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Projects/RFM Customer Segmentation Analysis
intermediate5 milestones · ~10 hours

RFM Customer
Segmentation Analysis

Marketing wants to know who their best customers are. You'll bring data into Python, compute Recency / Frequency / Monetary features per customer, define segments the marketing team can actually act on, and ship the analysis as a GitHub portfolio piece. Real database, real ambiguity, real deliverables.

What you'll learn

  • —Bring data from a production database into pandas via SQLAlchemy
  • —Translate a vague business question into RFM features
  • —Define and defend customer segments numerically
  • —Communicate segment-level recommendations to non-technical readers
  • —Ship a portfolio-grade GitHub repo with notebooks + findings + README

After this project

What you'll be able to claim — credibly — once your repo is shipped.

Roles you can credibly apply to

Junior Data Analyst (SaaS or Ecommerce)
Customer segmentation is the most common first-90-days project assigned to junior analysts.
— Pair with one SQL-only project (e.g. ecommerce-sales-analysis-sql) so your portfolio shows breadth, not just Python.
Marketing Analyst
RFM is the canonical framework marketing teams reach for when targeting customers as groups, not individuals.
— Add a campaign-attribution or A/B-test project to be competitive at companies with mature marketing analytics.
Customer Insights / CRM Analyst
Segment definition + behavioural profiling + a campaign-recommendation brief is this role's core deliverable.
— More common at Series B+ companies; earlier-stage startups fold this into the data analyst role.

Keywords on your resume after this

Tools
PythonpandasSQLAlchemyPostgreSQLJupyterGitGitHub
Methods
RFM analysisCustomer segmentationQuantile binningpd.qcutgroupby aggregationsDistribution analysis
Concepts
Recency / Frequency / MonetaryCustomer lifetime value (proxy)Long-tail distributionsSegment-level revenue contribution

Interview questions you'll be ready for

  1. 01Walk me through how you'd build customer segments from raw transaction data.
  2. 02What does RFM tell you that ranking customers by total spend doesn't?
  3. 03Your top segment is 8% of customers but 42% of revenue — what's the marketing implication?
  4. 04How would you handle the case where 50% of customers are one-time buyers — does RFM still work?
  5. 05If a CFO challenged your segment definitions, how would you defend them?
Resume-ready bullets — unlocked when you reach Milestone 5
After completing the project, you'll get a copy-ready resume bullet template phrased the way hiring managers want to hear it, with the numbers your own analysis produced.

The 5 milestones

Each milestone is reviewed before you advance.

  1. 01

    Set up your project repo

    Before any analytical work: create a public GitHub repository, push the standard skeleton, and paste the URL into your workspace.

  2. 02

    Get the lay of the land — in pandas

    Bring the ecommerce data into Python and form a first-pass understanding of who the customers are.

  3. 03

    Build the RFM features

    Compute Recency, Frequency, and Monetary features per customer, and analyze how each is distributed.

  4. 04

    Score and segment

    Assign RFM scores and group customers into actionable segments.

  5. 05

    Pull it together for your portfolio

    Curate four milestones into a portfolio piece a recruiter can read in five minutes.

Reading & references

Pre-flight setup: Python environment for this project
Setup Guide · Premium
How to read an unfamiliar database
Article
Loading SQL into pandas (the right way)
Article
Pandas describe + value_counts in 5 minutes
Article
What is RFM analysis?
Article
Pandas groupby — the patterns you'll use
Article
pandas qcut — quantile binning
Article
Designing customer segments that marketing can actually use
Article · Premium
Anatomy of a great portfolio README
Article
Writing for non-technical readers
Article

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