Marketing
Customer churn analytics
Customer churn analytics is the process of assessing how many customers you are losing over the course of a year.
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Customer churn analytics vs Customer segmentation analytics
These models cover closely related ground. Compare their purpose, scope, practical guidance, and supporting resources to choose the better fit.
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Marketing
Customer segmentation analytics
Customer segmentation analytics is the process of finding sub-groups or segments within the overall market.
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Use this when.
Set frequency according to industry dynamics and Customer Lifetime Value Analytics, but a monthly stream is a useful default. In highly competitive subscription markets, monitor frequently enough to intervene before departure and to evaluate retention tests.
Keep the segmentation current because it should guide targeting, service design and marketing investment. Review it before a major campaign to confirm that the audience and offer fit. In a stable market an annual refresh may be sufficient; in a volatile one, monitor segment size, behaviour and economics more often.
Extracted signals
Strengths, limits, and pitfalls.
Customer churn analytics
- Standardise the unit and churn event company wide. Decide whether one person with several products is one customer or several accounts, how households are treated and whether inactivity of six months, a year or three years means loss in a non contract business.
- Define the customer, active state, observation period and churn event first. Track customer retention rate (CRR) and customer turnover rate (CTR) by cohort and segment. These KPIs describe the past; prediction requires prior behaviour and context.
- Combine tenure, campaign, usage, service, payment and sales data. With lawful collection and clear purpose, Text Analytics can summarise customer feedback and Regression Analysis can estimate associations with churn. Separate correlation from cause, validate on later cohorts and test whether the proposed offer produces incremental retention above its cost.
Watch for
- Not every departure is harmful. Combine Customer Lifetime Value Analytics with Customer Profitability Analytics so retention resources protect valuable relationships. Address the service model for unprofitable segments fairly rather than manipulating customers into leaving.
Customer segmentation analytics
- Create the fewest segments needed to support genuinely different actions. A useful cluster has a recognisable behaviour, a reachable audience and a proposition or service decision that would change because the segment exists.
- Begin with a decision, not with every available variable. Select features that can explain or predict the behaviour relevant to that decision, including location, purchase history, age, stated needs, channel use and service patterns.
- External and digital data can enrich first party records, but only when collection and use are lawful, ethical and accurate. Text Analytics can structure language data and Data Mining can identify patterns across many records and variables. After generating candidate groups, test each segment with six questions:
Watch for
- Do not mistake a statistically detectable cluster for a viable market. Tiny, unstable or inaccessible groups create complexity without value, and sensitive inferred characteristics can introduce serious privacy and discrimination risk.
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