For ages, achieving optimal model accuracy has been revered as the ultimate goal, with marketers relying on model fit as the benchmark for discovering the ideal equation that harmonizes with their data. However, what if there’s a deeper layer to model accuracy beyond mere numerical alignment? What if we challenge conventional wisdom and explore alternative dimensions of accuracy that transcend traditional models?
In this blog post, we will embark on a journey to reimagine model accuracy, pushing the boundaries to truly grasp the essence of marketing insights. Come along as we venture into unexplored territory, uncovering a novel perspective on accuracy that promises to transform our approach to data-driven marketing decision-making.
Key Takeaways Covered in this Post:
The Importance of Parameter Alignment in Maximizing Media Mix Effectiveness
The Importance of Testing at Scale in Marketing
The Profit-Driven Nature of Publishers and Its Implications
Overcoming External Factors: Using Marketing Response Models
Exploring the Connection Between Generative AI and Model Accuracy
The Search for Accurate Testing: Where to Go?
Unlock the Power of Accurate Testing and Optimization with Marketing Evolution
The Importance of Parameter Alignment in Maximizing Media Mix Effectiveness
In the process of optimizing the media mix, achieving proper parameter alignment is critical for obtaining accurate test outcomes. Parameter alignment entails the degree to which selected parameters align with the goals and objectives of media campaigns. By meticulously selecting and refining appropriate parameters, marketers can fine-tune their media mix, facilitating closed-loop marketing adjustments. This involves establishing parameters that accurately capture the target audience, reflect key performance indicators (KPIs), and are in line with the overarching marketing strategy. When parameters align effectively, test results become dependable and actionable, empowering marketers to make data-driven decisions and enhance the effectiveness of their media mix.
The Importance of Testing at Scale in Marketing
Conducting large-scale tests aids marketers in collecting dependable data and making well-informed decisions regarding their campaigns. Nonetheless, a hurdle marketers often encounter is attaining statistically significant results during campaign testing. Limited resources and time constraints pose challenges in testing multiple variables and thoroughly analyzing their impact. A potential remedy to this issue is A/B testing, enabling marketers to compare two campaign versions and identify the superior performer. However, A/B testing has its constraints as it only evaluates two variations at a time and may overlook the intricate nuances of consumer behavior. Consequently, conducting tests at scale becomes imperative to ensure comprehensive and precise insights for optimizing marketing strategies.
The Profit-Driven Nature of Publishers and Its Implications
Publishers typically maximize their financial returns, prioritizing revenue generation over rigorous testing and evaluation of marketing metrics. Rather than thoroughly testing and refining their marketing endeavors, publishers might lean towards strategies that yield immediate profits. However, this approach could overlook opportunities for more effective or innovative marketing tactics. Additionally, the profit-driven focus of publishers can result in unpredictable fluctuations in testing campaigns. They may hesitate to allocate resources to experimentation and swiftly abandon campaigns that do not yield immediate positive outcomes. Consequently, this hampers long-term growth potential and prevents potentially successful strategies from being uncovered.
Overcoming External Factors: Using Marketing Response Models
Marketing response models, such as generative attribution, aid marketers in gauging the influence of diverse marketing tactics and external variables on consumer behavior and response. Consequently, marketers can evaluate the efficacy of their marketing campaigns and implement necessary adjustments for enhanced outcomes. These models also facilitate an understanding of how external factors, such as economic shifts or shifts in consumer preferences, impact consumer responses.
The benefits and challenges lie in integrating non-media and environmental factors, thereby providing a comprehensive understanding alongside traditional factors like media, individual, geographical, and temporal details simultaneously.
Moreover, marketing response models assist marketers in identifying and mitigating the impact of external variables on testing campaigns. By integrating these models into their testing methodologies, marketers can gain deeper insights into how external factors shape consumer behavior and responses. This comprehension empowers them to make well-informed decisions and devise strategies to navigate external fluctuations.
Exploring the Connection Between Generative AI and Model Accuracy
Traditional models often require assistance in accurately attributing the impact of various marketing touchpoints, leading to incomplete insights and less-than-optimal decision-making. As we strive to expand the horizons of model accuracy, it becomes crucial to explore emerging technologies like generative attribution, which have the potential to enrich our understanding of marketing insights. Among these technologies, generative AI emerges as a particularly promising candidate. By harnessing the power of machine learning, generative AI can forge new and innovative marketing strategies, uncovering previously obscured patterns and insights.
But how does generative AI intersect with the concept of model accuracy? The answer lies in generative attribution.
Generative AI has the potential to revolutionize marketing attribution by analyzing vast amounts of data and identifying the genuine drivers of success. Envision a scenario where every marketing effort is precisely attributed, enabling you to optimize your strategies with unparalleled accuracy. Generative AI opens up new horizons for marketers, empowering them to unveil hidden patterns, identify unexplored opportunities, and make data-driven decisions that truly make a difference.
The Search for Accurate Testing: Where to Go?
It’s imperative for marketers to prioritize investments in precise measurement and analysis, yet discovering dependable testing methodologies can pose a challenge. Marketing response models could be one option for a viable solution. These models take into account consumer behavior, market trends, and external influences, resulting in more dependable outcomes. By leveraging such models, marketers can mitigate external variables and gain precise insights to steer marketing strategies and enhance performance.
A modern and scalable approach is to implement advanced analytics and machine learning algorithms that have the capability to sift through vast datasets, identifying patterns and correlations that may elude traditional testing methods. These tools empower marketers to unearth insights and make informed, data-driven decisions to optimize their strategies. However, it’s crucial to underscore the importance of using predictive analytics in conjunction with marketing response models to ensure accuracy.
explored the origins and convergence of Data warehouses and Customer Data Platforms (CDPs) and how their integration has become the cornerstone of modern enterprises’ growth strategies. As we continue the series, we’ll focus on building a cross-functional team to maximize the potential of data warehouses and CDPs working in tandem. We’ll identify critical stakeholders from engineering, data, and marketing and discuss strategies for uniting them to address use cases and drive innovation in customer engagement and retention.
Part 2: The Significance of a Cross-Functional Team:
Organizations must foster collaboration across various departments to fully leverage the combined power of data warehouses and CDPs. If we break down the acronym CDP, we should always remember that the goal of working together is to focus on the Customer first. This requires fostering a customer-driven culture that leads by listening to the voice of the customer in the Data and building a robust Platform for collecting and activating their preferences so we can adapt to the customer’s needs as we engage with them.
A cross-functional team made up of members from engineering, data, and marketing is not only beneficial but crucial for ensuring the seamless integration and utilization of these technologies. Organizations can develop a holistic approach to data management and customer engagement by bringing together diverse skill sets and perspectives.
Key Stakeholders:
Engineering: The engineering team is crucial in implementing and maintaining the technical infrastructure required for data warehouses and Customer Data Platforms (CDPs). Engineering teams are also vital in implementing event capture libraries across applications, websites, and mobile platforms. These libraries standardize the capture of behavioral events on the front end before they are sent downstream to various destinations. Engineers work closely with data and marketing teams to understand the specific behavioral events these teams need to capture to meet their requirements.
Data: Data scientists and analysts form the core of data-driven organizations, tasked not only with extracting insights from extensive data in warehouses and captured by CDPs but also with empowering business users. Their job involves providing the necessary data to make informed decisions for analytics while ensuring a unified definition of the customer across operational systems such as help desks, marketing tools, and sales platforms. By employing advanced analytics like machine learning and predictive modeling, data teams reveal patterns and trends in customer behavior, informing marketing strategies and personalizing experiences. However, synchronizing all systems to operate off a single customer record definition presents a significant challenge.
Marketing: The marketing team is a key player in customer engagement and retention, leveraging insights from data warehouses and CDPs to tailor campaigns, personalize content, and enhance the customer journey across various platforms. However, when marketing tools are isolated from the data stored in warehouses, marketers face significant challenges in accessing and utilizing this data directly for complex targeting efforts. Typically, they must coordinate with data teams to extract sophisticated audience lists, a process that can slow down campaign execution and reduce agility. While CDPs assist in forming customer profiles, they often fall short in enabling more intricate targeting without further engineering support. This necessitates a collaborative effort with data teams to devise and implement data-driven strategies that effectively resonate with segmented customer groups.
Uniting Stakeholders to Address Use Cases
Organizations must foster collaboration and communication among key stakeholders to leverage data warehouses and CDPs effectively. Here are some strategies for uniting these teams
Establish clear goals and objectives:
This is a critical first step in leveraging the integration of data warehouses and Customer Data Platforms (CDPs) to their fullest potential. This involves clearly defining the specific use cases and desired outcomes that the organization aims to achieve through this integration. It’s essential to articulate what success looks like, whether it’s improved customer insights, enhanced marketing campaigns, or more personalized customer experiences.
To ensure a cohesive effort toward these goals, it’s vital that all stakeholders—from engineering and data teams to marketing and executive leadership—are not just aligned, but they are the driving force behind the objectives. This alignment includes:
A thorough understanding of each party’s role in the integration process
How each group will contribute to achieving the established goals.
Setting clear goals and objectives aids in the evaluation of the integration’s effectiveness over time. By having predefined metrics for success, organizations can measure progress, identify areas for improvement, and make informed decisions about future data strategies. This strategic approach maximizes the benefits of data warehouse and CDP integration and facilitates a culture of data-driven decision-making within the organization.
Encourage cross-functional collaboration:
Once clear goals and objectives are established, creating opportunities for engineering, data, and marketing teams to collaborate closely on projects and initiatives is crucial. By fostering a culture of data-driven decision-making, organizations can encourage all stakeholders to utilize insights from data warehouses and Customer Data Platforms (CDPs) in their everyday processes. This mindset shift promotes a more strategic approach towards leveraging data, resulting in projects and initiatives that are deeply informed by actionable insights.
To ensure these cross-functional meetings are outcome-focused and adept at overcoming blockers, organizations must prioritize setting clear objectives. Design these sessions to drive collaboration and innovation by enabling team members with varied backgrounds to contribute their unique insights and skills towards achieving specific goals and resolving any challenges that may arise.
By setting up cross-functional teams and defining specific use cases that require input from engineering, data, and marketing, companies can ensure that these teams have a focused direction to work towards together. Creating shared workspaces, either physical or digital, can further enhance this collaboration, providing a platform for continuous communication and idea exchange. This collaborative environment not only accelerates project development but also breeds innovative solutions that may not have been discovered in siloed teams.
Conclusion:
Building a cross-functional dream team is not just essential for maximizing the potential of data warehouses and CDPs in driving customer engagement and retention – it’s a gateway to a world of possibilities. It’s about maximizing the potential of data warehouses and CDPs in driving customer engagement and retention. By bringing together key stakeholders from engineering, data, and marketing, organizations cannot only develop a holistic approach to data management and customer-centricity but also pave the way for innovation and growth. Through collaboration, training, and continuous iteration, businesses can stay ahead of the curve in delivering personalized experiences and fostering long-term customer loyalty, and that’s where the real excitement lies.
In our next post, we’ll explore real-world examples of how leading companies are leveraging data warehouses and CDPs to drive growth and innovation. Stay tuned for insights and inspiration on applying these strategies to your organization.
You’ve heard the buzz about customer data platforms. You’ve heard the letters CDP bandied about. Maybe your boss asked you if your company needs a CDP. Maybe you’re a boss wondering the same thing.
Whatever questions you have about CDPs, you’ve come to the right place. We’re here to give you all the info you need on this crucial marketing technology.
Key Takeaways
Ecommerce professionals can simplify their tech stacks and easily personalize customer experiences with a customer data platform (CDP).
A CDP is a smart, user-friendly software that confidently consolidates and manages all customer data, creating a unified, enduring record of each customer’s attributes.
Unlike some other database software programs, a CDP is a tool built mainly for marketers. But having access to technical support will be essential for integration and operation concerns.
While a CDP is similar to customer relationship management (CRM) software, it is also distinctly different and a CDP with marketing automation capabilities gives marketers additional options to power ecommerce personalization.
What Is a Customer Data Platform (CDP)?
A Customer Data Platform (CDP) is a type of marketing technology software. Specifically, it’s a kind of unified customer database software: one that creates persistent, consolidated records of all your customers, their attributes, and their data. A good CDP should easily integrate with your existing data and allow for easy retrieval of the data it stores.
A CDP builds a complete picture of your customers on an individual level. It collects first-party customer data (transactional, demographic, and behavioral data) from a multitude of sources and systems, and links that information to the customer that created it.
This creates a 360-degree customer profile, also called a single customer view, which can then be used by third-party tools or built-in marketing automation toolstoexecute marketing campaigns and analyze their performance.
How to Build a Customer Data Platform
So, how do you build a CDP? For any customer data platform to function, there are three main steps involved:
Integration
First and foremost, compiling and assembling all relevant data into a single database is the primary task of any CDP. It works to solve the problem of disconnected data sets by linking all your sources and systems together in one place.
Organization
Once your data is integrated, a CDP needs quality control protocols. It needs to identify and address any missing information, remove duplicate data sets, and cross-check for accuracy so that segments and audiences can be identified.
Identity Resolution
After connecting all the dots, merging data from multiple sources and attributing it to specific customer profiles is where a customer data platform really shines. This is called data unification and it lets you build complete profiles of every individual customer, where you can build and expand on insights as they interact with your business.
What are the Values and CDP Characteristics
There are a variety of businesses offering various CDP offerings, but the best of the best provide a few essential characteristics that every marketer should look for:
Ready‑to‑Use Solution
All customer data is neatly organized and available for immediate use. Some technical resources are required to set up and maintain the CDP, but it does not require a high level of technical skill compared to a traditional data warehouse.
Single Customer View
Customer data collected and organized with a CDP is visualized through individual data profiles for each user. This 360-degree view of the customer is possible due to the fact that all customer data is located in one central location.
Customer Data Unification
Inconsistent data from multiple online and offline sources is combined to create a unified single customer view.
Accessible Data for Third Parties
Data contained within a CDP is ready for use in third-party systems focused on adtech and campaign delivery.
Who Needs a CDP
Unlike some other database software programs, a CDP is a tool built mainly for marketers. That doesn’t necessarily mean that a CDP can be operated without any technical support. To get the most out of a CDP, an organization will typically need these three roles:
Marketer: a person who understands the market and can suggest business-tailored use cases for the CDP.
IT Person: someone to help support the marketer during the implementation phase of the CDP, and can help manage tasks like using webhooks, deploying recommendations on the web, or helping with integrations. Knowledge of HTML, CSS, and Javascript is also helpful for building powerful weblayers.
Analytical Person: someone that knows how to work with data and what to track in custom dashboards, how to analyze A/B tests, and can report results to the marketing team.
These don’t have to be three separate people, but for maximum value from a CDP you’ll need all those skills.
What Is the History of Customer Data Platforms?
Managing customer data is nothing new. From handwritten filing cards and massive independent mainframes to modern cloud-based solutions, the search for the best tool has been going strong for decades. Modern computing power has significantly increased the pace of progress, allowing for more and more useful tools.
Online customer relationship management (CRM) software was introduced in the 90s and allowed companies to manage their interactions with both current and potential customers. These customer relationship management platforms could also perform customer data analysis that could help drive retention and sales. While useful, these tools had some limitations: They only managed data for registered clients and only used predefined first-party data.
Things changed in the 2000s with the rise of data management platforms (DMPs). These were aimed towards advertisers and helped with the planning and execution of media campaigns. Unlike CRMs, DMPs worked with second- and third-party data, and could segment anonymous IDs.
The customer data platform (CDP) was introduced a few years back as a reaction to the demand for an improved customer experience and omnichannel marketing initiatives. Older tools, while useful for their purposes, had created data silos. CRM data was one thing, DMP data was another — and marketers weren’t able to productively use all the data they had access to.
CDPs solved this problem by offering a unified customer view that gathers a company’s first-party data (and to some extent, second- and third-party data) into a single, comprehensive platform. A major advantage of CDPs is their ability to store extremely granular first-party data, such as events on a website.
Bloomreach Engagement: A CDP Since 2012
Bloomreach acquired Exponea in early 2021, a SaaS company that built its CDP architecture from the ground up starting in 2012. This has allowed Bloomreach to refine and improve its customer data platform, and build powerful tools on top of it to help modern businesses manage customer data and harness its utmost potential.
Thanks to years of hard work and growth, Bloomreach has an industry-leading CDP, made even more powerful by user-centric analytics, predictions, recommendations, and marketing automation layers. We call it Bloomreach Engagement.
Why Is Customer Data Important?
Today’s customers expect a lot from companies. They’ve experienced good personalized service, and if you want to keep their business, you need to provide that elevated standard. A consistent customer experience across channels, appropriate recommendations, tailored communications — for today’s customers, these are necessary.
Not many companies can actually deliver these personalized experiences. But if you can’t meet customers heightened expectations, you have a problem. If customers think you don’t care about them, they’ll take their business somewhere else — and they won’t be coming back. The fight to win those customers back will be much more difficult than getting their business in the first place.
This is why it’s so crucial to have well-maintained, accessible, and insightful customer data. And now, a good CDP makes that possible. It’s only a matter of getting the right data.
What Kind of Customer Data Does a CDP Work With?
The sheer volume and speed of digital data is hard to comprehend, and overwhelms traditional database software. A CDP, however, is purpose-built to manage this flow of data.
The most reliable way for a CDP to collect this type of data is via their own SDK, but most CDPs can also ingest data from other systems via JSON or batch ETL transfers.
The types of data a CDP can work with include:
Events: behavioral data that arises from a user’s actions in a session on a website, in an app, or on a mobile browser.
Customer Attributes: this includes names, addresses, contact details, birthdays, etc. Advanced CDPs can also store machine learning-powered predictions, such as likelihood to purchase.
Transactional Data: purchases, returns, and other info from ecommerce or POS systems.
Campaign Metrics: engagement, reach, impressions, and other metrics from campaigns.
Customer Service Data: live chat data, number and length of interactions, frequency, NPS scores, and other data from CRM systems.
What Makes Customer Data Platforms Different From DMP and CRM?
When comparing data gathering software, it’s easy to get overwhelmed. There’s a sea of similar acronyms, product descriptions that look almost the same, and lots of claims about which program best suits your needs.
You might have come across customer relationship software (CRM), CDPs, and data management platforms (DMP). While their capabilities might sound similar, it’s important to understand the distinctions between them so you can evaluate vendors and choose the right product for your business needs.
CDP vs. DMP vs. CRM: Table Explained
Holistic Customer Data: Does the platform manage customer data from all available sources (behavioral, demographic, personal, transactional, device, etc.)?
Lasting Customer Profiles: Does the platform retain data for a long period of time?
Packaged System: Can the platform exist as a ready-to-use piece of software?
Real-time Capability: Does the platform update data in real time, allowing for quick reactions to changes?
Open Platform: Is it simple to get data into the platform? Is it easy to share data from the platform with other services?
Cross-channel Personalization: Does the platform allow for the personalization of messages across different customer touchpoints?
Only Anonymized Data: A data management platform by design works with anonymized customer data. CRMs and CDPs work with identified customers, and allow for granular views of individual customers.
Identity Resolution: Does the platform allow you to connect the customer behavior of anonymous visitors with known customers after they have given their consent? Does the platform recognize customers across devices?
First-party Data Priority: Does the platform primarily deal with data from first-party sources?
Third-party Data Priority: Does the platform primarily deal with data from third-party sources?
Requires IT Support: Does day-to-day operation of the software require support from IT?
Finding the right platform is no easy task. But understanding what you can expect your CDP to do for you on a daily basis helps.
In our knowledge card, you’ll get essential know-how on CDPs and learn more about the features that your company should be looking for in its CDP.
Types of Customer Data Platforms
The customer data platform market has matured, leading to a number of different providers. These providers are differentiated based on their target market and their intended use cases. Let’s take a look at some of the differences.
A Standalone CDP vs. CDP + Marketing Automation
A key distinction among CDP vendors is whether they provide a product which is only a CDP, or a CDP plus other capabilities. It’s crucial to understand what your vendor is providing, because this distinction can cause large differences in how your business uses the CDP.
A Standalone CDP
A standalone CDP is exactly what it sounds like: a customer data platform without extra capabilities. It ingests all of a company’s first-party data and builds complete pictures of all of your customers (a single customer view). Usually, a standalone CDP will offer analytics capabilities, allowing for granular segmentations of your audience.
This data is accessible for use by other systems, but the standalone CDP cannot execute campaigns. It needs dedicated tools that can make use of the comprehensive data it collects.
For companies that already have campaign execution tools, a standalone CDP might make sense. But companies that lack those capabilities should consider a CDP + marketing automation platform.
CDP + Marketing Automation
A customer data platform coupled with marketing automation is the next generation of the CDP. It combines all the benefits of a standalone CDP with marketing campaign tools, creating a single, powerful, customer-centric marketing platform.
This gives marketers the complete toolset they need for creating incredible customer experiences by bringing together AI-driven marketing capabilities, real-time analytics, and UX optimization with a CDP.
A CDP combined with marketing automation simplifies workflows and increases productivity by collecting frequently used tools into one integrated interface. But it is also flexible and can fit into your existing tech stack — it molds around what you already have and fills gaps.
Facilitates more precise targeting and higher-quality interactions with customers
Allows for meaningful analysis of marketing initiatives across different channels
Enables agile responses to changes in the market or customer preferences
Bloomreach Engagement: The Most Versatile Platform on the Market
Bloomreach offers you the flexibility to pick and choose which features you want to use; it’s not an “all-or-nothing” solution. Although Bloomreach is a CDP + marketing automation, it can act as a standalone CDP to provide a unified source of customer data to an existing technology stack, or it can be used to handle all marketing activities using the additional layers of campaign execution and analytics.
If you already have a CDP, Bloomreach Engagement’s customer data engine can help you fully activate your data and maximize the ROI of your ecommerce marketing efforts. Our customer data engine is what makes our platform truly stand out with capabilities that go beyond the standard scope of marketing toolsets. Our powerful data core combines CDP capabilities and advanced analytics to help marketers understand the customer journey in real time and create omnichannel campaigns that drive results.
The Difference Between Enterprise-grade CDPs and Small Business CDPs
There are multiple CDP providers out there, each with differing purposes and capabilities.
A key consideration when choosing a CDP is the intended scale of the software. Is it built for small businesses? Or is it a full-fledged enterprise solution? There are some key points to remember when answering these questions:
Scalability. Enterprise-level companies need to work with massive amounts of data. That data can change quickly, and for a CDP to be useful, it needs to respond to those changes swiftly and accurately. This means that CDP architecture needs to be built for scale from the very beginning.
Flexibility. No two companies are the same. For enterprise-level companies, a plug-and-play solution will almost never be suitable for the unique needs of a company — therefore flexibility in a CDP is a must-have. A customer data platform must be able to ingest a company’s data from all its unique sources, as well as interface successfully with the platforms the company uses to function.
Integrity. A CDP needs to be trusted with the sensitive data it uses, and that can mean data for millions of customers. This requires rigorous security protocols and a dedication to privacy. These need to be core values of the CDP provider if they are to be trusted with customer data.
Bloomreach Engagement: An Enterprise-grade CDP
Bloomreach Engagement was built from the ground up as an enterprise-grade CDP. Thoughtful product planning and experience with world-class clients has made Bloomreach an industry leader in customer data platforms for the most demanding of applications.
Scalability: Bloomreach’s agile in-memory framework is scalable by design and is ready to handle massive volumes of rapidly changing data at the speeds necessary for business success.
Flexibility: Bloomreach easily adapts to the needs of enterprise-class businesses. A quick onboarding process integrates Bloomreach with existing data. A rich API makes third-party integrations smooth and painless. And native integrations with best-in-class tools means Bloomreach works with the tools you already use.
The Benefits of a Customer Data Platform (Key CDP Use Cases)
There’s a mutlitude of benefits to using a CDP, but most the types of advantages you get from a platform really boils down to the way your business wants to employ it.
And just as there are numerous benefits, there is the large number of CDP vendors on the market, which can be overwhelming. When choosing a vendor, it’s helpful to consider the use cases you hope to accomplish with a help of CDP.
While it’s important to have high-level goals (improve the customer experience, foster loyalty, etc.), you also need to know how a CDP can help you achieve those goals through lower-level use cases.
We’ve collected what we believe to be some of the most important use cases, and benefits, below.
CDP Use Cases:
1. Online to Offline Connection
Merge online and offline activities to create an accurate customer profile. Identify customers from online activities when they enter a brick and mortar store.
2. Customer Segmentation and Personalization
Segment customers according to their behavior (RFM, LTV prediction) to deliver a personalized, omnichannel experience throughout the entire customer lifecycle.
Integration with Facebook Ads, Google Ads, Google Analytics, and Doubleclick enables you to leverage insights and run powerful acquisition and retention (lookalike) campaigns outside of your website.
Create different recommendation models such as “similar products” or “customers also bought” and deliver the best shopping experience to drive engagement, increase brand loyalty, and sell, up-sell, or cross-sell your products or services.
Quickly transform the appearance of your pages. Use our smart website overlays (pop-ups) or send cart abandonment emails to increase your ROI. Create different designs and determine which variant performs better with the automatic A/B testing feature.
7. Omnichannel Automation
Guide your customers through their entire lifecycle with personalized messages sent to their preferred channel, significantly enhancing your opportunities to both acquire and keep a loyal customer.
Increase email open rates. Thanks to an AI-powered algorithm, you can determine the ideal distribution time for each user based on their email opening habits and reach them at this optimal hour.
9. Reviews Optimization
Get better and more frequent online reviews from your customers through personalized omnichannel communication and NPS survey analysis.
How Can a CDP Improve Customer Lifetime Value and Foster Customer Loyalty?
The most effective way to foster customer loyalty is to give your customers exactly what they’re looking for: a consistent, high-quality, and personalized experience. Customer data platforms make it possible to deliver these experiences at scale, personalizing the journey of each customer.
If your data is siloed, you can’t create a consistent experience for your customers. Without that central data hub, you can’t provide the omnichannel experience customers expect, which is receiving up-to-date interactions regardless of which channel the customer communicates through.
How Long Does It Take to Implement a Customer Data Platform?
The short answer? It depends. A very rough estimate would be 4-12 weeks.
The long answer? Without knowing the details of your organization and business needs, there’s no one-size-fits-all answer. There are a few things you’ll need to take into consideration:
Integration complexity — how many tools will you need to integrate with?
CDP output requirements —what will you need from the CDP?
Current state of your data — data cleansing can lead to a longer implementation
Unique business rules — are there business-specific stipulations to consider?
Identity merging needs — siloed data can lead to a single customer having multiple profiles across different platforms, and merging these profiles takes time
Level of detail in data attributes
Every business that wants the benefits of a CDP will have different requirements and goals, making it impossible to give a precise answer to how long the implementation process will take.
Nevertheless, most businesses can expect to go through a similar set of steps when implementing a CDP.
Let’s walk through the typical steps in the process of implementing a CDP.
We’ll also look at the differences between implementing a standalone CDP and CDP with built-in campaign execution and analytics capabilities.
The 3 Necessary Stages To Implementing a CDP
1. Planning Phase
All the necessary groundwork for integrating a CDP needs to be taken care of before any technical work can begin. Some necessary parts of this stage include:
Project Scope Creation: describe business goals, use cases, step-by-step integration and implementation.
Tracking Document Creation: describe customer attributes, consents, and custom events to be tracked.
2. Integration Phase
Once ouy lay the groundwork, it’s time for the technical integrations to begin. Most of the integration steps will be standard for any data collection tool, but this process will vary slightly depending on what type of CDP you choose. Let’s go over the routine integration steps first:
CDP Initialization: This is the process of connecting the CDP to your online & offline data sources, allowing you to identify your customers and analyze their actions. With Bloomreach, this is very simple: just paste a snippet of code into the header of your website. Other solutions might look quite different.
Customer IDs and Attributes Tracking: After initializing the CDP, set up customer IDs and attributes tracking for the information you’ve decided to collect. This data is helpful for segmenting your audience, triggering campaigns, sending personalized information, and more.
Events Tracking: Follow and get insight into customer behavior by tracking purchases, clicks, returns, browsing behavior, and more. Connect this to a customer’s unique identifier to build complete pictures of each customer.
Data Imports: Connect all your existing data (customer data, event data, product catalogs) to your new platform.
The steps in this importing process depend entirely on your CDP.
If you are using a Standalone CDP, you will want to integrate it with your other tools and platforms so you can make the most of its capabilities. Consider which of the below platforms you want to use. They will each need to be integrated with the CDP.
These integrations are unnecessary with a CDP + marketing automation, since analytics and automation abilities are built in.
If you are using a CDP + Marketing Automation, there’s no further integration process needed. A CDP + marketing automation platform doesn’t require any integration with analytics and execution tools, since those capabilities are native. As soon as the platform is integrated, you can begin analyzing data and executing automated marketing campaigns.
Note: If you want to keep some of your existing third-party tools, you can integrate them with Bloomreach Engagement, just like a standalone CDP.
3. Execution Phase
You’ve finished initializing your CDP, you’ve set up customer identifiers and event tracking, you’ve integrated all your tools and platforms — now you can start using your platform to power insightful analytics and marketing automation.
But again, this process will look different depending on what type of CDP you employ.
Because a standalone CDP was not built together with your analytics and execution platforms, you can expect the following:
A CDP + marketing automation offers some advantages for execution. Thanks to an all-in-one solution, marketers can expect:
One User Interface
Unified Technology
Bidirectional Data Flow
Easy Omnichannel Orchestration
Real-time Response
How to Choose the Right CDP for Your Company
After you’ve decided that a CDP is the right tool for your business, you’ve got to decide which vendor to choose. The number of possible vendors might make the choice seem overwhelming, so it’s important to have a plan for your buying process.
Each company will have different requirements and use cases, but some parts of the buying process should look the same for most businesses.
First, you need to define your use cases. How do you plan to use a CDP? Do you want a CDP with execution layers and ecommerce personalization capabilities? Or do you just need identity resolution and customer segmentation (standalone CDP)? Answering this question will help you better understand your requirements.
Once you’ve done that, you can start to match your requirements to potential vendors. Can they handle the use cases that you require? This allows you to create a short list of candidates.
Next, evaluate the vendors you’ve selected. Ask them to demonstrate their platform executing a use case that you require, instead of relying on a canned demo that only showcases the best that platform has to offer. This will show you if a potential solution is right for you or not.
Finally you can make your decision. This might involve an RFP or a pilot project to make sure that the solution you’ve chosen actually meets your needs. If it has, congratulations! You’re ready to start taking advantage of all a CDP has to offer.
If you dream of achieving better business results with less effort, allow us to introduce you to marketing automation.
Because it streamlines and optimizes digital marketing efforts, marketing automation helps businesses increase revenue and elevate the customer experience. Just ask the 77% of marketing automation users who report an increase in conversions thanks to these tools.
However, implementing marketing automation can be confusing and overwhelming, especially for anyone who isn’t already familiar with the concept.
This guide will demystify marketing automation for you. Keep reading to learn the ins and outs of marketing automation, from the basics of what it is and how it works to best practices for implementing a successful marketing automation strategy.
Marketing Automation: A Definition
What is automated marketing? Automated marketing refers to the use of marketing software or other tools to automate repetitive tasks, streamline workflows, and manage campaigns across multiple channels, including email, social media, digital ads, and websites.
With marketing automation, ecommerce brands increase efficiency and enjoy a host of other benefits, including better customer engagement and revenue growth.
What Are the Benefits of Marketing Automation?
Automated marketing provides businesses with several advantages that help them gain a deeper understanding of their customers. With automated marketing in place, ecommerce businesses are more efficient, improve lead management, boost customer engagement, and more:
Free up Time and Lower Costs
Automated marketing reduces costs and increases efficiency by automating repetitive tasks. This gives marketers more time to focus on high-level tasks, like creating strategies and improving the customer experience. This increased efficiency also leads to cost savings, as automated marketing reduces labor costs and reduces the likelihood of human error.
Personalize the Customer Experience
Marketing automation places your brand in the right place at the right time, so you can personalize the customer experience like never before. Target customers with personalized messages after they make a purchase, browse specific products, or abandon a shopping cart. Stay consistent with customization and you’ll increase customer engagement and loyalty over time.
Monitor Target Audiences
Automated audience monitoring segments customers by demographics, behavior, interests, and brand interactions. Assign lead scores to promising prospects so you can target them more effectively. Identify trends in customer data points and use this intel to make informed decisions about which touchpoints to deploy and when.
Improve Marketing ROI
Increased conversions go hand-in-hand with a marketing automation process. This is because automated marketing makes it easier for ecommerce businesses to send targeted content to customers at various stages of the buyer’s journey. When customers receive marketing messages that they like and respond to, they view your brand as trustworthy and credible, and are more likely to shop with you.
How Does Automated Marketing Work?
While marketing automation may seem like magic, there are logical processes behind the scenes that make it work.
It begins with workflows. Workflows are a series of automated steps that a contact goes through based on their behavior, preferences, and demographics. Workflows are triggered by the actions your customer takes, like a form submission or website visit.
Messages are the pieces of content you send along to your customer via SMS, push notifications, emails, and social media posts. To be successful, your message should always include personalization and a call-to-action that’s relevant to that subscriber’s unique position along the buyer’s journey.
If you’ve ever received a welcome email after signing up for a newsletter, you’ve seen workflows and messaging in action. The automatic response is a type of workflow. The email you received with that 10% discount code is the marketing message.
Marketing Automation Tools and Platforms
Marketing automation is powerful and can sometimes intimidate marketers who are unsure of where to start with marketing automation tools. Fortunately, there are many platforms available to help marketing teams automate tasks, analyze customer data, and deliver a personalized omnichannel experience.
Here are a few popular marketing automation solutions known to help ecommerce brands elevate their customer experience and drive growth:
Email marketing. In the digital marketing world, email still reigns supreme. An email marketing automation tool can build your send list for you, segment your customers based on demographics and preferences, and enhance deliverability metrics so your emails stay out of the spam folder.
SMS, MMS, and WhatsApp messaging. 74% of consumers have a positive impression of brands that send them text messages. With this type of marketing automation software, you can contact customers with the right message at the right time.
Mobile app marketing. Custom notifications, unique messaging, and more are at your fingertips with mobile app marketing. Recommend relevant products your customers are likely to buy and solicit helpful feedback right in the app. Gather data about how your shoppers respond and use it to zero in on marketing messages that’ll convert.
Web personalization. Your website is ground zero for your ecommerce business. Ensure that your web experience is top-notch with a platform that helps you deliver real-time product recommendations, choose a banner most likely to convert, and more. You can also gather zero-party data to refine your automation over time.
Bloomreach Marketing Automation Platform
Instead of setting up and trying to connect different pieces of marketing automation software, you can use a tool like Bloomreach Engagement to bring the best digital marketing automation tools together to work perfectly in sync. Bloomreach Engagement makes it easy to personalize messaging across multiple channels, track customer behavior, and analyze data to inform marketing strategies. Engagement is also scalable and user-friendly, making it a clear choice for businesses of all sizes.
Marketing Automation Process and Best Practices
By now, it’s clear that marketing automation is a game-changing way to drive revenue by automating email sends and text messaging, while also uncovering data-rich insights that sharpen your efforts over time.
But, what does a successful marketing automation process look like, and which best practices should you follow to make the biggest impact on your goals?
In this section, we’ll outline the marketing automation process and share a few tips that will take your automated marketing to the next level.
What Is the Marketing Automation Process?
Marketing automation stacks include a combination of software platforms and processes that, when used together, streamline and automate repetitive marketing tasks. The typical marketing automation process will involve:
Collecting data about your prospects and current customers. Data is often gleaned from a variety of sources, like website sessions, form fills, and email campaigns.
Segmenting your target audience after data collection so that similar customers are grouped alongside others. By grouping customers by their identifying factors, you can distribute marketing messages that are most likely to resonate and convert.
Creating campaigns across multiple channels that present a personalized offer, based on segmentation-sourced data.
Automating delivery schedules, social media marketing posts, and other campaigns based on preestablished triggers, so your marketing team can focus on more important tasks.
Analyzing the results of your automated efforts and tweaking the approach as necessary.
Know Your Audience
Automating your marketing may feel impersonal, but if you want to be successful at it, you have to make sure that every automated touchpoint is designed with your audience’s needs in mind. Knowing your audience allows you to segment them properly, so that they receive only the most relevant messages possible and, in turn, convert at higher rates.
But it can be challenging to make sense of all the customer data that’s available to your business and activate it in meaningful ways. With multiple channels and a myriad of data sources that need to be consolidated and organized, you need a tool that can help you make sense of all the information you’ve gained.
That’s why a customer data platform (CDP) is so essential for any automated marketing strategy. A CDP is a marketing technology that collects data from all your customer interactions and combines them into a single customer view. This view provides a unified customer profile for each individual, giving you insights into their behavior that can be easily activated with marketing campaigns.
A CDP and marketing automation is one of the most valuable technology pairings available to marketers. With a unified data source fueling your automated marketing strategy, you can send effective, personalized messaging that feels relevant to each and every customer.
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.
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.
