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E-commerce funnel, conversion and channel ROI dashboard

I built a funnel and ROI dashboard showing where customers drop out and which channels stay profitable after marketing costs and returns. It focuses on traffic quality, not vanity volume.

I analysed 90,000 synthetic sessions using session-to-order conversion, revenue per session, return effect, channel spend and gross profit after marketing. Python summaries were shaped into Power BI-ready outputs.

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.

E-commerce funnel, conversion and channel ROI dashboard main dashboard preview

Why this matters

Build summary

Marketing traffic only matters if it converts profitably. This project looks beyond visits and asks which channels actually pay back.

I analysed 90,000 synthetic sessions using session-to-order conversion, revenue per session, return effect, channel spend and gross profit after marketing. Python summaries were shaped into Power BI-ready outputs.

Key numbers

90,000
sessions
4.9%
order conversion
£262,561
net revenue

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 session, basket and order-stage summaries.
  • Calculated channel-level conversion and revenue per session.
  • Included returns and marketing spend in ROI logic.

Technical inspection notes

  • Modelled the funnel from session to order so drop-off could be reviewed by channel.
  • Calculated revenue per session and profit after marketing to avoid overvaluing high-traffic channels.
  • Included returns in the commercial view because channel quality can change after refund behaviour is included.
  • Used Python summaries shaped into Power BI-ready outputs for cleaner dashboard logic.

Dashboard snapshots

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

Click any screenshot to inspect the dashboard evidence more closely.

E-commerce funnel, conversion and channel ROI dashboard snapshot
Funnel overview: Shows funnel volume, conversion and commercial result.
E-commerce funnel, conversion and channel ROI dashboard snapshot
Channel conversion: Helps compare traffic quality.
E-commerce funnel, conversion and channel ROI dashboard snapshot
Channel ROI: Shows which paid channels pay back.

What the analysis showed

  • The synthetic dataset included 90,000 sessions.
  • Order conversion was 4.9%.
  • Net revenue was £262,561.

What I would improve next

  • Add attribution windows.
  • Add device and product-category cuts.
  • Add confidence intervals around channel differences.