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Customer segmentation and profitability dashboard

I segmented customers to show which groups are most valuable and where retention or reactivation should be prioritised. It shows how customer history can support targeted marketing decisions.

I calculated RFM scores using synthetic order-history data, grouped customers into readable segments and compared revenue, profit, frequency, recency and acquisition channel performance.

Commercial analyticsSynthetic data

Dataset type: Synthetic data. This label is shown clearly so the project is honest about whether the evidence is public, mock, synthetic or portfolio data.I separate source type from method because the reliability and limitations depend on where the data came from.

Customer segmentation and profitability dashboard main dashboard preview

Why this matters

Build summary

Customer value is easier to act on when customers are grouped by behaviour, not only by demographics or one-off sales.

I calculated RFM scores using synthetic order-history data, grouped customers into readable segments and compared revenue, profit, frequency, recency and acquisition channel performance.

Key numbers

15,000
customers
£4.79M
net revenue
RFM
segmentation model

What I wanted the report to help with

How I approached it

  • Make the main performance question easier to scan.
  • Separate headline totals from the detail that explains them.
  • Show enough context for a non-technical user to trust the next action.
  • Created recency, frequency and monetary value scores.
  • Grouped customers into readable portfolio segments.
  • Compared segment value by revenue, profit and acquisition channel.

Technical inspection notes

  • Built RFM-style features from order history: recency, frequency and monetary value.
  • Compared segment value using both revenue and profit so high spend did not automatically mean high commercial value.
  • Added acquisition-channel comparison to show whether some channels attract stronger customer groups.
  • Kept segment names readable so the output could support marketing actions rather than just analysis notes.

Dashboard snapshots

These snapshots show how the analysis was turned into decision-ready visuals.

Click any screenshot to inspect the dashboard evidence more closely.

Customer segmentation and profitability dashboard snapshot
Segmentation overview: Shows customer segments and value measures.
Customer segmentation and profitability dashboard snapshot
Profit by segment: Helps prioritise customer groups.
Customer segmentation and profitability dashboard snapshot
Channel value: Shows how acquisition channel relates to customer value.

What the analysis showed

  • The model segmented 15,000 customers.
  • The sample produced £4.79M net revenue.
  • Segment value varied meaningfully by acquisition channel.

What I would improve next

  • Add retention-window logic.
  • Test segment movement over time.
  • Connect campaign response data to each segment.