Understanding customer data: Types, and how to collect and segment

From website visits to emails, reviews, purchase history, psychographics, support tickets, and more, it’s likely that your business accrues a massive amount of customer data every single day. 

That data can be a rich source of actionable insights for your company — or it can go to waste. In order to put it to use, you’ll need to first collect it and then segment and manage it so that you can distill those insights. 

If you don’t yet have a customer data strategy (or if it’s been a while since you’ve revisited it) we know that it can be difficult to know where to start. That’s why we have pulled together answers to some of the most common questions business owners and marketers tend to have about customer data. This includes a review of the different types of customer data that exist, how you can collect it, the different ways that you can begin putting it to use, and more.

What is customer data?

At the most basic level, customer data refers to any information you know about your customers, whether they be individuals or other businesses or organizations. This includes contact information, demographic data, customer preferences, online interactions, and more. 

Collecting and analyzing customer data helps you understand your target audience more deeply. It reveals their needs, wants, and even the beliefs that drive those needs and wants. This empowers you to incorporate more data-driven decisions into your marketing, sales, and promotional strategies to attract and retain more customers.

Types of customer data

There are four main types of customer data:

1. Demographic data
2. Psychographic data
3. Behavioral data
4. Transactional data

Here’s a closer look at each one.

1. Demographic data

Demographic data refers to characteristics by which you can identify your customer base, such as their age, gender, income, and education level. 

This type of data is useful for understanding who your customers are and what their needs may be. It can also be used to segment your customer base for marketing strategies and full-blown campaigns.

For example, if you’re selling an anti-wrinkle cream, you may want to focus your marketing efforts on the age groups most likely to purchase your product. Gathering demographic data on your customer base will make your campaigns more targeted and improve results.

2. Psychographic data

Psychographic data — made up of a customer’s values, personality traits, opinions, attitudes, beliefs, and lifestyle — can shape your selling approach and messaging. 

It’s no longer enough to target a part of the market with demographic data. Think of psychographic data as the next step up. It’s simple: while demographic data may help you appeal to a customer's logical or analytical side, psychographic data helps you appeal to the emotional component that goes into a buying decision.

Consider how Bryan Kramer, marketer and author of Human to Human: H2H, puts it: “The fact is that businesses do not have emotions. Products do not have emotions. Humans do. Humans want to feel something.” 

What motivates your customer? What kind of messaging will they respond to? What brand voice or attitude will push them to hit the buy button? All these questions are easier to answer once you’ve gathered enough psychographic data to paint a more comprehensive view of the customer.

3. Behavioral data

Behavioral data includes anything from purchase history to website interactions or opting in to your newsletter in exchange for a discount. In other words, it’s the actions and behaviors that your customers take. 

This type of data helps you understand how your customers have interacted with your business in the past, which may provide insights as to their wants and needs in the future. 

For example, continuing with the wrinkle cream scenario above, users of a certain age that land on your homepage and click through some of the featured product pages show some level of brand awareness as well as buyer intent. Buyer intent insights strengthen when you track user behavior data and see that they came back a day later to sign up for your brand newsletter. 

That example is just a small scenario illustrating how analyzing behavioral data helps you further optimize the touchpoints throughout your customer journey. You’ll learn as you begin to understand what actions your customers are most likely to take, when they’re likely to act, and most importantly, why they’re doing what they do. This will also improve your lifecycle marketing efforts as you re-engage return customers.

4. Transactional data

Transactional data is information about the transactions your customers have completed with your business. This can include data points like purchase amounts, purchase frequency, how long it takes the customer to make those purchases, and how many items they’ve returned. 

This type of data helps marketers understand spending patterns and trends among your customers. It’s especially important as market forces shift, new technology is introduced, and new variables enter the buying equation.

How do you collect customer data?

There are many potential ways to collect customer data. We offer a handful of options below. Keep in mind: Customer data is only useful if it is accurate and up-to-date, so factor those points into your collection strategies.

Website forms

Website forms give your customers a means of communicating with your brand even if they aren’t ready to make a purchase. Examples can include a form which must be completed to sign up for a newsletter, receive a promo code or discount, or download an asset like an ebook. In this way, you can use forms to collect a customer’s name, date of birth, contact information, and other key data. 

Surveys and focus groups

Another way to collect customer data is by sending surveys. You can either give customers a paper survey or use an online survey tool like Survey Monkey. Old-fashioned focus groups can also be a great way to get detailed feedback from your target audience.

Collecting direct feedback

You can collect data by speaking to your customers directly. This could be done over the phone, through email, or in person. If you speak to customers directly, you will better understand their needs and wants. This approach can also uncover objections, attitudes, or behaviors that weren’t so apparent before. Ultimately, it’s all about getting closer to your customers and creating a brand that appeals to more than just their logic.

Other data sources

It should be noted that you can also collect a lot of customer data without your customer actually needing to do anything. That’s because customer data can live in many of your business’s systems, passively collected as a customer interacts with your brand. Examples include:

• Transaction data, living in your CRM, accounting software, or POS
• Behavior data, living in your website and CMS tracking software

A note on compliant customer data collection

We’d be remiss not to touch on a few best practices for data privacy in this guide. 

Now more than ever, customers are wary about sharing their personal data. Marketers must respect customers’ privacy and ensure their data collection processes comply with data collection laws and regulations. Even in the absence of these regulations, respecting and protecting your customers’ data helps you build brand trust which can translate into greater sales.

Here are three essential practices to follow:

1. Right to opt out: Provide customers the option to opt out of providing their data.
2. Legal compliance: Ensure your data collection practices comply with all relevant laws and regulations, such as GDPR and CCPA.
3. Transparency: Be transparent about how you will use any customer data you collect. Make sure your website’s Terms and Conditions page explicitly details how you collect customer data, what you do with it, and how visitors or users can opt out.

While the conversation about ethical customer data collection goes well beyond the scope of this post, following these best practices will set you on the right track to collect valuable customer data without crossing any lines.

How do you segment customer data?

There is no one-size-fits-all way to segment customer data. Ultimately, how you segment your data will highly depend on your end goal. Let’s look at a few examples:

Segmenting by demographic data

The most common way to segment your customer data is by using demographic data such as age, gender, location, or income level. 

With this information at your fingertips, it’s easier to make assumptions about what products or services interest your target market.

Middle class income couples who have a driveway, are between the ages of 23-60, and live in southern California, for example, won’t be interested in buying a high-quality snow blower—it doesn’t snow in southern California. That’s an example of why segmenting by demographic is essential if revenue is the goal.

Segmenting by behavioral data

Another way to segment your customers is by behavioral data — which includes information on what kind of customers are more likely to make a purchase, how often they purchase, and what products they tend to buy.

For example, let's say you know a customer has recently searched for “best crockpot” on Google. Based on that search history, you might conclude that the customer has a high intent to buy — and that they might be primed to make a purchase with a gentle nudge in the form of an email or promotion.

Behavioral data can be a key part of shaping how, when, and why you’ll run certain marketing campaigns, like paid ads.

Segmenting by psychographic data

You can also segment your customer data by using psychographic data. This includes information on your customers' values, beliefs, and attitudes. 

This type of data can be helpful in understanding what motivates your target market and how best to appeal to their needs.

Suppose, for example, you run a website that sells holiday cards, but you don't cater to one particular religion. If you know what religion a customer practices, you can tailor your promotional emails, product recommendations, and even messaging so that it aligns with the values that matter to them. Then, when a Christian holiday like Easter comes around, for example, you can send a promotional eblast to customers you know are Christian, who may have an incentive to make a purchase — without accidentally alienating customers who practice other religions and who might be offended or turned off by receiving said promotion.

Segmenting by transactional data

What if you wanted to run a remarketing campaign? This is where segmenting by transactional data can be useful. 

By segmenting your audience by customers who have made several repeat purchases in order to target them, your remarketing campaign has a bigger chance of creating more revenue. 

Why? Because it’s more targeted to an audience that’s familiar with your brand and primed to buy. This is infinitely better than running a remarketing campaign that includes customers still on the fence about making their first purchase (or haven’t at all).

Validating and analyzing customer data

Once you’ve created your customer database, you need to validate it and ensure it’s usable within your data systems. 

It’s critical to note that data analysis can’t happen before data validation. To validate data, you'll need to run it through a set of rules against your existing database. The rules and even your approach to data validation will depend on the solutions you use as part of your marketing data collection and management process. 

For example, with a data platform like Simon CDP, all the data you ingest — whether it’s first- or third-party data — goes through a validation process that checks for items like the following:

• Syntax
• Empty data fields
• Accurate timestamps
• Unique field names
• Appropriate identifiers
• Unauthenticated users

Any errors or omissions detected during this process will either be flagged or updated automatically, depending on the validation rules you’ve set. Once the data is validated, Simon CDP analyzes it for marketing insights. For example, you can match website visitors to first-party data to create unified customer profiles. You can also pinpoint customers with unique identifiers that plug them into the right conversion campaigns.

Benefits of customer data analysis

One of the most important benefits of customer data analysis is that it helps businesses to understand their customers more deeply. 

Customer data analysis enables businesses to group their customers into segments, understand their needs and wants, and develop more sophisticated selling strategies. 

Additionally, customer data analysis helps businesses identify trends and patterns in customer behavior, which improves the products and services they offer. And there are many other benefits:

• Accurately gauging customers’ satisfaction with your products or services
• Gathering accurate metrics to measure the health of your business
• Increasing revenue and profit margins
• Improving customer retention rates
• Developing more successful marketing campaigns
• Increasing customer lifetime value

Do more with Simon Data

At Simon Data, we know that customer data is an important part of understanding your target market, meeting their needs, and providing a stellar customer experience. That’s why we enable marketing teams with the tools and features they need to create a better-rounded view of their customers. 

Simon CDP centralizes, unifies, and manages all customer data so you can easily track your customers' interactions with your business. You can gather contact information, demographic data, customer preferences, and more. 

Whether you’re looking for a more comprehensive way to execute data orchestration or you want to improve your lifecycle marketing efforts, Simon Data helps you get there. Request a Simon Data demo today.