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E-commerce · Analytics ·

Cohort analysis

Measure retention and customer value at 1, 3 and 6 months after acquisition. Identify the most profitable cohorts.

Illustrative preview of the SAP Analytics Cloud dashboard Cohort analysis for the E-commerce industry: metrics LTV by cohort, Retention rate M1/M3/M6, Revenue by cohort, Churn, analyzed by Cohort, Acquisition channel, Segment.
Illustrative preview of a possible rendering in SAC. Brand colors and structure; synthetic figures.

KPIs included

  • LTV by cohort
  • Retention rate M1/M3/M6
  • Revenue by cohort
  • Churn

Analysis dimensions

  • Cohort
  • Acquisition channel
  • Segment

About this template

Measure retention and customer value at 1, 3 and 6 months after acquisition. Identify the most profitable cohorts. Designed for teams in the E-commerce industry, the model pre-wires 4 key metrics — including LTV by cohort and Retention rate M1/M3/M6 — analyzable across 3 analysis axes (Cohort, Acquisition channel, Segment). You start from an already-bounded base (units, aggregations and business labels defined) rather than a blank sheet.

After downloading, import Cohort analysis into SAC Modeler (Files → New Model → Import data from a file), map the 3 dimensions and 4 measures, then build your Story. The provided dataset contains 720 to 960 rows with realistic values for the E-commerce industry, available as .xlsx (multi-sheet workbook), .csv (flat table) and .package (ZIP bundle with model.json, data.csv and README).

FAQ

What is the "Cohort analysis" template for?

It provides a ready-to-use SAC structure to drive analytics in the E-commerce industry. The standard business KPIs and dimensions are already defined, saving you the modeling phase.

Which KPIs are included?

The template includes 4 metrics: LTV by cohort, Retention rate M1/M3/M6, Revenue by cohort, Churn. Each is computed across the dimensions Cohort, Acquisition channel, Segment.

How do I import it into SAP Analytics Cloud?

Download the .csv or .xlsx format, then in SAC: Files → New Model → Import data from a file. Map the columns (Dimensions then Measures), validate the types and build your Story. Allow 5 to 10 minutes for an operational model.