E-Commerce Analytics: KPIs (Conversion, AOV, Churn), Funnels, Cohorts and A/B Testing Basics

Overview

E-commerce is data-rich: every click, view, add-to-cart, payment attempt and return generates information. Analytics converts this raw data into decisions that improve conversion, reduce returns, and increase profitability.

This topic usually appears as:

• define KPIs like conversion rate, AOV, churn,
• explain funnel and cohort analysis,
• write steps of A/B testing and precautions.

Learning objectives

You should be able to:

• Define major e-commerce KPIs (conversion rate, AOV, churn, repeat rate).
• Explain a funnel and identify where drop-offs occur.
• Explain cohort analysis and interpret a simple cohort table.
• Explain churn vs retention and how they are measured.
• Describe A/B testing steps and common mistakes.

Why analytics is important in e-commerce

Analytics helps in:

• improving conversion rate (identify drop-offs and fix them) (concept)
• reducing cart abandonment and payment failures (concept)
• improving product assortment and pricing decisions (overview)
• reducing returns (identify mismatch reasons) (concept)
• improving retention and LTV (measure repeat behavior) (concept)

Core KPI definitions (quick)

Key terms you can write in 1–2 lines:

• Conversion rate = Orders / Visits (concept)
• AOV (Average Order Value) = Revenue / Number of orders (overview)
• Cart abandonment rate = % carts not converted to orders (concept)
• Churn = % customers who stop purchasing in a period (concept)
• Retention rate = % customers who continue purchasing (concept)
• Repeat purchase rate = % customers who purchase again (overview)

Important e-commerce KPIs (table)

Funnels: meaning and example funnel

A funnel is a step-by-step view of customer journey where some users drop off at each step (concept).

Common e-commerce funnel:

Visits -> Product Views -> Add to Cart -> Checkout Started -> Payment Success -> Order Placed

How to interpret funnel drop-offs (table)

Cohort analysis: meaning and use

A cohort is a group of users who share a common starting event/time, e.g., “customers acquired in January” (concept).

Cohort analysis tracks how behavior (repeat purchase/retention) changes over time for each cohort (concept).

Uses:

• measure retention improvements after changes
• compare quality of customers from different campaigns
• detect if repeat purchase is improving or worsening (concept)

Example cohort table (simple)

Example: retention % by month for cohorts acquired in Jan/Feb/Mar (illustration):

Interpretation: improving Month 1 and Month 2 retention suggests better retention actions or better customer quality (concept).

Churn and retention (basics)

• Churn: customers who stop buying (lost customers) (concept)
• Retention: customers who continue buying (retained customers) (concept)

In e-commerce, churn can be reduced by improving:

• delivery reliability, returns/refunds speed,
• personalization and loyalty programs,
• product quality and accurate catalogues (concept)

A/B testing basics (process + rules)

A/B testing compares two versions (A vs B) to find which improves a metric like conversion (concept).

Process:

1. set hypothesis (e.g., “new PDP layout increases add-to-cart”)
2. create A (control) and B (variant)
3. split traffic randomly
4. measure outcome metric
5. implement the winner if significant (concept)

Rules (exam points):

• test one change at a time (concept)
• choose one primary metric (conversion/add-to-cart)
• run test for sufficient time/sample
• avoid changing other major things mid-test (concept)

Common mistakes in analytics and A/B tests

Common errors (write any 4–5):

• tracking wrong KPI or multiple KPIs without clarity (concept)
• stopping A/B test too early (insufficient data)
• testing many changes at once (can’t identify cause)
• ignoring segmentation (new vs returning behave differently) (concept)
• data quality issues (bot traffic, duplicate users, wrong attribution) (overview)

Exam-ready notes (short points)

• KPIs: conversion, AOV, churn, retention, return rate, payment success.
• Funnel shows where customers drop off; fix friction points.
• Cohorts track retention of customers acquired in same period.
• A/B testing: hypothesis → variants → split traffic → measure → decide.

Quick recap

• Analytics drives decisions in e-commerce by tracking KPIs and behavior.
• Funnels identify drop-offs; cohorts measure retention over time.
• A/B testing is the controlled method to improve conversion safely.