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Any agency can raise your install count – there is always someone willing to sell cheap volume. The number that decides whether an app succeeds is how many of those people are still opening it a month later, and whether they were worth more than they cost to acquire. That is a different job, and it starts after the download.
App store optimisation is the storefront: listing, screenshots, keywords, conversion from page view to install. It is essential and it is one stage of a much longer sequence.
App marketing covers the rest – where demand comes from before the store visit, what happens in the first ninety seconds after opening, why people stop coming back in week two, and whether the users you bought will ever be worth what they cost. Most apps that fail do not fail at the storefront. They fail at day seven.
Our app growth service covers the store-side work in detail. This page is about the full lifecycle around it.
This is the uncomfortable part of app growth: several of the stages that decide whether it works belong to product, not marketing. An agency that only talks about the stages it controls is not describing the problem accurately.
If the audit finds the problem is onboarding or activation, we will say so – and recommend fixing that before spending on acquisition. It is a smaller engagement for us and the only advice that makes sense.
What is a user worth, and how long before they have paid back what you spent acquiring them? Until you can answer that, every cost-per-install target is arbitrary and every scaling decision is a guess.
Most app marketing goes wrong here rather than in the campaigns. A cost per install that looks cheap is expensive if those users never activate, and one that looks costly is a bargain if they subscribe for two years.
Organic channels cost time and compound. Paid channels cost money and stop the moment you stop. Almost every app needs both, weighted by how long you can wait and how much you can spend.
Push is the most abused channel in mobile. Sent well it rescues users who were drifting; sent badly it is the fastest route to a disabled notification permission you can never get back.
We measure notification programmes on retention and uninstall rate, not on open rate. A campaign with excellent opens that quietly increases uninstalls is a loss, and open rate alone will never show you that.
Mobile attribution changed permanently when the platforms tightened privacy. Some of what agencies still promise is no longer technically possible, and pretending otherwise leads to decisions based on numbers that are quietly wrong.
What replaced precise per-user tracking is a mix of aggregated reporting, incrementality testing and cohort analysis. It is coarser, slower, and honest – and it is what we build reporting around.
Growth work is a sequence of small tests, most of which fail. The value is in running enough of them properly that the ones which work are recognisable rather than lucky.
Creative testing is the exception to slow experimentation – ad creative decays fast and needs constant refresh, so that loop runs weekly while product-side tests run over months.
If the audit finds retention is broken, spending on acquisition makes the problem more expensive rather than solving it. We would rather delay the campaign than run it into a leaking funnel.
What the app does, who it is for, what a user is worth and how the business makes money from them.
Category competitors, what they charge to acquire, which channels they use and where the gaps are.
Cohort analysis of the existing user base. Where people drop, and whether the problem is acquisition or the product itself.
Channel mix, budget, targets and the sequence – usually fixing the biggest leak before opening the tap wider.
Tracking verified end to end, creative built, audiences defined and measurement agreed before a penny is spent.
Deliberately small at first, across a few channels, to establish real cost and quality figures rather than assumed ones.
Lifecycle messaging configured against behaviour, so acquired users have reasons to come back before they drift.
Weekly creative refresh, monthly channel review, and reallocation toward whichever source produces users who stay.
Widening spend only on channels whose cohorts have proven they retain and pay back. Scaling a bad cohort just loses money faster.
Targeting shifted toward the cohorts that reach the core value, which changes what you can afford to pay for them.
Cost per activated user usually falls even when cost per install rises – because the mix improves.
Lifecycle messaging and onboarding fixes turn a decaying curve into one that levels off, which is what makes an app viable.
Higher lifetime value and lower effective cost shorten the time before a user repays their acquisition.
Cohorts, retention curves and channel-level quality rather than an install count that tells you nothing.
ASO, content and referral compound quietly, so the proportion of free installs rises over time.
A mix that includes organic and referral is far less fragile when a paid channel changes its rules or costs.
Budget moves toward the channels whose cohorts retain, and away from the ones producing cheap users who vanish.
A fitness app and a food delivery app have completely different natural usage frequencies, so comparing their retention curves directly is meaningless. We benchmark against your category, not a global average.
An install number always goes up if you spend more, which makes it a poor way to judge whether anything is working. Every report we produce is organised by cohort – so you can see whether the users acquired in March are still there in May.
We ask what a user is worth before proposing a cost per install target. Without that figure, any target is invented.
It inflates a chart position, corrupts your data permanently, and risks removal from the stores. We will decline the work instead.
If retention is the problem, we say so and recommend fixing it before spending. That is a smaller engagement and the right advice.
We do not claim measurement precision that privacy frameworks no longer permit. Incrementality testing replaces the certainty that was lost.
Push programmes are measured against retention and uninstall rate, not open rate – because opens can rise while the app is being deleted.
ASO, content and referral run alongside paid so the organic share grows and the whole thing is less fragile.
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Reliable process, clear communication.
We're here when you need us.
Tailored to your business goa
Reliable process, clear communication.
We're here when you need us.
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Everything involved in getting the right people to install an app and keep using it – demand generation, store conversion, paid and organic acquisition, onboarding, lifecycle messaging, retention and monetisation. Installs are the midpoint of that sequence rather than the goal.
App store optimisation is the storefront: listing, screenshots, keywords, and conversion from page view to install. App marketing covers everything before and after – where demand comes from, what happens in the first ninety seconds, and why people stop returning in week two. ASO is one essential stage of the wider job.
By targeting on what happens after the install rather than on install cost. Campaigns are optimised toward activation and retention events, which usually raises the headline cost per install and lowers the real cost per user who matters. Cheap installs from low-intent sources are the most common waste in mobile budgets.
Apple Search Ads, Google App Campaigns, Meta, short-video platforms and selected networks – chosen by where your audience actually is rather than by habit. We start narrow to establish genuine cost and quality figures before committing budget at scale.
Marketing can improve the retention of the users it acquires by targeting better, and lifecycle messaging can recover people who are drifting. What marketing cannot fix is an app people do not want to use. If the audit points at onboarding or core product value, we will say so plainly.
By cohort. Every report shows how the users acquired in a given period behaved over the following days and weeks – retention, activation, revenue, and cost against value. Install totals appear but are never the headline, because they always rise when you spend more.
Day one, seven and thirty retention, activation rate, cost per activated user, lifetime value and payback period. Whether the retention curve flattens matters more than any single number – a curve that levels off means a viable business, one that keeps falling does not.
Yes, and they need separate treatment. Store mechanics, attribution, user behaviour and typical economics all differ. Running one strategy across both and averaging the results hides which platform is working.
Store listing improvements can move conversion within weeks. Establishing reliable cost and quality figures takes six to eight weeks of running campaigns. Retention improvements need a full cohort cycle to prove, so meaningful conclusions take two to three months.
It should – they are complementary and we usually run both. Store optimisation raises the conversion rate on all the traffic that acquisition sends, so improving the listing first makes every subsequent campaign cheaper.
Send us your app and your analytics. We will map the retention curve, find the biggest drop, and tell you honestly whether more marketing is the answer or whether something upstream needs fixing first. Free, no obligation, findings yours either way.
The businesses that win aren’t just found – they’re found first. We make that happen, from local search to your entire digital presence.
