Are you getting the most from your customer data? Our customer data management best practices will help you build an end-to-end CDM solution.
Customer data management (CDM) helps organizations create a trusted, unified view of both information about known customers and context about unknown customers. Following best practices for CDM allows a business to drive more relevant interactions for greater customer engagement and satisfaction.
Customer data management predates master data management (MDM) and customer data integration (CDI). In fact, you could argue that “customer data management” is the original generic term: as soon as businesses started to collect data about their customers, they needed to figure out what to do with it and how to use it to generate business value.
CDM technology emerged in the 1990s with customer relationship management (CRM) systems, which tracked and managed data about known customers. In the 2000s, data management platforms (DMPs) emerged as AdTech, with data warehouses primarily managing cookie IDs and generating look-alike audience segments for targeted online ads to unknown customers. Then, starting in the mid-2010s, customer data platforms (CDPs) brought the two together with features like multichannel campaign management, tag management, and data integration in a single platform. The constant throughout this evolution has been the need to gather data, manage its quality, and keep it fit and available for use.
Today, by breaking down departmental siloes to combine customer data with product data and other information, organizations can reveal previously hidden relationships and connections and create truly 360-degree customer views to form the foundation of an enterprise strategy.
You can’t take an enterprise approach to the customer without taking an enterprise approach to customer data—and with an unprecedented volume and variety of data to manage, companies today must develop a CDM strategy that incorporates data governance, data access, master data management, data catalogs, metadata management, and more. As you build a CDM infrastructure to help you better understand and deliver on what your customers want and expect, keep these three best practices in mind:
A customer who’s making regular purchases from one of your lines of business may still be a prospect to another line of business. One department may consider anyone who’s made a purchase in the last five years an active customer, while another may drop them from the list if their last purchase was more than a year ago. As you determine which customer attributes to track and manage, use your enterprise-wide business goals to come to an agreement about how to identify and define customers.
Using AI and natural language processing (NLP) across systems lets you automatically combine transactional data (orders, quotes, incidents, assets, entitlements) and interaction data (web chats, call notes, etc.) into an intelligent omnichannel customer view that’s searchable across all data, structured and unstructured. The goal is to create a consistent 360-degree view of the customer across marketing, sales, fulfillment, finance, and corporate teams for more effective analysis, strategy, and execution.
If you can’t trust your data, you can’t expect it to deliver solid analytics on which to base your actions, so manage it like the strategic asset it is. Collect all your important customer data in a data lake or other repository that works seamlessly with analytics tools and other applications and can handle almost limitless simultaneous tasks or jobs. Implement privacy measures throughout the data pipeline. Transform, cleanse, enrich, and standardize your data and ensure that it is fit for use before you share it across applications. Take full advantage of new AI and machine learning capabilities while automating data management tasks as much as possible to boost efficiency and productivity.
You’re generating more customer data than ever, in more varieties than ever, and collecting it in more places than ever. At the same time, you have more users demanding access to it through self-service tools that don’t require them to wait for IT’s help or permission to start creating data-driven initiatives. Meanwhile, new data science tools and technologies need vast amounts of data to drive new algorithms. To support a solid CDM strategy, start with a flexible, scalable data management platform that offers baseline capabilities right out of the box and allows you to add new functionality or data as your needs evolve.
A true end-to-end CDM solution lets you manage and master all your customer data across the enterprise, with data quality and enrichment, data integration, business process management (BPM), data governance, and data privacy capabilities all in one place. It should also be able to support different deployment models (on-premises or multi-cloud) depending on your requirements. And, critically, it should leverage AI and machine learning to streamline and accelerate data management tasks that would be difficult or impossible for a human to do at the speed of today’s business. Informatica provides that single platform to help you achieve the complete, comprehensive customer 360 view that drives a superior customer experience.