Customer data types and collection methods explained

Author: Adam Bunker

Subject Matter Experts: Michelle Buretta & Anthony Aerni

What is customer data?

Customer data is the collective term for all the points of information that arise from the interactions customers have with your business. That can be as obvious as the number of sales you make, or as abstract as the written opinions somebody gives in a feedback survey.

Customer data is wide-ranging. It includes people’s personal identifying information; the digital footprints customers leave across online touchpoints; and qualitative assessments of their attitudes, behaviors, and opinions.

In an incredibly competitive market, where customer loyalty is hard fought for, being able to accurately collect and utilize all this data has never been more important.

The businesses that work to join the dots between seemingly disparate datasets are the ones that’ll unlock insights that can help them edge ahead of their rivals.

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Why is customer data important?

When it’s collated, combined, and put to work, customer data becomes a goldmine of actionable insight. This is true on a basic, fundamental business level (how old is our average customer?), as well as a more advanced, analytical one (where along the customer journey do we lose people?).

Knowing everything you can about your customer purchasing habits, their demographic, and their opinions can help you better tailor your products, experience, and marketing strategies. And that can help you give customers exactly what they’re expecting.

In fact, customers are increasingly keen for businesses to use their data if it means the experiences they get in return are more compelling. Better personalization, for example, can help drive conversion, customer loyalty, and satisfaction.

This has been proved time and again by various research studies. Trustpilot, for instance, suggests that online conversion can shoot up by 8% when the customer experience is personalized. According to Zendesk, 60% of customers think businesses should be using the data they collect to better personalize the customer experience, while Deloitte research shows that customer loyalty tends to be 1.5x higher for leaders in this space.

And our own research shows that nearly two-thirds (63%) of consumers think companies need to do a better job of listening to them.

In other words? Customer data is an enabler, and – if used to its full potential – a potential point of differentiation. But that’s only true if you can cast your net wide and collate as many data types as possible.

Types of customer data

We’ve already mentioned that customer data comes in a range of shapes and sizes. Some is quantitative, some is qualitative. Some data is solicited, and some is collected automatically using intelligent analytics software. Making sense of all of this data starts by understanding the differences between the various types, and knowing what insights it can unlock for you and your business.

At Qualtrics, we separate customer data into three distinct categories: behavioral (B), operational (O) and experience (X) data. Here’s how they shake out:

Behavioral Data

Behavioral data is derived from actions, behaviors, or interactions. It’s usually gathered through observation, tracking, or analysis of customer activities, rather than being provided by the individual.

Operational Data

When we talk about hard numbers and solid stats, we’re talking about operation data. O-data covers sales figures, customer demographics, conversion rates, and metrics like churn and customer lifetime value (CLV). In other words, O-data tracks quantitative, tangible information.

Experience Data

X-data tracks less obviously-apparent information. By monitoring elements of the customer experience – like opinions, sentiment, and effort – businesses can better understand why things are going the way they’re going. This usually means utilizing traditional data collection methods, like surveys, alongside software suites that can monitor contact center conversations and understand customer behavior.

An easy way to think about the difference between B, O, and X-data is that while the first two show you WHAT happened, the latter explains WHY. Each of these customer data types has strengths and weaknesses – but you’ll need all of them to paint a full picture of customer behavior and opinions, as well as your reach and target audience.

How to collect customer data

Given that there are so many types of customer data, it’ll come as no surprise that there are a ton of ways to collect it – and a raft of sources. But before we explore data sources, let’s quickly explore what a customer data point actually is – and what triggers one – on a basic level.

The foundational elements of customer data

When we talk about collecting any piece of customer data, we’re discussing a process that tracks a given session, assigns people with a profile, and then links that profile to a time, event, and descriptive attributes:

Session identifier

A session identifier is a unique value assigned to the customer journey. This could be a unique identifier natively found in the interaction channel, or a combination of different fields that create a unique identifier.

Visitor or customer identifier

A visitor identifier is a data element that, where possible, recognizes the person or account holder navigating within that interaction channel.

Date and time stamp

Date and time stamps are used to sequence events in the order in which they occur.

Events

Events are the moments your customers experience during their journey. That could be clicking through menus, routing to an agent in the contact center, or navigating web pages.

Attributes

An attribute is any additional, descriptive information carried along with the behavioral data content that adds relevance about the customer (e.g., segmentation/demographic data).

Customer data sources

Ok, so how do you actually go about collecting all this customer data? The incredibly short answer is: it varies depending on the type of data.

The longer answer: You’ll use a combination of smart software analytics tools and traditional feedback methods to form a robust view of customer opinions, demographics, and the overall customer experience.

Here are a few data collection sources and methods to explore:

Digital experience analytics

If you want to collect B and X-data, you’ll need a robust digital experience analytics suite that can accurately track customer journeys along digital touchpoints. Qualtrics® Digital Experience Analytics, for example, joins customers throughout their experience to build a holistic, rock-solid picture of their behavioral patterns and potential pain points they encounter.

So that might be an abandoned checkout, a frustrating experience with customer support, or times of the year in which they’re more likely to purchase.

Contact center software

The contact center is the hub of direct customer communications, so software solutions that bring all these interactions together are vital for revealing pain points, frequently asked questions, and service preferences. Especially useful here are Natural Language Processing (NLP) tools, which can listen to calls, scour through emails and record live chats.

The most comprehensive tools can then unpick what was said and attribute effort, sentiment, and emotional analysis, helping you turn unstructured customer data into insights that can help drive decision-making.

Tag Managers

Tag managers are software suites designed to implement code snippets that live behind the scenes on websites or apps. These tags have unique IDs that collect data on specific user actions and events – for example, clicking on a CTA – offering granular insights into customer touchpoints and behavior.

Chatbots

Increasingly powered by AI with NLP abilities built-in, these automated messaging systems track customer queries automatically, providing insights into immediate needs and potential automation opportunities in the journey.

Social media listening

Customers are talking to and about you on social media sites, so it pays to be able to capture what they’re saying and turn it into useful customer data. That requires social listening tools with the ability to spot trends – and use those trends to flag emerging issues or opinions.

Marketing efforts

Any marketing you do has an impact, and that impact can be measured as useful customer data points. This encompasses campaign and outreach data, which reveals things like engagement levels, conversion rates, and the most effective touchpoints for each demographic or segment – helping you refine your targeting and improve journey optimization.

Surveys

Tired and tests, surveys are still a fantastic way to learn what customers really think – and they feed into data-driven metrics like CSAT and NPS scores. Even though they often result in unstructured, written data, surveys are inherently direct; they collect customer feedback and perceptions with explicit sentiment, expectations, and satisfaction levels.

Bought data

Lastly, businesses sometimes opt to buy third-party data from external suppliers or partners. While this data won’t have the same innate relevance to your exact customer base, it can be useful for assessing wider market trends or understanding opinions and segments within your business sector.

Customer data privacy

One thing to remember is that, while customers increasingly expect businesses to use their data to provide more compelling, personalized experiences, they also don’t want to be snooped on or have their personal information exposed to third parties. After all, customers who have shared their information with you haven’t necessarily given you free reign to do anything you like with it.

Best practice here means opting to use digital tools with robust privacy policies, and that adhere to regulations in international markets, including GDPR in Europe. In some instances, that may mean using tools that provide you with one level of customer insight in one market, and a lesser degree in others.

Any digital analytics suite worth its salt should have a clearly outlined customer privacy policy and framework. Here’s ours, for example.

How to use customer data

You’ve searched through every touchpoint with a fine-toothed comb and gathered every bit of customer data there is. What now? The most important thing to remember is that data is a blunt instrument – and it’s pretty useless in a vacuum.

To start turning data into insight, you’ll need to get analytical:

Turn data into metrics

Metrics are the key ingredient for Key Performance Indicators (KPIs) – the method by which you’ll understand if you’re hitting your goals or not. So when you collect information that X many customers churn in a given period, you’ve got a trackable metric. Importantly, you can combine this info with other data points to get more rounded metrics. Churn becomes a churn rate, for example, when you divide it by the total number of customers at the beginning of that period. And this metrication feeds into the next step…

Measure today against yesterday

Customer data, described as metrics, work hardest when they’re used as a yardstick. Are this quarter’s metrics underperforming compared to the last? If so, you’ve got something to investigate, and maybe an action to take as a result.

Make changes that the data suggests

Data often leads to insights, which should become action. X-data is especially powerful here. Often, it’ll pinpoint trends and patterns that suggest emerging pain points. Customer data that suggests customers are having a hard time checking out, for instance, should prompt an investigation and resulting UI changes.

Bridging digital data divides

Making the most of customer data means being able to combine information that – in many organizations – is siloed and fragmented across departments. Where the really game-changing stuff happens in the space between those silos, and that’s unlocked by software that can see across different types of data.

AI-powered data analytics software can do this at a massive scale and in real-time, linking customer data from social media, the contact center, website or app interactions, behavioral patterns, and operational figures. By bridging those gaps, you’ll be able to act proactively against emerging threats and make quick decisions based on trackable, provable insights.

Qualtrics can help here. Our XM® for Customer Experience software unites data from every touchpoint and source – and turns all that info into simple, actionable suggestions.