CDPs are increasingly essential to integrated digital marketing programs. Deploying these technologies reveals gaps and challenges throughout the entire enterprise and the digital supply chain serving the customer. If the customer cannot be fully understood from every point of view of the enterprise, it is not possible to serve them optimally. These gaps and challenges can only be remediated with board- and C-level resources and attention. If you are not serving your customers optimally, they will go to your competitor.
Successful use of customer data requires the development of a robust model, judicious selection of data, careful interpretation of analytics, and the ability to act on the results. Each of these steps poses its own challenges. By providing access to data from numerous systems in one database and supporting the systems that can produce an appropriate customer experience, the CDP overcomes the limitations imposed by fragmented point solutions and presents a holistic approach to customer interactions.
CUSTOMER DATA MODEL CHALLENGES
A customer data model identifies the factors that a marketer believes to be important in
understanding and predicting a customer’s behavior. Without a model, there is no way to systematically segment customers into groups and test the effectiveness of different marketing strategies. A data model captures a variety of data, from unambiguous, clearly defined attributes like name, address, and demographic details to data that can be derived and inferred through interactions and by processing data produced by other systems.
The data model contains attributes that might be created when an event occurs—for example, when a customer’s purchases exceed a certain threshold. Or an attribute can be based on who the customer is and where they live. Segmentation models can be based on a combination of explicitly defined data points, such as purchase history, user-declared values (for example, an expressed preference), externally referenced information (subscription or membership information), and attributes and values that are inferred by comparison to large numbers of customers with similar characteristics. Some techniques find hidden or latent attributes, or create relationships based on numerous subtle data signals.
Data models can be mathematical, rules-based, visual, or based on a list of the relevant factors that are believed to produce certain behavior; for example, all people under the age of 40 will prefer slim-cut jeans and those over the age of 40 will prefer a looser fit. (This is not a valid general statement, but it could form the basis of a hypothesis that could then be tested.)
A data model represents the customer and the collective insights and understanding of their real world needs.
Creating a data model is a valuable exercise for non-technical specialists because it allows marketers to use language to describe what they know, believe, or can infer about their customer. These characteristics are converted to a structure that the system leverages (either capturing the details or defining rules and algorithms for inferring them). Data models can also inform marketers about the types of metrics they should track, and how well their strategy and specific campaigns are performing relative to a particular customer segment or characteristic.
Customer data is usually collected from a large variety of systems that come from different vendors or, if homegrown, are created by different groups. Therefore, they will have varying formats, architectures, and naming conventions. As a result, customer data models could be inconsistent, which makes it challenging to create a unified model that incorporates meaningful data in actionable formats. For example, one system might define one customer at the individual level and another at the household level. If one system totals all of the purchases for multiple members and another tracks individual purchases, the analysis of sales per customer will produce different results.
Despite variations in the data, the model must contain enough detail and the correct attributes to support advanced functionality such as effectively predicting purchase patterns or recommending an appropriate product that meets the precise needs of the customer. A customer data model is analogous to a content data model (typically called a content model). Customer profiles contain attributes that are used by other systems to improve their outputs. For example, for personalization to work correctly, the model needs to provide signals to customer engagement systems that tell those systems how to differentiate the customer’s experience—what content to present, what products or solutions to offer, and the overall experience that will move them forward in their journey. What is it about the customer that can be captured as metadata (or attributes) in the customer data model and represented in the details of their profile that will drive a unique interaction? It might be the customer’s age, or whether they were active on social media, or whether they had children. The CDP stores data about the customer that can be leveraged by various downstream systems to predict and influence the customer’s behavior.
These signals can come from many sources. Some are based on explicit attributes such as demographics, content preferences, and account information (see Figure 2). Others come from subjective or behavioral attributes (see Figure 3). These might be interest profiles, past purchase behaviors, social media patterns, loyalty scores, and real-time website behaviors.