Types of Customer Data

Customer data is both a valuable business asset that can be used for marketing and business growth, as well as a sensitive source of information about individuals that must be protected.

Our article What Is Customer Data? explains in depth the definition and purpose of customer data, and where it can be collected.

In this article, we’ll break down the specific data types that make up each of the major categories of customer data: identity data, engagement data, behavioral data, and attitudinal data.

Identity data

Identity data is also referred to as basic customer data or personally identifiable information (PII). It is used to identify a person and pin together the other types of data so that they can be assigned to an individual. Identity data can be split into linked and linkable information.

Linked and linkable customer identity data

Linked information

Some identity data can uniquely identify a customer without the need for any additional information. This is called linked data and includes real-world data about the person, such as their full name, phone number, or email address. It is often provided by the user in the form of government IDs like a driver’s license, passport, or other documents used to verify a user's identity. It also includes site-specific data like login credentials, internal application user IDs, and payment information.

Linkable information

Other identity data may not be able to uniquely identify a customer on its own but can be combined with other collected data to build a profile on a user. This identity data is called linkable data and often includes location, gender, race/ethnicity, age, and employment status.

Although this data might not be sufficient to uniquely identify a customer, it is considered unique enough that privacy laws like GDPR and CCPA recognize it as protected personal data. Further examples of this type of data include a user's IP address, browser cookies, and unique device IDs, like a phone’s International Mobile Equipment Identity (IMEI).

It’s important to protect identity data containing PII with robust security strategies and technologies to ensure that you are compliant with the regulation covering the use of the data where both you and your users are located.

Engagement data

The digital touchpoints a customer creates while interacting with your business and the metadata about those touchpoints are called engagement data. These touchpoints include:

Advertisements: ad impressions, click-through and conversion rates, cost-per-click and time-of-day information
Triggered email campaigns: open and click-through rates, bounce and forwarding statistics, and conversion rates
Social media: video views and post likes, shares, and replies
Web and app interactions: page views and referrer origin, user journey, downloads, and specific actions taken
Support and customer service: categories and content of queries, complaints, and user feedback
Economic transactions: order history, average order value, average customer lifetime value, use of loyalty programs and discount codes, cart abandonment statistics, purchase details and history, and subscription details

Engagement data is unique to each business and each application. What is considered a valuable interaction will differ based on what kind of product or service you provide, and how it is monetized or how the value of your users is recognized. Engagement data focuses on the result of a user's interaction — did they convert after clicking on an ad, did they read an article all the way through, were they satisfied with the result of a support session, and so on.

Engagement data is often used for marketing purposes. For example, a company may email users who have had negative support interactions in order to find out how their support agents can improve, or notify users of a newly released application feature that they’ve not yet interacted with.

Behavioral data

Behavioral data is a subset of engagement data. It also captures the customer experience through their raw interactions, but the intent differs: rather than monitoring the results of an interaction, it focuses on the interaction itself.

Any user action can be monitored as behavioral data once the business process tooling is adapted to capture these points. Captured behavioral data can reveal the underlying patterns in users’ interactions — which can provide a foundation for understanding your existing audience and help create models for future audience expansion. Some examples of behavioral data include:

Quantitative data: devices and operating systems used (along with their versions)
Qualitative data: customer journey decision points, and attention/usage heatmaps (typically comprised of user interface interactions like mouse movements, clicks, and scrolling, and touch inputs like taps and drags)

By focusing on the interactions themselves, behavioral data is especially useful for finding out what features of a product users do or do not use, or may be struggling with. This gives product designers empirical feedback on how to change and improve the product itself. For example, you may discover that users consistently mis-click a button, and decide to make it more prominent in your user interface.

Attitudinal data

In contrast to the above types of customer data, attitudinal data captures not actions but rather opinions, feelings, and emotions. Customers who provide attitudinal data are a self-selecting group — whether their feedback is complimentary or inflammatory — so this is always worth taking into consideration when interpreting any insights derived from attitudinal data.

While customers who volunteer this data are not necessarily a statistically significant representation of your audience at large, all feedback is valuable. Attitudinal data is an indicator of customer opinions, and includes customer sentiments (whether or not they are satisfied with your product, or find it frustrating), a wish to purchase a particular product, feature requests, and the challenges and motivations that have led them to seek to use your product or service.

Attitudinal data is harvested through feedback and complaint forms, surveys, and support channels. Online reviews and wish lists will reveal purchasing habits and product satisfaction, while directed interviews and paid focus groups can provide a direct conversation with existing or potential consumers

Attitudinal data is often used to inform business planning decisions, answering questions about what features users would like to see implemented next, and what customers are complaining about the most. It is important, however, to weigh the decisions you make based on attitudinal data carefully. Users are much more likely to leave a negative review when they have a problem than leave a positive review when a product performs satisfactorily, and this must be considered to avoid wasting resources trying to satisfy a vocal minority.

How to use customer data

The specific way you use the customer data you collect will depend on how you extract value from your customer base, but the majority of businesses will use it for one or all of the following purposes: marketing, customer experience, and sales.

Customer data can be used to fine-tune your marketing campaigns. First-party data collected directly from your users can be enriched with third-party data to provide a detailed picture of your audience, allowing you to target them with advertisements and offers that will directly appeal to them.

Your customer experience can be enhanced using customer data — find out what your users like, and where your product falls flat, and update it to fix the real problems users are experiencing. Feedback about your support channels can be used to improve support delivery, while the in-app actions that led to your customers seeking support can be used to make sure that your agents can solve your users' most common problems quickly.

The use of customer data culminates in better products and happier users, which should direct them — and encourage them to direct others — to your sales channels. These sales funnels can be heavily optimized based on the engagement and attitudinal data you collect from your customers to ensure that those arriving via your marketing channels are more likely to convert to valuable customers.

Customer data platforms help you collect, transform, and store data

Once you’ve started gathering the customer data you require from different sources and formats, you still have to handle the non-trivial tasks of transforming and storing it.

A customer data platform (CDP) will allow you to transform this data into a consistent, reportable format in transit, and store it in a data storage solution of your choice. Additionally, a CDP will allow you to resolve user identities from linked and linkable information, and flag potentially sensitive information, so that it can be handled in compliance with privacy regulations (or removed entirely).

Once your CDP is consuming your customer data and automatically transforming and storing it, you can model, query, and derive valuable insight from your customer data — equipping your business for real-time data-driven decision-making.