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How to Analyse and Monitor the Health of Your Customer Base


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eMetrics 2016 deck

Published in: Data & Analytics
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How to Analyse and Monitor the Health of Your Customer Base

  1. 1. How to Analyse and Monitor the Health of Your Customer Base @CarmenMardiros
  2. 2. This is how it all started…. What is a “cohort”? first_order_month customers Jan 2016 7325 Feb 2016 2344 March 2016 2355 …. ….
  3. 3. My first cohort analysis at navabi... My two CEOs were not impressed: “The data is wrong”
  4. 4. Aha moment - I hadn’t “equalised” customers 2013 customers had 3 extra years of lifetime vs 2016 customers! ANY metric will look better for older customers.
  5. 5. A fairer comparison painting a truer picture. Cohort performance is capped at X weeks of age An emerging trend? Should we be worried? How cohorts are doing at 8 weeks of age
  6. 6. A truly fair comparison that reflects reality. Only cohorts at least X weeks old are eligible How cohorts are doing at 8 weeks of age
  7. 7. What age is best to measure cohort behaviour at? Long term view sacrifices visibility into recent cohorts but gives better insight into lifetime behaviour. On-the-fly age capping is best depending on the purpose. March Cohort July Cohort
  8. 8. Key takeaway #1 Always, always “equalise” customers prior to analysis to avoid false alarms.
  9. 9. What metrics best reflect cohort health? Leading indicators of LTV Orders per Customer (or other actions) Discount Rate (promos, sales etc) Revenue per Customer Return/Cancellation Rate Marketing Cost per Customer (acquisition and retention) Profit per Customer (actual and forecast) Always capped at age X. Never measured for cohort as a whole. Other leading indicators: website visits, products added to basket/wishlist, newsletters clicked on, customer service tickets/complaints
  10. 10. “Per customer” metrics can be very misleading Cohorts are rarely normally distributed and the average can be misleading. So: Use median to calculate “per customer” instead of mean, if feasible. Measure milestones reached.
  11. 11. What metrics best reflect cohort health? Milestones reached % Cohort reached 2nd order % Cohort migrated from 2nd to 3rd order (3rd to 4th etc) Digital equivalents: % User Cohort added product to basket within 3/7 days etc % Cohort with discount rate over y% / avg price paid over €z % Cohort reached profitability (measures to what extent profitable customers subsidise the rest of the cohort!) % Cohort reached VIP status (high LTV value) Always capped at age X. Cross-reference with acquisition channel for invaluable nuggets of insight.
  12. 12. Key takeaway #2 Use “milestones reached” as health checkpoints for a wide range of cohort behaviours.
  13. 13. Combine “calendar time” with “cohort time” Activity in week X comes from customers at different ages. Understanding “what works for whom” is challenging without analysing the mix.
  14. 14. Response is relative to Active Customer Base # Active Customer base = Anyone active in previous 57 weeks % Reorder Rate = Anyone active in previous 57 weeks and active again this week
  15. 15. Why Active Customer Base and not Total? Removes the effect of churned customers Total Customer Base keeps going up and up. But it also includes an increasingly high segment of churned customers. A much greater re-order rate would have to happen in order to register as strongly if we calculated Reorder Rate relative to the total customer base. Removes the effect of new customers Total Customer Base usually also counts customers acquired that week. This can muddy the picture significantly.
  16. 16. Key takeaway #3 Identify the customer segments your organisation most depends on and monitor their size and response like a hawk.
  17. 17. Questions? @CarmenMardiros