Customer Segmentation – An introduction

Customer segmentation is a means by which you group customers into an identifiable category upon which you as a company can analyze for among other activities strategic planning, market campaigns and architecting customer experiences.

Factors that could be used in customer segmentation include

  • Firmographic could include # employees, industry, etc.
  • Demographic could include age, marital status, location, income


I like behavioral rather than segmentation based on attributes or a derived factor. Behavioral is based on something the customer DID, rather than an attribute externally assigned to the customer. Some good sources of behavior-based attributes include.

  • Transactional history (soon to come my post about churn management and customer accounting). Within transactional history, a popular method of segmentation is using cohorts based on time of transaction. Other metrics include segmenting customers on recency, frequency and monetary value of the transaction.
  • Social profile, which included connections, networks and affiliations both on- and offline.
  • Sentiment, which is associated with attitude, but more specific to sentiment analysis as online (watch out for future blog on this).

Factors used for customer segmentation need to be SMART.

  • Specific – Clearly defined scope
  • Measurable & Manageable. You need to be able to measure it and collect the data reliably.
  • Should be based on a hypothesis that you can prove or disprove with data or information available to YOU at a reasonable cost
  • Actionable within your organization – somebody should own the decision and thus the ability to prove/disprove a hypothesis
  • Relevant to your goal: Current drives + future accelerators = Value bringing you towards your goal
  • Trending – The data and information should be available over a significant window of time & available at the necessary intervals

Rule based and analytics driven

  • Rule-based segmentation is based on criteria such as Boolean logic or thresholds and is usually two dimensional
  • Analytics driven segmentation such as k-means clustering is a data-driven means of determining customers who are similar along specific variables. Because these clusters are based on similarity and statistical significance, they are helpful in predictive analytics.

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