Customer Data Platforms (CDP) in 2020 and Beyond

Marketers have always known that customer data is the key to delivering great customer experience (CX) and is full of insights just waiting to be gleaned. It’s the foundation of winning the loyalty of today’s demanding customers.

Savvy marketers have also learned the challenges of working with customer data: it’s scattered everywhere, it comes in many different types, and it has varying degrees of completeness. Marketers and marketing technologists recognize and confront these challenges when pulling this data together in a meaningful way.

And customers? They just don’t have the patience or the inclination to wait for good experiences. Instead, they have become much more willing to jump ship after just one bad experience. This puts the pressure on marketers to not only satisfy customer needs, but to exceed their expectations. 

Many marketers have figured out that they can build loyalty and retention by increasing their focus on how data is used across every stage of the customer lifecycle: from running highly targeted and segmented marketing campaigns, to driving successful personalized cross-sell offers, or keeping customers informed through well-timed (and appreciated) individualized communications.

What is a CDP?

A Customer Data Platform (CDP) is a tool that marketers use to manage customer data so they can drive smarter, more effective campaigns. That’s because CDPs are the enabling technology that delivers micro-segmentation, hyper-personalization, and deeper customer insights for orchestrating customer relationships.

But what is a CDP, exactly? And what should marketers look for when they’re trying to manage customer data and improve customer experience? 

According to The Customer Data Platform Institute, a CDP is “packaged software that creates a persistent, unified customer database that is accessible to other systems.”

In order to simplify the CDP vendor landscape, the Institute developed the RealCDP certification program, which outlines five capabilities:

  • Ingest data from any source
  • Capture full detail of ingested data
  • Store ingested data indefinitely (subject to privacy constraints)
  • Create unified profiles of identified individuals
  • Share data with any system that needs it

The next-generation CDP

Traditionally, marketing departments have had the sole responsibility for selecting, maintaining, and leveraging customer data platforms. As marketing continues to play a bigger strategic role in how organizations deliver and compete on CX, and as new applications support new ways of interacting with customers and create new data types, there is a need to expand the type of data and the scope of data that a CDP can support and manage.

Enterprise data—which consists of all interactions, transactions, and other data beyond marketing—holds significant value. But in order to incorporate and intelligently understand how enterprise data is connected to customer records, additional capabilities are required.

Next-generation capabilities

Next-generation CDPs are being adopted by forward-leaning marketing teams to deeply connect customer data across the enterprise into a 360 graph, allowing them to compete at the edge of data and find meaningful micro-segments in which they can differentiate and personalize CX. Next-generation CDPs incorporate Artificial Intelligence (AI)/ Machine Learning (ML), and Natural Language Processing (NLP) to help marketing overcome the data challenges they face and maximize the value of their efforts. These next-generation capabilities enable marketers to:

  1. Automatically create a customer identity and relationship graph using Deep 360 Graph Matching: A deep matching engine evolves beyond answering the simple question of “Are these two customers the same?” to answer the more important question, “What is this data entity—a customer, prospect, email, service record, or quote—connected to?”  Its purpose is to manage unique identities and connect everything to everyone.  Deep matching is based on Artificial Intelligence and Machine Learning—it learns from hundreds of match decisions made by your marketers, and then automates billions of matches as it builds the 360 graph. Understanding how people are connected, their relationships, and networks provides both context and insight to marketers. For example, a marketer could use insight into one household member’s recent positive experience with a product and choose to target another member of the household or social circle that may have been positively influenced, making them a good candidate for a similar offer.
  2. Fine-tune recommendations with continuous analytics: With the amount of data that’s being generated every day by every individual, it takes a lot of horsepower to make sense of it all. Continuous analytics involves both an analytic repository and real-time analytic capabilities.  The analytic repository is a sandbox in which data scientists and analytic users can experiment and find new patterns and insights in CX.  The real-time analytics component can monitor data changes, detect insights such as next best experience, leading indicators of customer churn, or purchase indicators, and trigger real-time individual level actions and orchestration. Which means that when Sue Stern communicates through a web chat, the recommended next best action will be based on her most relevant history with your organization, and even other customers that look like Sue.
  3. Leverage a customer 360 view to create multiple unique perspectives: Taking into account your confidence in the data and the accuracy of how records are unified, you can activate different perspectives of the customer view for different uses. The customer view used for broad email campaigns can be different that the view used for personalized 1:1 outreach. This enables a richer customer dataset for use in campaigns since marketers can often use less-than-perfect data but have always struggled with traditional CDPs and data management that either oversimplify or overelaborate the match process in the pursuit of the 100% match. 
  4. Build a complete view of all enterprise data and marketing data: With the insights you can infer by using all enterprise data—instead of just marketing data—you gain a more complete and comprehensive view of your customers. Enterprise data—which includes purchases, contracts, warranties, quotes, service requests, returns, and omni-channel interactions—is the key to truly understanding customer behavior: their intent, attitude, and actions. For example, understanding contract entitlements and purchase cycles for B2B buyers can help optimize timing of marketing campaigns to stimulate repurchase—timing that is based on the buying cycle of the customer, instead of the marketing calendar or sales forecasts of your organization. 
  5. Ensure quality, trust, and compliance: Building trust with customers and complying with changing use and access customer data regulations is now more important than ever for customer experience. Enabling adherence to privacy and preferences makes governance a critical element of a next-generation CDP. Next-generation CDPs blend privacy-by-design with deep matching and profile activation and separate the function of matching and linking data from creation of the entity record. This enables consent to be the driving force behind if and when a customer profile is made available for use. In other words, if consent isn’t granted, then a customer profile isn’t generated for that specific use.

With these advanced capabilities, a next-generation CDP helps you to recognize customers every time they interact with you, across all types of channels, departments, and functions. A CDP lets you develop individualized marketing campaigns, improved customer experience, and context-driven sales offers. As you evaluate whether or not you need a CDP to help you reach your marketing and customer experience goals (you do), consider how a next-generation CDP helps you to

  • Discover non-obvious relationships, measure sentiment, and infer life events
  • Derive important attributes about customers such as occupation, product/competitor mentions, personality, and location-based events (such as travel and patterns)
  • Blend all interactions, transactions, and events into a comprehensive customer journey to analyze and personalize customer experiences
  • Use the individual customer journeys and apply ML to predict the next likely interaction for each customer
  • Determine customer patterns like churn indicators and retention
  • Fuel advanced customer analytics and increase campaign effectiveness with micro-segmentation, RFM (recency, frequency, monetary) analysis, market basket analysis, and so on

Learn more

You can learn more by attending the Informatica CX VIP Summit in  NYC on December 3rd, which includes a Master Class hosted by David Raab, the founder of the CDP Institute. This is a great opportunity to learn more about next-gen CDPs, network with others who have embarked on a CDP journey, and pose your most pressing questions directly to David himself. Join me there.

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