Interactive sales lift demo · sample data
Sales Lift Explorer
Sample data
Sample data

From campaign spend to business impact

Campaign overview

Compare sales with a baseline. Then see what remains after marketing costs.

October 1–14, 2026Synthetic retail campaign data · USD
All campaigns

Sales above baseline

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Compared with the illustrative baseline

Sales lift vs. baseline

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Observed sample sales

Campaign spend

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Marketing costs for this selection

Net contribution

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Incremental gross profit minus campaign spend

Sales with context

All campaigns · October 1–14, 2026

Observed salesIllustrative baseline

Select a point to inspect a day.

Recent campaigns

Select a campaign to update the chart and explore its budget.

CampaignSalesLiftNet contribution

Sorted by net contribution. Higher sales do not always mean a profitable campaign.

Budget scenario · One campaign

Explore a campaign

Observed sales
Marketing spend

Choose Budget scenario to explore a spending change.

Illustrative data · October 1–14, 2026 · USD. Difference from an assumed baseline does not establish causation.

Follow the money.

  1. Choose a channel or product. The headline figures and trend update together.
  2. Explore a campaign. Select its name to compare observed sales with the assumed baseline and see whether its incremental gross profit covers marketing spend.
  3. Try a budget change. The sandbox shows an illustrative scenario. Reset the scenario to return to the campaign's original budget.

Everything here uses synthetic data. No campaign is changed and no budget is spent.

How to read this demo

All figures are fictional and expressed in USD. The baseline is an assumed comparison, not a measured control group. The difference does not establish that marketing caused the sales change.

Sales above baseline = observed sales − baseline salesSales lift (%) = sales above baseline ÷ baseline × 100Incremental gross profit = sales above baseline × product marginNet contribution = incremental gross profit − campaign spend

The budget sandbox uses a fixed response curve with diminishing returns. It is a learning tool, not a trained model or a forecast of real results. Product margins are illustrative.

Daily sales figures

DateBaselineObservedDifference