Use this when... you want to compare groups of students, families, tutors, leads, or lessons by shared characteristics such as start month, branch, subject, tutor, year group, programme, source, or trial outcome.

Before you start... define the cohort clearly. Decide who belongs in the group, what period you are measuring, and which outcome matters. Vague cohorts create reports that are easy to misread.

Use Analytics to compare cohorts and understand patterns over time.

Cohort reporting helps you answer questions that simple totals cannot. You might compare students who started in September with those who started in January, leads from different campaigns, tutors with different retention patterns, or branches with different attendance rates.

The aim is not to create complicated reports for their own sake. It is to understand whether a group behaves differently and what action that suggests.

Pick one clear grouping: start date, lead source, subject, branch, tutor, programme, trial month, or student level. Avoid changing the definition halfway through the review.

Expected outcome: everyone reviewing the report understands exactly who is included.

Choose the result you want to compare: conversion, attendance, retained students, revenue, unpaid balances, lesson frequency, assignment completion, or report completion. A cohort can look strong on one metric and weak on another.

Before trusting the report, check whether lead sources, branches, tutor assignments, student statuses, and dates are consistently recorded. Missing or inconsistent tags can distort cohort analysis.

Lead source quality matters when comparing enquiry and trial cohorts.

Do not compare a new cohort with an older cohort unless both have had enough time to produce the outcome. For example, a cohort that started last week cannot fairly be compared on three-month retention.

A cohort looks unexpectedly small: check date range, filters, branch, archived records, and whether the tag or source field was used consistently.

The result contradicts another report: compare definitions. One report may use invoice date while another uses lesson date or student start date.

The data is too messy: clean the source fields first and document which period is reliable enough to use.

Use cohorts to decide what to change. If a lead source converts poorly, improve the form or campaign. If one subject has low attendance, review tutor capacity and scheduling. If a branch has higher aged debt, review billing follow-up.

Explain the definition, period, and caveats when sharing. A simple note beside the numbers can prevent people from treating a directional report as a precise audit.

Examples include students who started after a specific campaign, trial students by tutor, GCSE maths students by branch, students with no lesson after two weeks, families from a referral source, or tutors onboarded in the same month. Each cohort should connect to a decision you can make.

For growth and retention questions, compare cohort activity with billing outcomes. A cohort may convert well but pay late, attend regularly but generate low revenue, or produce strong revenue but high tutor workload. These combinations make the analysis more useful.

Use cohorts monthly or termly rather than changing the definition every week. Stable review periods make patterns easier to see and keep the team focused on action rather than report tweaking.

Add a note when sample size is small or when data quality is uncertain. Be careful with very small cohorts. A group of three students can change dramatically because of one family, one tutor change, or one unpaid invoice. Use small cohorts as prompts for investigation, not as proof of a broad trend.

Save the cohort definition beside the report so the same comparison can be repeated later.

After reviewing a cohort, record the action you will take and the date you will check again. Cohort reporting is most useful when it leads to a practical operational decision.