Customer data platforms (CDPs) are software applications that are designed to consolidate and manage customer data across all touchpoints to provide a unified view of the customer and their entire journey. They are designed to help organizations understand how customers have been interacting with the brand, from marketing, through sales, and post-sale, all in one place.
Customer data captured in CDPs is often generated through mobile devices, laptops, the web, or mobile apps, and then combined with customer data to help companies understand who the user is, whether they have interacted with the company before, what actions they took, and why they took them. CDPs can then be used in the following ways to help organizations create better customer experiences, enhance operations, and improve efficiency.
Unifying customer data is perhaps the most common CDP use case. As organizations move to create a 360-degree view of the customer, they need to ensure that data held in a variety of systems can be unified and verified, creating a single source of truth. Essentially CDPs make sure that all customer data is pulled together from disparate silos so that any interaction with a customer is driven by the most recent and complete record available.
This unified, single source of customer data can then be used to enhance personalization efforts, allowing a more curated and relevant experience for each customer. Data contained in a CDP can be used to create customer segments, based on shared characteristics and behavior patterns, so that future engagements are specifically tailored to their interests, buying patterns, and preferences. Taken a step further, personalized marketing at the individual level can be driven by using insights from your CDP.
Today’s customers often utilize a combination of online and offline channels to interact with companies. A CDP can be used to optimize omnichannel marketing campaigns. If a customer begins a journey through one channel, and then abandons that communication, a CDP will be able to track that customer, and then reach out to them via another channel to re-engage with them, and complete the interaction. Similarly, companies can use CDP to combine online data from your digital channels, including social media, communities, or SMS, with offline customer data from your point-of-sale (POS) system, preventing a product marketing message being sent to a customer that had recently purchased that item in a physical retail store.
CDPs can also be used to improve the efficiency and efficacy of marketing automation systems. While a CDP is not designed to replace a marketing automation system, it can be used to assist in automating marketing tasks. Working in tandem with a marketing automation system, a CDP equipped with machine learning (ML) tools can identify specific patterns in customer activity that can then inform the most efficient marketing actions to take, based on each individual customer’s history and preferences.
Similarly, the insights captured by CDPs can also be used to identify the characteristics of VIP or other high-value customers, and then determine the adjacent characteristics of these customers, such as their specific buying habits (like preference for online versus in-store shopping, presence on community boards, or social media), and then created targeted campaigns to leverage these insights. Additionally, analytics teams can also use this data to find “lookalike” customers with similar behavioral or demographic characteristics who can be cultivated and transformed into becoming VIPs themselves.
Ultimately, a CDP can be characterized as a catalyst for activating customer data. By serving as the mechanism for unifying vital information from various points within an organization, a CDP consolidates and normalizes disparate sets of data that can then be accessed and used by several other functions to make customer engagements more efficient, personalized, and effective.
Author Information
Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
