--- title: How do I compare my apps speed to others in my niche? description: Learn how to benchmark your app's speed against rivals, spot performance gaps users actually notice, and turn findings into better product decisions. image: https://weareaffective.com/hubfs/learning-centre-images/how-do-i-compare-my-apps-speed-to-others-in-my-niche.webp --- [Skip to content](https://weareaffective.com/learning-centre/how-do-i-compare-my-apps-speed-to-others-in-my-niche#main-content) [![we\_are\_affective\_logo\_200](https://weareaffective.com/hs-fs/hubfs/we_are_affective_logo_200.png?width=175&height=48&name=we_are_affective_logo_200.png "we_are_affective_logo_200")](https://weareaffective.com) - [Home](https://weareaffective.com) - About Us - [Our Story](https://weareaffective.com/about) - [How We Work](https://weareaffective.com/how-we-work) - Our Services - [App Planning & Strategy](https://weareaffective.com/app-planning-strategy) - [App Design](https://weareaffective.com/app-design-agency) - [App UX Design](https://weareaffective.com/app-ux-design) - [App UI Design](https://weareaffective.com/app-ui-design) - [App Technical Architecture](https://weareaffective.com/app-architecture) - [Existing App Audits](https://weareaffective.com/app-audit) - [Case Studies](https://weareaffective.com/case-studies) - [Pricing](https://weareaffective.com/pricing) - [Learning Centre](https://weareaffective.com/learning-centre) - [Get Started](https://weareaffective.com/get-started) Expert Guide Series # How do I compare my apps speed to others in my niche? Table of Contents Speed is one of those product qualities that users rarely praise and always punish. No one leaves a review saying the app loaded quickly. They just leave. And the ones who stay rarely know they are staying partly because of how fast things moved. So when a product team asks how their app's speed compares to others in their niche, they are asking something genuinely useful, but they are often asking it in a way that makes it harder to answer than it needs to be. > Speed comparisons only matter where users feel the gap, not where dashboards show one. The instinct is to reach for a benchmark number. Two seconds, three seconds, some threshold that makes the comparison feel objective. But benchmarking app speed against competitors is a question about [whether your speed is causing problems](https://weareaffective.com/app-audit) and whether the gap is large enough for users to feel it. Those are different questions, and they point to different kinds of work. What follows is a practical guide to working out where you actually stand, which comparisons are worth making, and what to do when the numbers point clearly in one direction or another. The goal is not a scorecard. The goal is a set of decisions you can act on. ## What benchmarking your app's speed actually means Benchmarking speed sounds like a technical exercise, and at one level it is. You run performance tests, collect load times, and compare them to whatever data you can find about similar products. But the more useful framing is behavioural. Speed benchmarking, done properly, is asking whether the performance gap between your app and a competitor's is large enough to change what users do. That distinction matters because raw speed numbers exist on a different scale from human perception. A difference of 200 milliseconds between two apps is real. It shows up in monitoring tools and in lab measurements. Users, though, generally cannot perceive differences below about 100 to 200 milliseconds without assistance, and their sense of "fast" or "slow" is shaped as much by context and expectation as by actual elapsed time. A two-second load feels different in a checkout flow than it does on a content feed. Same duration, different weight. So benchmarking should answer two things. First, where does your measured performance sit relative to what comparable apps deliver? Second, are the gaps in areas users care about? The second question is the one that drives decisions. An app that is technically slower than a competitor in a part of the flow users never stress about is not meaningfully slower in any way that changes behaviour. When setting up your benchmarking process, define in advance which user journeys you are measuring. Load time on the home screen matters very differently from load time inside a checkout or booking flow. ## Which speed metrics competitors can and cannot see One of the honest things to say upfront is that direct competitor speed data is mostly unavailable. You cannot access a competitor's server response times, their real-user monitoring dashboards, or their crash rates. What you can access is what any user can access: the experience of using the app on a real device, in real conditions, on a real network. That experience is the competitor benchmark. For web-based products or apps with public web components, tools like Google PageSpeed Insights, WebPageTest, and Lighthouse give you testable numbers. For native apps, the picture is less tidy. You can install a competitor's app and time specific interactions yourself, which is slower work but produces genuine comparable data on the journeys that matter. #### What third-party tools can surface App store reviews are an underused source of comparative speed data. If a competitor's reviews mention loading problems, lag on a specific screen, or crashes after updates, that is publicly available user signal about a real performance problem. Aggregating this across reviews gives you a sense of where competitors are hurting and, by comparison, where you are not. #### What stays hidden Backend response times, API latency, CDN configuration, and server-side processing time are all invisible unless a competitor publishes them, which they will not. The gap between what you can measure and what actually drives performance is wide. Work with what is observable rather than trying to infer what is not. ## Design that understands *your* users We build app experiences around real user behaviour, not assumptions. Research, psychology-driven design and technical specs that turn users into loyal advocates. [See how we work](https://weareaffective.com/how-we-work) [Get started](https://weareaffective.com/get-started) No commitment ## How to find comparable apps in your niche Finding genuine comparators is harder than it looks. The obvious move is to list the apps that come up in the same App Store category or appear in the same search results as yours. That is a reasonable start, but category overlap does not mean performance parity in the parts that matter. A skincare brand's app and a vitamins subscription app might both sit in the Health and Fitness category, but their critical flows are completely different: one is content-led browsing, the other is a recurring purchase journey. Comparing raw load times across those two is mostly noise. Better comparators share the same critical user journeys. If your app's most important moment is a product discovery flow leading to a first purchase, look for apps where that same flow exists and benchmark against the experience of completing it. Time from home screen to product detail. Time from add-to-cart to confirmation. These are journeys your users are also completing in other apps, and those other apps set the expectation. > Users calibrate their patience against every app they use, not just the ones in your category. Something worth holding here is that users do not benchmark your app only against direct competitors. They benchmark it against the fastest apps they use regularly. A user who opens their food delivery app and gets to a product listing in under a second will bring that expectation to your wellness app, even though the two have nothing to do with each other. Your real competition for perceived speed is often not the obvious name in your niche. Run competitor apps on the same device, same network, and same conditions each time you benchmark. Variations in testing environment will produce more noise than the actual performance differences you are trying to find. ## Benchmarking against adjacent markets when no direct comparison exists Some products operate in spaces where there is no direct equivalent to benchmark against. A new category, a niche tool, a product that combines functions no single existing app covers. When no close comparator exists, the honest move is to look at adjacent products and use those as a baseline with appropriate adjustments. We did this for a niche trading platform where no direct equivalent existed. Rather than leaving the revenue projections unsupported, we identified products that were performing some aspects of what the platform would do, examined their subscription and revenue models, and used those as a reference. Where a comparable product already had strong traction, we scaled the projections down to reflect the reality of launching a new product into the same space. The same logic applies to speed benchmarking: if no direct comparator exists, identify the apps performing the closest adjacent functions and use their performance on comparable journeys as a proxy baseline. For a fitness and activewear app with no close equivalent, you might look at general e-commerce apps for purchase flow benchmarks, content streaming apps for media load benchmarks, and community apps for social interaction speed benchmarks. None of them is a perfect match. Together they sketch a performance landscape your users are already operating in, and that landscape sets the floor your app needs to clear. When building from adjacent benchmarks, document which app you used, which specific journey you timed, and the conditions you tested in. This gives you something to return to and retest against as your own app changes. ## Which performance gaps users actually feel The relationship between measured speed and felt speed is not linear. Users perceive performance through a combination of actual elapsed time, feedback signals during loading, and the context of the task they are trying to complete. A technically slower app can feel faster than a technically faster one if it manages the wait better. The flows where users feel slowness most acutely are the ones they are motivated to complete. A checkout flow that takes two seconds longer than a competitor's is felt, because the user is primed and ready to act and the delay is friction against intent. A category browse screen that loads in the same additional two seconds is much less likely to register, because the user is in discovery mode and not pressing against a goal. Research from [Google](https://www.thinkwithgoogle.com/_qs/documents/9757/Milliseconds_Make_Millions_report_hQYAbZJ.pdf) supports this: a three-second delay in page load increases bounce probability by 32%, but that effect is strongest where users arrive with specific intent. We saw a version of this dynamic on a health and wellbeing product where [drop-off in a multi-step onboarding flow](https://weareaffective.com/learning-centre/how-to-read-a-user-session-recording-for-emotional-signal-rather-than-task-compl) was running at around 80 to 85%, almost always within the first three or four questions. The technical speed of the flow was not the problem. The problem was that users had no sense of how long the process would take. Once we introduced upfront priming, telling users what to expect before they began, completion rates rose to around 95%. The flow did not get faster. The experience of waiting became acceptable because users knew what they had signed up for. ## Which metrics are technical vanity and why they mislead Some speed metrics look impressive in a product review and tell you very little about whether users are having a good experience. Average load time across all sessions is one of them. It smooths over the variation that actually matters: the slow-network user who churns, the device segment where performance degrades, the specific screen where median load time looks fine but the 95th percentile is catastrophic. Session length and daily active users are similarly unreliable as proxies for whether your app feels fast. An app can log long sessions because users are waiting, and high daily active users can coexist with significant performance complaints buried in review data. These are, as we tend to describe them, [vanity metrics: numbers that look healthy](https://weareaffective.com/learning-centre/5-things-that-make-the-difference-between-so-so-apps-and-stellar-apps-what-your-) in a deck without confirming that the product is actually serving users well. #### The metrics that describe real experience | Metric | What it measures | Why it matters | | --- | --- | --- | | Time to interactive | When the user can first act | Closer to felt speed than full load time | | 95th percentile load time | Worst-case experience at scale | Reveals the users most likely to churn | | Flow completion rate by journey | Whether users finish key tasks | Speed problems show up as drop-off | | Day 3 and day 7 retention | Whether users return after first use | Performance problems compound over repeat visits | #### What retention numbers reveal Download numbers rising while day-3 and day-7 retention stays flat is a pattern that masks real product health problems. Teams often focus on acquisition because the numbers are moving, without realising that a proportion of those new users are encountering a slow experience and leaving quietly. The numbers look like growth. The product is cycling through users without holding them. ## How to test whether a speed difference changes user behaviour Once you have identified a speed gap between your app and a comparator, the next step is finding out whether that gap is causing users to behave differently. The most direct way to do this is through controlled testing, where you vary load time artificially and measure the downstream effect on completion rates, return visits, and task success. This is more tractable than it sounds. Feature flag systems let you serve different loading states to different user cohorts. A cohort that sees a skeleton screen during loading and a cohort that sees a spinner experience the same underlying load time differently. Comparing their completion rates tells you whether the gap is about raw speed or about how the wait is handled. Where a direct speed improvement is not yet possible, this kind of test also tells you how much performance-related friction is perceptual rather than measured. If adding a skeleton screen closes most of the behavioural gap without changing actual load times, the problem was never purely technical. That is useful to know before committing engineering time to infrastructure work that may not move the metrics you care about. [Review data from competitors provides a parallel signal](https://weareaffective.com/learning-centre/why-your-best-users-are-often-your-worst-source-of-product-direction). If a competitor's one-star reviews mention specific screens or features as slow, and your app handles those same moments without complaint, that is a meaningful advantage even if your raw benchmark numbers are close. User-generated performance data is imprecise but it reflects real frustration, which is the thing that actually drives churn. ## Where expectation-setting outperforms raw speed improvements There are situations where improving actual load time is technically complex, expensive, or constrained by third-party dependencies. In those situations, managing how users experience the wait often produces a faster improvement in felt performance than the engineering work would. We tested this directly on a health and wellbeing product. A multi-step onboarding flow was seeing drop-off rates of around 80 to 85%, and the conventional fix would have been to reduce the number of steps or speed up the transitions. Instead we introduced priming: telling users upfront what the process involved and roughly how long it would take. Completion rates moved to approximately 95%. The flow itself was unchanged. The expectation was set before the experience began, and that single change did more than any speed optimisation would have in the same timeframe. The same principle applies at the screen level. Progress indicators, skeleton screens, and loading animations do not make an app faster. They give users information about the wait, which changes how the wait feels. A user who can see that something is happening, and has a rough sense of when it will finish, tolerates the same elapsed time far better than a user staring at a blank screen with no signal. Before committing to infrastructure improvements for a slow screen, run a test with a well-designed loading state first. If that closes the behavioural gap, the problem was perceptual. If it does not, the problem is genuinely in the performance numbers. ## How to turn benchmark findings into product decisions A benchmark finding is only useful when it connects to a decision. "We are 0.8 seconds slower than the category average on product listing load" is an observation. The decision it points to depends on where that screen sits in the user journey, what the drop-off rate on that screen looks like, and how much of the gap is recoverable through design versus infrastructure. The useful output of a benchmarking exercise is a prioritised list of performance gaps, ranked by their likely effect on user behaviour rather than by their size. A 1.5-second gap on a screen users visit three times per session and are actively trying to complete something on outranks a 3-second gap on a settings screen users visit once a month. Size of gap does not equal priority. #### Turning findings into action 1. List every journey where your measured speed falls below the comparator. 2. Score each by [user intent level: how motivated is the user](https://weareaffective.com/learning-centre/what-a-development-team-actually-needs-to-know-about-the-user-before-sprint-one) at this point? 3. Cross-reference with drop-off data from your own analytics. 4. Separate gaps that are perceptual from gaps that are measured, and route them to design or engineering accordingly. 5. Set a review date so the benchmark does not become a one-off exercise. #### Connecting to retention The framing that makes benchmarking work useful in product reviews and roadmap conversations is retention, not speed. A finding that says "this screen is slow" is easy to deprioritise. A finding that says "this screen is likely contributing to day-3 drop-off at a rate that matches our slowest competitor" connects directly to a metric the team is already watching. Frame the gap in terms of what it is doing to the numbers that matter, and the decision about whether to fix it becomes much cleaner. ## Conclusion Comparing your app's speed to others in your niche is a worthwhile exercise, but the value sits in what you do with the comparison rather than in the numbers themselves. A benchmark that ends at "we are X milliseconds slower" has not done its job. The work is tracing that gap to specific user journeys, working out whether those journeys are ones users care about enough to feel the difference, and then deciding whether the fix is technical or perceptual. The most common failure in this kind of exercise is optimising for the wrong measurement. Teams spend engineering cycles reducing load times on screens that do not drive churn, while overlooking the onboarding flow where expectation-setting alone would close most of the gap. On the health and wellbeing product we described, a single priming change moved completion rates from around 80% to around 95% without touching the underlying code. That is the kind of finding a benchmark exercise should produce. Speed comparisons are most useful when they stay connected to behaviour. Where do users leave? Where do review scores suggest competitors are struggling? Where does your 95th percentile performance diverge from your median in ways that affect a specific user segment? Those questions produce decisions. Chasing a faster average load time for its own sake mostly produces a tidier dashboard. If you are working through a benchmarking exercise and want a second perspective on where the gaps are and which ones are worth addressing, [let's talk about your app's performance](https://weareaffective.com/get-started). ## Frequently Asked Questions Why does app speed matter if users do not explicitly mention it in reviews? Users rarely praise fast apps but they will quietly abandon slow ones without explaining why. Speed shapes behaviour without users being consciously aware of it, which makes it easy to underestimate as a product quality. What does benchmarking your app's speed actually involve? Benchmarking is not just about collecting load time numbers and comparing them to a threshold. The more useful approach is asking whether the performance gap between your app and a competitor's is large enough to change what users actually do. Can users really tell the difference between small speed improvements? Generally, users cannot perceive differences below around 100 to 200 milliseconds without assistance. Their sense of fast or slow is also shaped by context, so a two-second load in a checkout flow feels far more damaging than the same delay on a content feed. Can I access my competitors' real speed and performance data directly? Direct competitor performance data such as server response times or real-user monitoring dashboards is not publicly available. What you can measure is the actual experience of using their app on a real device, in real conditions, which is the most honest benchmark available. What tools can I use to measure competitor speed for web-based products? Tools like Google PageSpeed Insights, WebPageTest, and Lighthouse can give you testable numbers for web-based products or apps with public web components. For native apps, you will need to install the competitor's app and time specific interactions manually, which takes more effort but produces genuinely comparable data. Which parts of my app should I focus on when benchmarking speed? You should define the specific user journeys you are measuring before you begin, rather than looking at overall load times in isolation. A slow home screen load and a slow checkout flow carry very different weight in terms of user impact and business risk. Is my app a problem if it is technically slower than a competitor in some areas? Not necessarily. If the slower areas are parts of the flow that users rarely stress about or spend little time in, the gap may not be large enough to change behaviour. The key question is whether the difference occurs somewhere users actually care about. Are app store reviews a useful source of competitor speed information? Yes, app store reviews are an underused source of comparative speed data. 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