Ecom App Builders Research 01
Ecommerce Mobile App Statistics: Conversion, Revenue and Retention Benchmarks for 2026
See current ecommerce mobile app statistics for adoption, conversion, order value, revenue, retention and push notifications, with methodology and sources.
- Smartphone share of US online transactions
- 56.4% Adobe, 2025 holiday season
- Median app conversion lift vs mobile web
- 1.8x Poq, 21-retailer comparison
- Median in-app AOV lift
- 9% Poq, 21-retailer comparison
- Average Android ecommerce push CTR
- 3.78% Pushwoosh, Q4 2024 to Q2 2025
Editorial disclosure: This report includes anonymized first-party data supplied by MobiLoud, alongside research from other app platforms and industry data providers. Ecom App Builders has a commercial relationship with MobiLoud. Vendor datasets are identified throughout, and all calculations and editorial interpretation are our own.
Ecommerce apps don't win by replacing your mobile website or attracting every shopper. They win by giving a smaller group of high-intent customers a better place to buy again.
That pattern appears across several current datasets. In Poq's like-for-like comparison, 20 of 21 retailers converted better in their app than on mobile web. Tapcart found that customers who added an app to their relationship with a brand increased their total spend. In MobiLoud's smaller first-party sample, apps generated 24% to 65% of mobile revenue from only 2% to 16% of mobile traffic.
The opportunity is real, but it isn't automatic. Most installers never make a first purchase. Day-one retention is low. App users are also self-selecting, so a strong app-versus-web comparison doesn't prove that the app caused the entire difference.
This report brings the strongest current evidence together around the questions that matter to ecommerce operators: how widely shopping apps are used, how much revenue they can generate, how app conversion and order value compare with mobile web, what happens after installation, and how push notifications contribute to engagement and sales.
Key findings
The channel's value comes from customer depth, not universal adoption.
- 56.4%Smartphones accounted for 56.4% of US online transactions during the 2025 holiday season.
- 6.6 billionRetail apps recorded 6.6 billion downloads and 41.9 billion hours of use worldwide in 2024.
- 1.8xIn Poq's 21-brand comparison, the median app converted 1.8 times better than mobile web.
- 24% to 65%In MobiLoud's three-brand comparison, apps generated 24% to 65% of mobile revenue from 2% to 16% of mobile traffic.
- 9%Poq found a 9% median in-app AOV lift, while all three MobiLoud brands recorded lifts of at least 23.7%.
- 3.78%Ecommerce push click-through rates averaged 3.78% on Android and 3.05% on iOS in Pushwoosh's 2025 benchmark.
01
The most useful ecommerce app benchmarks
There isn't one universal ecommerce app benchmark. Reports use different samples, time periods and definitions, so the figures below shouldn't be blended into one average.
They do give you a useful starting point.
Current ecommerce app benchmark table
Thirteen current metrics from eight sources, with the source and measurement period kept beside every value.
| Metric | Current benchmark | Source and period |
|---|---|---|
| Smartphone share of US online transactions | 56.4% | Adobe, 2025 holiday season |
| Global retail app downloads | 6.6 billion | Sensor Tower, 2024 |
| Overall ecommerce app conversion rate | 2.84% | Poq platform, Jan 2025 to Jan 2026 |
| Median app conversion lift vs mobile web | 1.8x | Poq, 21 like-for-like brands |
| Retailers with higher app conversion | 20 of 21, with one at parity | Poq, 21 like-for-like brands |
| Retailers with higher in-app AOV | 80% | Poq, 21 like-for-like brands |
| Median in-app AOV lift | 9% | Poq, 21 like-for-like brands |
| Average total revenue lift associated with an app | 21.13% | Tapcart, measured through Apr 2025 |
| Ecommerce app day-one retention | 13% | Adjust, H1 2025 |
| Installers who became buyers | Fewer than 10% | AppsFlyer, Oct 2022 to Apr 2024 |
| First-time app buyers who bought again | 60% | AppsFlyer, Oct 2022 to Apr 2024 |
| Ecommerce push CTR | 3.78% Android; 3.05% iOS | Pushwoosh, Q4 2024 to Q2 2025 |
| Contextual vs generic push open rate | 14.4% vs 4.19% | Batch, Jul 2024 to Jul 2025 |
02
Mobile commerce and shopping apps are already established
The shift toward mobile commerce is no longer a forecast. Mobile is already the main way many people browse and buy online.
Adobe analyzed more than one trillion visits to US retail sites during the 2025 holiday season. Smartphones accounted for 56.4% of online transactions, up from 54.5% the previous year. On Christmas Day, mobile's share reached 66.5%.
Shopping app use is substantial too. Sensor Tower reported 6.6 billion retail app downloads and 41.9 billion hours spent in retail apps worldwide during 2024. Both figures increased for the fourth year in a row.
The category isn't growing evenly. Adjust found that global ecommerce app installs fell 14% year over year in the first half of 2025 while sessions increased 2%. Its broader 2026 trends report then recorded another 5% increase in ecommerce and shopping app sessions over the full 2025 calendar year.
Falling installs and rising sessions point to a more mature channel. The value isn't only in acquiring more app users. It's in getting more activity from the audience already there.
- Global retail app downloads
- 6.6B Sensor Tower, 2024
- Hours spent in retail apps
- 41.9B Sensor Tower, 2024
- Installs fell while sessions rose
- -14% / +2% Adjust, H1 2025 year over year
In Adjust's H1 2025 data, marketplace apps generated 60% of ecommerce sessions from only 20% of installs. Their day-one retention rate was 24.8%, compared with a 13% ecommerce average.
Amazon, Temu or another large marketplace isn't a sensible benchmark for a standalone fashion, beauty or wellness brand. The broad data shows that consumers are comfortable shopping in apps. It doesn't show that every retailer can expect marketplace-style adoption.
03
How much revenue can an ecommerce app generate?
Revenue is the question behind almost every app business case. The available research gives us three useful ways to examine it: how total revenue changes after a brand adds an app channel, how customer spend changes after someone adopts the app, and how much mobile revenue a smaller app audience can generate.
Tapcart's 2025 analysis covers 330 million orders, 120 million shoppers and $31.5 billion in Shopify revenue. Across that dataset, launching an app was associated with a 21.13% average increase in total revenue.
The customer-level result is more useful than the headline average. Customers who first bought on the web and later adopted the app increased their total spend by roughly 36% after both channels were part of the relationship. Tapcart also reported that approximately 11.2% of customers were app-first, meaning their first recorded purchase with the brand happened in the app.
These are large, observational platform results. They aren't controlled experiments. A shopper who downloads a brand's app is likely to be more engaged than the average website visitor before installing it. An app-first receipt also doesn't prove that the customer would never have bought on the web.
The defensible conclusion is that app adoption is associated with higher customer revenue, not that the app caused every additional dollar.
App share of mobile traffic vs mobile revenue
A relatively small app audience generated a disproportionate share of mobile revenue in all three examples.
View exact data
| Brand | App traffic share | App revenue share |
|---|---|---|
| Brand A, wellness | 16% | 65% |
| Brand B, luxury fashion | 2% | 35% |
| Brand C, cosmetics | 9% | 24% |
MobiLoud's first-party sample shows what that relationship can look like inside individual brands. Its 2025 Ecommerce Mobile App Benchmark Report analyzed five anonymized ecommerce and retail brands. Three included comparable app and mobile web data for Q1 2025.
Each app generated a much larger share of revenue than traffic. Brand B's app represented only 2% of mobile traffic but produced 35% of mobile revenue. Brand A's app reached a larger share of its mobile audience, but the pattern was similar: 16% of traffic produced 65% of revenue.
This doesn't prove that the apps created all of that value. The app audiences probably contained more repeat customers and fewer low-intent acquisition visits than mobile web. It does show why download volume and traffic share are incomplete measures of app performance.
A brand doesn't need to move every mobile visitor into its app. It needs to move enough valuable customers into a channel where they can buy more easily and return more often.
Average revenue lift associated with app launch by category
Fashion and beauty led the named categories, while every category in the published analysis recorded a positive average association.
View exact data
| Category | Average revenue lift |
|---|---|
| Fashion and apparel | 23.97% |
| Beauty and cosmetics | 21.31% |
| Food and beverage | 16.36% |
| Home and hobby | 11.66% |
| Health and wellness | 9.82% |
The shape of the results makes intuitive sense. Fashion and beauty brands can create frequent reasons to return through new collections, drops and early access. Food, beverages and supplements have replenishment use cases. Home and hobby purchases often follow longer cycles.
Tapcart doesn't publish the sample size for each category, so these figures are directional. They shouldn't become a revenue forecast for your store.
04
Ecommerce apps usually convert better than mobile web
Poq's 2026 platform report gives us the strongest current like-for-like conversion dataset we found.
Across 178 million app sessions and 5.05 million transactions between January 2025 and January 2026, apps on the platform converted at 2.84% overall. That provides a broad app benchmark, but the same-brand comparison is more valuable.
1.8x median app conversion lift vs mobile web
Poq compared app and mobile web conversion for 21 retailers over the same period. The brands covered four regions and more than 15 retail sectors.
Maximum observed lift: 4.2x
View exact data
| Measure | Result | Count or note |
|---|---|---|
| At least 2x lift | 33% | 7 of 21 retailers |
| At least 1.5x lift | 62% | 13 of 21 retailers |
| Higher than mobile web | 20 of 21 | One retailer was at parity |
| Maximum observed lift | 4.2x | 21-retailer comparison |
The result wasn't driven by one or two outliers. Twenty brands recorded a higher conversion rate in-app, while the remaining brand was at parity. Seven brands, or 33% of the sample, achieved at least a 2x lift. Thirteen, or 62%, achieved at least a 1.5x lift.
The range still matters. The strongest result was 4.2x, while several retailers landed between 1x and 1.4x. Category, traffic quality, price point, purchase frequency and the maturity of the app channel can all influence the comparison.
Three MobiLoud conversion examples show the wider range
Brand C sits close to Poq's median. Brands A and B are major outliers, not universal benchmarks.
View exact data
| Brand | Mobile app | Mobile web | Observed difference |
|---|---|---|---|
| Brand A, wellness | 9.06% | 1.14% | 8.0x |
| Brand B, luxury fashion | 2.56% | 0.23% | 11.1x |
| Brand C, cosmetics | 2.79% | 1.49% | 1.9x |
Brands A and B have unusually low mobile web conversion rates, and their apps likely concentrate loyal customers. That helps explain the gap.
There isn't one honest answer to the question, "What is the average ecommerce app conversion rate?" A platform-wide app rate, a median app-versus-web lift and a brand case study measure different things.
For planning, the 1.8x median across 21 same-brand comparisons is a better reference than the old claim that apps convert three to five times better than mobile websites. That older figure is usually traced to research from 2017 and 2018, and often uses buyers divided by product viewers rather than purchases divided by sessions.
Even the 1.8x figure isn't a forecast. Compare your app with your own mobile website over the same dates and with consistent definitions. Where possible, separate existing customers from new shoppers so a change in audience mix isn't mistaken for a pure channel effect.
05
Average order value and revenue per customer
Conversion is only one part of the revenue equation. Order value and purchase frequency determine how valuable the customer relationship becomes over time.
In Poq's 21-brand comparison, 80% of retailers had a higher average order value in-app. The median lift was 9%.
Poq, 21 like-for-like retailers. Vendor/platform dataset.
Average order value in three MobiLoud examples
The direction matches Poq's broader finding, but the uplifts in this smaller sample were much larger than the 9% median.
View exact data
| Brand | Mobile app | Mobile web | App uplift |
|---|---|---|---|
| Brand A, wellness | $97.85 | $63.83 | 53.3% |
| Brand B, luxury fashion | $103.57 | $59.17 | 75.0% |
| Brand C, cosmetics | $54.78 | $44.29 | 23.7% |
All three brands recorded a higher app AOV, with uplifts ranging from 23.7% to 75%. The direction is consistent across both datasets, but the size of the MobiLoud uplifts is well above Poq's median.
Average revenue per user shows how conversion, order value and return frequency can compound.
Revenue per user by channel
The figures describe a large commercial difference between the audiences. They aren't a clean estimate of the revenue caused by the app.
| Brand | App revenue per user | Mobile web revenue per user | Observed difference |
|---|---|---|---|
| Brand A, wellness | $25.27 | $0.73 | 34.6x |
| Brand B, luxury fashion | $39.63 | $1.45 | 27.3x |
| Brand C, cosmetics | $10.43 | $3.23 | 3.2x |
The figures are still useful because they show why app adoption among existing high-value customers can matter even when the app never becomes the brand's largest traffic channel. Keep that distinction visible when you build an ecommerce mobile app ROI case.
06
Installation, first purchase and repeat purchase
The strongest revenue and conversion numbers describe people who actively use an app. Getting someone to become one of those customers is the hard part.
Adjust recorded 13% day-one retention for ecommerce apps in the first half of 2025. AppsFlyer found that fewer than 10% of installers became buyers, with the average first purchase happening 3.6 days after installation. Once that first purchase happened, 60% of first-time app buyers made at least one more purchase.
Three thresholds, measured in separate datasets
Installation creates an opportunity. The first few sessions and the first purchase determine whether it becomes a valuable customer relationship.
- 0113%
Install and early activity
Day-one retention
Adjust, H1 2025 - 02Fewer than 10%
First purchase
Installers who became buyers
AppsFlyer, Oct 2022 to Apr 2024 - 0360%
Repeat purchase
First-time app buyers who bought again
AppsFlyer, Oct 2022 to Apr 2024
View exact data
| Stage | Benchmark | Definition | Source and period |
|---|---|---|---|
| Install and early activity | 13% | Day-one retention | Adjust, H1 2025 |
| First purchase | Fewer than 10% | Installers who became buyers | AppsFlyer, Oct 2022 to Apr 2024 |
| Repeat purchase | 60% | First-time app buyers who bought again | AppsFlyer, Oct 2022 to Apr 2024 |
Your onboarding should do more than introduce the interface. It needs to get the customer signed in, surface the value of using the app and shorten the route to a meaningful action, ideally a purchase, saved item or loyalty interaction.
This also changes how you should evaluate app promotion. Cost per install is easy to optimize, but a cheap install that never buys has little value. Track cost per first-time buyer and revenue per acquired user alongside CPI.
Source: Adjust, Q1 2025. These acquisition costs only become useful when paired with buyer conversion and customer value.
07
Engagement and retention after installation
App users tend to return more frequently, but retention still has to be earned.
Poq reported that app users opened between 2 to 8x as many sessions as mobile web visitors across its merchant base. It didn't publish a median session-frequency lift, so the range shouldn't be presented as one benchmark.
Engaged sessions per user
App audiences returned 1.75 to 4.3 times as often as mobile web visitors in these three examples.
View exact data
| Brand | Mobile app | Mobile web | Observed difference |
|---|---|---|---|
| Brand A, wellness | 4.7 | 1.1 | 4.3x |
| Brand B, luxury fashion | 3.1 | 0.8 | 3.9x |
| Brand C, cosmetics | 2.1 | 1.2 | 1.75x |
All three app audiences generated more engaged sessions per user. Their average app session lasted between 4 minutes 58 seconds to 6 minutes 41 seconds, although MobiLoud didn't publish directly comparable mobile web session-duration figures.
Adjust's broader ecommerce benchmark recorded an average session length of 9.89 minutes in H1 2025, down slightly from 10.23 minutes in 2024. Average revenue per monthly active user was $7.80 globally in 2024, with large differences by market.
These figures are useful context, but the most important retention benchmark is your own cohort curve. Track the percentage of each install cohort that returns after 7, 30 and 90 days. Then connect that activity to purchases, not just opens.
An app lives on the customer's phone, can keep them signed in and can bring them back through push notifications. Those advantages create more opportunities to buy. They don't create a reason to return by themselves.
08
Push notification benchmarks for ecommerce apps
Push notifications are one of the biggest functional differences between a mobile website and an installed app. They give you a direct way to bring an opted-in customer back without paying a per-message carrier fee.
The benchmark depends heavily on what you send.
Pushwoosh ecommerce CTR
Android recorded a slightly higher average click-through rate.
| Measure | Result |
|---|---|
| Android | 3.78% |
| iOS | 3.05% |
Batch campaign open rate
Contextual campaigns opened at more than three times the generic rate.
| Measure | Result |
|---|---|
| Contextual | 14.4% |
| Generic | 4.19% |
Pushwoosh's 2025 study analyzed more than 600 apps across over 20 industries. It calculated CTR as opens divided by recipients, weighted campaigns by recipient count and excluded silent and transactional notifications.
Batch's 2025 benchmark shows how much message context can change the result. Contextual campaigns were triggered by an action or defined moment. In ecommerce, that could be an abandoned cart, a price drop, a back-in-stock event or a replenishment reminder. Generic campaigns were sent manually to broad groups with little or no targeting.
CTR and open rate are different measures from separate datasets, so the four values shouldn't be treated as one comparison. The shared lesson is that relevance and timing matter.
Batch also reported a 61% overall push opt-in rate, split between 67% on Android and 56% on iOS. These are cross-industry figures, not ecommerce-specific benchmarks.
Airship's 2025 benchmark adds a behavioral comparison. Across more than nine billion users, push-opted users recorded 13% more purchases than opted-out users. Among top-performing apps, the observed difference reached 39%. Opt-in status isn't randomly assigned, so this is a relationship worth measuring, not guaranteed causal lift.
09
What push-attributed revenue can look like
MobiLoud published one-month push revenue examples for three brands. The examples weren't necessarily measured in the same calendar month.
One-month push-attributed revenue
Abandoned-cart automation represented 45.9% of the combined attributed revenue across the three published examples.
View exact data
| Brand | Total push revenue | Abandoned-cart revenue | Cart share |
|---|---|---|---|
| Brand A, wellness | $31,176.35 | $14,491.37 | 46.5% |
| Brand B, luxury fashion | $7,494.60 | $5,765.60 | 76.9% |
| Brand E, cosmetics | $15,804.23 | $4,725.78 | 29.9% |
total attributed revenue, including $24,982.75 from abandoned-cart flows
The mix varied substantially. Cart automation generated more than three-quarters of Brand B's push revenue but less than one-third of Brand E's. Push can work both as an automated lifecycle channel and as a campaign channel, but the message needs to match the customer and moment.
Push-attributed revenue isn't the same as incremental revenue. Attribution windows can claim purchases that would have happened anyway. Use campaign reporting for day-to-day optimization, but add holdout groups where possible if you want to understand the sales caused by a notification.
These are gross attributed revenue examples. They aren't ROI.
10
The ecommerce app metrics your brand should track
Industry statistics help you set expectations. Your own measurement system tells you whether the app is working.
An eleven-metric measurement framework
Use like-for-like definitions and periods, then segment new, existing and high-value customers before drawing conclusions.
| Metric | What it tells you | Recommended comparison |
|---|---|---|
| App share of mobile users | How much of your mobile audience uses the app | App users divided by app plus mobile web users |
| App share of mobile revenue | How important the app is as a sales channel | Compare with app share of mobile users |
| Conversion rate | How efficiently sessions become orders | App vs mobile web over the same dates |
| Average order value | Whether app customers build larger baskets | App vs mobile web by customer segment |
| Revenue per active user | The combined effect of conversion, AOV and frequency | App vs mobile web using consistent active-user definitions |
| First-purchase rate | Whether installs become customers | Buyers divided by new installers within a fixed period |
| Engaged sessions per user | Whether app customers return more often | App vs mobile web using the same engagement definition |
| Day-7, day-30 and day-90 retention | Whether installs become active users | Cohorts based on install date |
| Repeat purchase rate | Whether app buyers buy again | App buyers vs comparable known web customers |
| Push opt-in and CTR | How much of the audience is reachable and responsive | iOS and Android, split by campaign type |
| Push incremental revenue | Whether notifications create additional orders | Holdout group vs messaged group |
Segmenting matters. New customers, existing customers and known high-value customers won't behave the same way. If your app audience mainly consists of loyal customers, a simple app-versus-web comparison will overstate the incremental effect of the app.
For the strongest business case, add cohort analysis. Compare total customer spend and purchase frequency before and after app adoption, then compare that change with a similar group that didn't adopt the app. That gives you a more credible view than assigning every in-app order to the app.
11
What the statistics say overall
The current evidence doesn't support the idea that every ecommerce brand needs an app, or that an app will automatically produce a fixed conversion or revenue lift.
It does support a clearer conclusion.
Mobile shopping is established. Retail apps serve billions of users and account for tens of billions of hours of activity. Across current vendor datasets, app users usually convert better, often place larger orders and return more frequently than mobile web visitors.
The audience is the key. Your website remains the broad acquisition surface. It serves new and occasional visitors arriving from search, ads, social media and direct links. Your app serves a narrower group that has chosen a closer relationship with your brand.
That smaller audience can become a major revenue channel. MobiLoud's examples show apps producing 24% to 65% of mobile revenue from 2% to 16% of mobile traffic. Poq's wider comparison shows the direction of the conversion advantage across 21 retailers. Tapcart's cohort data associates app adoption with higher total customer spend.
The challenge sits between download and first purchase. Most installers don't buy, and most don't return the following day. You need a clear reason to install, a smooth first-session experience and an ongoing plan for relevant push, loyalty, content or replenishment.
The best way to use these benchmarks is to build a conservative business case, compare the app with your own mobile website and measure customer behavior before and after adoption. An app is most likely to work when you already have meaningful mobile traffic, repeat customers and natural reasons for those customers to return.
If you're still deciding whether the channel fits, start with the app readiness guide. The ecommerce mobile app builder guide explains the main implementation model.
Methodology
How this report was assembled
We reviewed current original reports, platform datasets and first-party benchmark pages available as of August 10, 2026.
We prioritized sources that publish the measurement period, metric definition and sample. Vendor data is included where it contains useful transaction, usage or campaign data, but it's identified as platform data. A vendor's customer base isn't a random sample of every ecommerce brand.
The first-party MobiLoud figures come from its 2025 Ecommerce Mobile App Benchmark Report. MobiLoud analyzed five anonymized brands across wellness and pharmacy, luxury fashion, cosmetics and cannabis. The main dataset covers Q1 2025, with some supplemental examples from early Q2.
Three MobiLoud brands included comparable app and mobile web performance tables. Three included complete one-month push revenue and abandoned-cart figures. Those push examples aren't necessarily from the same month.
Ecom App Builders calculated the conversion multiples, AOV uplifts, session multiples, combined push revenue and abandoned-cart share shown on this page from MobiLoud's raw tables. Figures are rounded where appropriate.
Sources
Evidence used in this report
Vendor and platform datasets are labeled in the report. Source periods and sample notes remain close to the figures they support.
- 01Poq, The Revenue Case for Mobile Apps
Published 2026; 178 million app sessions, 5.05 million transactions and $454 million in GMV from January 2025 to January 2026. Like-for-like comparisons cover 21 retailers.
- 02Tapcart, The Mobile App Incrementality Story
Published September 2025; 330 million orders, 120 million shoppers and $31.5 billion in Shopify revenue, measured through April 30, 2025.
- 03MobiLoud, 2025 Ecommerce Mobile App Benchmark Report
First-party anonymized ecommerce brand data, primarily from Q1 2025.
- 04Adobe, 2025 US Holiday Shopping Season
Published January 2026; analysis of more than one trillion visits to US retail sites.
- 05Sensor Tower, State of Mobile Retail 2025
Published March 2025; global retail app downloads and time spent during 2024.
- 06Adjust, Shopping App Trends and Performance Insights 2025
Published October 2025; ecommerce app installation, session, retention, CPI and revenue benchmarks for 2024 and H1 2025.
- 07Adjust, Mobile App Trends 2026
Published 2026; global app performance benchmarks for 2025.
- 08AppsFlyer, State of Ecommerce App Marketing 2024
Covers 1,600 ecommerce apps, 4.6 billion downloads and 21.5 billion remarketing conversions from October 2022 to April 2024.
- 09Pushwoosh, Push Notification Benchmarks 2025
More than 600 apps across over 20 industries; ecommerce CTR period Q4 2024 to Q2 2025.
- 10Batch, The Great Push Notifications and Mobile Engagement Benchmark 2025
800 billion messages sent to 1.2 billion unique visitors from July 2024 to July 2025.
- 11Airship, 2025 Mobile App Push Notification Benchmarks
January to December 2024 data from more than nine billion users across thousands of apps.
Claims reviewed but excluded
We left these claims out because they weren't defined clearly enough, conflicted with raw data or overstated causation.
| Excluded claim | Reason |
|---|---|
| Apps convert 3x to 5x better than mobile web | Usually traced to older 2017-2018 research with a different conversion denominator |
| App customers have 2.8x to 7x higher LTV | No current, consistently defined source supporting one universal range |
| In-app cart abandonment is approximately 20% | The current primary source and definition couldn't be verified reliably |
| Push recovers up to 22% of abandoned carts | Recovery event, sample and attribution window weren't sufficiently defined |
| Push notifications achieve 90% open rates | Not representative of typical ecommerce marketing campaigns |
| MobiLoud Brand D generated $360,496 from push | The supplied report's summary conflicted with its underlying table |
| Gross attributed revenue divided by app cost is ROI | Gross revenue isn't incremental profit and shouldn't be described as ROI |
| App-first customers are automatically net-new | First purchase in-app doesn't prove the customer would never have bought elsewhere |