What is a cohort table?

A cohort table is a matrix where rows represent cohorts (e.g., customers who signed up in January, February, March) and columns represent time periods since cohort formation (Month 0, Month 1, Month 2). Each cell contains the metric being tracked (e.g., percentage still active). This format makes it easy to compare how different cohorts perform over the same relative time period.

How many customers do you need for cohort analysis?

There is no strict minimum, but each cohort should ideally contain at least 100 members for meaningful patterns to emerge. Smaller cohorts produce noisy data that is hard to interpret. If your customer base is small, use larger time windows (quarterly instead of monthly) to create larger cohorts.

What tools can be used for cohort analysis?

Cohort analysis can be performed using analytics platforms (Amplitude, Mixpanel, Google Analytics 4), BI tools (Power BI, Tableau, Looker), spreadsheets (for simpler analyses), SQL queries against your data warehouse, or programming languages like Python or R for more advanced analysis. The right tool depends on your data infrastructure and analytical sophistication.

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Cohort Analysis

Also known as:cohort reportcohort trackinggroup analysis

An analytical technique that groups users or customers into cohorts based on shared characteristics or experiences within a defined time period, then tracks their behaviour over time.

In-Depth Explanation

Cohort analysis divides customers or users into groups (cohorts) that share a common characteristic - most commonly, the time they first became a customer - and then tracks how each cohort's behaviour evolves over time. This reveals patterns that aggregate metrics would hide.

Types of cohorts:

Time-based (acquisition) cohorts: Grouped by when they became a customer (month, quarter)
Behavioural cohorts: Grouped by actions taken (first purchase category, feature used, channel)
Demographic cohorts: Grouped by attributes (industry, size, location)
Value cohorts: Grouped by initial purchase value or engagement level

What cohort analysis reveals:

Retention trends: Are newer cohorts retaining better or worse than older ones?
Revenue patterns: How does customer spending evolve over time?
Engagement decay: When do customers typically disengage?
Product changes impact: Did a product change affect subsequent cohort behaviour?
Seasonal patterns: Do cohorts acquired at certain times perform differently?

How to create a cohort analysis:

Define the cohort: Choose the grouping criteria (e.g., signup month)
Define the metric: Choose what to measure (retention, revenue, usage)
Define the time period: Set the observation window (weeks, months, quarters)
Build the cohort table: Create a matrix showing each cohort's metric over time
Visualise: Use heatmaps or line charts to identify patterns
Analyse: Identify trends, anomalies, and actionable insights

Common cohort analysis pitfalls:

Cohorts too small for meaningful analysis
Not accounting for seasonality
Confusing cohort effects with general trends
Ignoring the composition of each cohort

Business Context

Cohort analysis reveals whether a business is genuinely improving customer outcomes over time, providing insights that aggregate metrics like overall retention rate can mask.

How Clever Ops Uses This

Clever Ops implements cohort analysis capabilities for Australian businesses, building automated cohort reports that track customer behaviour over time. We help clients understand how product changes, marketing strategies, and operational improvements are affecting customer outcomes for each new group of customers.

Example Use Case

"A subscription business uses cohort analysis to discover that customers acquired through referrals retain at 2x the rate of those acquired through paid advertising, leading to increased investment in the referral program."