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Customer Personality Segmentation In Minutes

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Running customer personality segmentation analysis...

Running customer personality segmentation analysis...

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How it works

K-means clustering segments customers into behavioral personas based on spending patterns across product categories and demographics. Clusters reveal distinct customer archetypes enabling targeted marketing campaigns.

Use when you have customer transaction data with spend categories and want to identify distinct behavioral segments for targeted marketing.

Do not use if customer records are fewer than 50 or if spend categories are mostly zero/missing.

Built for: Marketing managers, CRM analysts, customer insights leads, ecommerce data analysts, growth marketers, loyalty program managers

Typical data source: Customer records with spend across product categories (wine, meat, fish, sweets, gold), demographics (income, education, family size), purchase channel counts (web, store, catalog), and campaign response flags over 1-2 years

retailecommerceconsumer packaged goodsfinancial services

What data do you need?

Dataset with 25 columns

customer_id (identifier) year_birth (numeric) education (categorical) marital_status (categorical) income (numeric) kidhome (numeric) teenhome (numeric) recency (numeric) mnt_wines (numeric) mnt_fruits (numeric) mnt_meat_products (numeric) mnt_fish_products (numeric) mnt_sweet_products (numeric) mnt_gold_products (numeric) num_web_purchases (numeric) num_catalog_purchases (numeric) num_store_purchases (numeric) num_web_visits_month (numeric) accepted_cmp1 (binary) accepted_cmp2 (binary) accepted_cmp3 (binary) accepted_cmp4 (binary) accepted_cmp5 (binary) response (binary) complain (binary)

Minimum 100 rows

What's in the report?

Cornerstone #19 — K-means clustering + RFM on customer personality (2,936 votes)

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Cluster Profile Summary

Defining characteristics of each segment - income, wine/meat spend, recency

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Average Total Spend by Segment

Total spend comparison across all customer segments

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Income Distribution by Segment

How household income separates the customer clusters

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Spend Category Correlations

Cross-sell opportunities revealed by spend category correlations

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Purchase Channel Mix by Segment

Channel preferences (web/catalog/store) by customer segment

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Income vs. Wine Spend by Segment

Income vs wine spend relationship colored by segment

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Campaign Acceptance Rate by Segment

Campaign acceptance rates by customer segment for marketing ROI

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AI Insights

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

Related tools

Need something simpler? Tf038 Live Ttest — When you have predefined customer groups and want to test whether a single metric like spend or income differs significantly between them, rather than discovering unlabeled groups through clustering.

Similar: Churn Drivers

The Question This Answers

Discover distinct customer personas from purchase and demographic data

Run K-means clustering on spend categories, income, and campaign response data to reveal 3-5 natural customer groups, each with a distinct behavioral profile that marketing can act on.

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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