How to categorize customer data and gain actionable insights

Marketing success depends on truly understanding your customers. But how can you make sense of all the deluge of customer data that floods modern businesses?

The answer lies in the organization. Grouping your customer data into larger categories allows you to think more broadly and deeply about how you can use this data to improve your business.

• A better perspective on your customers.
• More targeted and personalized marketing.
• Improving product development.
• Effective market segmentation.
• A more satisfying customer experience.
• Improving risk management and regulatory compliance.
• Improving work efficiency.
• Better sales strategy.
• Competitor Insights.

This article covers how to categorize and organize customer data, and provides examples of how companies can leverage that data to make a profit.

How to organize and group customer data

There are many different types and characteristics of customer data, so there are many ways to think about customer data. Data-driven business decisions require a solid foundation of well-structured and organized information.

Categorizing your customer data helps you better understand your target audience and tailor your services and products accordingly. This structure also helps you identify which technologies are best suited to collect and evaluate customer data, and how to effectively act on that information.

Not all of these data points will apply to every business, but thinking of each group as customer data can spark new ideas and creative thinking.

Dig deeper: How can marketers make customer data available automatically and quickly?

core characteristics

Demographic data

Data such as age, gender, income, and education level are important to understand the market. These factors can influence who and how you want to advertise, as well as how your product is presented and packaged.

A company’s target audience is often not a single one, so demographic information can be used to create different segments within the overall market. Segmentation helps companies find profitable niche markets rather than focusing on her one homogenized view of customers.

• example: Procter & Gamble realized that skin care needs differ by gender and age. They develop and target specific products for these market segments.

farm graphic data

A company’s size, industry, and location can inform and guide product development, advertising messages, and sales activities and processes. It also helps with risk assessment (such as the creditworthiness of potential customers), competitive analysis, and pricing strategy.

• example: IBM used company information to inform its strategic shift to cloud computing.

technical data

Data about your preferred technologies and devices enriches our demographic data, informs product development, and helps us target and adjust our marketing and sales strategies. It also helps with competitive analysis, customer support, market segmentation, and risk management.

• example: Netflix uses technology data to improve streaming quality and improve its user interface.

geographic data

This informs sales territory management, retail and service site selection, regulatory and legal compliance, supply chain issues, advertising campaigns, disaster response issues, regional trends and preferences, and more. Please note that some customers, such as Snowbird, may have multiple locations.

• example: Geographical data can include things like commute times, which can be very useful if you’re trying to locate a Starbucks store.

Behavioral and engagement data

Web behavior and digital content viewed

This can tell you a lot about your customers’ preferences, including topics and products. Because it can change over time, it’s important to keep this data in a timeline where newer data is weighted more heavily than older data.

• example: Amazon developed the Kindle after observing customer interest in e-books.

engagement data

Data about social media interactions, comments, and shares can give businesses insight into how customers interact with their messages. Engagement data should also include trends and velocity of web behavior.

• example: Lego noticed that fans were sharing their Lego designs, so they devised the “Lego Ideas” platform where fans could submit their own Lego set ideas. Lego used the group’s reactions to these ideas to decide which new products to pursue.

chronograph data

Behavior and engagement change over time, so this needs to be factored into these data points. Additionally, knowing when your customers are most likely to purchase or renew is key to an effective marketing campaign.

• example: Netflix uses time series data to time the release of new content. They often release entire series on Fridays, realizing that users are more likely to binge-watch series over the weekend.

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Other customer data points

Psychological and attitudinal data

psychographic data, values, attitudes, opinions, interests, preferences, personality traits, etc. can inform advertising campaigns, product development efforts, and personalization. You can use this data to create detailed customer personas and profiles.

• example: Spotify collects data about your listening habits, including what you listen to, when you listen to it, and how often you listen to it. This provides insight into your customers’ moods, preferences, and lifestyle choices. They use this information to create personalized playlists and music recommendations.

Feedback and satisfaction data Essential for customer service and product development.

• example: Apple uses customer satisfaction surveys after support interactions to evaluate the effectiveness of our customer service. This has led to continuous improvements in our service approach, including personalizing customer support and streamlining technical support processes.

Transaction data and quantitative data

transaction dataInformation such as purchase history and subscription details can be used for predictive modeling and identifying patterns in market behavior.

• example: Target is famous for developing an algorithm that predicts pregnancy based on shopping patterns.

quantitative data Things like purchase frequency can show trends and customer lifecycles.

• example: Sephora uses this data to personalize product recommendations online and in its mobile app.

Identity and descriptive data

unique customer identifier, email addresses, phone numbers, postal codes, etc., help businesses integrate data from multiple sources. This type of data is essential for integrating records into your customer data platform. Many companies use email addresses or mobile phone numbers as unique data points for each account.

• example: Uber uses your email address and mobile phone number as the primary identifiers for your user account. This allows us to maintain secure, personalized communications with our users and collect feedback.

descriptive dataBy leveraging information such as job title, marital status, occupation, religion, and hobbies, companies can build a multidimensional view of their customers. This helps with his identity resolution, but is most valuable in effective personalization and improving customer experience.

• example: Nike’s NikeID service, also known as “Nike By You,” allows customers to customize their Nike merchandise, allowing users to add personality to their gear and create personalized gifts.

Understand customer data to drive business growth

By categorizing customer information, businesses can:

• Comprehensive understanding of the market.
• Get new ideas for advertising, marketing, product development, and customer service.
• Adjust your strategy accordingly.

It’s also important to think about how these categories can be integrated to create a more holistic view. For example, combining demographic and behavioral data can result in more accurate segmentation and better customer insights.

Utilizing AI and machine learning enables more sophisticated customer data analysis. But don’t forget about data from other sources as well. Zara, a global fashion retailer, used AI algorithms to analyze current fashion trends by scanning fashion-related images and posts on social media and the internet. This helped me understand what styles, patterns and colors are trending.

Dig deeper: How to build customer trust through data privacy and security