Your app is bringing in orders, customers are using it, and the channel report is heading in the right direction. When you review the investment, though, there’s another question to answer: how much of that revenue did the app add to your business?
If a loyal customer moves a $100 order from mobile web into your app, the app records $100 without necessarily increasing your sales. If that customer starts buying more often or stays with your brand longer, the extra spending may be incremental revenue.
Separating those effects takes more than an app sales report. You need a reasonable estimate of what the same customers would’ve spent without the app. This guide explains the main ways to build that comparison, where each method falls short, and how to turn the result into a useful business measure.
The Incremental Revenue Calculation
The calculation compares two outcomes for the same customers: what they spent with access to your app, and what they would probably have spent without it.
Incremental app revenue =
total customer revenue across channels with the app
- expected revenue from those customers without the appYou can observe the first number in your sales records. The second is an estimate, usually called a baseline, and the quality of that estimate determines how much confidence you can place in the result.
Your baseline might come from customers’ spending before adoption, a comparable group that didn’t adopt, or a randomized test. The right choice depends on the records you have and whether you can control who receives an app promotion.
Keep app-attributed revenue and incremental revenue as separate lines in every report.
Five Ways to Measure Incremental Revenue
The right comparison depends on the customer data you have and whether you can run a controlled test. These methods offer different levels of confidence; you don’t need to use all five.
1. Compare Customers Before and After Adoption
Your customers’ purchase history provides a starting point. Looking at what the same people spent before and after installing the app can reveal a change that a channel sales report would miss.
To make that comparison, group customers by the month they first became active app users, then compare their spending across all channels over equal periods. A hypothetical six-month comparison might look like this:
| Period | Average revenue per customer |
|---|---|
| Six months before adoption | $240 |
| Six months after adoption | $300 |
| Observed increase | $60 |
The apparent lift is 25%.
That doesn’t prove the app caused the full increase. Customers may install during a period of growing loyalty, after a first purchase, or in response to a promotion. Seasonality can also distort the comparison.
Accounting for seasonality and how long each person has been a customer helps make the comparison more useful. Breaking the change into purchase frequency and order value also shows where the extra spending came from. Even then, you still need to consider whether similar customers increased their spending without installing the app.
2. Compare Matched Customer Groups
A before-and-after comparison can credit the app for growth that was happening anyway. Perhaps a seasonal promotion increased spending across your whole customer base.
Comparing app adopters with similar customers who didn’t install gives you a way to account for some of that wider change.
Match them using factors that predicted spending before adoption, such as order count, recency, category, loyalty status, market, acquisition source, and prior revenue.
Then calculate the difference in how both groups changed:
Estimated incrementality =
change among app adopters - change among comparable non-adoptersIf adopters increased from $240 to $300 while the comparison group increased from $235 to $255, the estimated app-associated lift is $40 per customer rather than $60.
Matching reduces bias, but the groups may still differ in ways your records don’t capture. A customer who chooses to install may already be more interested in your brand. That makes the result an estimate of app-associated lift, rather than proof that the app caused the whole difference.
3. Use Randomized App-Promotion Holdouts
If you’re planning an app promotion, you have an opportunity to set up the comparison before customers respond. Randomly deciding who receives the promotion reduces the selection bias that makes existing app users difficult to compare with everyone else.
Split an eligible audience into a group that receives a sustained app promotion and a holdout that doesn’t. Measure total customer revenue across all channels, not only app orders.
The result captures the effect of your promotion and adoption strategy together, including any launch incentive. It doesn’t isolate the app’s effect on each person who installs. Some people in the promoted group won’t install, and some in the holdout may find the app on their own.
The test needs enough time to capture repeat behavior. Otherwise, you may mainly be measuring purchases encouraged by a launch incentive, before you know whether those customers will return.
4. Test Individual Push Campaigns With Holdouts
Once customers are using the app, you may want to understand which activities encourage additional purchases. A cart reminder, for example, can receive credit for an order the customer was already planning to complete.
A campaign holdout helps you test that effect. A randomly selected share of eligible app users doesn’t receive the notification, giving you a comparison with those who do.
Compare total orders and contribution during a defined window. Don’t rely only on last-click push attribution, which can credit orders that would have happened anyway.
The effect can vary by message type, so cart reminders, restock alerts, replenishment messages, and broad promotions deserve separate tests. Results from one successful campaign won’t tell you the incremental value of the entire push program.
5. Compare App, Web, and Total Revenue Trends
You may not have a reliable way to recognize the same customer across your app and website. In that case, comparing app, mobile web, and total revenue over time can still show whether app growth coincides with wider business growth.
Look for whether total mobile revenue rises as app share grows, whether mobile web declines, and how the pattern compares with seasonality and prior periods.
This is weaker evidence because many things change at once. Marketing spend, promotions, inventory, site performance, and economic conditions can all affect revenue.
Use aggregate analysis as a directional check, not a precise causal estimate.
What Current Platform Research Shows
Published app research faces the same measurement challenges. Provider studies can reveal patterns across many retailers, but their findings depend on whether they can follow individual customers or only compare channel totals.
Tapcart’s 2025 analysis examined 330 million orders and $31.5 billion in measured revenue. It reported a 21.13% average total revenue lift associated with app adoption, while web-first customers who later adopted the app increased total spend by about 36%.
Poq’s 2026 report found no aggregate mobile web cannibalization signal across five brands tracked for 13 months. The report also states that its data can’t link individual users across app and web, which limits causal claims.
These studies give you context for your own results, with limitations to keep in view. Both providers also have a commercial interest in app adoption. Your own customer comparison remains the basis for estimating what the app adds to your business.
What the Added Revenue Means for Profit
Additional orders bring additional costs. An app might encourage customers to buy more often, but heavy discounts or expensive fulfillment can leave little of that revenue available to cover the cost of running the channel.
Contribution margin accounts for product cost, discounts, returns, fulfillment, payment fees, and other variable expenses. Applying it to your incremental revenue estimate shows what those extra sales contribute before app operating costs:
Incremental contribution = incremental revenue × contribution marginFrom there, subtract the app’s setup and operating costs for the period you’re evaluating. That connects the revenue estimate to the investment decision: whether the additional contribution covers what you’re spending on the app.
Common Errors in Incremental Revenue Estimates
A calculation can be mathematically correct and still give you a misleading answer. The problem is often in what you counted or who you compared, especially when loyal customers are more likely to install in the first place.
These choices can overstate the app’s impact:
- Treating every app order as new revenue.
- Comparing loyal app users with all mobile website visitors.
- Counting app conversion, order-value, and frequency lifts that overlap.
- Using last-click push attribution as proof of incrementality.
- Ignoring revenue that moves out of mobile web.
- Measuring only a launch period with unusual incentives.
Document every assumption and limitation. A conservative estimate that leadership trusts is more useful than a precise-looking number built from weak comparisons.
Tracking the App’s Impact Over Time
An incrementality estimate becomes more useful as you see how it changes over time. Early gains may fade once a launch offer ends, while improvements in repeat purchasing can take several months to become visible.
Keeping app revenue, customer spending, and contribution together in a regular report helps you understand those changes. For example, rising app sales with flat total spending per customer may indicate that existing orders are moving between channels. The measures below give you that wider view:
| Metric | What it helps you understand |
|---|---|
| App-attributed revenue | How much revenue is processed through the app |
| App share of mobile and total revenue | How the app’s role in your channel mix is changing |
| Total revenue per customer by cohort | Whether the same customer groups are spending more across your business |
| Orders per customer | Whether customers are buying more frequently |
| Estimated incremental revenue | How much revenue exceeds your estimate of spending without the app |
| Incremental contribution | How much of that additional revenue remains after variable costs |
The comparison becomes more informative as you follow the same customer groups over several purchase cycles. Consistent definitions and time periods help you see whether the change is holding up.
The Bottom Line
A growing app sales report tells you that customers are using the channel. A credible incrementality estimate tells you whether their relationship with your business is becoming more valuable.
You won’t get a perfect view of what would’ve happened without the app. But a consistent baseline, a fair comparison, and a clear view of contribution will give you a much stronger basis for deciding what to invest in next.
