How Do I Set Realistic Growth Targets for My Mobile App?

Setting growth targets for a mobile app sounds like a planning exercise. In practice, it is a test of how well you understand your own product. The numbers you put in a deck or hand to an investor are only as good as the assumptions underneath them, and the majority of teams have not examined those assumptions at all.

Growth targets built on downloads alone are wishes dressed as numbers.

We see this regularly at WAA. A team arrives with confident projections built on download forecasts, market size estimates, and a rough idea of how many users they want by the end of the year. What they have not done is ask what percentage of those users will still be in the app on day three, day seven, or day thirty. That gap between downloads and active retained users is where most growth targets fall apart.

The good news is that building realistic targets is not complicated. It requires a clear acquisition model, an honest retention baseline, and a willingness to stress-test projections against what the data actually shows. This article walks through each of those steps.

Why Most Growth Targets Are Just Guesses

The typical growth target starts with a market opportunity. Someone looks at how many potential users exist in a category, applies a percentage they feel is achievable, and calls that a target. It feels analytical, but the input figures are almost entirely speculative. There is no connection to how the product actually behaves with real users.

The more honest problem is that teams set targets before they have the data to set them. They have not yet run a cohort through the product. They do not know their day-one retention rate, their activation rate, or how long a typical user stays engaged. So the target is essentially a guess with a formula applied to it.

This matters because a target built on guesses can easily be hit for the wrong reasons. Download numbers can grow while retained users stagnate, and a team celebrating milestone downloads may be watching a product quietly losing ground. We have seen this pattern on products where acquisition was strong and retention was invisible, and the two were never connected in the same report.

Targets become realistic when they are built backwards from what the product has demonstrated, rather than forwards from what an addressable market suggests is possible.

Define Your Acquisition Model Before Setting Any Number

Before any growth number goes on paper, the team needs to agree on how they plan to acquire users. Organic discovery through app store search, paid social advertising, referral programmes, and partnership placements all produce very different user quality, conversion rates, and costs. A target that does not name the acquisition channel is not a target at all.

The channel matters because it shapes the kind of user who arrives. A user who found the app through a targeted social campaign is likely at a different level of intent than someone who found it organically while searching for a solution to a specific problem. Their likelihood of staying beyond day one differs meaningfully.

We worked with a health and wellbeing product where the team had set user number targets without distinguishing between paid and organic acquisition. When we separated the two cohorts, the organic users retained at almost twice the rate of paid users. That distinction completely changed the economics of the growth plan, and the targets needed to be rebuilt around it.

Before writing any growth projection, list every channel through which you plan to acquire users. For each one, estimate the cost per install and your best guess at the day-seven retention rate for that cohort. If you cannot estimate the latter, the target is not ready to be set.

Acquisition model and retention model are inseparable. The number of users you plan to bring in is only meaningful alongside the number you expect to keep.

Establish a Retention Baseline First

A growth target without a retention baseline is built on sand. Retention tells you what fraction of the users you acquire are still present at each point in time, and it is the single most important variable in any long-term user number projection.

On an art-based auction game we worked on, the product launched with retention in the mid-sixties to low seventies. That was a strong baseline, and the team's growth targets were set with that figure in mind. But as the quantity of available games on the platform declined over time, usage became sporadic and retention fell to around 30 to 35 per cent. The team raised this concern, but the client pushed for new features rather than addressing the core content supply issue.

By the time retention hit that low point, the gap between the original targets and actual user numbers was significant. Fixing the underlying problem eventually restored retention to the mid-sixties to low seventies, but the recovery took time because user trust had to be rebuilt alongside the product experience.

Retention metrics can take time to recover even after the product problem is fixed.

That project illustrated something we come back to with every client: the retention baseline is the foundation on which every growth number sits. If the baseline shifts, the targets shift with it, and any projection made against the old figure becomes unreliable.

Anything below 50 per cent retention after day one should be treated as a warning. The goal is to push that figure as high as possible, and then sustain it through to day thirty and beyond, particularly if the team is spending money on user acquisition and needs that spend to pay back.

The Danger of Using Downloads as a Proxy for Growth

Download numbers feel like growth because they go up. They appear on dashboards, they feature in investor updates, and they tend to be the first metric a team reaches for when asked how the product is doing. But a download is an expression of interest, not a measure of engagement, and the gap between the two is where most growth stories quietly unravel.

Around 25 per cent of mobile apps are abandoned after a single use, according to Localytics. That means for every four users a team celebrates acquiring, one of them closed the app and never returned. If that proportion is not built into the growth model, the model is wrong from the first month.

We developed a water tracking app where we were interpreting user silence as approval. Download numbers were growing. There were no support complaints. The product felt healthy. When we looked properly at retention data, the pattern looked similar to what we see in personal finance apps, where approximately 71 per cent of daily active users are lost between day one and day thirty, according to figures from the top ten apps in that category. The downloads were telling us one story, and the retention data was telling us a completely different one.

Growth targets built on download volume will almost always overstate the size of the active user base. The number that actually matters is retained engaged users, and that figure requires a much more honest accounting.

Create two columns in any growth projection: downloads and retained users. Run both numbers at every time point and check whether the gap between them is growing or narrowing. A growing gap is a signal that the acquisition engine is outpacing the product's ability to hold users.

How Early Churn Distorts Your Projections

The most common modelling error we encounter is treating churn as a flat monthly rate applied uniformly across the user base. In reality, churn is front-loaded. Users are most likely to leave in the first three days, and if they survive that window, they are meaningfully more likely to stay long-term.

On average, 77 per cent of apps lose their daily active users within the first three days of installation, according to Business of Apps. Even well-built products typically see a 40 to 50 per cent drop in retention after three days. The difference between those two figures, 77 per cent and 40 to 50 per cent, represents the strategic value of strong onboarding and a clear early product experience.

If a growth model applies a flat monthly churn rate, it will miss the spike that happens in the first 72 hours completely. That means any projection past the first week is likely overstating active user numbers, and compounding errors carry forward into every subsequent month.

The practical fix is to model churn in stages rather than as a single rate. Build a projection that accounts separately for day-one retention, day-three retention, and day-thirty retention. Each has a different curve and a different cause, and conflating them produces targets that are structurally wrong.

If you are building a projection in a spreadsheet, use three separate retention columns rather than one. Model day one, day three, and day thirty as distinct drop-off points and apply the appropriate rate to each. Then build your forward projections on top of the day-thirty cohort size, not the download figure.

Building Milestone-Based Targets That Account for Attrition

A single annual user number is a destination with no map. Milestone-based targets break the journey into stages, and each stage must account for the attrition rate the product has demonstrated rather than the rate the team hopes to achieve.

The structure works like this. Start with a projected download number for month one. Apply your day-one retention rate to get the cohort still present after the first day. Apply your day-three and day-thirty rates in sequence. The number at the end of that chain is your actual active user base from month one's acquisition. Repeat for each subsequent month and stack the cohorts.

Between funding rounds on one of our own products, we made the decision to put in much better tracking of user retention and to ask users proactively how things were going. We had been using investor confidence as a proxy for product health, and the actual retention picture had not been properly examined. When we built milestone targets on top of real retention data rather than optimistic assumptions, the numbers changed, and the decisions about where to invest changed with them.

Milestones also serve a different purpose when talking to investors or internal teams. They replace a single end-of-year number with a series of checkpoints, and that makes accountability far clearer.

What Happens When You Ignore the Core User Journey

Growth targets frequently focus on the top of the funnel and treat the product itself as a constant. The assumption is that if enough users are acquired, the numbers will work out. What that misses is that the quality of the core user journey directly determines what the retention rates are, and therefore what the targets can realistically be.

On the auction game project, the team's instinct when retention fell was to add new features. The assumption was that more product would equal more engagement. What the data was actually showing was that the core reason people used the product, playing auction games, was no longer available in sufficient quantity. Adding features on top of a broken core experience did not help. It delayed the real fix.

We worked on a health and wellbeing product where onboarding completion was lower than expected. Rather than redesigning the whole flow, we looked at where in the journey users were dropping off. The issue was a single progress bar that moved by a tiny amount with each completed step, giving users no sense of forward momentum. We replaced it with a segmented bar showing progress within each section of the onboarding flow. Users completing the same number of steps felt meaningfully further through the process, and completion rates improved. The core journey had a friction point, and fixing it changed the retention numbers that the growth targets depended on.

Any growth target that does not account for the experience quality of the core user journey is making an optimistic assumption about something that deserves scrutiny.

How to Stress-Test Your Targets Before Presenting Them

A growth target that only works under the best conditions is a hope. Stress-testing means running the same projection under several different scenarios and checking whether the business still makes sense when the numbers are less favourable.

The three scenarios worth running are a base case built on current retention data, a downside case where retention drops by 15 to 20 per cent from that baseline, and an upside case where acquisition runs ahead of plan. The question each scenario answers is different.

1. The base case tells you whether the target is achievable given what you already know.
2. The downside case tells you how much headroom the business has if early churn is worse than expected.
3. The upside case tells you whether faster acquisition would actually improve the business or just accelerate the same retention problem at greater cost.

Getting an accurate app store listing is one practical lever that affects the downside case directly. If the listing accurately communicates what the app does, the users who download it have already self-selected as the target audience. They arrive with correct expectations, which reduces the early abandonment caused by a mismatch between what the marketing promised and what the product actually does. That alone brings the abandonment rate down in a way that improves the base case without any changes to the product itself.

Stress-testing is also useful as an internal check before a presentation. If the downside scenario produces a number that makes the business plan unworkable, the targets need to change before they reach an investor, not after.

Presenting Growth Targets to Investors and Internal Stakeholders

The way a growth target is presented changes how it is received. A single number with no methodology behind it invites scepticism, and rightly so. A target accompanied by the retention data it was built on, the acquisition channels it assumes, and the scenarios it has been tested against reads as something a team actually understands.

Lead with Retention, Not Acquisition

Sophisticated investors know that download figures are a weak signal. Framing the conversation around retained active users rather than download volume signals that the team understands where value actually accumulates. Showing a clear day-one, day-three, and day-thirty retention rate alongside the target tells the reader those figures are being tracked and taken seriously.

Explain What Changes the Number

The most useful part of any target presentation is the list of assumptions that, if wrong, would change the number. If retention drops below a certain point, what does the model look like? If a paid acquisition channel becomes more expensive, what happens to the cost per retained user? Naming these dependencies is a sign that the team has done the thinking.

Internal teams benefit from the same discipline. A growth target presented to an engineering or design team without retention context gives them no way to connect their work to the number. Showing how a retention improvement of five percentage points compounds over six months makes the relationship between product quality and business outcome visible, and that is a more useful conversation than a target alone.

Conclusion

Realistic growth targets are built from the product outward, not from the market inward. The market tells you what might be possible. Your retention data tells you what is actually likely given how the product is performing with real users right now.

The work is honest. It means tracking what happens to users after they download, alongside counting the downloads themselves. It means building projections that account for the front-loaded churn that every mobile app experiences. It means naming the acquisition channels and the assumptions attached to each one. And it means stress-testing the numbers before presenting them so that a downside scenario does not arrive as a surprise.

The teams that get this right are the ones who accept that silent users are not happy users. Around 25 per cent of apps are used only once, and most of those users never submit a complaint or a cancellation reason. They simply leave. Building targets that account for that reality rather than assuming it away is what separates a growth plan from a wish list.

Targets built on retention data rest on firmer ground than any alternative. That is worth fixing before the next investor conversation or board presentation, and it is the kind of work we do directly with teams at WAA.

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