Last updated: Types of customer data: Definitions, value, examples

Types of customer data: Definitions, value, examples

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Customer data is everywhere. The different types of customer data can position companies advantageously – if they do the work to interpret and make use of it. To stay competitive is to embrace the power of data. Every time you engage with a brand you leave a trail of breadcrumbs behind you. Individually, these bits of information say something about you as a consumer. But when combined, they create a valuable customer profile that businesses can use.

In general, there are four different types of customer data that companies collect, and each serves its own purpose in helping them get to know you – and they inform how to deliver customer experiences that set them apart.

“Who are you?”

What are types of customer data: Definitions

Let’s first define the types of customer data. Understanding how data is collected and why can put you on the path to a better strategy for your company.

  1. Identity data says: “I’m John, I live in San Francisco.” It’s the name, contact, account login, and other personalization information.
  2. Descriptive data adds: “I’m a man in my 30s. I’m married, I have children, a dog, and I write for a living.” Descriptive data delves further into the details of who the person is.
  3. Behavioral data says: “Here’s the best way to reach me” (and has the receipts to back it up). Behavioral data shows how a consumer likes to engage with a brand, from purchase history to social to how many emails from the brand are opened.
  4. Qualitative data or attitudinal data says: “Here are the things I care about most.” These types of data help businesses understand the motivations, opinions, preferences, and attitudes of consumers and customers.

What is identity data: It’s personal

Identity data is probably what most people think of when they first think of companies gathering and holding their data.

Identity data is your name, contact information, account login, demographics, the unique links to your social media profiles – the information databases use to distinguish you from everyone else.

Your identity data is going to be the basis of your customer profile. It’s the digital equivalent of an introduction: “Hi, my name is John and I live in San Francisco.” Table stakes stuff in this day and age.

Companies use this data for basic personalization (e.g., addressing you by name in an email), but it’s also what CDPs use to aggregate your information from the various data sources. It’s how they cross-check that this John Norris is the same John Norris who made a purchase from you recently and also tagged you in an Instagram post.

What is descriptive data: It’s relevant

Descriptive data starts to paint a fuller picture of who you are beyond your name and address. The types of customer data companies collect will vary from business to business.

Descriptive data gives a more complete view of customer profile information. It can include details like family and marital status, career details and educational information, lifestyle information like what type of home and vehicle you own, how many children you have, what types of pets you have, etc.

For example, a dog groomer may want to know what type of dog you have, whether you rescued them, etc…  A clothing store, on the other hand, is unlikely to ask about pets at all.

Going back to our introduction metaphor, descriptive data is the equivalent of answering a quick follow-up question like, “Where are you from?” or “What do you do?” It provides a little more context about who you are but isn’t necessarily prying or intrusive.

Businesses use this information in a few different ways:

  • To create more accurate audience segments
  • To develop customer personas
  • To predict buying habits
  • To take marketing personalization beyond the basics.

It’s never about the volume of data – success comes based on the quality of your data. And, your willingness to allow the data to transform how you communicate.

What is behavioral data: It’s complicated

Behavioral data encompasses all the different ways you interact with a company or brand – from transactional data like past purchases to customer service tickets you’ve submitted. It’s also interactions you’ve had with sales reps, how often you open their emails, and so on.

And this is not limited to online interactions. For example, a retailer may note what store location you visit the most or notice that while you purchase online, you always do returns in-store.

Behavioral data information shows how customers engage with brands and can be used to improve the overall customer experience in a number of ways.

Examples of behavioral data include:

  1. Like descriptive data, behavioral data helps with audience segmentation. It can be used to develop personalized communications (like sending retargeting emails to customers who have abandoned their shopping carts).
  2. It helps brands identify which channels consumers and customers prefer to engage on (like when you choose to be contacted by email instead of text message for service reminders, etc.)
  3. At a large scale, behavioral data can help identify trends and issues in the company’s overall experience (e.g., they may notice that a large portion of their online customers bounces off the site at a certain point, indicating a potential problem in the UX.)
  4. It can inform which SEO keywords the company should be targeting for their products, the social media sites their customers frequent, and on and on.

Behavioral data is the equivalent of the early-stage interactions in any relationship – like noticing that your new friend is much more likely to respond to a text message than answer a phone call.

What is attitudinal data: It’s emotional, value-based, and always evolving

The final level of depth comes from the attitudinal customer data, which is also called “qualitative data”.

Attitudinal or qualitative data gets to the heart of what motivates you as a customer – why are you more likely to buy this t-shirt versus the one next to it. This type of data includes things like motivations, opinions, preferences, and attitudes, which aren’t as easy to collect as demographics or purchase history.

This type of data adds richness to customer profiles and, when used well, is what gives customers that sense of feeling seen by a brand.

Companies usually obtain attitudinal or qualitative data through things like customer interviews, feedback reviews, and surveys. And in order to get high-quality data, brands need to ask the right questions in the right way, because when they do, it unlocks a deeper level of engagement between customers and brands.

A company may uncover that customers choose them because of a cause they support versus the price or even quality of their products. They may realize that a ton of their customers feel really strongly about a particular product feature they otherwise wouldn’t have considered.

This is the equivalent of really beginning to know someone – not just their likes and dislikes, but also the why behind them.

Other classifications of customer data explained

Beyond the four types of customer data mentioned above, you may come across several other types of customer data.

Here are just a few other ways data is broken up:

What is First- vs. third-party data:

  • First-party data is the data a company collects directly from the customer  (e.g. asking for your name and contact info, tracking your order history, keeping tabs on your interactions with the brand across different channels).
  • Third-party data, on the other hand, is collected by a separate entity and sold to the company (e.g., internet browser cookies that track your movements online). The data is first scrubbed of any personally identifying information (PII), so it’s not useful for things like personalization. But, at scale, it is incredibly valuable for identifying trends and uncovering insights.

What is Structured vs. unstructured data:

  • Structured data is well-defined and highly organized so that it’s easy to search and filter through it. (Think, multiple-choice questions or checkboxes.)
  • Unstructured data is looser in format and typically takes on a more narrative/open-ended form that may require a person to read and interpret. (Think, short-answer questions on a survey or notes from a sales call.)

Get more from your customers’ data with CDP

Understanding the various types of customer data makes it easier for businesses to turn their insights into effective engagements.

Customer data platforms have emerged as a sophisticated solution for reconciling and aggregating all of a company’s customer data and using it to build a full customer profile. In doing so, the value of the data grows substantially.

Crush revenue + growth targets.
Power up your competitive edge.
Win customers.
It all begins HERE.

 

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