What is customer data?

The modern business world is driven by data and some of the most valuable is customer data. Finding the best way to satisfy customers requires studying their needs and that means analyzing the data trail they leave behind. 

Customer data comes from a variety of sources. Some of the most concrete information comes from completed sales; successful transactions contain a wealth of decision-making data that spells out what went right during the sales process and offers a solid guidepost for planning future sales presentations. 

The actual transaction details, though, are just the beginning. Businesses also routinely capture information from advertisers and sales channels that track the customers as they explore their options and move to a decision. These details from ad channels, websites and in-store venues offer useful waypoints that can help revise and focus the entire sales narrative. 

Finding the best way to gather this data, clean up any irregularities, organize it for consistency and then analyze it is the challenge for modern enterprises. This requires a data scientist’s knowledge of statistics along with a marketer’s instincts for the customer and a visionary’s impression of what the future could bring. 

“All of this [data] is converging and the [customer data platform] is the central hub to marketing teams and more,” said Ryan Fleisch, director of product marketing for Profile & Activation at Adobe. “It's so important that more teams are using and flowing data into a central place where everybody's reading off the same playbook.”

Key attributes 

There are many different forms of customer data, and they come from a variety of sources. Customer data specialists often categorize these by making several key distinctions:

    Types of customer data

    Data about the customers comes from a wide variety of sources and the list of possible resources is getting longer and longer. Some of the traditional sources are:

      Customer data collection: Key methods

      Much of the work for enterprise data managers is collecting the data from a variety of sources and then finding the best way to integrate it into reports, charts and tables that can guide future decisions. Some of the data comes directly to the enterprise and some comes from third parties or government agencies. 

      The challenge is to start to integrate it so the enterprise can understand what’s happening to all customers and users. Careful collection and analysis leads to a more complete picture. 

      “I think most companies have a natural reaction to attach themselves to a really bad example, when someone writes in with some really bad consequences that happened for them,” noted Chris Martinez, co-CEO of Idiomatic, which specializes in using AI to understand customer data. “You surface that one case and you say, ‘Hey we have to fix this because look how bad of an experience one person had.' But oftentimes you're missing the slow-burn issues — the things that are not as shocking, but happen to a lot more people. By having a data-driven kind of rigorous approach instead of an anecdotal approach to this, you can actually solve problems for more people.”

      Some of the best data collection tools and methods are:

        One of the most popular data collection approaches is to integrate much of this information with a tool known as a customer data platform (CDP) or a data management system (DMP). There are dozens of companies that are making tools that fall into one or both of these categories and they’re being widely adopted. 

        The tools are popular because they are optimized for integrating data from all of the possible sources. They often have predeveloped pathways for importing data from the most common ad platforms and store tracking software. Some are also connected with some of the best customer service platforms. 

        “Companies are already sitting on [data] they basically just are not using and we really see this as a lost opportunity,” said Kevin Yang, co-CEO of Idiomatic. “Just to give you a concrete example, customer service companies spend an incredible amount of money just having customer service agents to answer these tickets. But they don’t use the opportunity to actually analyze this to make their experience better. If they were able to do this, customers would be really much happier to share information with them, because they'll be a lot more responsive and the companies that are best at doing this.”

        Top 10 best practices for customer data integration and analysis in 2022

        Here are 10 best practices for data collection, integration and analysis: