How to categorize customer data for actionable insights

Organize customer data into categories like demographics, psychographics and transactions to gain actionable insights for marketing success.

Marketing success depends on truly understanding your customers. But how do you make sense of it all with the deluge of customer data flooding modern companies?

The answer lies in organization. Grouping customer data into larger categories can help you think more expansively and deeply about how to use this data to improve your business with: 

• A better view of your customer. 
  
• More targeted and personalized marketing.
  
• Improved product development. 
  
• Effective market segmentation. 
  
• A more satisfying customer experience. 
  
• Better risk management and regulatory compliance. 
  
• Improved operational efficiency. 
  
• Better sales strategies. 
  
• Insights on your competition. 

This article tackles a way to categorize and organize your customer data and gives examples of how companies have used that data to their advantage.  

How to organize and group your customer data

There are many ways to think about customer data because customer data comes in many types and flavors. Data-driven business decisions require a firm foundation in well-structured and organized information.

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

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

Core characteristics 

Demographic data 

Data such as age, sex, income and education level are crucial to understand your market. These factors can influence how and to whom you want to advertise and how you wish to present and package your product offerings. 

Often, a company’s audience is not monolithic, so demographic information can be used to create different segments within an overall market. Segmentation can help a business find profitable niches rather than focusing on one homogenized view of the customer.

• Example: Proctor & Gamble realized that skin care needs vary by sex and age. They develop and target specific products for these market segments. 

Firmographic data

Company size, industry and location can inform and guide product development, advertising messages, sales efforts and processes. It’s also useful for risk assessment (such as the creditworthiness of potential clients), competitive analysis and pricing strategies. 

• Example: IBM used firmographic information to inform their strategic shift to cloud computing. 

Technographic data

Data on preferred technologies or devices can enrich your demographic data, inform product development and help target and tailor marketing and sales strategies. It’s also useful for competitive analysis, customer support, market segmentation and risk management. 

• Example: Netflix used technographic data to improve streaming quality and to improve their user interface. 

Geographic data  

This will inform sales territory management, site selection for retail stores and services, regulatory and legal compliance, supply chain issues, advertising campaigns, disaster response issues and regional trends and preferences. Remember that some customers — like snowbirds — might have multiple locations. 

• Example: Geographic data might include things like commutes, which can be very valuable if you’re trying to place a Starbucks store.  

Behavioral and engagement data

Web behavior and digital content viewed 

This can tell much about your customers’ preferences, including topics or products. It can change over time, so it’s important to keep this data in a timeline where the newer data is weighted more heavily than the older data. 

• Example: Amazon developed the Kindle after observing customer interest in ebooks. 

Engagement data

Data on social media interactions, comments and shares can give a company insight into how customers interact with its message. Engagement data should also include the trend and velocity of web behavior. 

• Example: After Lego noticed that fans were sharing their own Lego designs, they came up with the “Lego Ideas” platform, where fans could submit their own Lego set ideas. Lego used group reactions to these ideas to decide which new products to pursue. 

Chronographic data 

This should be incorporated into these data points since behavior and engagement change over time.  Also, knowing when customers are most likely to purchase or renew is key to effective marketing campaigns. 

• Example: Netflix uses chronographic data to time the release of new content. They noticed that users are more likely to binge-watch a series on a weekend, so they often release an entire series on a Friday. 

Other customer data points

Psychological and attitudinal data

Psychographic data, such as values, attitudes, opinions, interests, preferences and personality traits can inform advertising campaigns, product development efforts and personalization. This data can be used to create detailed customer personas and profiles. 

• Example: Spotify collects data on users’ listening habits, including what they listen to and how often and when they listen. This provides insights into customers’ moods, preferences and lifestyle choices. They use this information to create personalized playlists and music recommendations. 

Feedback and satisfaction data are vital for customer service and product development. 

• Example: Apple uses customer satisfaction surveys after support interactions to gauge the effectiveness of its customer service. This has led to continual improvement in their service approach, including personalizing customer support and streamlining the technical support process. 

Transactional and quantitative data

Transactional data, like purchase history or subscription details, can be used for predictive modeling and to discern patterns in a market’s behavior. 

• Example: Target famously developed an algorithm to predict pregnancy based on shopping patterns. 

Quantitative data like purchase frequency can show trends and customer life cycles. 

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

Identity and descriptive data

Unique customer identifiers, like an email address, a phone number or a postal address, help companies to merge data from multiple sources. That kind of data is essential to merge records in a customer data platform. Many companies use an email address or a mobile phone number as the unique data point for each account. 

• Example: Uber uses email addresses and mobile phone numbers as the primary identifiers for user accounts. This allows them to maintain secure, personalized communication with users and to collect feedback. 

Descriptive data, such as job title, marital status, occupation, religion or hobbies, allows businesses to create a multi-dimensional view of their customers. This can help with identity resolution but is most valuable in creating effective personalization and improved customer experiences. 

• Example: Nike’s NikeID service — also known as “Nike By You” — allows customers to customize their own Nike merchandise so users can add a personal touch to their gear or create a personalized gift. 

Making sense of customer data to drive business growth

By dividing customer information into categories, your business can:

• Gain a comprehensive understanding of your market.
  
• Get new ideas for advertising, marketing, product development and customer service.
  
• Tailor your strategies accordingly. 

It’s also important to think of ways to integrate these categories to create a more holistic view. For example, combining demographic and behavioral data can lead to more precise segmentation and greater customer insights. 

AI and machine learning can be used for more sophisticated customer data analysis. But don’t forget data from non-customers. Zara, a global fashion retailer, used AI algorithms to analyze current fashion trends by scanning fashion-related images and posts across social media and the internet. This helped them to understand what styles, patterns and colors are trending. 

The post How to categorize customer data for actionable insights appeared first on MarTech.

About the author

Contributor

Greg Krehbiel

Greg’s decades-long career in B2B and B2C publishing has included lengthy gigs in editorial, marketing, product development, web development, management, and operations. He’s an expert at bridging the intellectual and cultural divide between technical and creative staff. Working as a consultant, Greg solves technology, strategy, operations, and process problems for publishers. His expertise includes Customer Data Platforms, acquisition and retention, ecommerce, RFPs, fulfillment, and project management. Learn more at krehbielgroup.com.

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