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 engagement analytics
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Marketing
Customer engagement analytics
Customer engagement analytics is a highly evolving field at the moment where businesses are trying to map the entire customer interactive journey on- and offline.
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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.
Review the full model at least every six months and monitor leading signals between reviews. Reassess it when a product, service or system changes the experience.
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 engagement analytics
- Identify the few experiences most strongly associated with emotional attachment and repeat behaviour, then test improvements against a baseline rather than trying to please every customer in every way.
- Define engagement for the business and separate emotional state, rational loyalty and behaviour.
- Measure perception through Quantitative Surveys and Qualitative Surveys; use Social Media Analytics for relevant participation and advocacy.
Watch for
- Do not wait for a perfect composite score or treat the first score as truth. Start with a documented model, validate it, monitor drift and refine it without changing definitions so often that trends become meaningless.
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