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Project 10IntermediateBusiness6-8 focused hoursE-commerce segmentation case study
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Customer Segmentation and Growth Strategy

Discover, profile, and cautiously use customer segments to recommend growth actions.

EDAK-MeansPCAPreprocessingBusiness Strategy
FRAME→DATA→ANALYZE→MODEL→EVALUATE→COMMUNICATE

BUSINESS CONTEXT

Why this project matters

An e-commerce company treats thousands of active customers alike and wants actionable segments for loyalty, reactivation, and premium offers. No correct segment label exists.

PROJECT OBJECTIVE

What you are expected to accomplish

Use defensible unsupervised analysis to identify useful customer structure, explain its limits, and recommend evidence-based growth actions.

DATASET OVERVIEW

Expected dataset structure

Expect customer-level spending, frequency, recency, value, discount, return, and diversity measures with very different scales and possible redundancy.

Main entities / rows

One row per active customer

customer_id

string

Identifier, excluded from similarity features.

annual_spend

float

Annual customer value.

order_count

integer

Purchase frequency.

average_order_value

float

Typical basket value.

days_since_last_purchase

integer

Recency.

account_age_months

integer

Customer tenure.

discount_share

float

Discount dependence.

category_diversity

integer

Breadth of categories purchased.

return_rate

float

Returned-order proportion.

Data quality issues to expect

  • Features have incompatible numeric scales.
  • Spend and order values may be skewed or contain outliers.
  • Correlated measures can create redundant similarity signals.

QUESTIONS TO ANSWER

Focus the work around meaningful decisions

  1. 01

    What would make a segment actionable and stable?

  2. 02

    Which features should define similarity?

  3. 03

    Which K values create compact, interpretable groups?

  4. 04

    What actions follow from profiles without treating clusters as truth?

PROJECT ROADMAP

Move through the case study in a practical sequence

  1. 01

    Step 01

    Define useful segmentation

    State what actionable, distinct, understandable, and stable groups would mean.

  2. 02

    Step 02

    Audit customer data

    Inspect distributions, missingness, outliers, scale differences, and redundancy.

  3. 03

    Step 03

    Choose preprocessing

    Justify scaling, transformations, and outlier treatment for distance-based clustering.

  4. 04

    Step 04

    Run K-Means experiments

    Compare plausible K values with inertia, silhouette evidence, sizes, and interpretability.

  5. 05

    Step 05

    Profile clusters

    Create a table of count, spend, frequency, recency, and other characteristics before naming groups.

  6. 06

    Step 06

    Explore with PCA

    Project scaled data to two components, report variance, and explain limitations.

  7. 07

    Step 07

    Recommend strategy

    Connect evidence-backed profiles to growth actions.

  8. 08

    Step 08

    Document limitations

    Discuss K choice, sensitivity, changing behavior, stability, and operational validation.

TASKS / MILESTONES

Concrete work to complete

  • Write a segmentation objective.
  • Audit and justify similarity features.
  • Compare multiple preprocessing and K choices.
  • Create a cluster profile table and PCA view.
  • Name groups only after profiling.
  • Propose one action and rationale per useful segment.
  • Write a limitations and validation note.

SUGGESTED VISUALIZATIONS

Visuals worth creating

  • Feature distributions
  • Elbow and silhouette comparison
  • Cluster-size chart
  • PCA cluster projection
  • Profile comparison chart

DELIVERABLES

What the learner should produce

  • Segmentation objective
  • EDA and preprocessing rationale
  • K-selection analysis
  • Cluster profiles
  • PCA visualization
  • Growth recommendations
  • Limitations note and README

SUCCESS CRITERIA

What strong completion looks like

  • Similarity features and scale choices are justified.
  • K is not selected from inertia alone.
  • Clusters are profiled before interpretation.
  • PCA is treated as exploratory, not proof.
  • Recommendations follow evidence and limitations are explicit.

GUIDANCE

Support when you need a nudge

Reveal practical hints, checkpoints, and framing help without exposing a full finished solution.

Complete this project when the work is yours

Use this only after you have worked through the roadmap, milestones, and your own analysis.

KEY LEARNING OUTCOMES

Skills reinforced by this project

  • Apply preprocessing and K-Means with appropriate caution.
  • Interpret PCA and cluster profiles correctly.
  • Translate exploratory groups into defensible growth hypotheses.
  • Communicate unsupervised limitations to stakeholders.

RELATED LESSONS

Revisit published lessons that support this build

Published lesson

Clustering with K-Means and Choosing K

Open lesson→

Published lesson

Dimensionality Reduction and PCA Intuition

Open lesson→

Published lesson

Feature Scaling and Transformations

Open lesson→
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