Marketing
Customer churn analytics
Customer churn analytics is the process of assessing how many customers you are losing over the course of a year.
Read article
keymodelsAll comparisons
Compare
Customer churn analytics vs Non-customer analytics
These models cover closely related ground. Compare their purpose, scope, practical guidance, and supporting resources to choose the better fit.
Related modelsConceptual / qualitativecustomerFinance
Marketing
Non-customer analytics
Non-customer analytics is about understanding what people who are currently not your customers think about your product, services or brand.
Read articleChoice logic
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 it when a market is stagnant, declining or narrowly contested, or when customer only research keeps reproducing the same opportunities.
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.
Non-customer analytics
- Ask what non customers do instead and why. The strongest opportunity often comes from removing a common barrier, not adding another feature for existing buyers.
- Define the current market and the three tiers. Use transaction data to identify lapsed or declining engagement, but do not infer reasons from absence alone.
- Recruit participants lawfully through panels, intercepts, community partners and transparent outreach. Use Quantitative Surveys, Qualitative Surveys, Focus Groups and Interviews. Do not scrape or target individuals from personal social posts merely because a purchase is visible. Obtain consent and protect privacy.
Watch for
- Do not recruit current customers and call the result non customer insight. Also avoid treating refusal as ignorance; it may be rational, ethical or structural.
Read next
Open the full model articles.
Each comparison links back to the full articles so you can inspect examples, steps, caveats, and related templates before choosing.
Application bridge
Related templates.
Interface Management PlanInterface Management PlanMarketing organization design canvasA practical template / form addressing this question: How should marketing capabilities be organized for the company's product, customer, and growth model?Customer-facing roadmap strategyA practical template / form addressing this question: What should a public or customer-facing roadmap communicate, to whom, and with what confidence?Compensation philosophy canvasA practical template / form addressing this question: Which principles should govern compensation decisions and trade-offs?