# Cohort Retention & RFM Funnel Analyzer

Paste an order or event CSV and get the growth stack in one page: a cohort retention triangle with a heatmap, RFM quintile scoring mapped to the ten standard lifecycle segments, and a strict-order funnel.

> Canonical page: https://elysiatools.com/en/tools/cohort-retention-rfm-funnel-analyzer

- **Category:** Data Visualization

- **Keywords:** cohort analysis, retention, rfm, churn, funnel analysis, customer segmentation, growth analytics, lifecycle segments, repeat purchase

## Overview

A semantic layer over your transaction log, not another generic chart. Cohorts are formed by each customer's first-order month (or ISO week), and retention is the share of the cohort that orders again in each later period — rendered as the classic triangle heatmap with per-cell user counts, size-weighted average retention per period index, and best/worst cohort callouts. RFM follows the standard method: rank every customer on Recency (days since last order), Frequency (order count) and Monetary (total spend), cut each dimension into quintile scores 1–5, and map the three-digit code to the ten lifecycle segments — Champions, Loyal Customers, Potential Loyalists, New Customers, Promising, Needs Attention, At Risk, Can't Lose Them, Hibernating, Lost — each with share of base, average spend, and a recommended play. The funnel counts users who reached each step in strict chronological order (a user only counts for a step if they completed the previous step earlier in time). Everything runs on a header-flexible CSV (user/customer, date/timestamp, amount/revenue, event/action), so an ecommerce export, a Stripe CSV or a product-event dump all work without reformatting.

## Inputs

- **订单 / 事件 CSV** (textarea): user_id,date,amount u1,2026-01-05,120 u1,2026-02-03,80 u2,2026-01-08,200
- **队列周期** (select)
- **显示期数** (number)
- **分析日期** (text): defaults to the newest date in the CSV
- **漏斗步骤（可选）** (text): signup, cart, checkout, purchase — needs an event column

## When to use

- Evaluating repeat purchase rates and long-term customer churn over weekly or monthly cohorts.
- Segmenting an ecommerce customer base into RFM tiers (Champions, At Risk, Lost) to tailor lifecycle marketing campaigns.
- Tracking strict-order conversion drop-offs from product event logs or checkout flow exports.

## How it works

- Paste an order or event CSV containing flexible column headers such as user/customer, date/timestamp, amount/revenue, and event/action.
- Select the cohort aggregation granularity (month or ISO week), configure the number of periods to display, and optionally define an analysis cutoff date.
- Provide comma-separated funnel step names if analyzing sequential conversion milestones from an event column.
- Generate the report to inspect retention triangles with cell counts, size-weighted retention averages, RFM lifecycle segment distributions, and strict-order funnel drop-offs.

## Use cases

- E-commerce stores analyzing repeat-order curves across acquisition months to measure seasonal cohort retention.
- CRM marketers identifying high-value customers needing re-engagement, such as At Risk and Can't Lose Them segments.
- Product teams measuring strict chronological drop-off across onboarding or multi-step checkout funnels.

## Frequently asked questions

### What CSV column headers are recognized?

The analyzer recognizes standard variations for user IDs (user_id, customer, user), timestamps (date, timestamp), amounts (amount, revenue, spend), and event names (event, action).

### How are customer cohorts defined?

Cohorts are grouped by the timestamp of each customer's first recorded order or transaction, grouped by month or ISO week.

### How does the strict-order funnel calculate conversion?

A user is only counted for a given step if they completed all preceding steps chronologically earlier in the dataset.

### What segments are generated by the RFM model?

Customers are ranked by Recency, Frequency, and Monetary quintiles (1–5) and mapped into 10 standard lifecycle segments, including Champions, Loyal Customers, At Risk, and Lost.

### What happens if I leave the analysis date blank?

When omitted, the tool automatically defaults to the latest date found in the provided CSV dataset.

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