Customer segmentation models

Customer segmentation models is an analytical project: based on your data (behavior, purchases, product, CRM) we select features and build segmentation using clustering methods (k-means etc.) or business rules, identify groups, describe their 'portraits' and give recommendations on working with each. Honestly about the nature, this is key: segmentation is an EXPLORATORY model, not an objective truth; the result depends on the chosen features, method and number of segments — a different dataset/parameters will give different groups; a 'single correct' segmentation does not exist, we pick a stable and interpretable option together with you. Honestly about data: segment quality depends on data completeness and cleanliness — garbage in gives meaningless groups (garbage in, garbage out); sufficient volume is needed. Honestly about correlations: segments describe how groups differ (correlations), but do NOT prove the causes of behavior. Honestly about the essence: this is segmentation and recommendations, NOT implementation and NOT sales growth — what to offer each group and how to work with it is decided and implemented by you or your marketer (implementation — separately); the model itself does not raise revenue. Honestly about aging: customers and behavior change — segmentation is worth periodic recomputation. Requirements: access to data. An important boundary: these are segmentation models, not RFM (simpler segmentation — separate/cheaper), not predictive models (LTV/churn — separate) and not campaign implementation. If there are few customers or poor data, a complex model is overkill — simple rules/RFM are enough. Picture this: instead of 'we split customers by gut feel' you get stable groups by real behavior and clear portraits. The base price starts from 25,000 ₽ per project; it depends on data volume and features.

• You work with all customers the same way, without natural groups.
• 'Eyeball' segments by intuition do not reflect real behavior.
• You do not understand which customer groups you have.
• There is no basis for targeted offers and communications.

• Feature selection and data preparation
• Building segmentation (clustering/rules)
• Selecting a stable and interpretable number of segments
• Segment portraits and their differences
• Recommendations on working with each group
• Checking segment stability and meaningfulness
• A report and review with you
• Exporting segments for further work

• Natural customer groups by real data
• Clear segment portraits
• A base for targeted offers (implementation — separately)
• Priorities for working with groups

• We clarify goals, data, features; collect access
• We build and check segmentation, describe portraits
• We compile a report and export, review with you

• Will segmentation raise sales?

  Not by itself. It gives groups and recommendations, but what to offer each and how to work is implemented by you or your marketer (separately). The model is a base for targeted work, not revenue growth itself.

• Is this the 'correct' segmentation once and for all?

  No. The result depends on features, method and the number of segments — there is no single correct segmentation. We pick a stable interpretable option, but over time behavior changes and segmentation is worth recomputing.

• How is this different from RFM?

  RFM is simple segmentation by recency/frequency/amount. Segmentation models are more complex: they use many features and behavior to reveal non-obvious groups. For a small base or simple tasks RFM may be enough and cheaper.