Types of Customer Data and How to Use Them

You email a customer a coupon for cat food, but all they ever buy from you is dog food. Or you double your ad budget with The New York Times, but most of your audience reads Time Out. Maybe you launch a direct mail promo for in-store purchases . . . but include a bulky subset of folks who have only ever made online purchases.

It’s the kind of uninformed marketing that can happen when organizations don’t know their audiences, and it’s why customer data is so important. By collecting the right information, businesses not only allocate resources more effectively, they also improve the customer experience (CX), which helps the bottom line.

That’s because organizations that understand their audiences improve their marketing. Product recommendations are on target, communications are personalized, messaging resonates better — and it isn’t just marketing teams who benefit from customer data. Product teams can track which features users struggle with most often, while finance and operations teams can review where customers are clustered when deciding where to open their next brick-and-mortar location.

In short, customer data is all powerful, and organizations would be well-served if they knew the universe of data available to them. Let’s take a look at the most common categories and customer data examples.

The different types of customer data

Broadly speaking, customer data sits under two umbrellas: quantitative and qualitative. The former consists of data that can be attached to numerical values, such as the average age of buyers or the clickthrough rate (CTR) of an email campaign.

Qualitative data, on the other hand, doesn’t take numeric form, consisting instead of opinions, feelings, or beliefs — think Yelp reviews and survey responses. Evaluating this data can be thorny because the information you receive is unevenly distributed. Not every customer leaves a Yelp review, for example, and those who do generally give your business four or five stars, but a minority leave lengthy one-star reviews in colorful language detailing their dissatisfaction. How to weigh those reviews — good and bad — against those of the silent majority?

As bothersome as that problem may be, it’s a good one to have because it means you’re collecting data. Now, let’s go a little finer grain than just the categories of quantitative and qualitative data.

Profile data

Think of profile data as the foundation of your customer data. It offers the most basic personal information about your audience, such as their:

•  Name
• Address
• Telephone number
• Email address
• Date of birth
• Occupation
• Income
   

Keep in mind that much of this data can be classified as personally identifiable information (PII), which may be regulated. The European Union’s General Data Protection Regulation (GDPR), for instance, legally obligates organizations to safeguard PII, as does the California Consumer Protection Act (CCPA).

Ensure your business complies with all regulations, which helps build trust with audiences, and then you can use profile data to better personalize your marketing, which can yield dramatic ROI. Luxury leather goods brand MCM, for instance, saw its return increase by 3X when it began personalizing its email outreach.

Transactional data

Transactional data is exactly what it sounds like: information about your customers’ purchases. This could include but is not limited to:

• Whether they tend to buy online or in-store
• Which items they purchased or returned
• How many items were bought 
• Whether buyers used online or physical coupons
• How often your customers shop and how recently
• Which products were left in carts and where in the customer journey that occurred
• Which events they registered for
• The full value of each transaction and lifetime value of an individual customer
   

This is rich, first-party data that can offer meaningful insights. Is there a certain sweater that’s returned often? Food for thought for your design team. Which web page trips up would-be shoppers, causing them to abandon virtual carts? Useful information for your UX and web teams. Does a certain region consistently shop online? Are some products frequently bought together? Maybe it’s time to shutter your brick-and-mortar location and invest more heavily in digital marketing for that part of the country.

You see, then, the possibilities that transactional data holds for your business. Combined with profile data, transactional data allows businesses to further enhance their marketing. Not only would an email campaign greet them by their name, for example, it would also alert them of the special in-store yoga classes taking place next week and recommend related products — leggings made by the brand they buy regularly or in the mauve color most of their purchases seem to be. 

Such targeted marketing is even desired by some: In our 2022 CX Trends Report, we found that the number of consumers open to sharing their data in exchange for better experiences has grown since 2020 — those very comfortable with such an exchange rose 7% percent between then and now, while those fairly comfortable with it also rose from 42% in 2020 to 49% in 2022.

Engagement data

There’s one more category of customer data considered important, and that’s engagement data, which includes both quantitative and qualitative information. Sometimes labeled interaction, behavioral, or attitudinal data, it’s how audiences encounter and respond to your brand across multiple touchpoints. Examples include:

• Which of your web pages gets the most traffic and how long visitors stay on each page
• Sentiment analysis — when people talk about your brand online, do they use positive or negative language?
• Which of your social media accounts receives the most likes, shares, follows, comments, or mentions
• How many people sign up for your newsletter and what they click on when they receive it
• Responses at focus groups or to social media polls
• How many people interact with your display and paid social efforts
   

While sometimes more difficult to parse — recall the Yelp example from earlier — engagement data is another thrilling trove that can yield customer gold. Your net promoter score (NPS), for example, can tell you how likely someone is to recommend your products or services. Engagement data can also suggest which products or services could use more R&D love and whether you should dial down efforts on a particular social media platform.

There are a number of insights that engagement data offers and, when paired with profile and transactional data, can further hone an organization’s understanding of its audience. Customer personas take on greater heft because organizations better understand who their buyers are, where they cluster, their communication preferences, income level, and so on.

Get more from your customer data

The examples of customer data cited here are by no means exhaustive, pointing to an ongoing issue that bedevils organizations today: data chaos. They may collect heaps and heaps of key data, but it may be spread across multiple teams and tools. The sales team may use a CRM to record information about leads, for instance, while the marketing operations team may use a push notification system for its SMS promos.

Organizations can unify these data points with a customer data platform (CDP). No more sending three separate, potentially conflicting messages to the same person because disparate systems list her as Josephine, Josie, and Jo — who happens to have the same email address. A CDP with an identity resolution engine gathers information across siloed channels and systems to produce a comprehensive and unified 360° profile of each customer.

Updated in real time, these profiles allow businesses to serve products, services, and messages that are timely and relevant. Did a customer just buy a tennis racket? Send her an automated email with a coupon for tennis balls and racket tape.

Or perhaps there’s a waiting list for a certain item — deploy a SMS alert when it’s finally in stock and mention another product that shoppers who buy the waitlisted item tend to gravitate toward. A CDP with machine learning capabilities can easily uncover such patterns to help organizations improve their CX.

But there are many uses for a CDP. Discover the advantages that CDP-powered organizations enjoy and let us show you how the platform can boost your company’s business intelligence!