Are you getting the most from your customer data? Our customer data management (CDM) best practices will help you build an end-to-end solution.
What is customer data management?
Organizations use customer data management (CDM) to gain a better understanding about their customers. With CDM, a business can gain a trusted view of information about known customers. They can also broaden their knowledge about unknown customers. This knowledge can be used by the company to create more engaging and relevant interactions for their customers.
A brief history of customer data management
Customer data management predates master data management (MDM) and customer data integration (CDI). That’s because businesses have needed to figure out what to do with this information ever since they started to collect data about their customers.
The evolution of customer data management begins in the 1990s. Customer relationship management (CRM) systems tracked and managed data about existing customers. In the 2000s, data management platforms (DMPs) began to offer businesses the ability to manage new types of customer information. DMPs could use data from data warehouses about unknown customers to manage cookie IDs and target online ads to those customers.
Starting in the mid-2010s, customer data platforms (CDPs) brought the two types of data systems together in one platform. CDPs offer multichannel campaign management, segmentation and orchestration. The constant throughout this evolution has been the need to gather data, manage its quality and keep it fit and available for use.
Today, organizations can use customer data management systems to break down departmental silos. They can then combine customer data with product data and other information to reveal hidden relationships and gain a better understanding of that customer. This allows them to create truly 360-degree customer views to form the foundation of an enterprise strategy.
Three customer data management best practices
You can’t take an enterprise approach to the customer without taking an enterprise approach to customer data. And managing an unprecedented volume and variety of data takes a multifaceted CDM strategy. The right strategy will help you address issues like data governance, master data management and data cataloging. As you build your CDM infrastructure, keep these three best practices in mind:
- Create a standard definition of “customer” across your business
- Unify data and insights across all relevant systems
- Make sure your customer data is clean, protected, consistent and actionable
There's a good chance that coming up with a single definition of "customer" for your whole business will be harder than you thought. A customer who’s making regular purchases from one of your lines of business may still be a prospect for another line of business. One department may consider anyone who’s made a purchase in the last five years an active customer. Yet another group may drop them from the list unless they’ve bought something in the past year. Even though you don't need just one definition, you do need to know how to define a customer. To find out who your customers are and what they want, you need to know what customer characteristics you want to track and manage.
You can use AI and natural language processing across systems to combine different types of data into a single customer view. To get a better understanding of your customer, you can take data from transactions (orders, quotes, incidents, assets, entitlements) and combine it with interaction data (such as web chats, social media and call notes). You can then make this unified view of the customer searchable across all data, structured and unstructured. This 360-degree view of your customer empowers teams and leads to more effective analysis, strategy and execution.
Trusting data that is incomplete, inconsistent, outdated, or fragmented is difficult. If you can't trust your data, you can't expect it to provide solid analytics on which to base your decisions, so treat it as the strategic asset it is. Storing customer data in a data lake or other repository lets you use it with analytics tools and other applications, but it still needs to be managed and maintained to ensure it remains consistent, complete and accurate. Implement privacy measures throughout the data pipeline. Transform, cleanse, enrich and standardize your data and ensure that it is fit for use before you sync it across applications. And boost efficiency and productivity with AI and machine learning capabilities to automate data management tasks.
Now you have a foundation for a strong customer data management strategy
You’re generating more customer data than ever, in more varieties than ever, and collecting it from more places than ever. You also have more users demanding access to it through self-service tools. These tools mean users don’t have to wait for IT’s help or permission to start creating data-driven initiatives. Meanwhile, new data science tools and technologies demand vast amounts of data to drive new algorithms. You can support a solid CDM strategy by starting with a flexible, scalable data management solution that offers baseline capabilities right out of the box and allows you to add new functionality or data as your needs evolve.
Managing customer data at the speed of today's business
A true end-to-end CDM solution lets you manage and master all your customer data across the enterprise. CDM capabilities can include data quality and enrichment, data governance and more. You should be able to deploy on-premises or multi-cloud depending on your requirements. And you should be able to leverage AI and machine learning to streamline and speed up data management tasks. The right CDM solution will help you achieve the comprehensive customer 360 view that drives a superior customer experience.