture
User profiles for DMPs segment and categorize people tied to a cookie’s lifespan to capture their anonymous behavioral data.
Data selection involves several field values to collect the necessary data. Yet, as part of the field data, DMPs can gather important insights, including when people visited a website, how long they spent there, and what type of information they read on it. But to get the most out of DMPs, you need to turn to analytics tools to extract more patterns.
CDPs avoid anonymous data and focus on specific data that identifies individual customers. An email address is one example of the type of customer identifiers used by CDPs.
The role of CDPs and DMPs in your marketing strategy
Data management platforms Each platform can play a role in your marketing strategy. Through access to historical data, both platforms can illuminate and inform your digital marketing strategy, but in very different ways. For example, DMPs are effective for digital channels and audience segmentation.
CDPs, on the other hand, are beneficial for social media websites, offline interactions, and insights into customer needs and purchase behavior. With a CDP system that manages data, you’ll better understand customer needs and expectations based on their purchase behavior and past interactions with your brand.
Knowing when to use or choose a data platform
Deciding on whether to use a CDP, DMP, or both comes down to:
Understanding the differences between the two platforms
Determining how each platform can help you achieve your marketing objectives.
Knowing how you want to use your data
Establishing if you can dedicate enough resources to using these platforms to optimize their potential
Not CDP vs. DMP, but CDP and DMP
A CDP and DMP can work together. However, if you need third-party data for short-term customer leads and conversion, you should work with a DMP. If you seek long-term customer engagement that requires first-party data, you should work with a CDP. Both platforms offer ways to enhance the customer experience (CX) and can help you create, provide value, and maximize return on investment (ROI).
Depending on the type of CDP you select, there are also opportunities to combine these platforms to take advantage of more marketing opportunities. For example, you can use DMP data in real time to personalize the interaction with first-time site (anonymous) visitors to establish and maintain trust. You can also deepen your customer profiles with the third-party data that a DMP delivers.
CDPs draw data from DMPs and share information back with them. The two systems work well together, with DMPs driving in new prospects and leads and CDPs helping brands connect and engage with them. So when a DMP is integrated with a CDP, you can gain access to first-party data that shows what customers are doing beyond their interactions with you. This insight helps you find out more details about what they want or need.
Customer relationship management (CRM) is a complete software system that manages customer relationships, but it isn’t a single solution. To effectively manage, analyze, and improve your customer relationships, you need a comprehensive set of cloud solutions that supports your organization at every customer interaction point.
Which is why your CRM solution should include a sales cloud, service cloud, ecommerce cloud, and marketing cloud, as well as a customer data platform (CDP) that can combine online, offline, and third-party data sources for an always up-to-date customer 360 view.
What is a CRM system?
A CRM system gathers, links, and analyzes all collected customer data, including contact information, interactions with company representatives, purchases, service requests, assets, and quotes/proposals. The system then lets users access that data and understand what happened at each touchpoint. Through this understanding, a complete customer profile is developed, and a solid customer relationship is built.
Customer data can also be aggregated to populate incentive compensation modeling, sales forecasting, territory segmentation, campaign design, product innovation, and other sales, marketing, and customer service activities. CRM tools and software help you streamline the customer engagement process, close more sales deals, establish strong customer relationships, build customer loyalty, and ultimately increase sales and profits.
CRM tools have almost always been seen as sales tools. However, over time, these solutions have extended their reach and become integral to marketing, ecommerce, and customer service functions.
The power of customer relationship management is derived by constantly gathering customer data, analyzing that data, and then using those insights to deepen relationships and improve business results. It allows any customer-facing employee to convey, “We know you, and we value you.”
A set of data-driven CRM tools supports you beyond the sales process, which is crucial to business performance. With the in-depth knowledge of your customers, you can:
Offer and sell new, add-on products—at the right time in the right way at the right price
Help customer service teams resolve issues faster
Help development teams create better products and services
CRM software supports strong, productive, loyal customer relationships through informed and superior customer experiences. The goal? To improve customer acquisition and retention by providing experiences that keep your customers coming back. Customer relationship management is both a strategy and a tool that supports those experiences in five key ways.
1
Answer the most basic customer questions
Customer relationship management helps you find new customers, sell to them, and develop a loyal customer relationship with them. These systems collect many different types of customer data and organize it so you understand your customers/prospects better and can answer (or even anticipate) their questions.
2
Manage customer data
Bad decisions come from a lack of access to and inability to interpret customer data. Being able to store, track, and validate customer data within an automated system will allow sales and marketing teams to optimize customer engagement strategies and build better relationships.
3
Automate the sales process
Sales force automation makes selling more efficient, helping you sell more quickly. The best CRM systems use artificial intelligence (AI) and unified customer data to automate the sales process by prompting sellers with recommended next-best actions.
4
Personalize marketing campaigns
Customers and potential customers arrive through various channels, including websites, social media, email, online/offline events, etc. Unfortunately, many businesses struggle to connect marketing efforts across all these channels. Marketing teams can improve conversions, strengthen customer relationships, and align messaging across their digital customer channels by leveraging CRM systems.
5
Align sales and marketing
With customer relationship management, marketing and sales work better together to drive sales and increase revenue. When sales and marketing are in sync, sales productivity goes up along with marketing ROI.
CRM features and benefits
Customer relationship management solutions are one of the largest and fastest-growing enterprise application software categories. The CRM market size was valued at $41.93 billion in 2019 and is projected to reach $96.39 billion by 2027, growing at a CAGR of 11.1% from 2020 to 2027.
More and more companies are using CRM solutions to acquire more sales leads, improve the sales pipeline, boost productivity, and improve customer satisfaction. However, many have encountered problems ranging from cost overruns and CRM integration challenges to system limitations. These are avoidable problems, and you can help ensure success by focusing on a customer-first strategy.
It’s critical for businesses to have integrated, customizable, and comprehensive views into their customers’ and potential customers’ solution/product interests, customer service needs, and purchase history. A good CRM system should provide that view. All data is in a single location, viewable through optimized dashboards.
Additionally, your marketing team can leverage CRM solutions to orchestrate personalized marketing and lead generation campaigns. These systems can help track all cross-channel interactions—from engagement to purchase. Mature cloud CRM solutions do more. They are fully integrated with back-office solutions to successfully support the entire customer journey.
Because it manages prospect and customer engagement points across all channels, your CRM system can inform all your communications and marketing activities, delivering the 360-degree customer view needed for a truly connected omnichannel experience.
Many different vendors have many different types of solutions. However, a few capabilities are must-haves.
Provide accurate, consistent data for that much-needed, complete customer 360-degree view
Types of CRM
CRM software solutions, at their core, are used to manage customer relationships and sales interactions. Still, many businesses leverage these systems simply as a sales force automation tool. But these solutions, such as Oracle’s, offer many more valuable capabilities that span a wide range of marketing and sales functions, including marketing, customer service, sales, and partner channel management.
Today’s CRM software can support the entire customer journey. But what one company may need from a CRM system can be vastly different from what another company might require. To help you select the right CRM for your organization, it’s helpful to know that there are three main types of CRM solutions: collaborative, operational, and analytical.
Data is the most critical part of any CRM software solution. In fact, customer data is the starting point for all marketing and sales activities. Successful customer engagement and relationship strategies hinge on accurate, complete, and accessible customer profiles. Bad data comes from several places, including:
Fraudulently entered data
Keystroke errors
Duplicate customer information
Natural changes (company bankruptcy, job changes)
Incomplete and inaccurate data can increase quickly to degrade the value of your CRM tools, resulting in unnecessary expenses. Conversely, when customer data is complete and accurate, businesses stand a better chance of reaching their target customers and prospects. In short, your data is a valuable asset. So it’s important to focus on collecting and optimizing these four CRM data types:
Identity data
Identity data includes descriptive details to identify customers, leads, and contacts. This data should be used for marketing segmentation.
Descriptive data
Descriptive data includes lifestyle details relevant to your contacts. It is what completes that all-important 360-degree view of leads and contacts.
Quantitative data
Quantitative data includes measurable data points that can help you interpret how your leads and contacts have interacted with you.
Qualitative data
Qualitative data can help you better understand your contacts’ intent, including search behaviors related to buying decisions.
CRM vs. marketing automation
Both CRM and marketing automation systems are data-driven. They focus on gathering, storing, and using data. For example, marketing automation systems gather leads by communicating with potential and current customers.
Specifically, marketing automation looks to gather enough customer data points to show intent and then hands that person off to the sales team as a marketing-qualified lead (MQL). A CRM solution picks up where the marketing automation solution left off and works to convert those marketing-qualified leads into contacts.
AI in CRM
Discover the next generation of CRM (0:38)
The best CRM systems offer robust analytics coupled with AI and machine learning. AI is the future of customer relationship management, going beyond contact management and sales force automation to truly helping you sell.
AI in CRM can guide you toward the next-best actions and provide smart talking points—specific to each customer opportunity. AI also delivers timely customer intelligence that helps you optimize customer experience (CX) across marketing, sales, and customer service.
CRM vs. CX
When customer relationship management first arrived on the scene, businesses would capture data but not know what to do with it. Today, CRM systems are integrated with AI, which helps interpret and predict what that data means.
CRM AI capabilities are the foundation to using a 360-degree view of the customer that will start them on their way to becoming your customer. As these AI enhancements continue to evolve, CX will continue to improve—and in turn, customer expectations will continue to increase.
Your business needs to fully understand your customers (and how they buy) to not only meet their expectations but to provide them with compelling experiences. This is the future of CX and should serve as your guide to selecting the best CRM solution.
How CRM improves customer experience
A complete customer view is necessary for business success and growth. Without a CRM system, you’ll struggle to develop that much-needed 360-degree view of the customer that you need to:
Personalize customer interactions
Automate business processes (with appropriate CX integrations)
CRM software solutions help sales reps organize their leads, automate follow-ups, and manage their opportunities and pipeline. But sales isn’t the only department within your organization that can benefit from your CRM platform. Marketing, customer support, product development, content management, and HR can all achieve high ROI from a CRM solution.
For example, since your solution holds essential information about every customer, your customer support teams can put that customer data to good use. With CRM data, your customer support reps (CSRs) have more insight into who your customer are, their needs and motivations, and what type of relationship they’ve had with your brand in the past. This information gives your customer service reps context when interacting with those customers.
As with any other business application, the decision to host your CRM on-premises, in the cloud, or as a hybrid model depends on your business needs.
On-premises CRM
On-premises CRM gives you complete control over your system, but there is a trade-off. These systems must be purchased, installed and deployed, monitored, maintained, and upgraded. As a result, they can be costly, involve time-intensive installations and upgrades, and require in-house IT resources for ongoing maintenance.
With an on-premises CRM solution, access to new functionality can be a long, drawn-out, and expensive process. Also, sophisticated AI-based technology—to support virtual assistants, chatbots, next-best recommendations, and predictive analytics—will not be available.
Cloud CRM
Software-as-a-service (SaaS) options offer simple interfaces that are easy to use and require less IT involvement and investment than on-premises CRM tools. Because upgrades are pushed through automatically, you always have the most up-to-date functionality without significant IT effort.
This includes new advanced technology, such as AI and machine learning that can help you turn your customer data into relevant customer experiences. Cloud-based CRM also offers the convenience of anytime, anywhere access through mobile devices.
Hybrid CRM deployment
A hybrid CRM deployment requires trade-offs in all the areas mentioned above, but this deployment model can also deliver the best of both worlds. However, it’s important to recognize that IT technology is increasingly moving to the cloud. Companies that remain heavily invested in on-premises CRM risk being left behind as competitors advance to the cloud. Your ability to provide mobile access will also be limited.
There are so many things to take into consideration when selecting a CRM system for your organization. But in the end, the CRM model most suitable for you is the one that allows you to interact with your customers in meaningful ways to drive exceptional customer experiences.
Marketing has only become more challenging as the number of channels and touchpoints to integrate continues to grow. Successful brands need to build a cohesive and seamless cross-channel marketing strategy to bring all the aspects of their marketing plan together. That’s where marketing intelligence andmarketing attribution tools come in.
Brands can use marketing intelligence and attribution tools to track marketing strategies in real time, providing data that helps drive smart decisions and marketing mix choices. Recently,Salesforce and Mevo joined forces to create a unified solution for marketers looking to streamline their marketing efforts.
Let’s explore how this partnership can help you achieve your marketing goals and conquer the challenge of cross-channel marketing.
The Challenge: Creating Effective Cross-channel Marketing Campaigns
Marketers today face the challenge of cross-channel marketing, which includes integrating and normalizing data across many channels. Ensuring a consistent customer experience with various touchpoints like social media, email marketing, and search engine ads can be tough, if not impossible.
A recent survey revealed that most marketers crave a centralized platform to access cross-channel marketing data. Yet, less than 30% actually have such a system. Even among those with a plan, 60% still rely on manual data assembly.
Accurate data is necessary for marketers to know how well their marketing campaigns are doing across different channels. To get this data, they need to understand how customers move through multiple platforms and map their behavior between all of them concurrently. Although a fully-integrated, cross-channel marketing strategy might still be a ways off for some brands, marketers can still make great strides by using data and the right technology to bridge the gap in the meantime.
The Solution: Unification of All Channels and Touchpoints
Effectivemarketing mix modeling and accurate attribution are crucial elements in any successful marketing strategy. Attribution is a vital aspect because it shows how each step affects a customer’s decision-making, and ultimately informs marketers and their C-suite about which marketing channels and messages are most effective.
To achieve complete optimization, brands should strive to make all marketing decisions data-driven while also deploying insights gleaned from attribution and modeling programs as quickly as possible to business leaders who can drive real change. This knowledge-sharing reduces useless silos while optimizing the allocation of marketing resources and driving both return on investment (ROI) and return on ad spend (ROAS).
Tips for Implementing Cross-Channel Marketing Effectively
Implementing a cross-channel marketing strategy is a powerful tool that can improve brand recognition and increase sales, but it can also be overwhelming if not done correctly. Some tips for implementing an effective cross-channel marketing strategy simply and easily are:
Create a Consistent Brand Message Across All Channels: Ensure that your brand message is consistent and clear across all marketing channels.
Use Data to Personalize Your Campaigns: Use data to personalize your campaigns and provide your customers with relevant and timely content.
Optimize Your Campaigns for Mobile: Ensure that your campaigns are optimized for mobile devices, to capture the huge percentage of consumers who use their phones for shopping and research almost exclusively.
Measure and Analyze Your Campaigns: Measure and analyze your campaigns on a regular basis to find areas for improvement and optimization across all aspects of your cross-channel marketing strategy.
Examples of Brands with Successful Cross-Channel Marketing Campaigns
Many brands have successfully used cross-channel marketing to reach their target audience. Some used social media and email marketing to expand their reach, while others focused on in-store experiences and personalized recommendations to boost sales. Examples of brands that used effective cross-channel marketing include:
Nike: Utilizes social media, email marketing, and in-store experiences to create a seamless customer journey.
Sephora: Offers personalized recommendations through its mobile app, email marketing, and in-store consultations.
Coca-Cola: Utilizes TV commercials, social media, and experiential marketing to create a cohesive brand message.
Amazon: Uses email marketing, mobile app notifications, and personalized recommendations to drive sales across multiple channels.
The advertising industry has undergone significant change, with new technologies and strategies constantly emerging. One recent innovation that is causing a stir is generative AI advertising. This groundbreaking approach is revolutionizing the creation and delivery of ads, resulting in more personalized and captivating consumer experiences.
Generative AIis a groundbreaking tool that enables marketers and advertisers to create dynamic and interactive campaigns. These campaigns can adapt in real time according to user data and preferences. By leveraging artificial intelligence, brands can deliver highly targeted and relevant content. This content captures attention and generates meaningful results.
This post will examine how generative AI advertising is revolutionizing the ad industry and explore the benefits it offers to advertisers and consumers alike.
What is Generative AI Advertising?
This tool uses AI technology to create and personalize ads. It employs machine learning algorithms to generate highly personalized ad content for individual users. This approach enables advertisers to create more engaging and relevant ads, increasing the likelihood of attracting and converting customers. Generative AI also helps optimize ad campaigns by automatically generating and testing variations of ads to find the most effective ones. With AI, advertisers can enhance their targeting capabilities and deliver impactful ads to their target audience.
Why Generative AI is Important in the Ad Industry
Generative AI presents a ground-breaking method for developing tailored advertisements based on unique tastes and interests. Using algorithms to evaluate data quality, advertisers can create advertising that appeals to specific target demographics. This technology increases the chances of converting consumers into customers by delivering more relevant and engaging content. Generative AI also helps companies save time and money while increasing the efficacy and efficiency of ad development. This innovative technology has the potential to transform the advertising sector by improving ad targeting and raising the overall effectiveness of marketing initiatives.
What is the Role of AI Targeted Advertising in Marketing?
AI-targeted advertising is crucial in modern marketing strategies. It uses artificial intelligence algorithms to analyze data and identify the most relevant audience for products or services. AI gathers information on consumer preferences, behavior, and demographics, allowing marketers to tailor advertisements to specific target groups. Generative AI advertising is essential but not meant to replace humans; it enhances their capabilities. AI provides reliable and accurate insights in real-time, improving return on advertising spend. This collaboration between humans and AI allows marketers to focus on strategic tasks requiring creativity and intuition while AI handles data analysis and marketing optimization.
What are the Benefits of Generative AI Advertising?
Unlike humans, who can make mistakes when handling data, generative AI algorithms process and analyze vast amounts of information with precision and consistency. By relying on AI technology, advertisers can minimize errors and ensure that their data-driven marketing and advertising decisions are based on accurate and reliable insights. Additionally, humans have inherent biases that can unintentionally influence advertising strategies. Generative AI advertising eliminates this bias, allowing for a more objective and unbiased approach to targeting and engaging with the audience.
Increased Efficiency
Artificial intelligence allows marketers and advertisers to automate and optimize advertising campaigns, saving time and resources. Generative AI algorithms analyze large amounts of data and create personalized and targeted ads that resonate with the target audience. It improves advertising effectiveness, leading to higher conversion rates and return on investment. With this amazing tool, marketers and advertisers can streamline processes, reach customers at the right time, and achieve better results.
Enhanced Creativity
Generative AI advertising offers enhanced creativity through machine learning and data analysis. This technology allows marketers to explore endless creative possibilities and stand out in a competitive market. By leveraging Generative AI, advertisers can create tailored ads that engage their target audience, leading to higher conversion rates and overall business success.
Personalization
Advertising can be tailored to each consumer’s tastes, interests, and behaviors with the help of generative AI. With this level of personalization, marketers may give their target audience more exciting experiences. Marketers and advertisers can improve their chances of grabbing the attention and interest of potential customers, which will result in improved conversion rates and sales, by presenting tailored adverts. Additionally, since customers value brands that recognize their particular wants and preferences, tailored advertising can increase customer satisfaction and loyalty.
Real-Time Optimization
Unlike traditional advertising, generative AI continuously analyzes and adjusts based on insights from user behavior, allowing for the dynamic optimization of ad campaigns in real time. Advertisers can customize their messages and creatives based on their target audience’s preferences and interests at any moment. This level of customization increases the chances of engaging potential customers and maximizes the efficiency of advertising campaigns. With real-time optimization, generative AI ensures every impression counts and drives desired results, making it a powerful tool for advertisers looking to optimize their advertising efforts.
Cost-Effectiveness
This tool allows marketers and advertisers to create personalized and targeted ads at a lower cost. AI algorithms analyze consumer data and behavior to generate relevant advertisements, ensuring effective reach to the target audience. Furthermore, generative AI advertising enables real-time optimization, allowing marketers to refine and enhance ad campaigns based on performance data continuously. This increases the chances of success and minimizes the risk of wasting money on ineffective advertising strategies.
Improved Customer Experience
With Generative AI, it can change how advertisers and marketers connect with customers. One significant benefit is the improved customer experience. Generative AI analyzes customer data and preferences through AI algorithms and machine learning to create personalized ads. It improves client satisfaction, boosts conversion rates, and encourages repeat business. Advertisers can customize their adverts for specific consumers by offering pertinent and appealing material that speaks to their needs and interests. Long-term success and increased customer satisfaction result from this.
Public Perception
Generative AI positively impacts public perception. AI algorithms generate ads that resonate with the target audience, improving brand perception. Consumers feel connected to the brand when ads are tailored to their preferences. This fosters a favorable view of the brand and increases loyalty, engagement, sales, and revenue. This tool delivers highly relevant and engaging ads, enhancing the user experience and positively influencing public perception.
What are the Potential Risks in AI-Powered Advertising?
As AI-powered advertising continues to gain prominence in the digital marketing landscape, it is essential to be aware of the potential risks associated with this technology. Here are some of the potential risks in AI-powered advertising.
Ensuring Data Used for Model Training is Representative
Ensuring that the training data used for AI-powered advertising represents the real world. Results may be distorted, and prejudice may occur if the data is biased or unrepresentative. It may have detrimental effects on targeting particular demographics and the precision of the marketing and advertising strategy. To mitigate these risks, advertisers and AI developers must carefully curate and validate their training data to accurately reflect the diverse population and avoid perpetuating biases.
Legal and Ethical Concerns
AI algorithms can collect large amounts of personal data, raising concerns about its usage and consent. Discrimination and bias are also risks in AI-powered advertising. If algorithms are not adequately trained, they may perpetuate stereotypes or exclude certain groups. Transparency and accountability are also concerns. It is difficult to understand the decision-making processes of AI algorithms, raising questions about responsibility in case of legal or ethical problems. These risks emphasize the need for regulation and oversight to ensure responsible and ethical use of AI-powered advertising.
Lack of Control
With AI in charge of targeting and delivering ads, advertisers may need more control over ad placement and audience targeting. This lack of control can lead to ads appearing on irrelevant or inappropriate platforms, harming a brand’s reputation and wasting ad budget. Furthermore, AI algorithms continually evolve, posing risks of unintended consequences or biases in targeting.
Brand Inconsistency
There is a risk of brand inconsistency in AI-powered advertising. AI algorithms analyze data and make decisions based on patterns and trends. However, they may need to accurately interpret the brand’s values, tone, and messaging. This can lead to advertisements that are consistently unclear or misleading for consumers. AI-powered advertising can also lack the human touch and creativity a brand needs to communicate its message effectively. Investing time in training the AI model to learn about appropriate branding and positioning is essential.
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.
