Quick Summary
The digital experience is comprised entirely of data. The mobile device effectively becomes the sales person. In the physical world, a sales person can read physical body language and adjust accordingly.
The same things happens when users interact with your website. We can capture their “digital body language” and adjust what we offer them based on that information.
At the end of the day, everything boils down to providing value for a customer. The more we know about our customers, the better we can provide that value and differentiate from our competitors. Although this objective sounds simple, many challenges arise along the way. These fall into four broad categories related to systems that produce and consume customer data: the ways that customer data is modeled, the data itself, gaining insights from the data, and acting on that data.
Customer data platforms (CDPs) aggregate data from many different sources to provide a 360 degree view of the customer. The platforms are designed to be managed and used directly by marketers. In addition, they eliminate the need to access multiple systems in order to collect the information needed to create customer profiles, develop marketing campaigns, test the effectiveness of marketing strategies, and predict customer behavior.
Customer data model challenges
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, the customer data models may be inconsistent. This inconsistency makes it difficult to create a unified model that incorporates all the relevant data.
Despite the variations in the data, the model must contain enough detail and the correct attributes to support advanced functionality. The customer data model is analogous to the content data model (typically called the content model). Customer profiles contain attributes that are used by other systems to improve their outputs. For example, in order 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, the overall experience that will move them forward in their journey? What is it about the customer that can be captured as metadata and represented in the details of their profile that will drive a unique interaction? The CDP stores data about the customer that can be leveraged by various downstream systems to predict and influence their behavior.
These signals can come from many sources. They might be interest profiles, past purchase behaviors, social media patterns, loyalty scores, and real time web site behaviors. The overall information architecture (customer data models and content models) needs to be aligned so that specific pieces of information can be surfaced to the user depending on the real-time signals from their digital body language. This step requires human understanding—which pieces of information most contribute to a solid model for behavior. While some of these features can be defined in advance, many are based on upstream and downstream system architecture and algorithms.
Mining the data stream
What does customer data reveal about what the customer needs? Many different data sources are available. For instance, social media activity or actions on a website. Social media data may contain information about a preference. How is that preference identified and captured? Clickstream tells us something about how they are consuming content and traversing the website, whether they click through on an offer or not, whether they respond to a promotion, whether they are able to complete their purchase. The data tells a story--the question is how to understand that story.
Every tool and technology in the digital engagement ecosystem produces streams of data which need to be interpreted in order to be acted upon. The challenge lies in identifying what data is important, understanding what it is saying and determining what to do with it. These questions must be asked over and over again to keep the focus on what the purpose of collecting and analyzing data, which may in fact change over time.
Metrics fall into several classes:
• Search metrics
• Behavior metrics
• Utilization metrics
• Content metrics
• Response metrics
Each category of metric can have dozens of details and reports. The goal of understanding these metrics is to drive an action to optimize or improve an outcome. When users browse to a certain point and then leave the site, they were unable to complete their task. What can be changed to impact this behavior? Experiments need to be designed to find the best combination of user outcomes. These are the insights that lead an organization to change its strategies for campaigns and offers The CDP not only allows ready access to all the metrics associated with customer behavior but is also able to execute an appropriate response based on the data, sometimes by accessing external systems.
Deriving customer insights
A good sales associate knows both the customer and the products, and recommends the one that meets the customer’s needs. In the digital world, the organization needs a way to capture and act on the insights that come from the data. This means first interpreting the signals, which arrive in a different form from those a sales associate receives. Marketers and merchandisers have been dealing with the issue of signals for years, but in a different context. The new pieces of the puzzle are the scale of the challenge, the velocity of commerce, and the number of variables that need to be interpreted. It is not possible to manually design and create the combinations of products, services, and solutions that meets each customer’s unique needs. In the physical world, that is what a great salesperson does – they know the customer and offer solutions based on that knowledge. Digital technology now serves as the stand-in for the best salesperson in the organization.
The process begins with a hypothesis about what offers the customer will respond to and what components of that offer can be varied. It may be as simple as shopping basket analysis where purchase history combinations are mined and presented to customers exhibiting similar signal patterns. Other sources of variables can be mined from human experts, product engineering documentation, maintenance manuals, and support call chat logs.
An analysis might reveal that for a specific segment of customers, a particular combination of products leads to increased conversions under certain circumstances. The hypothesis is that extending the product breadth with new audiences will also lead to increased revenue. In order to test this, the CDP needs to integrate with downstream systems that orchestrate the user experience. These systems include content management systems, ecommerce applications, product information systems, and the customer data platform.
Once the insights are gained, the challenge becomes one of converting knowledge into action.
Acting on insights from customer data
The goal of understanding the customer is to take action based on that understanding. How does the organization interpret the interactions, preferences, experiences and all the signals that stream from every customer relationship? What do you do with the data? It’s the “What next?” question that has to be continually asked and answered. What do we do when we know our customer’s needs? The obvious answer is that we try to meet those needs as cost effectively as we can. The question is always one of “how?” How do we act on the information?
Human intervention is critical at this stage. Acting on insights requires a human to interpret the data and recognize a pattern, the ability to test hypotheses about actions will create the desired behavior, and the machinery to operationalize the confirmed (or fine-tuned) hypothesis. The end result is dynamic functionality derived from a combination of human judgement, expertise and creativity. However, this outcome cannot be achieved unless the foundational information, data, and content architecture is in place for the digital machinery. CDPs are intended to be accessed by business users who can test their ideas and design marketing strategies.
In conclusion
Customer data platforms 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. These can only be remediated with board and C level resources and attention. If the customer cannot be fully understood from every point of view of the enterprise, it is not possible to serve them optimally. If you are not serving them optimally, then they will go where someone else can - your competitor.
Successful use of customer data requires development of a robust model, judicious selection of data, careful interpretation of analytics, and finally, 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.
To learn more about the data architecture approaches that support a “personalization stack”, see the following additional content from Earley Information Science:
[Webinar] How Organizations Are Using Customer Data Platforms
[Webinar] The Customer Data Platform – A Path to a Unified Customer Experience
[ITPro Article] The Problem With AI