Have an idea for the next big app? Be careful. It’s probably going to fail.

After all, 99.5% of consumer apps fail.

Below 1000 downloads: 67.8%. Below 1000 users: 17.9%. No user retention: 6.6%. No revenue: 5.8%. No profit: 1.4%. Success: 0.5%.

A graph of apps that succeed and apps that fail based on our estimates.

It’s a scary number, but you can’t fight the truth. Trust us: we tried.

After examining statistics like “9,999 in 10,000 apps fail,” we set out to prove them wrong.

We did, but 0.5% isn’t much better, is it?

But remember: it’s a lot more nuanced than just a number. Countless factors can make your app more or less likely to succeed. In this post, we’ll go through the statistics, debunk some myths, and show you how we calculated these numbers. At the end, we’ll talk about how to find any hope of success.

And if you’re strategic enough (possibly with the help of our expert guide to fail-proofing your app), you may end up in the lucky 0.5%.

Only 0.5% of Consumer mobile apps succeed

You read it: 0.5% of consumer apps succeed.

It’s not 50%, nor is it 5%. It’s 0.5%.

Why so low?

Because it’s really hard to make any money with them.

Flappy bird

Flappy bird was incredibly popular but got taken down because the creator did not want to keep up with the burdens of maintaining an app.

  • Most apps don’t have product-market fit. They may be good, but there isn’t a market. We talk all about how to find product-market fit in this guide.
  • Consumer apps can topple in a day. Remember Flappy bird? The second you stop investing all your time and energy into it, the public forgets about it.
  • Consumer apps need a ton of marketing. The app store is full of good ideas no one knows about.
  • These apps are expensive. Read all about how much it costs to build an app
  • They don’t have any good business infrastructure. An app needs a business to succeed.
  • Most forget about analytics

Business apps have it a little better, though.

About 13% of B2B mobile apps succeed

Business apps have it a little easier. They come with a few key advantages.

  • Businesses are already starting with capital, revenue, and product-market fit
  • More often than not, the business needs to remove a cost, not to make more revenue
  • Business teams already know about the power of marketing and strategy

Consider the R&G Medical app. It lets legal professionals view medical scans from any web-based device — no special hardware needed.

Medical scans

The app we built for R&G Medical made medical scans viewable through a web browser instead of a special device. This saved them a lot of time and money in the courtroom.

This app was a game-changer. It didn’t bring in revenue, but it made the company’s job significantly easier and saved a ton of money.

That’s why business apps tend to do better: they succeed without making profit.

But that doesn’t mean business apps can’t fail.

  • Bureaucracy can make a business far too slow to develop an agile app
  • Some businesses are out of touch with employee or customer needs
  • An over-focus on business metrics can destroy the user experience
  • Sometimes, the app just doesn’t succeed.

Of course, these numbers depend on the industry, the idea, and so many other factors.

But in general, apps are high-risk, high-reward products. Unless you’re really building something special, 0.5% of consumer apps and 13% of business apps will succeed. We’ll get to the details later if you’re interested.

That doesn’t mean that other people won’t use other numbers, however.

What about the 0.01% statistic?

People say that 9,999 out of every 10,000 apps (99.99%) fail, but it’s not true.

In fact, it’s pretty easy to disprove.

As of March 2021, there were 2.9 million apps on the Google Play store (over 8.9 million apps total). If the 0.01% success statistic were true, only 290 google play apps would be considered successful.

That’s a pretty narrow definition of success.

So why does everyone think so many apps fail?

How the myth started

Way more than 0.01% of apps succeed. But for some reason, when you google “How many apps fail,” you get the same untrue statistic.

Featured snippet with incorrect information

The results for “How many apps fail” are wrong.

Here’s the issue: the original number was taken way out of context.

The original source of this statistic is this 2014 report from Gartner, Inc: a major research firm and member of the S&P 500.

Gartner's report

The original report is always taken out of context

The report itself is fine, but people misinterpret what it’s trying to say.

Gartner is making a rough estimate that most consumer app developers won’t make as much money as they expect.

It’s a pretty reasonable guess: everyone thinks they’ll be in the top 29, but you probably won’t make it.

But people all across the internet took the number completely out of context. Here’s why most citations aren’t relevant

  • Gartner is only talking about consumer mobile apps (which are notoriously unsuccessful), not all mobile apps
  • They only talk about financial failure. Brand-building apps are successful without making money (and Gartner makes this distinction very clear)
  • The number is a rough estimate, not an exact figure
  • Every prediction is based on 2014 data. The report also predicts that cross-platform frameworks and free-to-install apps will flop

This context is really important. Gartner never claimed that nearly every app fails. Instead, they looked at the 2014 app landscape and warned amateur developers about a potential gold rush.

California Gold Rush

During the California Gold Rush, people rushed to California to find gold that wasn’t there.

So how can we get a more accurate number?

We asked ourselves the same question. Here’s how we came across 0.5% for consumer apps and 13% for business apps.

Methodology

It’s really hard to estimate how many apps succeed. After all, “success” means different things for different apps.

  • Some apps succeed with in-app purchases
  • Other apps succeed by building brand awareness
  • Still more apps succeed by removing a barrier for a business
  • Many apps succeed by collecting data or running ads
  • Some apps even succeed by displaying or organizing data

No single metric can measure an app’s success, especially not money. We used a few methods to control for these different factors. Each method contributed something different to the bigger picture, allowing our experts to make better predictions.

Step 1: Interviews

We started with interviews. We interviewed dozens of experts, whose perspectives on the market influenced how we analyzed data

Some of the most valuable insights came from the team. Since our team works with apps every day, they have a very good idea of what succeeds and what fails.

Our team

This was mostly qualitative research. Their answers informed where we looked next.

Results

Most of the people we interviewed agreed that it depends on the app. An amateur coder’s consumer app without any market research will almost certainly fail. However, an automated business process will probably succeed (not to the same extent as Uber, though).

What this means

These testimonies aligned with the data: most smaller, inexperienced projects will flop. But the longer you survive, the more likely you will succeed.

Drawbacks of this method

Testimonies are anecdotal evidence. They can send you in the right direction, but they can’t find an exact number.

What we learned

The interviews confirmed our professional suspicion that most apps fail and sent us in the best direction for the next steps.

Step 2: Industry Comparison

Since a successful app is a business, we looked at US Bureau of Labor Statistics data presented by lendingtree for comparison.

Results

20% of small businesses fail in the first year, 50% fail by the fifth year, and 66% of businesses fail by the 10th year.

What this tells us

Most small businesses don’t last ten years. But those numbers seem scarier than they are since a business that lives longer is less likely to fail.

After year 1, an additional 30% of the original business population goes away. That means only 7.5% of the original business population fails each year after year 1.

It gets better after that. After year 5, only 16% of the original business population goes away over the next five years. That means only 3.2% of the original business population goes away for the next five years after year 5.

In other words, the longer you last, the more likely you are to survive.

Drawbacks of this method

This method isn’t perfect because small businesses are hard to compare with apps. Apps have a very high startup cost, very low personalization, and very high competition. It is not a perfect map.

This data maps an overall trend, and it varies within industries. The pattern will likely be similar in apps, but the specific numbers will differ.

What we learned

We learned that a lot of businesses fail early on because of poor planning. The longer a business lives, the more likely it will survive. The same is likely true of apps to some extent, assuming the relationship is similar.

Step 3: Data Collection

Finally, we looked at data to determine how many apps fail. Three key performance indicators were especially important.

  • Ratings. An app should be well-used and loved by its users. The number and quality of ratings can indicate success.
  • Installations. A successful app should be installed frequently enough to meet its goals. The number and quality of installations indicate success.
  • User Retention. A successful app keeps its users and gets used somewhat regularly. Usage statistics strongly indicate an app’s success.
  • Financial Data. A successful app either saves money or makes money. The break even point is a good way to measure whether an app succeeds or not.

For ratings and installations, we used Android Rank’s data pulled from the Google Play store. For user retention, we used Andrew Chen’s Quetta data and Statistia’s Google Play and Localytics data. Finally, for financials, we used survey data from App Promo and data from Survey monkey intelligence.

Step 4: Analysis

Once we had data, we needed to anayze it. This took a few steps.

Splitting the apps

We quickly discovered that enterprise and consumer apps needed to be measured seperately. A consumer app needs a constant stream of downloads, while an internal process app only needs a handful. Success is measured so differently that we couldn’t possibly look at them together.

While there’s still a lot of variation within consumer apps, they have similar goals. A brand awareness app still needs lots of downloads, even if profit isn’t as important as a mobile game.

Discrediting metrics

We started by trying to find the success rate, but it didn’t work.

The Android Rank data showed that of 305,378 apps with over 100 ratings, over 187,000 apps are rated above 4 stars (about 61%).

But we encountered an issue with this information: likeability doesn’t always mean success. Vine was well-liked, but not financially successful.

Additionally, ratings aren’t always trustworthy. TikTok’s ratings plummeted to 1.8 stars until 8 million reviews were deleted. Then, the ratings jumped to 4.4 stars.

Instead of defining “success” like most people do, we decided to define “failure.”

Initial sample of failed apps

To begin, we determined how many apps received below 1000 downloads. Of course, many apps succeed with fewer than 1000 downloads, but the first 1000 users have been a staple metric around the world for years, so it was a good starting place.

Immediately, this eliminated most apps. Statista’s Google Play statistics reveal that 68.07% of apps never hit the 1000 download mark.

This number roughly aligns with other data, such as App Promo’s discovery that 67% of developers don’t break even.

The graph is especially revealing.

A graph of app downloads. It forms a bell curve scrunched around the 100-500 range (or lower)

Downloads form a bell curve scrunched toward the failure side of things, revealing that most apps do fail.

In fact, the numbers look pretty bleak. The majority of apps will only receive 100-500 downloads, and very few apps will ever move far beyond that threshold.

But things get bleaker because 1000 downloads are not a success if you can’t keep them.

Narrowing the sample with retention rate

After removing most consumer apps that never get enough downloads, we accounted for user retention.

We used Chen’s data from this post, as graphed below.

Average retention curve is very steep. Most users leave in three days.

In three days, 77% of users leave. After three months, 95% of daily active users are gone.

This information is extremely useful because it explains why so few apps succeed. They get a spike of traffic, then lose almost all of it over the course of a few months.

The graph explains why so many apps don’t have sustained traffic. It also casts doubt on installations as a metric: an app with 10,000 installations may not maintain them.

Yet it also spells out hope for some businesses.

  • If apps maintain a steady stream of new installs, new users replace the old
  • After the first three days, most of the people who left are already gone.
  • 5% of the original installations stick around. That’s higher than the eCommerce conversion rate of ~2%

Essentially, it means that if you can get past the first wave of departures, you’re much more likely to survive.

But retention curves aren’t constant.

Adjusting retention rate based on popularity.

The more popular an app gets, the less likely people are to leave. Retention rates are higher in the top 150 apps than any others, as graphed below.

The more popular apps lose fewer people on this graph

The top 150 apps only lose 20-60% of users in the first three days and 40-80% in the first three months. Note that only the curve for the top 10 apps flattens out pretty high up. The rest trend toward zero.

This information reveals two important points:

  1. If you don’t get more users, your app is guaranteed to fail
  2. Size prevents a quick death

It also explains why App Promo discovered that successful app developers spend $1000+ per month marketing their app. They need more users to replace the old ones, so they strike that balance.

Thus, for an app to survive, it must do one of the following

  • Keep new users coming in to replace the old
  • Engage users to reduce the drop-off rate

Only about 13.9% of apps will have 1,000 users after 3 months according to the above data. However, even many of these apps will fail if they don’t engage users. We estimate that only 7.3% of consumer apps will survive past the first three months.

With the right strategy, many of these users can be replaced. You simply need a good funnel and engagement strategy. Here is an example from clevertap.

30% of people in the funnel complete app registration

With ads, conversion rates can get extremely high. This isn’t true of every app, but if it works for Clevertap, it’s not impossible.

Here’s the issue: this funnel costs money. Most apps don’t have a marketing budget for whatever reason. Thus, hopes aren’t very high if you don’t set money aside for marketing.

But it gets worse when you consider the financial side of success

Calculating financial success

Though these apps may keep users, very few will financially succeed. Consider Survey Monkey’s data. Average revenues are warped by the top few apps.

Most apps make almost zero money A select few make almost all the money

The average revenue for a health app is a whopping $1500/day, but the median revenue is actually zero dollars.

Why? Because most apps don’t make money. Within any given category, only the top 20% actually make money.

It’s even worse as a whole. Take a look at this graph of daily revenue.

The tp 200 apps make $82,500 per day. The next 8,000 make $3,500.

The top 200 apps make 23x more than the next 8,000 combined.

If you define finacial success as getting into the top 200 apps, you have a 0.00007% chance. Even if you want to make it into the top 8,000 apps, you have a 0.003% chance.

But this isn’t a fair comparison. Instead, look at the industry numbers: only the top 20-40% actually makes money.

That means only 1.46% of apps will have any revenue at all.

Breaking even

Revenue alone isn’t a success, either. You have to make money, or at least break even.

As it turns out, that’s hard to do. App Promo illustrates it in the below infographic.

Infographic that says you have to spend money to succeed

Note: This 80% statistic represents only those surveyed, who are much more likely to have a successful app. Our data is more accurate because it uses app store data and includes apps too small to make it into the survey.

Essentially, to make money, you need to have more money to spend than you think.

As a result, most people don’t break even.

It’s impossible to tell for sure, but we estimate that only about 0.5% of these original apps will break even.

Now for B2B.

Adjusting for B2B

This isn’t an exact number, since businesses tend to be less open about negative results, but we can make some very general estimates.

Most B2B apps have a much higher chance of succeeding. Here are a few reasons why

  • The goal is to solve an internal blocker. Once it’s solved, the app succeeds and the company makes money.
  • Companies are almost always far more strategic than the 80% or so independent developers that build consumer apps
  • Businesses have the power of brand and big marketing budgets
  • Most importantly, a business already has the business infrastructure that keeps an app alive.

Because this business infrastructure exists already, it’s less likely to fail. We estimate that about 13% of b2b or internal apps succeed, versus an estimated 0.5% of consumer apps.

Flaggerade app

This is a huge difference, but it makes sense. Consider business apps like Flaggerade (you can read about how we built Flaggerade for more details). Flaggerade is a shift management and automation app for one of the biggest road sign and work zone companies in Arizona. It succeeds by making an internal process more efficient, not by bringing in more revenue.

An internal app like this pretty much succeeds by default.

In fact, consumer apps are at a huge disadvantage as a whole.

  • Product market fit hasn’t been proven yet
  • Customers need to be discovered
  • Business infrastructure needs to be built from scratch
  • Funds are much harder to come by
  • The risk is much higher. Businesses have more room for failure and quality testing
  • They must generate revenue. Eliminating a cost isn’t an option for consumer app success.

Business apps succeed because they eliminate a problem that costs money. That’s much easier to do than to make money from scratch.

But even if they don’t, they have the necessary business infrastructure to survive.

Look at how many business apps get installed each week.

 

Infographic: New Downloads of Business Apps Plateau | Statista You will find more infographics at Statista

Most of these apps are tied to a business and can float on that extra revenue until they make money of their own.

Launching an app is so much easier with a business on the backend. It’s that simple.

But don’t worry. There’s still hope

The hope

That 0.5% figure seems scary, but it’s not the end of the world.

This number gets better if you think strategically.

Think about it: we’ve made some important discoveries about why apps die. Your app can succeed if you take the right steps.

Most importantly, remember the “great filter.”

The Great Filter is a possible explanation for why we haven’t found any aliens yet.

It’s a weird analogy, so hang in there.

There are 10^18 planets capable of sustaining life, but zero civilizations have colonized the universe. The Great Filter basically describes the steps to universal colonization as filters that planets have to pass through.

The Great Filter

Earth has already passed the multi-cellular life and civilization filters. We don’t know what filters are ahead of us, but we do know that we’re further along than planets without civilization.

Apps are the same way.

Let’s look at that graph again to get an idea of what the filters are.

Below 1000 downloads: 67.8%. Below 1000 users: 17.9%. No user retention: 6.6%. No revenue: 5.8%. No profit: 1.4%. Success: 0.5%.

The biggest filter is the first 1000 downloads.

Once you have 1000 downloads, you’re better off than 67.8% of all apps.

That’s huge.

And once you hit 1000 active users, you’re better off than 85.7% of all apps. Again, that’s huge.

That’s why it’s so important to find 1000 users before you build.

If you establish product-market fit, you’ve likely passed the first three filters. Then, it’s just up to your monetization strategy.

There’s hope. You just have to be really strategic.

That’s why we made this in-depth guide to building a fail-proof app. But if you want more information, use this contact form. We’ll walk you through how to fail-proof that app idea.

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Notes on the estimates

0.5% is not a prophecy. Further research may reveal a number much higher or lower.

The math discussed in this post was used as an aid to help our expert designers and developers make an estimate. It opperates on lots of assumptions that just can’t be measured, so there’s no way to tell if we are right or wrong without a massive study well beyond our resources. Do not prescribe it as an undeniable truth.