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Volta — User Segmentation

Stack
Pythonpandas / NumPyscikit-learnMatplotlib / Seaborn
Impact
  • 4 segments: Power 12% / Growth 24% / Casual 32% / Dormant 32%
  • Lorenz: 12% users → 41% revenue; 68% → 92%
  • +€310K/yr via segment migration scenarios

Volta — User Segmentation

Business Context

The fourth project in the optimize loop: who are the users and how do we monetize each segment? Turning analytics into money.

Hypothesis

Users split into a small number of homogeneous segments with distinct monetization patterns.

Data & Method

  • StandardScaler + KMeans.
  • Data-driven K: marginal-gain elbow, silhouette plateau K = 2–4, collapse at K = 5.
  • Lorenz-curve analysis of revenue concentration.

Insight

  • 4 segments: Power 12% / Growth 24% / Casual 32% / Dormant 32%.
  • Lorenz: 12% of users → 41% of revenue; 68% → 92%.
  • Migration scenarios (Casual → Growth, Growth → Power) yield +€26K/mo.

Impact

  • +€310K/yr through migration between segments.
  • A per-segment monetization strategy instead of an averaged approach.

Documentation

Other Volta subprojects