Organizations increasingly strive to provide more relevant, personalized and meaningful customer experiences
(CXs). Data fuels the modern CX engine. Digitization has created unprecedented levels of customer data to record transactions, interactions and even observe customer behavior. Mar Tech innovations have enabled new customer insights and actions such as social listening and engagement, proximity marketing, browsing intent and retargeting, account-based marketing and others that build upon traditional CRM tools. While it may feel that enterprises are checking all the right boxes with each new CX investment, many organizations are now realizing that customer data, insights and actions are now siloed by these different sources and systems, resulting in a disjointed customer experience.
Customer Data Platforms
(CDPs) are emerging to solve this challenge by enabling nontechnical business users to easily and quickly extend the customer profile, generate customer insights and deliver finely tailored instructions to the last-mile tools that execute personalized messages. According to Gartner, “a CDP is a marketing system that unifies a company’s customer data from marketing and other channels to analyze customers, enable modeling, and optimize the timing and targeting of communications. It includes a user-friendly interface that helps activate customer data and enable personalization across multiple channels
.” In fact, Gartner recently identified CDPs as one of four technologies that have the capability to transform how marketers run their technology ecosystems
CDPs are hot because traditional approaches to customer data management and value creation aren’t keeping pace and hindering customer growth strategies. This isn’t getting any easier when there’s always a new source of customer data, new digital screen to interact with customers and more granular personalization required to meet and exceed customer expectations. No doubt this is a real challenge that demands addressing.
While CDPs are roughly aligned within the analyst and vendor community on the problems they purport to solve, they diverge wildly in their methods and approaches. Understanding these differences and asking the right questions will make all the difference between solving the problem or adding to it.
A CDP’s capabilities can be broken down as follows:
Customer Data Integration
CDPs are designed to put the Marketer and CX professional at the helm. These business users have been burned by overly relying on IT to provision new customer data sources. It is an understandable perspective from business users that IT is slow and rigid when it comes to enterprise data management. These business users often need to seize data with rapidly perishable value to respond to market forces in an agile manner. Many CDP vendors play off this grievance between departments with a seductive pitch to business professionals that they no longer have to rely on IT. This is a gross oversimplification to solving the customer data integration problem within large enterprises.
The reality is that both IT and business users need to play bi-modal roles in building out the 360° view of the customer. IT has expert data integrators skilled at building customer data integration environments that are scalable, reliable, governed and secure. It is tedious work that generates long term value. Business users have willing citizen data integrators who can jump on near-term opportunities and experiments where good-enough is good-enough. Look for a CDP that contemplates and addresses the tooling for both IT and business user roles.
Deep analytics to generate customer insights
Making an impact on driving incremental customer revenue and reducing cost to serve requires enterprises to have thousands of narrowly defined algorithms sensing and reacting to customer opportunities and pain points. Some will be created by data scientists using languages like Python and frameworks like TensorFlow. Others will be created by business analysts using SQL with callable analytic functions.
But to truly scale to thousands of algorithms trawling the data to find opportunities that otherwise go undetected an un-acted upon, more business users need to apply their acumen without the bottleneck of scarcely available data scientists and citizen data scientists. Look for a CDP that has no-code
machine learning and procedural analytics such as sentiment analysis and pathing. As the name implies, business users can invoke these powerful analytics by simply clicking on buttons and selecting drop down menus.
Doing all the work mentioned earlier around building out the customer profile and generating customer insights is worthless if an enterprise doesn’t take action. CDPs activate insights by integrating with applications in the Mar Tech stack (e.g., eMail, CRM, Social Listening & Engagement, others) and customer facing systems (e.g., company website, digital product, kiosk and others).
Antiquated list pulls fail to meet expectations for personalized engagements. Look for a CDP that supports autonomous decisioning and real-time personalization. Autonomous decisioning leverages machine learning to test and learn messages across extensive customer attributes to ultimately converge on the best outcome. The most effective real-time personalization uses data on what the customer is doing in the moment (e.g., browsing mortgage rates) combined with historical data (e.g., credit rating, current mortgage rate with bank, etc.) to appropriately tailor the message.
Choosing a CDP with these capabilities will help you break down the existing customer silos to enable breakthrough CX business outcomes rather than creating yet another silo that will further hinder your CX initiative.
To learn more about how Vantage Customer Experience can help, try our Demo
Gartner, Demystifying the Promises and Implementation of Customer Data Platforms
, 25 March 2019
Gartner Press Release, Gartner Identifies Four Emerging Trends That Will Transform How Marketers Run Their Technology Ecosystems
, 29 Aug 2019
Chad Meley is Vice President of Solutions Marketing at Teradata, responsible for Teradata’s Artificial Intelligence, IoT, and CX solutions.
View all posts by Chad Meley
Chad understands trends in machine & deep learning, and leads a team of technology specialists who interpret the needs and expectations of customers while also working with Teradata engineers, consulting teams and technology partners.
Prior to joining Teradata, he led Electronic Arts’ Data Platform organization. Chad has held a variety of other leadership roles centered around data and analytics while at Dell and FedEx.
Chad holds a BA in economics from The University of Texas, an MBA from Texas Tech University, and performed post graduate work at The University of Texas.
Professional awards include Best Practice Award for Driving Business Results in Data Warehousing from The Data Warehouse Institute and the Marketing Excellence Award from the Direct Marketing Association. He is a regular speaker at conferences, including O’Reilly’s AI Conference, Strata, DataWorks, and Analytics Universe. Chad is the coauthor of the book Achieving Real Business Outcomes From Artificial Intelligence published by O'Reilly Media, and a frequent contributor to publications such as Forbes, CIO Magazine, and Datanami.