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 profitability analytics
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Finance
Customer profitability analytics
Customer profitability analytics is the process of identifying which of your customers are actually making you money.
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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.
Use this analysis continuously to understand where customer value is being created, and give it particular attention when revenue is falling, costs are rising or margins are under pressure. It can reveal whether the problem comes from customer mix, acquisition channels, service demands, pricing or operational cost.
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 profitability analytics
- Assess profitability across the customer’s complete relationship and expected lifetime. When systems treat a person who buys five products as five unrelated customers, each product view can look weak even though the combined relationship is highly profitable. Resolve customer identities before acting on the result.
- The method also applies where “profit” means efficient use of a constrained budget. In one NHS project, only 5 per cent of patients accounted for more than 200 accident and emergency visits. Identifying these super users allowed the organisation to address their underlying needs through different support while freeing resources for other patients.
- The same logic helps broadband providers identify customers whose use of an unlimited plan makes the relationship uneconomic. Regression analysis (Regression Analysis), correlation analysis (Correlation Analysis) and data mining (Data Mining) can help identify the characteristics associated with different profitability groups.
Watch for
- Do not leave customer profitability inside the finance function. Finance understands cost allocation, while sales, service and operations understand the behaviours that create those costs. Their combined interpretation is what turns the metric into sound pricing, service and marketing decisions.
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