The Mobile App Revenue Case

How Native Commerce Apps Drive Incremental GMV for Enterprise Retailers

Poq Commerce | May 2026

Executive Summary

Mobile commerce accounts for 59% of ecommerce revenue globally, reaching $2.5 trillion in 2025 (eMarketer). In the US alone, mobile retail hit $710B. In the UK, over 70% of online retail orders are placed on mobile devices. Yet the dominant commerce platforms do not offer native mobile app capabilities as part of their core stack. Third-party providers exist in each ecosystem, but for merchants it remains a separate procurement, a separate integration, and a separate support relationship. Conversion rate, order value, and customer lifetime value are left on the table.

This paper presents evidence from Poq’s own platform data ($454M in app GMV across 80+ merchant apps, January 2025 to January 2026) alongside independent industry research to quantify the revenue impact of native commerce apps. The core findings:

• Apps convert at 1.8x mobile web across our merchant base (median, across 21 brands where we have both app and mobile web data for the same period, spanning UK, US, Europe, and Australia)
• All measured merchants convert better in-app than on mobile web, with top performers exceeding 4x
• ~70% of app revenue is incremental after subtracting a conservative mobile web baseline those users would have generated
• 20% of app users are still active after 12 months, with best-in-class brands retaining 40%, driven by push notifications, an app-exclusive channel
• No aggregate cannibalisation signal: across 5 brands measured over 13 months, mobile web revenue does not decline as app revenue grows

For enterprise retailers and the platforms that serve them, the implication is significant: native app capabilities drive measurable, incremental revenue that mobile web alone cannot deliver.

1. The Mobile Conversion Gap

There is a structural problem in mobile commerce. Smartphones generate the majority of retail traffic globally (79% on Shopify, approaching 80% in the UK) but convert at roughly half the rate of desktop. The data has been consistent for nearly a decade:

Contentsquare’s 2026 Digital Experience Benchmark, based on 99 billion sessions across 6,500 websites, confirms the gap: mobile web conversion at 2.03% vs desktop at 3.81%. Criteo’s Global Commerce Review established the 3-5x app-vs-web conversion advantage in 2018, drawing on data from 5,000+ retailers across 80 countries. Button’s cross-platform studies land at a similar 3x.

The gap extends to every downstream metric. Cart abandonment runs 80-86% on mobile web vs ~20% in-app (Barilliance, SaleCycle 2024-25). Average order values are materially higher in-app (Criteo’s matched-shopper data shows up to 17% higher in-app AOV). Customer lifetime value is consistently higher in-app: Think with Google research shows app customers purchase 33% more frequently and deliver multiples of the LTV of web-only customers.

A 4x gap in cart completion and a 3x gap in conversion translate directly to GMV. For every merchant losing 85% of mobile web carts, the revenue left on the table is substantial.

The gap is also persistent. Mobile’s share of commerce continues to grow (up 21.1% in 2024 per Oberlo/Statista), while mobile web conversion rates have stagnated. Consumer expectations have shifted: 74% of global consumers say they are likely to use a retailer’s mobile app when shopping in-store (Airship/Sapio Research, 11,000 consumers across 10 countries). Retailers in the Digital Commerce 360 Top 1000 who have apps rank at a median of #209; those without, #584.

2. How Native Apps Close the Gap

The conversion advantage comes from five structural differences between native apps and mobile web browsers, and they compound.

1. Persistent authentication and saved payment. App users log in once. Subsequent purchases use saved addresses, stored payment methods, and biometric checkout (Face ID, fingerprint). Mobile web users re-enter credentials, re-type card numbers, navigate 3D Secure flows. The friction gap shows up directly in cart abandonment: ~20% in-app vs 80-86% on mobile web.
2. Push notifications: a demand-creation channel with limited web equivalent. While iOS added basic web push support in iOS 16.4, adoption remains minimal and the capability cannot match native push for rich content, reliability, or opt-in rates. Native apps can reach users directly, at zero marginal cost, with personalised triggers: abandoned cart reminders, price drops, back-in-stock alerts, flash sales. The data on push is striking:

• Opt-in rates: 75% Android, ~52% iOS (Pushwoosh 2025)
• A single push notification increases 90-day retention by 120% (Airship 2025 Mobile App Push Notification Benchmarks)
• Automated push drives 21% of push-attributed orders from just 2.9% of push volume (Omnisend 2024)

Push is arguably the single most important app advantage. It creates demand rather than merely capturing it. There is no web equivalent to this capability.

3. Performance and session depth. Native apps load from device storage, not over the network. App users spend 64% more time per session than mobile web visitors (Contentsquare 2024 Digital Experience Benchmark, 43+ billion sessions). More time browsing means more products discovered, more items added to cart, and more transactions completed.
4. First-party data. Apps provide authenticated, deterministic user identity, login-based rather than cookie-based. Behavioural signals (browse patterns, wishlists, dwell time) are collected with full user consent and are immune to browser-level privacy changes (Safari ITP, Chrome Privacy Sandbox). This data feeds personalisation engines and retargeting audiences with higher match rates than typical email lists deliver. Google and BCG found that mature use of first-party data delivers 2.9x revenue uplift and 1.5x cost savings. As commerce platforms invest heavily in AI-powered personalisation and search, native app data is the highest-fidelity training signal available: persistent identity, in-app behaviour, push response, and the offline-to-online bridge connecting app usage to physical store visits. All without cookie consent friction.
5. Session frequency. App users return more often. Our platform data shows session frequency multipliers of 2-8x vs mobile web. Google’s research confirms app customers purchase 33% more frequently. The app icon on the home screen is a persistent, zero-cost brand touchpoint, always visible, always one tap away.

These five mechanisms compound. A user who returns more often (frequency), sees more products (session depth), encounters fewer checkout barriers (saved payment), and receives timely nudges (push) is structurally more likely to convert, and to convert at a higher basket value.

3. What the Data Shows: Poq Platform Evidence

We operate a multi-tenant native app platform serving 80+ merchant apps across the UK, US, Europe, and Australia. Our transactional data is held in our central analytics environment, aggregated from in-app SDK data across the customer base. This section presents what we observe at scale.

3.1 Platform Performance

Between January 2025 and January 2026, the Poq platform processed:

• 178 million app sessions
• 5.05 million transactions
• $454 million in app GMV
• Platform CVR: 2.84% | Platform AOV: $89.99

Peak performance came during BFCM (November 2025): $1.95M in daily GMV, 3.63% conversion rate, 21,100 daily transactions. December sustained the momentum at $1.74M/day.

iOS dominates revenue in both regions (73% of EMEA app revenue, 85% in North America), with iOS users converting at 3.67% in EMEA (the highest geographical segment) and carrying a ~25% AOV premium over Android.

3.2 App vs Mobile Web: Like-for-Like Comparison

For 21 brands where we have both app data (from our platform) and mobile web data (from client-shared analytics), we ran a direct, same-period comparison. This spans the UK, US, Europe, and Australia across 15+ retail sectors.

Median CVR lift: 1.8x. All 21 brands show in-app conversion at or above mobile web.

The range matters as much as the median. The top tier (footwear, family lifestyle, loyalty retail) exceeds 3x because saved payment and one-tap checkout eliminate friction on repeat purchases. The mid-tier (premium fashion, beauty, intimates, homewares) clusters at 1.8-2.5x across both US and UK. Even in considered-purchase categories like luxury fashion and premium denim, the lift holds at 1.4x.

Twenty of twenty-one brands convert strictly better in-app, with the remaining brand at parity. Seven brands (33%) show a CVR lift of 2.0x or higher, and thirteen (62%) a lift of 1.5x or higher. The conversion advantage is consistent across geographies: US brands average 1.9x, UK brands 1.8x.

This comparison excludes two brands in our broader analysis whose app use cases differ fundamentally from their web traffic profiles (one content/engagement-led, one gifting/seasonal where web and app audiences have very different purchase intent). They are noted in methodology rather than included in the table, for clarity.

Eighty percent of brands also show higher AOV in-app (median +9%), with the strongest lifts in premium lifestyle (+19%), footwear/premium (+17%), and US premium fashion (+16%). The exceptions are high-frequency, low-AOV categories where the app advantage shows up in conversion rather than basket size.

3.3 How This Compares to Industry Benchmarks

Our 1.8x median CVR lift is lower than the headline figures reported by industry benchmarks. The reasons are specific and worth understanding.

Criteo’s widely cited 3-5x (Global Commerce Review, 2018) uses a non-standard denominator: buyers divided by product page viewers, not buyers divided by sessions. This produces a higher figure because it excludes homepage-only visits, browse-without-clicking sessions, and bounces from the baseline. It also draws from 2017-2018 data and has not been updated. Other vendor benchmarks measure conversion rate per user rather than per session, which similarly inflates the app figure because app users are a self-selected, high-intent cohort. Tapcart’s current published figure is 2.3x (down from earlier claims of 3.2x), based on same-brand comparison across their Shopify merchant base, methodologically closer to our approach.

Our comparison uses sessions as the denominator, the same brand on both sides, the same time period, and the same currency. It controls for brand strength, marketing mix, and seasonal variation. The result, 1.8x across 21 brands in four geographies, is the most conservative published methodology that we’re aware of, and the fact that every brand shows a lift under this methodology makes the finding more robust, not less.

Tapcart’s Incrementality Index, based on 330M orders and $31.5B in measured revenue, reports a +21.13% total revenue lift from app adoption, with most verticals clustering at +16-24%. This figure accounts for CVR, AOV, and frequency combined, not just CVR alone. Our 1.8x CVR lift is consistent with a total revenue impact in that range once the compounding effects of higher AOV (+9% median) and increased session frequency (2-8x) are factored in.

4. Decomposing Incrementality

4.1 The Honest Challenge

The hardest question in mobile commerce is not whether apps convert better. The data is overwhelming. The question is whether app revenue is incremental or whether apps simply cannibalise the best customers from other channels.

We are transparent about the limitations. Poq does not own the analytics relationship. Our in-app SDK data is aggregated across the customer base. No client has implemented a cross-platform unique user ID linking app users to web users. User-level difference-in-differences analysis (the gold standard) is not possible with our data.

But aggregate analysis is possible, and the results are clear.

4.2 No Cannibalisation Signal

We tracked monthly app and mobile web revenue for 5 brands over 13 months. If app cannibalises web, we should see web revenue decline as app revenue grows. We don’t.

One homewares/lifestyle brand holds a stable app share throughout the year. Both channels grow together, both peak at BFCM, and web revenue in early 2026 exceeded the prior year’s despite steady app revenue.

A specialty retail brand shows a remarkably stable app share over time. Web revenue tracks seasonal demand independently: it peaks during gifting season, not when app users stop spending.

The most telling case is a premium lifestyle brand. At peak season, web revenue tripled while app revenue also grew. If the app were stealing web customers, peak season would show the opposite: app surging, web declining.

A luxury fashion brand shows both channels scaling together at peak — app and web revenue both hit their yearly highs in the same month.

Across all 5 brands, the pattern is consistent: both channels co-vary positively with demand. App share is stable over time. There is no progressive displacement of web by app.

4.3 The Incrementality Framework

Beyond co-movement, we use a principled decomposition framework in our client ROI models. The logic is deliberately conservative:

1. Assume 100% of app installs come from mobile web. This maximises the cannibalisation baseline. In reality, many installs come from paid UA, social, app store search, and other channels that never touched the website.
2. Model the web-baseline revenue those users would have generated, using the brand’s actual mobile web CVR and AOV as inputs.
3. Subtract the full web baseline from app revenue. Whatever remains is incremental revenue that would not have existed without the app; generated through higher conversion, higher AOV, and higher session frequency.

Applied to a representative enterprise retailer over three years:

The model’s key assumptions have been validated against our platform data:

Every input is at or below what we actually observe. The 70% incrementality figure likely understates reality.

4.4 What the Academic Research Shows

Two peer-reviewed studies address app incrementality directly:

Narang & Shankar (Marketing Science, 2019) remains the closest to a gold standard. Using difference-in-differences methodology on a retailer’s matched customer data, they found app adoption increased spending by 37% and purchase frequency by 33%. Critically, the lift appeared across both online and offline channels: genuine behavioural change, not channel shift.

Tapcart’s Incrementality Index (2025) is based on 330 million orders, 120 million shoppers, and $31.5 billion in measured revenue. They report that 11.2% of app customers are app-first (net new to the brand), and web-to-app customers spend 36% more in total (not instead of web, but on top of it). The methodology is observational, not experimental, but the dataset is enormous.

4.5 The Push Notification Argument

Push notifications are the cleanest incrementality case. They are effectively an app-exclusive channel (web push on iOS remains nascent, with minimal adoption and limited capability). They create demand that would not otherwise exist: a flash sale notification at 6pm, an abandoned cart reminder at lunchtime, a back-in-stock alert for a wishlisted item.

Airship’s data (thousands of apps, billions of users) shows that a single push notification increases 90-day retention by 120%. Daily push engagement keeps 50% of iOS users retained at 90 days, a 257% increase over baseline.

This is demand creation, not channel shift.

5. Retention: The Compounding Effect

Conversion lift is a point-in-time metric. Retention is where app economics compound.

5.1 Platform Retention Data

Across 19 brands on the Poq platform (2024 cohorts, iOS and Android):

A 20% M12 retention rate means one in five users who install the app are still active a year later. At best-in-class (40%), it is two in five. This compares favourably to industry benchmarks for retail apps: Adjust and AppsFlyer both report 5-9% Day 30 retention for ecommerce apps. Our 12-month figures exceed what most apps achieve in their first month.

5.2 What Drives Retention

The brands with the strongest retention share common characteristics: regular purchase cycles (fashion, homewares), active push notification strategies, and loyalty programme integration. The best performers are lifestyle brands with seasonal collections that give users a reason to return.

Push notifications are the primary retention lever. Airship’s cross-industry data shows:

• Any push engagement increases 90-day retention by 3x
• Retail-specific: Android retention up 212%, iOS up 110% with push
• A single push notification increases retention by 120% vs zero

Bain’s research shows that a 5% improvement in customer retention increases profits by 25-95%. Bluecore’s analysis of 100+ retailers found that active buyers spend 69% more per purchase than new customers, and place 58% more orders. Retention compounds the conversion advantage over time.

5.3 iOS Consistently Outperforms Android

Across our 19-brand dataset, iOS users retain at higher rates than Android at every milestone, particularly at M6+. This aligns with industry-wide patterns (iOS users tend to have higher intent and spending power) and has practical implications: if there are budget constraints limiting the number of platforms, iOS should be the lead platform for a new app launch, with Android following once the iOS experience is proven.

6. Methodology and Data Sources

6.1 Poq Platform Data

All first-party data in this paper comes from Poq’s central analytics environment. App transactional data covers 80+ active client apps across 395 days (January 2025 to January 2026). Mobile web data for the app-vs-web comparison comes from client-shared mobile web analytics, filtered to mobile device traffic.

Standard filters exclude staging and UAT environments. Operating system values are normalised for mixed-case inputs. Cross-brand comparisons use USD; same-brand app-vs-web comparisons use local currency (GBP for UK brands) to ensure like-for-like measurement.

Retention data covers 19 brands using 2024 cohort averages.

The full data pack with source queries is available as a companion document for validation.

6.2 Industry Sources

This paper cites independent measurement and research sources (Contentsquare, Criteo, Airship, Adjust, AppsFlyer, Pushwoosh, Omnisend, Sensor Tower, Think with Google, Google/BCG), platform vendors with large primary datasets (Tapcart: 330M orders/$31.5B), retail benchmark providers (Bluecore, SaleCycle, Barilliance, Digital Commerce 360), and peer-reviewed academic research (Narang & Shankar, Marketing Science 2019).

Where vendor data is cited, methodology and dataset scale are noted in the body text.

6.3 What We Can and Cannot Claim

Supported claims:

• All measured merchants show in-app conversion at or above mobile web (1.8x median, 21 brands across UK, US, EU, AU)
• App users generate more revenue per session (1.5x median)
• No aggregate cannibalisation signal: web revenue does not decline as app revenue grows
• The ROI model’s incrementality framework is validated against observed platform data
• Push notifications create demand through an app-exclusive channel with no web equivalent
• Retention compounds the conversion advantage over time

Known limitations:

• No cross-platform user ID exists to track individual users between app and web
• Self-selection bias cannot be fully isolated at the user level (app users may be inherently higher-intent)
• The 1.8x median is based on 21 brands across 4 geographies; further expansion would continue to strengthen confidence
• Two further brands are tracked in our broader analysis but excluded from the headline comparison: one whose app primarily serves non-commercial fan engagement, and one in gifting/seasonal where app and web audiences have fundamentally different intent
• Install rate varies widely by brand (0.2-6.1%), reflecting marketing investment and brand strength

We present the data honestly. The evidence for app revenue impact is strong. The evidence for pure incrementality at the individual user level requires cross-platform identity that no commerce app platform, including Tapcart, can currently provide. The aggregate case, the economic model, and the academic research all point in the same direction.

Sources & References

Independent measurement & research

• Contentsquare. 2026 Digital Experience Benchmark. 99 billion sessions, 6,500 websites. https://contentsquare.com/guides/digital-experience-benchmark/
• Criteo. Q1 2018 Global Commerce Review. 5,000+ retailers, 80+ countries. https://www.criteo.com/news/press-releases/2018/05/criteos-q1-2018-global-commerce-review-reinforces-the-mobile-first-consumer-mindset/
• Button. Mobile Commerce Studies. https://blog.usebutton.com/
• Barilliance. Cart Abandonment Rate Statistics. https://www.barilliance.com/cart-abandonment-rate-statistics/
• SaleCycle. Cart Abandonment in UK and European Ecommerce, 2024-25. https://www.salecycle.com/blog/cart-abandonment-rate-in-uk-and-european-ecommerce-causes-impacts-and-solutions
• Pushwoosh. Push Notification Benchmarks. https://www.pushwoosh.com/blog/push-notification-benchmarks/
• Airship. 2025 Mobile App Push Notification Benchmarks. https://www.airship.com/resources/benchmark-report/mobile-app-push-notification-benchmarks-for-2025/
• Airship / Sapio Research. Global Consumer Survey on Retail Mobile Apps (n=11,000, 10 countries, 2023). https://www.airship.com/company/press-releases/global-survey-shows-more-consumers-turn-to-retailers-mobile-apps-to-streamline-in-store-shopping-experiences-and-score-personalized-offers-and-deals/
• Omnisend. Email, SMS and Push Marketing Statistics for Ecommerce, 2024. https://www.omnisend.com/blog/email-sms-push-marketing-ecommerce-2024/
• Adjust. Shopping App Insights. https://www.adjust.com/blog/shopping-apps-data/
• AppsFlyer. Ecommerce App Marketing 2024 Report. https://www.appsflyer.com/resources/reports/ecommerce-app-marketing-2024-report/
• Sensor Tower. State of Mobile 2025. https://sensortower.com/state-of-mobile-2025
• Think with Google. Customer Lifetime Value and the 80/20 Rule. https://www.thinkwithgoogle.com/intl/en-emea/consumer-insights/consumer-trends/80-20-rule-and-customer-lifetime-value/
• Google / BCG. Responsible Marketing with First-Party Data. https://www.thinkwithgoogle.com/future-of-marketing/digital-transformation/sustainable-first-party-data-strategy/
• Oberlo / Statista. Mobile Commerce Sales Statistics. https://www.oberlo.com/statistics/mobile-commerce-sales
• Digital Commerce 360. Retailers’ Mobile App Investments and Returns, August 2024. https://www.digitalcommerce360.com/2024/08/05/retailers-mobile-apps-investments-return/
• Bluecore. 2024 Customer Growth Benchmarks Report. 100+ retailers across seven verticals. https://www.globenewswire.com/news-release/2024/04/16/2863832/0/en/Retailers-With-the-Highest-Identification-Rates-See-53-Higher-Repeat-Purchase-Rates-Finds-New-Report-from-Bluecore.html

Platform-specific datasets

• Tapcart. Mobile Incrementality Data Story (Incrementality Index). 330M orders, $31.5B in measured revenue. https://www.tapcart.com/blog/mobile-incrementality-data-story

Academic research

• Narang, U. & Shankar, V. (2019). Mobile App Introduction and Online and Offline Purchases and Product Returns. Marketing Science, 38(5), 756-772. https://pubsonline.informs.org/doi/10.1287/mksc.2019.1169

Retention economics

• Bain & Company. Retaining Customers is the Real Challenge. https://www.bain.com/insights/retaining-customers-is-the-real-challenge/
• Reichheld, F. & Sasser, W. E. (1990). Zero Defections: Quality Comes to Services. Harvard Business Review. https://hbr.org/1990/09/zero-defections-quality-comes-to-services

Poq Commerce operates a multi-tenant native mobile app platform for enterprise retailers, processing $454M in annual app GMV across 80+ brands in the UK, Europe, and North America. Poq is a MACH Alliance certified member with integrations across Shopify, Salesforce Commerce Cloud, commercetools, and composable commerce stacks. For the full data pack, methodology, and source queries, contact analytics@poqcommerce.com.