The 4 Types of Consumer Data That Matter to Retailers

“More and more successful retailers are leveraging data and consumer understanding to become precise in understanding their consumer, what brands to carry and what categories to expand or contract,” Hill says. “Going off gut instinct isn’t enough nowadays.”

Good data also requires good analysis, and that analysis should boil things down in significant ways.

“Having the expertise to match the data available with real and meaningful findings is the difference between succinct and meaningful insights or drowning in data,” says Patty Altman, NPD Group’s executive vice president of analytics.

Data collection comes with its own challenges, including consumer privacy and data security concerns. Christian Beckner, the National Retail Federation’s vice president of retail technology and cybersecurity, says that failing to account for these issues could create potential liability or regulatory problems for retailers. However, it is just one of many security considerations to keep in mind.

“Those are definitely a critical area of focus, but it’s not the sole focus from a cybersecurity standpoint,” he says.

Types of Retail Customer Data: Identity Data

Of the four basic kinds of retail customer data, identity data is perhaps the most foundational, encompassing information such as a person’s name, gender, contact information, email address and social media profiles.

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These data points help ensure that the people being contacted are current customers, and that they want to be contacted.

A major trend in recent years has been identity resolution, which describes the process of confirming a person’s identity. As the consumer data company Experian notes in a white paper, many companies are looking to technology to better target customers and to use their data to gather a more holistic view of their identities.

Types of Retail Customer Data: Descriptive Data

Descriptive data is in many ways a more in-depth form of identity data, distilling different types of actions into data points. This includes purchase patterns, website visits, email opens and usage rates. Perhaps the most important source for this kind of information in the retail space is point-of-sale data, Hill says.

“Point-of-sale data is the gold standard for market share tracking and should always be the first choice when looking to understand sales performance,” he says, adding that “one data source can’t answer all questions.”

Collecting descriptive data is a key element for modern persona-based marketing, which takes data points from throughout your customer base and turns them into a customer profile, from which you can build a business strategy.

Types of Retail Customer Data: Behavioral Data

Behavioral data ties together the threads from identity and descriptive data into more in-depth patterns that reflect actions. For example, if you know that a significant portion of your online shoppers are young adults, your marketing message might include a back-to-school sale.

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Hill says that consumer trackers, often used in retail settings, can play a key role in capturing data on brand preference and end users. Online, these approaches can be automated in more in-depth ways — for example, tracking which links users click on a website, or how far they scroll (a classic use case for Google Analytics). In aggregate, this data can offer broader lessons that can improve the shopping experience online.

It can also be used for other purposes. A recent report from PYMNTS.com, for example, notes that analytics based on behavioral data can be used to detect fraud in e-commerce situations, potentially making it a suitable replacement for biometrics. “Behavioral analytics takes into account not only the possibility of human error but also the way the human brain analyzes information and makes decisions when using devices or when entering data online,” the website states.

Behavioral data aims to pull different lessons from data-driven trends so that businesses can execute on them.

Types of Retail Customer Data: Qualitative Data

If identity data and descriptive data represent who consumers are, and behavioral data represents what consumers do, qualitative data represents what they think. This is the direct response that you might get from customer feedback, such as from customers rating their experiences on a delivery app.

This can extend out in the form of longitudinal panels, in which specific sets of consumers are researched over time, something Hill says “can provide the ‘why’ behind sales metrics.”

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That’s not to say all qualitative data is perfect — for example, as the Pew Research Center notes, between 4 and 7 percent of online polls have bogus results, and a similar effect can be seen in longitudinal panels, according to Altman.

“Panelist wear-out is always a concern,” Altman says. “Asking too much of a consumer will lead to low-quality responses, or in the case of a continuous panel, high dropout rates.”

Altman also warns to be careful not to ask questions that could bias the respondent when gathering qualitative data.

What Technologies Can Retailers Use to Collect Consumer Data?

Consumer data can be gathered through a variety of inputs in retail, including collecting phone numbers or email addresses, signing customers up for loyalty programs, collecting data through point-of-sale purchases, and using analytics data through mobile apps and e-commerce websites. Altman says that depending on retailer needs, these data types may or may not work together.

“If it’s a simple question, something like a brand’s demographic profile, there is no need to go overboard combining more in-depth data,” she says. “However, if there is a more complex issue, such as creating a new buyer acquisition strategy, combining data types is key.”

A key element for organizing many of these data points is a customer relationship management (CRM) platform such as Microsoft Dynamics, which can help track and maintain the relationships uncovered by your data — something that, according to CMSWire, more than 90 percent of businesses with more than 11 employees do.

By gathering these inputs into a single place, retailers and other types of business can analyze consumer patterns to uncover new business opportunities and to tweak their models based on predictive analytics — something larger businesses are particularly adept at.

However, the process of collecting consumer data is an ongoing one — and over time, your tactics may change. One challenge that many retailers have faced, says NRF’s Beckner, has been maintaining consumer data — a consideration that has grown in recent years as a result of Europe’s General Data Protection Regulation and the California Consumer Privacy Act. He says it’s important to do periodic audits of your data to ensure the information is still relevant to your business.

“In some cases, it might be many years old and might not be relevant to your current business activities and operations,” Beckner says.

Data must be continually managed and updated as retailers attempt to better match their audiences.

“The best strategy a retailer can take is to work with a reputable company who can demonstrate and prove the regulator practices they follow,” Hill says.

Collecting data is just the first step to turning it into business intelligence, and the next step in that journey is creating a data analytics discipline, using tools like Tableau. With a little infrastructure help, retail businesses can better understand their customers and what’s needed to serve them better.