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RFM Analysis In Minutes

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Running rfm customer segmentation analysis analysis...

Running rfm customer segmentation analysis analysis...

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Sample Output

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How it works

Customer segmentation using Recency, Frequency, and Monetary analysis on retail transaction data.

Use this when you need rfm analysis on your data.

See related tools for alternatives.

Built for: Analyst, data scientist, business user

Typical data source: CSV with relevant columns

analytics

What data do you need?

Data for rfm analysis

customer_id (categorical) transaction_date (date) amount (numeric)
example1 example1 example1
example2 example2 example2
example3 example3 example3

Minimum 10 rows · Best with 100-5000 rows

What's in the report?

Segment customers using Recency, Frequency, and Monetary (RFM) analysis to identify high-value customers, at-risk churners, and growth opportunities using quintile-based scoring.

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Segment Treemap

Customer segments by size and revenue contribution

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Segment Profiles

Detailed RFM metrics for each customer segment

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R-F Heatmap

Customer distribution across Recency and Frequency scores

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RFM Distributions

Distribution of Recency, Frequency, and Monetary values

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Customer Scatter

Individual customers plotted by RFM scores, colored by segment

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Revenue Concentration

Cumulative revenue distribution (Pareto / Lorenz curve)

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Geographic RFM

RFM metrics by customer country

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Cohort Retention

Retention rates by acquisition cohort over time

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Marketing Actions

Recommended marketing actions per segment

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Order Distribution

Distribution of order frequency across customers

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AI Insights

Plain-English interpretation — what the numbers mean, what's significant, and what to do next.

Questions?

See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.

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