How Do I Work Out If My Retention Rate Is Good Enough?

An education app I worked on a few years back spent £40,000 on their launch campaign and managed to get 50,000 downloads in the first month. The client was thrilled—until we looked at the retention data. Only 8% of users came back after day one. By day 30, we had less than 2% still using the app. They'd essentially paid about £16 for each person who stuck around. The maths was brutal and the client was understandably frustrated because they'd been so focused on that download number that they hadn't prepared for what came after.

Here's what most people don't realise: downloads are vanity metrics. They make you feel good but they don't actually tell you if your app is succeeding. I mean, I can download fifty apps right now and never open forty of them again. User acquisition costs money—lots of it—but retention is where you actually make that money back. An app with 10,000 highly engaged users who keep coming back is worth far more than one with 100,000 downloads and nobody using it.

The difference between a successful app and a failed one isn't how many people download it; its how many people can't imagine their day without it.

When I'm working with clients now, I spend more time talking about retention strategy than I do about launch preparation. Sure, we need people to discover the app, but if they're leaving within hours because the onboarding is confusing or the value proposition isn't immediately clear, we've wasted our budget. Retention tells you if you've actually solved a problem worth solving. It tells you if your app has found product-market fit or if you're just temporarily interesting. And honestly? Most apps fall into that second category, which is why the average app loses 77% of its daily active users within the first three days. Those numbers should terrify anyone planning an app launch.

What Actually Counts As Good Retention?

The numbers people throw around for "good" retention can be really misleading if you don't know what you're looking at. I've seen clients panic because their Day 1 retention was 35% when they'd read somewhere that 40% was the magic number—but here's the thing, retention rates vary massively depending on what your app actually does and who its for. A meditation app is going to have completely different usage patterns than a banking app, and comparing the two is pretty much pointless.

When I'm evaluating retention for clients, I don't just look at one number and call it a day. We need to understand what "good" means in context. For most consumer apps, you're looking at roughly 25-30% Day 1 retention as a baseline—that means about a quarter of people who download your app will open it again the next day. Sounds low? It is, but that's just how mobile works these days. Day 7 retention typically drops to around 10-15%, and by Day 30 you're often looking at 5-8% for average apps. These aren't great numbers, they're just typical ones.

But—and this is important—those benchmarks don't tell you if your retention is actually good enough. I worked on a fitness app where Day 1 retention was only 22%, which looked poor on paper. However, the users who made it past Day 7 had an 85% chance of still being active at Day 90. That's bloody good retention where it matters. The app was doing exactly what it needed to do; filtering out casual browsers and keeping the people who genuinely wanted to get fit.

What Makes Retention "Good" For Your App

Instead of chasing arbitrary numbers, focus on these factors:

• Your retention curve should flatten out after the initial drop—if its still nosediving after 30 days, something's broken
• Cohort retention should improve over time as you make updates; newer user groups should retain better than older ones
• Power users (your top 10% most active) should have retention rates above 60% at Day 30
• Your retention should be improving month-on-month, even if its just by 1-2 percentage points

The real question isn't whether your retention matches some industry average—its whether your retention supports your business model. If you need users to make three purchases to break even on acquisition costs, work backwards from that. How many days of retention do you need to get those three purchases? That's your target, not some generic benchmark you found online.

The First 24 Hours: Where Most Apps Lose Users

Here's something that used to catch me off guard early on—I'd deliver a beautifully designed app with stellar functionality, watch the downloads roll in, and then feel a bit sick when I checked the analytics the next day. The drop-off in those first 24 hours was always brutal, even for apps I was genuinely proud of. We're talking about 70-80% of users never opening the app a second time. And I mean never. They download it, have a poke around for maybe 2-3 minutes, and that's it. Gone forever.

The problem is almost always the onboarding experience. I've seen finance apps lose users because they asked for too much information upfront—full KYC verification before people could even see what the app did. One healthcare client we worked with had a 12-screen onboarding flow that took about 8 minutes to complete. Their day 1 retention was sitting at 19%. Bloody awful numbers. We stripped it down to 3 screens with optional steps pushed to later, and day 1 retention jumped to 44%. Same app, just a different sequence of when we asked for things.

What Kills Users In The First Day

The mistakes I see most often are pretty consistent across industries. Apps that require account creation before showing any value lose users fast. Same with apps that have unclear navigation or bury their core feature behind multiple taps. One e-commerce client had their search function hidden in a menu—sounds mad, but it happens more than you'd think. Users couldn't find what they came for and just deleted the app.

Track your session length for day 0 users separately. If people are spending less than 60 seconds in your app on first launch, you've got an onboarding problem that needs fixing immediately.

The Notifications Trap

Another retention killer? Asking for notification permissions too early. I learned this the hard way on a lifestyle app where we prompted users on launch. About 65% denied the request, and here's the thing—once someone denies permissions on iOS, its incredibly hard to get them to go into settings and enable it manually. We moved the prompt to after users completed their first meaningful action in the app, explaining exactly why we needed it. Opt-in rates went from 35% to 58%, and day 1 retention improved because users who granted permissions got timely reminders to come back.

The numbers I aim for these days are around 40-50% day 1 retention for most app categories. Gaming can go higher because the engagement loop is tighter. Finance and healthcare usually sit lower, around 35-40%, because users have specific tasks they're completing. But anything under 30% means something is fundamentally broken in those first few minutes, and you need to address it before worrying about anything else.

• Reduce required fields during signup—only ask for what you absolutely need to function
• Show core value before asking for permissions or personal data
• Keep first-time user flows under 2 minutes from download to first valuable action
• Use empty states intelligently—don't show blank screens without guidance
• Test your app as a completely new user every single week

One retail app I worked on had a carousel of feature highlights on first launch. Five screens of "look what our app can do" marketing copy. Nobody read them. We added a skip button and tracked it—89% of users skipped through without reading. We replaced it with a single screen asking what they wanted to shop for, then dropped them straight into relevant products. Day 1 retention went up 23 percentage points. Sometimes the best onboarding is just getting out of peoples way and letting them use your app. Understanding how progress bars manipulate user behaviour during setup can also help you design more effective onboarding flows.

Day 7, Day 30, Day 90: The Three Numbers That Matter

If you're tracking retention properly, these three milestones will tell you almost everything you need to know about your app's health. Day 7 retention shows if you've successfully onboarded users and given them a reason to come back. Day 30 tells you if your app has become part of their routine. Day 90? That's when you know you've built something people genuinely need.

I learned this the hard way working on a fitness tracking app a few years back—the client was obsessed with day 1 retention (which sat at a healthy 40%) but completely ignored the fact that day 7 had dropped to just 8%. We'd built a beautiful onboarding flow that impressed people initially, but the core workout tracking feature was too clunky for daily use. By the time we spotted the pattern and fixed it, we'd already lost thousands of users who might've stuck around. This is why measuring progress systematically throughout development is so crucial.

What Good Looks Like At Each Milestone

For most apps, you want to see day 7 retention above 20%, day 30 above 10%, and day 90 holding steady around 5-8%. But here's the thing—these numbers shift wildly based on what your app does. A meditation app I worked on had 35% day 7 retention because users were genuinely forming a daily habit; whereas an e-commerce app in the same portfolio sat at 12% for day 7 but that was perfectly fine because people don't shop every day.

The Drop-Off Curve Matters More Than Individual Numbers

Its not just about hitting specific percentages—you need to watch how quickly users fall off between each milestone. A steep drop from day 7 to day 30 usually means your app solved an immediate need but hasn't proven its long-term value. If day 90 is significantly lower than day 30, you're probably dealing with seasonal users or your app lacks depth to keep people engaged. I track the ratio between these milestones religiously now because it reveals patterns that individual numbers miss entirely.

Industry Benchmarks That Actually Mean Something

Look, the numbers you'll find in those fancy industry reports? They're not wrong exactly, but they're not particularly useful either. I mean, telling you that the average day 1 retention across all apps is around 25% doesn't help much when you're trying to work out if your fintech app's 18% is a disaster or actually pretty decent.

Here's what I've seen work across dozens of apps: gaming apps typically see day 1 retention between 35-45% for casual games, dropping to 10-15% by day 30. But before you panic about your meditation app sitting at 22% day 1, understand that utility apps behave completely differently—people don't need to open them daily for them to be valuable. I worked on a parking app that had terrible daily retention but brilliant monthly retention because that's how people actually use parking apps.

The best benchmark isn't what everyone else is doing; its what your best users are doing and working backwards from there

Finance apps generally sit around 20-30% day 1, 8-12% day 30. Shopping apps? Expect 15-25% day 1, maybe 5-8% day 30 unless you've got something really special going on. But here's the thing—these numbers only matter in context. A 15% day 30 retention rate is bloody brilliant for a retail app if your competitors are at 6%. It's terrible if they're at 22%. You need to know where you stand relative to your actual competition, not some generic category average. And honestly? The apps that obsess over beating industry averages usually miss the point entirely; what matters is whether your retention supports your business model and keeps improving over time. If your app is struggling to stand out, consider making your app more shareable to drive organic growth.

Gaming vs Finance vs Retail: Why Your Category Changes Everything

I've built apps across pretty much every category you can think of and honestly, comparing retention rates between different industries is a bit like comparing apples to...well, something that isn't an apple at all. A casual mobile game with 20% Day 1 retention might be doing brilliantly, whilst a banking app with the same number would have me seriously worried about whats gone wrong.

Gaming apps—particularly casual games—have some of the most brutal retention stats you'll see. We're talking 15-25% Day 1 retention being perfectly normal, sometimes even lower. Why? Because people download games on a whim, try them for five minutes during their commute, and then never open them again. Its just the nature of that category. But here's the thing; games also have the highest potential for engagement when they do hook someone. I worked on a puzzle game where we had just 18% Day 1 retention, but the users who stuck around were playing 6-7 sessions per day.

Finance apps sit at the complete opposite end. A banking or investment app should be seeing 60-80% Day 1 retention minimum—and if you're not hitting those numbers, something's fundamentally broken. These are utility apps that people need, not want. When someone downloads their bank's app, they've got a specific task to complete and they expect it to work first time. I built a fintech app for managing savings goals and we were consistently above 75% because users had real money at stake. The complexity can be worth it though—adding sophisticated finance tools like loan calculators often drives deeper engagement.

Retail and e-commerce fall somewhere in the middle, usually around 30-40% Day 1 retention. But these apps live and die by purchase behaviour, not daily opens. You might only see users return every few weeks when they actually need to buy something, and that's completely fine as long as they're converting when they do show up. Features like receipt scanning capabilities can help retain users by providing ongoing value between purchases.

The Metrics I Track Beyond Basic Retention

Sure, retention rates tell you who's coming back—but they don't tell you why or what those users are actually doing. I've built apps that had decent retention numbers but were still failing because users weren't engaging with the core features we'd spent months developing. That's when I learned to look beyond the basic metrics and track what actually matters for long-term success.

The first metric I always check is session length. If people are opening your app but leaving after 10 seconds, you've got a problem even if they come back tomorrow. I worked on a healthcare app where Day 1 retention was at 45%, which looked reasonable, but average session length was only 23 seconds—turns out the onboarding flow was so confusing that users would open the app, get frustrated, and close it. We only discovered this by tracking how long people actually spent inside the app, not just whether they returned.

Feature adoption rate is another big one. Let's say you've built a fintech app with five main features—tracking which features users actually engage with tells you what's valuable and what isn't. I had a client whose users loved the budgeting tool but completely ignored the investment tracker we'd spent £40k building. Without tracking feature-level engagement, we would never have known to pivot our development focus. This is where aligning stakeholders on feature priorities becomes crucial—you need unified vision on what success looks like.

Track your North Star Metric—the one action that correlates most strongly with long-term retention. For social apps its usually daily active connections, for productivity apps it might be tasks completed. Find yours and measure it religiously.

Metrics That Actually Tell The Story

Here's what I track for every app project beyond basic retention rates:

• Session length and frequency—are users spending meaningful time in your app?
• Feature adoption rates—which features drive retention and which are ignored?
• Time to first key action—how quickly do new users experience core value?
• Cohort progression—how do user behaviours change over their first 90 days?
• Churn triggers—what actions (or inactions) predict someone's about to leave?

Churn prediction is something I've only started focusing on in the last few years, but its become one of my most valuable metrics. By tracking patterns before users leave, you can intervene with targeted messaging or feature prompts. On an e-commerce app I worked on, we noticed that users who hadn't added items to their wishlist within 5 days had an 80% higher churn rate—so we added a gentle prompt on Day 4 and improved retention by 12%.

The thing is, different app categories need different secondary metrics. A meditation app should track streak length and completed sessions. A delivery app needs to monitor order frequency and basket abandonment. There's no one-size-fits-all approach here, which is why understanding your specific user journey matters so much. For specialised industries like construction apps, the metrics you track might be completely different from consumer apps.

When Your Numbers Look Bad But Aren't

I've had clients nearly cancel projects because their retention numbers looked terrible, when actually everything was working exactly as it should. A financial planning app we built had only 12% day-7 retention, and the client was panicking—but here's the thing, users were opening it once per month like clockwork. They weren't retained in the traditional sense but they were absolutely engaged with the app when they needed it.

This is where standard retention metrics can lie to you. If you've built a utility app—something people use for a specific task rather than daily entertainment—then low day-7 retention might be completely fine. I worked on a medical appointment booking app that had 8% day-7 retention but 67% of users came back within 90 days. Were those numbers bad? Not at all, because people only book appointments every few months.

When Cohort Analysis Saves You

The problem with looking at raw retention percentages is they don't tell you what's actually happening. You need to dig into cohort analysis; breaking down users by when they installed and tracking their behaviour over time. We discovered one app's retention looked awful because a big marketing push brought in loads of the wrong users. When we filtered to look only at users from organic search, the numbers jumped from 15% to 34% day-7 retention. Sometimes poor retention is actually a visibility issue—if people can't find your app organically, you might need to improve your app store ranking quickly.

Seasonal and Use Case Patterns

Some apps are just meant to be used seasonally or sporadically. A tax filing app will have massive drops in retention after April, but that doesnt mean its failing. A recipe app might see lower weekday retention but spike on weekends. I mean, context matters more than the raw numbers ever will—you can't judge every app by the same standard retention curve. Understanding these patterns helps you plan for long-term app relevance rather than panicking over seasonal dips.

Conclusion: Making Sense Of Your Data

Here's what I've learned after years of tracking retention across dozens of apps—the numbers alone don't tell you anything useful until you understand the context behind them. I mean, you can stare at a 15% Day 30 retention rate all day, but it wont help unless you know what's actually happening inside your app. Are people leaving because they achieved what they came to do? Or are they frustrated and giving up? The difference matters.

What I do with every client is look at retention alongside behaviour data. If you've got low retention but high session times before people leave, that's a different problem than low retention with people bouncing after 30 seconds. I worked with a fitness app where Day 7 retention was only 22%—sounds terrible, right? But when we dug into the data, we found that users who completed their first workout had 61% Day 7 retention. The problem wasnt the app; it was getting people to actually start using it. Once we fixed the onboarding flow, overall retention jumped to 34% within six weeks.

The real skill is knowing which metrics to pair together. Retention plus feature usage tells you if people are finding value. Retention plus session frequency shows you if you're building habits. Retention plus crash data reveals technical issues you might have missed. I always say—and I know its a bit obvious—but your retention rate is just the starting point for a conversation, not the answer itself. The apps that succeed are the ones where teams regularly review this data, spot the patterns, and actually do something about what they find. If your retention looks bad, dig deeper before you panic; if it looks good, dig deeper before you celebrate.