Analytics

    Cohort Analysis

    Term #76

    Grouping customers by a shared characteristic (month of first purchase) and tracking their behavior over time. Reveals real changes that averages can hide.

    In practice

    Cohort analysis answers: are customers I acquired in January worth more than those acquired in July? Without it, you might improve CR while lowering LTV — bringing in cheap customers who never come back.

    Common misconception

    'Cohort analysis is too complex.' In Klaviyo: Analytics → Cohort Analysis — 2 clicks. The basic metric — what % of month X's customers bought again in month X+1 — can even be calculated in a spreadsheet.

    How to fix it

    Cohort retention declining? (1) Improve the post-purchase experience, (2) check whether products naturally drive repeat purchases, (3) run a win-back campaign for at-risk cohorts, (4) measure NPS by cohort.

    Example

    A supplements store: the Black Friday cohort's repeat rate was 8% the following month. Regular-purchase cohorts: 34% returned. Black Friday cut LTV by 60% — they changed their sales strategy.

    Priority

    Run cohort analysis quarterly. Question: 'What % of month X's customers bought again within 90 days?' Below 20% signals a retention problem. Above 40% is a healthy business.

    Jimmy tracks your Cohort Analysis, automatically

    WhatsApp alerts when something important changes. No dashboards, no hassle.