How to classify client information to provide useful insights

Segment consumer information based on transactions, psychographics, and demographics to obtain useful insights for effective marketing.

A true understanding of your clients is essential for successful marketing. But with the stream of customer data that modern businesses are facing, how can you make sense of it all?

Organization is the key to the solution. Organizing consumer data into broader categories will assist you in considering more broadly and thoroughly how to use this information to advance your company’s operations with:

• A clearer picture of your client.
• more individualized and focused marketing.
• enhanced creation of products.
• sensible division of the market.
• a more fulfilling encounter for the client.
• improved compliance with regulations and risk management.
• increased effectiveness of operations.
• improved methods for selling.
• information on your rivals.

This article discusses how to classify and arrange your customer data and provides real-world examples of how businesses have made use of it.

How to classify and arrange your client information

Customer data encompasses various types and variations, inviting diverse perspectives. Making data-driven business decisions necessitates a robust groundwork in structured and organized information.

Segmenting customer data facilitates a deeper understanding of your audience, enabling the customization of services or products. This framework also aids in pinpointing the most suitable technologies for gathering and analyzing customer data, guiding effective action based on insights.

While not all data points may be relevant to every business, approaching each category as customer data can inspire fresh ideas and innovative strategies.

Principal attributes

Demographic information

Understanding your market requires knowing information like age, sex, income, and educational attainment. These elements may affect how and to whom you choose to market, as well as how you present and package your line of products.

Since a company’s customer base is frequently not homogeneous, demographic data can be utilized to separate distinct market segments. Rather of concentrating on a single homogenized image of the client, segmentation might assist a corporation in identifying lucrative niches.

For instance: Proctor & Gamble discovered that age and sex affect the skin care requirements. For these market niches, they create and focus on particular product development.

Firmographic information
Product creation, advertising messaging, sales efforts, and procedures can all be influenced and guided by a company’s size, industry, and location. It is also helpful for pricing strategies, competitive analysis, and risk assessment (e.g., prospective clients’ creditworthiness).

Example: IBM, for instance, based their strategic transition to cloud computing on firmographic data.

Technographic data

Preferred technology or device data can help you target and customize your marketing and sales efforts, improve your demographic data, and guide product development. Additionally, market segmentation, customer service, competitive analysis, and risk management can all benefit from it.

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

Geographic data  

This will help with supply chain management, advertising campaigns, catastrophe response, regulatory and legal compliance, sales territory management, site selection for retail outlets and services, and regional trends and preferences. Keep in mind that certain clients, such as snowbirds, may have several locations.

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

Engagement and behavior data

Observing web behavior and digital content consumption offers valuable insights into customers’ preferences, spanning topics and products. Given its potential to evolve, maintaining a dynamic timeline of this data is crucial, prioritizing recent data over older data.

For instance, Amazon’s development of the Kindle stemmed from observing customer interest in ebooks.

Engagement data, comprising social media interactions, comments, and shares, provides companies with insights into how customers engage with their messaging. It’s essential to include trends and the momentum of web behavior. Lego’s introduction of the “Lego Ideas” platform, in response to fans sharing their own designs, serves as an illustration.

Moreover, integrating chronographic data into these metrics is essential, considering the fluidity of behavior and engagement over time. Understanding the timing of customer purchases or renewals is pivotal for effective marketing campaigns. For example, Netflix strategically times the release of new content based on chronographic data, noting increased user engagement on weekends and often launching entire series on Fridays.

Additional client information

Psychological and attitudinal insights encompass psychographic data, encompassing values, attitudes, opinions, interests, preferences, and personality traits. These insights play a crucial role in shaping advertising campaigns, guiding product development endeavors, and facilitating personalization efforts. Utilizing this data enables the creation of intricate customer personas and profiles.

For instance, Spotify gathers data on users’ listening behaviors, including frequency, timing, and content preferences, offering insights into their moods, tastes, and lifestyle choices. Leveraging this information, Spotify curates personalized playlists and music recommendations.

Feedback and satisfaction data are indispensable for enhancing customer service and refining product development strategies.

For example, Apple employs customer satisfaction surveys following support interactions to assess the efficacy of its customer service initiatives. This ongoing feedback loop has resulted in continuous enhancements to their service approach, including personalized customer support and streamlined technical assistance processes.

Quantitative and transactional data

Transactional data can be utilized for predictive modeling and to identify trends in the behavior of a market. Examples of this type of data include purchase history and subscription details.

As an illustration, Target is renowned for creating an algorithm that uses buying habits to forecast pregnancy.
Purchase frequency is one type of quantitative data that might reveal patterns and customer life cycles.

For instance, Sephora uses this information to customize product suggestions for its mobile app and website.

Identification information and descriptive data

Email addresses, phone numbers, and postal addresses are examples of unique consumer identifiers that assist businesses in combining data from many sources. Records in a customer data platform cannot be merged without that kind of information. Many businesses utilize a cell phone number or email address as the exclusive information for every account.

Uber, for instance, employs users’ email addresses and cell phone numbers as its primary means of account identification. This enables them to get user input and to keep safe, tailored communication with people.
Businesses can build a multifaceted picture of their clients by using descriptive data, such as job title, marital status, employment, religion, or interests. While identity resolution may benefit from this, effective personalization and enhanced customer experiences are the main uses for it.

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 client information to promote company expansion

1. Attain a comprehensive understanding of the market.
2. Generate fresh ideas for advertising, marketing, product development, and customer service.
3. Tailor strategies accordingly.

Additionally, it’s crucial to explore ways to integrate these categories to create a more holistic perspective. For instance, merging demographic and behavioral data can enhance segmentation precision and deepen customer insights.

Advanced techniques like AI and machine learning can elevate customer data analysis to more sophisticated levels. However, it’s essential not to overlook data from non-customers. For instance, Zara, a global fashion retailer, utilized AI algorithms to analyze current fashion trends by scanning fashion-related images and posts across social media and the internet. This enabled them to grasp emerging styles, patterns, and colors that are trending.