What to look for in a CDP
CDP adoption is on the rise and organizations have several options from which to choose. Some offerings feature CDP-like capabilities that have been built on to existing marketing campaign automation tools. Others meet all requirements established by the CDP Institute and are RealCDP™-certified, and some CDPs extend even further to provide advanced customer analytics, hyper-personalization and real-time capabilities that build deeper customer engagements and drive loyalty as an outcome.
Look for the following key features in a modular solution that provides the ability to comprehensively orchestrate and coordinate with your existing technologies:
Automated data unification: A CDP helps increase operational efficiency by automating data unification and ensuring teams and systems across the enterprise have access to clean, up-to-date customer profiles. With a single source of truth for customer data, marketers and enterprise systems can gain accurate customer insights without concerns about data inaccuracies. Further extending the value, some CDPs leverage data unification to provide a user-friendly interface for efficiently analyzing customer behaviors, segmenting audiences, and determining the next-best actions.
Embedded real-time AI and ML: An AI- and ML-driven CDP with real-time capabilities generates data-driven insights that help the business understand the relevance, impact, and trajectories of customer behaviors. In short, only such a CDP can deliver effective and optimized contextual next-best offers and recommendations with personalized journey orchestration in real time.
Persona discovery and dynamic customer segmentation: Dynamic customer segmentation is anchored in analytics to help businesses market to continuously optimize customer groups. With historical purchase information, behavioral data, AI, and machine learning models, a CDP with persona discovery and dynamic segmentation can help brands target their campaigns to achieve high conversion rates and lower costs. Dynamic customer segmentation is continuously refined to reflect changing customer interests, attitudes, and preferences.
Next best offers and recommendations: Real-time customer analytics algorithms can leverage CDP data to surface next-best-actions and offers for each customer. The recommendations can be based on the customer’s brand interaction history as well as the interactions of customers that fall within the same persona. Examples of recommendation types may include recommended just for you (RJFY), frequently bought together (FBT), journey next best action recommendations (Journey NBA), and top trending products.
Journey orchestration: Journey orchestration links what were once ad-hoc interactions into a connected series of frictionless interactions that lead the customer along an engaging journey no matter when and where they interact with your brand.
Each feature mentioned above yields incremental gain. When used in concert, they become a powerful engine for optimizing customer value and building deeper customer engagement.