1. Descriptive Analytics

Segments, Cohorts and Funnels

Compare like with like across customer groups, starting periods and process stages

Segments, Cohorts and Funnels

Segments group entities by attributes. Cohorts align them by a starting event. Funnels follow eligibility through ordered stages. They answer different questions.

Segment without inventing a person

HarborMart may segment orders by store, slot, fulfilment mode and basket size. These are operational groups, not permanent customer “types.”

Good segments are:

  • defined before the outcome being compared;
  • large enough for useful uncertainty estimates;
  • actionable through a distinct decision;
  • stable enough to reproduce;
  • checked for proxying sensitive attributes.

A cluster label such as “value seeker” is an analyst’s summary, not an observed psychological fact.

Cohort retention

Suppose customer activity is measured from first order:

First-order cohortMonth 0Month 1Month 2Month 3
January, n=200n=200100%58%45%39%
February, n=240n=240100%63%49%
March, n=180n=180100%60%

The blank March cells are not zero; those horizons have not matured. Compare cohorts at the same age. A calendar-month total confounds customer age, acquisition volume and seasonal conditions.

Retention at age kk is

Rk=cohort members active at age keligible cohort members.R_k=\frac{\text{cohort members active at age }k} {\text{eligible cohort members}}.

Define whether “active” means any order, paid order or contribution-positive order, and how account closures or observation-window loss are handled.

Funnel arithmetic

StageCustomersStage conversionEntry conversion
eligible visitors10,000100%
product viewed7,50075.0%75.0%
checkout started2,40032.0%24.0%
paid order1,92080.0%19.2%
fulfilled order1,82495.0%18.24%

“Checkout conversion” could mean 1,920/2,400=80%1,920/2,400=80\% or 1,920/10,000=19.2%1,920/10,000=19.2\%. Name the denominator.

Funnels require:

  • a stable identity across devices;
  • ordered event rules;
  • a time window;
  • treatment of repeats and backtracking;
  • consistent eligibility at entry.

Composition can reverse an aggregate

If mobile traffic rises and mobile converts less than desktop, total conversion can fall even when both channel-specific rates improve. That is a composition effect, not necessarily a product deterioration.

Use standardisation when the question is “what would the total rate be under a fixed channel mix?” Report both observed and standardised results; each answers a different question.

Quick check

February’s Month-1 retention exceeds January’s by five points. Can HarborMart attribute the change to a new onboarding message launched in February?

Answer
No. The cohorts also differ in acquisition source, season, prices and other conditions. The pattern motivates an experiment or credible adjustment; it is not itself a causal estimate.

Next: Visual Evidence

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