4 customer data types companies need to utilize

4 customer data types companies need to utilize

Nowadays, it’s absolutely essential for companies to gather and leverage customer data in order to create new business opportunities while providing the best customer experiences possible. After all, customers expect you to understand their unique tastes and preferences. According to Twilio’s 2023 State of Customer Engagement Report, 66% of consumers say they will quit a brand if their experience isn’t personalized. And while 46% of brands believe that they are doing an excellent job of providing personalization, just 15% of consumers agree.

Most customer data collected falls under three categories:

1. Zero-party data. This is data customers intentionally and proactively share with organizations. Examples include data from surveys, product quizzes, preference center settings, requests for updates and reminders, account creation, conversational pop-ups, contests, and product onboarding.

2. First-party data. Essentially, customer data that’s directly collected and owned by the organization doing business with them. This includes on-site or in-app user interactions, like web pages visited, questionnaires filled out, or purchases made.

3. Third-party data. This refers to customer data that’s not owned by the organization or its customers, but rather by a third party, such as a social network, an advertising network, a search engine, or a mobile platform. This type of data is often purchased by businesses to get a more complete picture of their customers, although this data can often be inaccurate or outdated. 

With the advent of big data and artificial intelligence (AI), data collection has become incredibly sophisticated. Here are four different customer data types companies need to become familiar with during their data collection efforts.

1. Identity data 

Basic examples of identity data include an individual’s: 

• Name

• Phone number

• Email address

• IP address

• Social media profile

• Gender

It can also include more specific customer information, such as annual spending or income. With enough data, you can start to piece together the demographics of your customers. Demographic data is useful because you can use it for customer segmentation, or grouping customers based on shared attributes.

Just a note of caution, because of the sensitive nature of personally identifiable information (PII), your business must collect and safeguard this data in accordance with privacy laws like the General Data Protection Regulation (GDPR) in the European Union, the Health Insurance Portability and Accountability Act (HIPAA) in the United States, and any other applicable regulations. 

2. Engagement data

Engagement data, also known as interaction data, shows how your customers engage with your brand across various channels and touchpoints. Some common examples of customer engagement data include:

• How customers interact with your website (page visits, clicks, etc.)

• How often they like, share, or comment on your brand’s social media posts

• In-app interactions

• Conversions

• Email opens, forwards, and bounce rates

3. Behavioral data

Behavioral data focuses on the way specific customers interact with your service or product directly. Unlike engagement data, which looks at all customer interactions, behavioral data focuses on the behaviors one specific individual takes with your brand. 

For example: 

• Purchase history 

• Shopping cart abandonments

• Subscription renewals

• Free trial offer usage

• Heat map data

• Average order value

• Device and browser type
  

With behavioral data, you can find out who’s following through with making a purchase or signing up for a subscription service — and who’s not. This data can help you better engage with and target your customers by sending them a personalized coupon or free trial invite to push them over the finish line, for example.

4. Attitudinal data

Attitudinal data refers to your customers’ first-hand opinions about your brand, product, or service. It can include things like your customers’:

• Motivations

• Challenges

• Purchase criteria and preferences

• Customer feedback

• Net Promoter Score (NPS)

Adopt a holistic approach to customer data collection

We know that brands want to understand customers, and customers want to be understood. But how do you really get that holistic understanding of who a customer is and what they want?

The answer lies in unifying different types of data from different digital customer engagement solutions so that it’s actionable and accessible for all customer-facing teams. When all of these types of data are unified in real-time and work in concert, they create a holistic understanding for every customer and act as an engine for precise personalization. That way, customer-facing teams can provide experiences tailored to each individual in order to drive loyalty and retention while growing customer lifetime value.