Shopify Price Testing and Mobile App A/B Testing in Explore 3.0
- Test product-level and variant-level prices in your Shopify store with Shopify pricing experiments in Omniconvert Explore 3.0.
- Run mobile app A/B testing inside iOS and Android apps on onboarding, in-app purchase screens, layouts and navigation.
- Use server-side testing for backend logic, such as recommendation rules, discount eligibility and caching, measured without browser data.
- Start a Shopify pricing test with price presentation, such as badges and in-cart messages, before you test price points.
- Keep each shopper on one price and judge the test on revenue per visitor, not conversion rate alone.
- Treat every result as specific to your traffic, audience and test design, and retest before a permanent change.
Shopify price testing, native mobile app testing and server-side testing are the three new features in Omniconvert Explore 3.0. Each one moves experiments past the look of a page and into the places that set revenue: the price, the app and the logic behind the store.
Shopify and eCommerce teams must do more with less. Traffic costs more, customers expect more, and the small interface changes that once gave growth no longer give enough. This release post explains what each new capability lets you test, how it helps stores and teams, and how to run a price test without damaging customer trust.
What is new in Omniconvert Explore 3.0
Most A/B testing in eCommerce changes what shoppers see in the browser: a headline, a button, a layout. Those tests still matter, and Explore still runs them, together with multivariate tests, split URL tests, personalization and targeted surveys. The three new features reach parts of the business that browser tests cannot reach.
| Feature | What you can test | What you measure |
|---|---|---|
| Native mobile app testing (iOS and Android) | Screens, layouts, buttons, copy, onboarding flows, UI components, in-app purchase journeys | Tap-through rate, onboarding completion, in-app conversion, feature adoption |
| Server-side testing | Backend logic, recommendation rules, discount and promotion logic, caching and API behavior | Speed, conversion influence, engagement lift, reliability |
| Shopify pricing experiments | Product and variant prices, price messaging, badges, in-cart messages, segment-specific tests | Average order value, conversion rate, margin, revenue per visitor |
Native mobile app testing: how mobile app A/B testing works in Explore
Apps often hold a store's most engaged customers, the people who installed the app because they plan to buy again. Until now, true experiments inside native apps were hard to set up and slow to repeat. Many teams know the value of codeless testing tools from quality assurance, where non-technical people create and check tests without waiting for engineering cycles or code freezes. Explore applies the same idea to app experiments.
With native mobile app testing, your team can answer questions such as these:
- Does a shorter onboarding flow increase onboarding completion and first purchases?
- How does a new layout of the in-app purchase screen change in-app conversion?
- Which promo message or offer placement keeps users moving toward checkout?
What you can test. Screens, layouts, buttons, copy, calls to action, micro-interactions and navigation patterns. Onboarding experiments cover step-by-step flows, tutorial screens and first-purchase paths. UI component tests cover modals, tabs, cards and menus. In-app purchase tests cover promo messages, offer placement, in-app price displays and funnel drop-off.
How it helps a store. You see which app experiences drive retention and repeat purchases, find where users leave the purchase path, and remove friction with evidence instead of opinion.
How teams use it. Product and CRO teams iterate faster. Designers check creative decisions against real user data. Leaders see what works on each device, because app results sit next to website results. If your mobile web store needs work too, the mobile CRO guide covers the audit steps.
Server-side testing for backend logic experiments
Most eCommerce optimization works on the interface: buttons, layouts, images. But the logic underneath, the rules that drive recommendations, discounts, system behavior and personalization, often has a bigger effect on what customers do. Server-side testing in Explore 3.0 lets you test that logic on live traffic without slowing development or risking instability.
What you can test. Backend logic variations, conditions and triggers. Recommendation rules, such as ranking strategies and selection logic. Discount and promotion logic, such as eligibility rules, promotion triggers and fallback logic. Performance settings, such as caching strategies, API response behavior and resource priority.
How it helps a store. You check a logic change on live traffic before you roll it out to everyone, which lowers the risk of each release. Explore measures speed, conversion influence, engagement lift and reliability without relying on browser data, so ad blockers and browser limits do not distort the result.
How teams use it. Engineers and CRO specialists plan tests together. Engineers own the variation in code, and the CRO team owns the hypothesis, the metric and the reading of the result. That closes the gap between a technical decision and its measurable effect on the business.
Shopify price testing: what a Shopify pricing test can compare
Price is one of the strongest levers a store has. In the McKinsey analysis of the average S&P 1500 company, a 1% price rise with stable volume would give an 8% increase in operating profit [McKinsey & Company, 2003]. Volume rarely stays stable, and that is the reason to test. Price has also been one of the hardest things to test on Shopify because of platform limits.
What you can compare.
- Price points: product-level and variant-level prices.
- Price communication: in-page pricing, in-cart messages, highlight styles and badges.
- Segments: new and returning shoppers, devices, and traffic from specific campaigns.
How it helps a store. You find the price that gives the best balance of conversion and revenue, see how sensitive your customers are to a change, and lower the risk of a permanent price change. Explore reports average order value, conversion rate, margin and revenue per visitor for each variant.
How teams use it. Price decisions rest on data instead of instinct. Marketing and product leads agree on one pricing strategy because they read the same result, and they see the revenue effect of a change sooner. For the theory behind price tests, read split testing for pricing. For prices that change by rule, read dynamic pricing in eCommerce.
Omniconvert Explore runs Shopify pricing experiments next to your A/B and multivariate tests, with revenue per visitor for each variant.
See how Omniconvert Explore works →How to run a Shopify pricing test safely
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Test the presentation before the priceStart with price messaging, badges, in-cart messages, bundles or a free-shipping threshold. These tests carry little trust risk and often show how price-sensitive your shoppers are.
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Pick products with enough trafficChoose best-sellers or a product group, so each variant collects enough orders to reach a reliable result in a few weeks.
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Limit who sees a different priceTarget new visitors only, or run variants one after another. Keep each shopper on the same price on every visit and on every device you can match.
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Set revenue per visitor as the primary metricA lower price usually wins on conversion rate and can still lose revenue. Watch average order value and margin as secondary metrics.
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Plan the sample size and stop at itDecide the sample size and duration before launch. Do not stop early because one variant looks ahead, and run full weeks to cover weekday and weekend behavior.
In some markets, showing different prices to different shoppers at the same time can also raise consumer-protection questions, so check your local rules before you test price points.
How Explore 3.0 can affect conversion, AOV and revenue per visitor
Conversion rate. When app flows and backend logic go into controlled tests, teams can find and remove friction that blocks purchases. Well-designed variations show which journeys run more smoothly.
Average order value. Pricing experiments show how customers respond to different price points and price messages. The results can point to prices that encourage larger orders while you watch the effect on conversion.
Revenue per visitor. When the app, the backend logic and the prices are tested and refined on real behavior, some stores earn more from each visit. How much depends on traffic quality and on the tests you choose.
Acquisition cost. If each visitor converts or spends more, paid acquisition becomes more efficient. That can lower cost per acquisition over time, if traffic volume and quality stay the same.
Here is a failure practitioners hit with Shopify price testing. A team tests a lower price on a best-seller, the variant wins on conversion rate after four days, and they roll it out to the whole store. Two months later margin is down, because the extra orders did not cover the price cut and the test never measured revenue per visitor. Returning customers also saw the old price in an earlier email and asked support why it changed. Judge price tests on revenue per visitor, and run them to the planned sample size.
Device-specific tests pay off when one device lags. CLX Gaming had almost twice as much mobile traffic as desktop traffic, but mobile converted less. The team used Omniconvert to test a mobile homepage redesign that added links to the three main product categories and moved the financing section higher on the page. The variant lifted conversion rate by 28.57% and revenue per user by 46.21% [Omniconvert, CLX Gaming case study]. That test ran on the mobile website, not inside a native app, but the method is the same: find the device that lags, form a hypothesis and test it. More results are in A/B testing examples and the CRO case studies.
Shopify price testing and mobile app testing FAQ
Shopify price testing is a controlled experiment that shows shoppers different product prices, variant prices or price presentations and measures the effect on conversion rate, average order value, margin and revenue per visitor. Omniconvert Explore runs these tests on product-level and variant-level pricing in Shopify stores, so you decide on prices from shopper behavior instead of guesswork.
Native mobile app testing is the practice of running experiments inside iOS and Android apps and measuring how real users interact with app screens and flows. It differs from mobile web testing, which runs in the phone's browser. Typical tests cover onboarding flows, in-app purchase journeys, layouts, buttons, copy and navigation.
Server-side testing is an experiment in which the variation is created on the server before the response reaches the visitor, instead of in the browser. It lets you test backend logic such as recommendation rules, discount eligibility, promotion triggers and caching strategies, and measure the results without relying on browser data.
Start with tests that change how a price is presented, such as price messaging, badges, bundles or free-shipping thresholds, before you test the price itself. When you do test price points, limit the test to new visitors or run variants one after another, keep each shopper on one price, use revenue per visitor as the primary metric and stop at your planned sample size.
You can test screens, layouts, buttons, copy, calls to action, micro-interactions and navigation patterns. You can also test onboarding steps and tutorial screens, UI components such as modals, tabs, cards and menus, and in-app purchase journeys such as promo messages, offer placement and in-app price displays. Measure tap-through rate, onboarding completion, in-app conversion and feature adoption.
Client-side testing changes the page in the visitor's browser, usually with JavaScript, which suits visual changes such as layouts, copy and buttons. Server-side testing changes what the server sends, which suits backend logic such as recommendations, discount rules and performance settings. Many teams use client-side tests for interface ideas and server-side tests for logic and speed.
No. Dynamic pricing changes prices continuously by rules such as demand, time or stock. Shopify price testing is a fixed experiment: you compare a small number of price variants for a set period, then pick the one that gives the best revenue per visitor and roll it out. A price test can also check whether a dynamic pricing rule earns more than a fixed price.
Choose one question per feature before you build anything. For pricing, test how one best-selling product's price is presented, then test the price point with new visitors only. For your app, test the onboarding step where most users drop out. For the backend, test one recommendation or discount rule that you already argue about.
Write the primary metric and the sample size next to each question, and run the tests one at a time so you can read each result clearly. See how Omniconvert Explore runs these tests.
Test prices, app flows and backend logic in one tool
Omniconvert Explore runs Shopify pricing experiments, native mobile app tests and server-side tests next to your A/B, multivariate and split URL tests.