Segmentation divides customers into groups with similar characteristics or behaviour, so products, messages and service can be tailored.
Approaches
- Rule-based: simple, explainable groups such as region, plan or tenure bands.
- RFM analysis: score customers on recency (how recently they bought), frequency (how often) and monetary value (how much). Combinations like "recent, frequent, high-value" or "lapsed high-value" are immediately actionable.
- Clustering: algorithms such as k-means find groups in many behavioural features at once.
- Needs-based: segments from survey data about goals and attitudes.
Doing Clustering Well
- Choose features that reflect behaviour you care about.
- Scale features so none dominates.
- Try several numbers of clusters and judge by stability and usefulness, not just statistics.
- Profile each cluster: size, averages and distinctive traits, and give it a descriptive name.
Making Segments Useful
A segmentation is only valuable if it changes actions. For each segment, ask what you would do differently — offer, message, service level — and test whether it works.
Keep It Current
Customers move between segments. Recalculate regularly and track movement between segments over time.
Cautions
Avoid segments based on sensitive attributes without a clear, lawful and fair reason, and beware stereotyping individuals from group averages.