Consumers have developed certain expectations of brands. They want them to know their preferences, personalize their content and most especially have a complete view of their interactions with them across websites, mobile apps and even brick and mortar stores.
But data silos prevent brands from meeting these customer demands and expectations. For instance, when it comes to tracking interactions across multiple touchpoints, many brands fall short because each channel relies on siloed and individual data sources. The majority of customer journeys involve a number of different channels, and customers tend to move seamlessly and quickly between these channels. Most companies, however, don’t have these channel data environments connected to create the needed velocity of real-time interaction management.
The result is disjointed, lagging and bumpy experiences for customers, as well as data quality problems for marketers and supporting analysts.
Customer Data Platform (CDPs) to the Rescue?
A customer data platform is formally defined by the CDP Institute as “packaged software that builds a persistent, unified customer database that is accessible to other systems.”
CDPs typically have four common functions:
- Ingesting audience data from multiple sources. That means not only stitching together a single customer ID from different operational and analytical CRM systems, but also tying together databases that traditionally don’t share customer data, such as marketing clouds, service software and ecommerce engines. This data management workflow and the ability to tie IDs across systems is what customer resolution is all about.
- Managing customer identities. Effectively, this is the process of reconciling identities brands have about identified customers (like email address or mobile phone numbers) with what the brand knows about customers before they share their data (anonymous cookies and mobile device IDs). Think of this as cross-device identification.
- Supporting real-time customer segmentation. CDPs allow marketers to build and maintain universal, omnichannel audience segments of customers with the unified data available in the customer data platform.
- Data provision and activation. Once customer profiles have been unified, the data must be available in real-time so brands can deliver relevant, personalized interactions. This is done through providing connectors and APIs to other marketing technologies.
The ultimate value of a customer data platform is to provide a unified omnichannel view of first-party customer data for real-time customer engagement.
Related Article: Curiouser and Curiouser – Drawing the Line Between DXP and CDP
How to Choose a CDP?
Every company will move at a different pace and have a different level of interest and state of readiness when it comes to managing data holistically in an on-premises, hybrid, or fully cloud-based manner. Here are several things to consider:
- The ability to go “hybrid.” If a brand’s analytical data exists today in an on-premises form then a hybrid architecture can support creating mechanisms to exploit that data without having to duplicate or rebuild it in another technology. To make the shift to becoming fully cloud — the ability to deliver detailed customer-level data collection, manage that data securely, AND support real-time activation — at scale, brands must look to hybrid platforms to accelerate this initiative. Organizations with established data infrastructure and architecture can incur significant implementation and data synchronization costs when importing their data assets into a cloud-based CDP solution. To work around this issue, companies may want to explore solutions that provide basic CDP capabilities (identity management, segmentation and data provision) without requiring a physical data move into a single database.
- The level of data collection needed. Collecting and managing customer behavior across all devices and digital channels when customer activity is both known and anonymous is outside the scope of many of today’s CDP solutions. In addition, data management best practices cannot overlook data hygiene, deterministic and non-deterministic integration approaches, transformations, federation and governance. Applying data management techniques to high-velocity data streams in digital marketing is critical for the future of customer engagement. Consider the level of detail and velocity of data collection needed when choosing a solution.
- Activating data in an analytically driven manner. While CDPs allow for audience segmentation, the core capabilities typically support rules-based approaches. When algorithmic applications of segmentation enter customer engagement use cases, real-time decisioning, triggering and next-best-offer execution frequently fall apart. While some CDP vendors are developing analytical capabilities to supplement the core CDP functions, the sophistication of these varies widely. If requirements extend past core CDP capabilities, it may be better to look at purpose-built tools that offer basic CDP functions but are designed for more broad-based journey orchestration and analytics activity.
- Moving beyond marketing. A common characteristic of a CDP is providing unified customer data to other marketing applications. But a common complaint among CDP customers is the integrations to other systems are more complex and time-consuming than advertised. This is a big problem when customer experience programs extend beyond marketing and into other areas that affect customers (sales, service, fraud and risk). All these areas need to apply analytically-driven contextual personalization to their customer activities, and the CDP is the logical place from which to activate the needed customer data.
As you look to build out your customer data platform capabilities, keep in mind the need for real-time contextual personalization and intelligent delivery of offers and interactions across your business.
Related Article: Understanding the Myths and Realities of a True CDP
Jonathan Moran covers global product marketing activities at SAS, with a focus on customer experience and marketing technologies. Prior to SAS, Jon gained over 20 years of marketing and analytics industry experience at both Earnix and the Teradata Corporation in pre-sales, consulting and marketing roles.