App Store Optimisation isn’t a formula — what we’ve learned from getting it wrong (and right)!
When we started working more deeply with clients on App Store Optimisation (or ASO), we assumed something fairly simple: If we apply best practice consistently, we’ll see consistent results.
The reality? We didn’t.
Some changes we were confident about delivered modest gains, some delivered nothing, and a few behaved in ways we didn’t expect at all.
That’s when we realised something important: ASO isn’t a checklist. It’s a context-driven discipline.
Here’s what we’ve had to rethink.
1. The “one size fits all” trap
There is no universal ASO formula. What works for a challenger fintech app, a niche B2B product, a consumer lifestyle brand or a global household name can vary dramatically.
Search intent differs. Brand awareness differs. Competition density differs. Category behaviour differs.
We’ve seen similar optimisations produce very different outcomes across clients. Not because the advice was wrong, but because context matters.
ASO should reflect:
Your product maturity
Your audience behaviour
Your brand recognition
Your competitive landscape
Not a generic blog post checklist (ahem…)
2. Keyword priority: don’t bury what matters
Another common mistake: treating keywords like decoration rather than hierarchy. Not all keywords are created equal 🙂.
Your most valuable terms should:
Appear prominently (even the app name carries weight — it’s maybe your biggest ASO asset!)
Be positioned intentionally
Align with how users actually search
We’ve seen well-written listings that technically include the right terms, but bury them so deeply they lose impact.
At the same time, the opposite extreme is just as risky…
3. Algorithms are smarter than we think (and getting smarter!)
Keyword stuffing is no longer clever. It’s obvious and it can work against you.
Apple and Google’s algorithms have evolved. Relevance, clarity and coherence matter far more than density.
Another recent shift is that Apple now analyses the written content within screenshot creative. Screenshots aren’t just there to drive installs any more — they also influence how the store interprets your app.
That means your metadata, messaging and creative need to work together for both discoverability and conversion optimisation. Optimisation today is about alignment, not volume.
4. Brand vs product search: know which game you’re playing
If you’re Apple, people are searching for “Apple”. If you’re not, they’re probably searching for “smartphone”.
There’s a big difference between a brand-led search strategy and a product/category-led one. It’s easy for early-stage apps to overestimate brand demand, and for established brands underplay it.
Before optimising anything, ask a simple question: are users searching for us or are they searching for a solution?
Your answer should shape everything else.
5. Seasonal trends: get there before everyone else
Seasonal trends absolutely matter for ASO keyword performance. But if you add “Black Friday” to your keywords the week Black Friday starts you’re already behind.
By the time everyone else is visibly optimising for a seasonal spike, competition is crowded and rankings are harder to influence.
The bigger gains come from anticipating intent early and planning ahead — not reacting late.
6. The biggest lesson: keep testing, keep iterating
If I had to distil it down to one piece of advice, it’s this: keep testing, keep iterating.
ASO should be treated as an ongoing discipline, not a one-off exercise.
A few tips for running A/B tests for ASO:
Change one thing at a time
Give it enough time to gather meaningful data
Resist the temptation to stack multiple changes (if you change everything at once, you won’t know what shifted performance)
Across our ASO work we’ve had wins, we’ve had inconclusive tests, and we’ve had results that forced us to rethink assumptions.
That’s part of the process.
ASO isn’t about finding a perfect formula. It’s about building a smarter approach to app store visibility, organic installs and conversion optimisation over time.
In an upcoming post, we will explore what running these experiments looks like in practice — because knowing what to optimise is one thing. Executing it well is another.
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