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Cohort Analysis
Group customers by acquisition date and track retention over time with a cohort-by-period heatmap.
Overview
Cohort Analysis groups customers by when they were acquired — based on each Contact's createdAt date — then tracks how actively each cohort stays in touch over time, using lastContact as the retention signal. It answers a question none of the other CRM analytics screens can: are customers acquired recently staying engaged at the same rate as customers acquired a year ago?
Retention, Not Revenue
Cohort Analysis measures contact recency, not money. A customer can show strong retention here (recent lastContact) independent of whether they have any open or won deals — it is purely a relationship-engagement view.
Screen preview
A quick look at the screens. Use the toggle in the corner of each preview to switch between the dark and light themes.
Cohort retention
Key Features
Cohort Grouping
Group acquisition by Month, Quarter, or Year using the pill filter bar.
Retention Heatmap
Cohort rows × period columns, with cell opacity scaled to that cohort's retention percentage in that period.
Acquisition-Based Cohorts
Every contact is placed in exactly one cohort based on its createdAt date — the period it first entered the directory.
UI Walkthrough
Choose a grouping with the By Month / By Quarter / By Year pill filter. Each row of the heatmap is one acquisition cohort (e.g. "Jan 2026"); each column is a period since acquisition (Period 0, Period 1, Period 2, …). A cell's opacity reflects what percentage of that cohort's contacts had a lastContact date falling inside that period — darker means stronger retention, lighter means the cohort has gone quiet.
Reading the Heatmap
Reading down a column compares cohorts at the same relative age — for example, comparing every cohort's Period 2 retention shows whether newer customers are engaging better or worse than older ones did at the same stage. Reading across a row shows a single cohort's engagement decay over time.
Watch for a Widening Light Column
If retention opacity fades faster in recent cohorts' early periods than it did in older cohorts, that is an early warning sign for onboarding or engagement quality — something worth catching well before it shows up as reduced revenue in Revenue Analytics.
AI Assistant
Cohort Analysis has a built-in AI Assistant, opened from the tab on the right edge of the screen. It receives the same cohort table shown on screen β each cohort's size and its retention-percentage series over subsequent periods β as structured context, and answers in Markdown.
Retention Health
Ask whether customer retention is improving or declining over time.
Cohort Comparison
Ask which cohorts retain best or worst, and any outliers worth investigating.
Drop-off Analysis
Ask where retention drops off most sharply across the periods.
Sizing Context
Ask whether any cohorts are too small for their retention percentage to be reliable.
Any assistant reply can be saved as a report via Save as Report, and relevant Knowledge Base sources can be attached with Add Knowledge before asking. The assistant's AI backend follows the Writer Engine setting in Settings β Agentic β Ollama and other local/in-browser engines run entirely on your machine, never through TotalApp's servers.