Review miningrankcusp's concrete return: why search visibility is your next growth lever
What is in this resource:
How to build your ASO strategy from end to end
Learn to pick target keywords, write metadata, follow rank and base every decision on data.
From data to action
Simplify the keyword data and stay in line with App Store rules.
Tools that lift rank
Carry the keywords drawn from competitor reviews into your metadata and take your app out of the cusp band into the top 10.
What can be measured and what cannot
The time spent on ASO has a measurable return - but the measurable part cannot be computed until it is separated from the part that is not. This guide is about separating them and deciding on the first only.
ASO's return is usually asked for in one sentence: what is this work earning us? The honest answer has two parts and they must not be mixed.
What we can measure is visibility. We can measure where you rank for a phrase, we can measure that phrase's relative popularity, and putting the two together we can say how your visibility changed. That measurement is real and verifiable.
What we cannot measure is conversion. How many of the people who saw your app's page installed it is shown only by your own developer account. No tool looking from outside can measure it.
So ASO's return is measured at the first link of the chain: did you become more visible in search results? The rest of the chain - visibility to install, install to use, use to revenue - is measured in your own data and nobody outside can see it.
Calculations that do not make that separation all end in the same place: a visibility increase multiplied by an invented conversion rate. The number comes out large, nobody can argue with it, and the decision is made on the wrong data.
Computing visibility
Visibility is rank combined with popularity. Rank alone does not say how many people you reached; popularity alone does not say what share of them is yours.
The basis of the calculation is a simple observation: the first page is where almost all of a search ends. Users do not scroll; they open one of the first few results. So the distance between ranks is not linear - going from 10th to 9th is far more valuable than going from 30th to 29th.
That is why the higher ranks carry more weight when visibility is computed. We do not publish an exact conversion curve, because that curve varies by category, country and search type, and we do not have enough data to measure it per category.
What we do have is relative comparison: where you are for the same phrase against last month, where you are against rivals in the same category, and before against after a move. Those three comparisons let you decide without an absolute figure.
The practical use: instead of rank times popularity per phrase, use the bands. If your count of top-10 phrases rose, your visibility rose. If your count in the 11-30 band rose, it will rise next month. If your count outside 200 fell, relevance is being built.
- Phrases in the top 10: today's visibility.
- Phrases at 11-30: next month's visibility.
- Phrases outside 200: ground with no relevance yet.
- The monthly change in those three gives direction without an absolute figure.
The cost of time
The other half of a return calculation is cost, and in ASO cost is almost entirely time. The tool fee is known and small; the real expense is the hours.
A weekly loop typically takes these hours. Looking at the measurements and pulling the band, half an hour. Choosing the move and writing the metadata, an hour. Shipping the release - if that is not already happening - half an hour. Comparing a week later, half an hour. Two and a half hours in total.
The monthly work is separate: re-scanning the pool, an hour. Updating the rival list, half an hour. Looking at review mining, an hour. Another two and a half hours.
So a systematic ASO loop is about twelve hours a month. That number matters because the comparison is made from it: where else could those twelve hours have gone?
There is an unmeasured cost too: attention. Changing metadata in a release widens that release's test surface and adds an item to the product team's agenda. In small teams that cost can weigh more than the hours.
The most effective way to lower cost is to narrow the scope: one country, six phrases, one move a week. Widening scope does not raise cost linearly - it raises it faster, because attention divides.
Which kind of move has a return
After a few months of measurement the most valuable question becomes: which kind of move works for our app? The answer is not in a general guide; it is in your records.
- Split the moves by typeAdding a phrase to the app name, rewriting the subtitle, cleaning the keyword field, filling a new country field, changing category. Five types is enough; finer distinctions leave too few examples to mean anything.
- Count how many times each was triedIf a type was tried fewer than three times you cannot say anything about it. That means the answer is "unknown", and writing unknown is better than writing zero.
- Subtract the control group every timeEvery move's result has to be computed with that week's average control-group movement removed. Otherwise you write the market's movement down to your own move.
- Work out the win rateFor each type: how many times tried, how many won, average places gained. Those three numbers set the next month's plan almost by themselves.
- Drop the lowest-rate typeThe biggest gain in a return calculation is here. Dropping work frees more time than increasing other work - and it is a hard decision, because the dropped work is usually somebody's favourite.
The four mistakes this calculation invites
This calculation is easy to get wrong, and when it is wrong it looks convincing. Here are the four.
The first: inventing a conversion rate. "Visibility doubled, our conversion is three per cent, so installs rose by this much." We cannot measure conversion from outside, and your own rate varies from phrase to phrase.
The second: forgetting the control group. In a period when every phrase rose, your phrases rising is not the result of your move. Every calculation made without subtracting the control group writes the market's movement down to itself.
The third: measuring over too short a period. A metadata move takes a few days to reach the index and a few weeks to settle. A return calculation built on two weeks of data is mostly measuring noise.
The fourth: counting only the wins. A move can win on one phrase while losing on another - a word removed from the subtitle weakens the phrases it was holding up. A net calculation has to include the losses.
All four err in the same direction: they make the return look larger than it is. That makes it easier to keep going, and it makes wrong moves repeat.
| Mistake | The correction |
|---|---|
| Inventing a conversion rate | Cut the chain at visibility; measure the rest in your own data. |
| Forgetting the control group | Subtract the untouched phrases' average every time. |
| Measuring over two weeks | At least a month; a move takes time to settle. |
| Counting only wins | Include the phrases the same move weakened. |
Comparing ASO with paid acquisition
The return question is usually asked in order to make a comparison: should this time go to ASO or to advertising? The two sides cannot be calculated the same way, and it is worth knowing why.
In paid acquisition cost is measured directly: money spent, installs received, the ratio between them. The whole chain is visible in your own account and the number is clean.
In ASO the cost is time and the return is visibility. The chain does not end in the same place: the step from visibility to install depends on the quality of your page, and that step is measurable but not attributable to ASO.
The real difference between them is persistence. When advertising stops the traffic stops; a rank you won does not - it stays as long as it is maintained. That is an argument in ASO's favour, but a structural one rather than a measurable one.
The second difference is compounding. A user arriving from search is a user who searched for that phrase; their intent is known and their conversion is usually higher. That is structural too, and again not measurable from outside.
An honest comparison is built like this: cost per install on the advertising side, first-page phrases won per month on the ASO side. Two different units that cannot be divided into each other - but tracked over time, it becomes clear which one is accelerating.
When to stop
The least liked and most necessary section of a return guide: when ASO should stop.
The first stopping point is the strength threshold. If the median rating count of the top 10 is far above yours across every phrase you target, there is no distance left to take with metadata. Continuing is time spent for nothing; the work has moved to the product.
The second is the band running dry. If nothing is left at 11-30 waiting to be spent and the pool is producing no new candidates, the loop is turning over nothing. A new country or a new source of phrases is needed.
The third is conversion. If your rank rises and your downloads do not, the problem is not ASO - it is the page or the product. One more keyword does not fix that; changing the screenshots and the first two lines does.
The fourth is the record. If three months of moves have produced no measurable effect, either the measurement was set up wrongly or the moves are in the wrong field. Do not spend a fourth month without checking both.
Stopping is not a failure. ASO is maintenance work; when there is nothing to maintain, what is being done is not maintenance but busywork.
- Strength threshold out of reach: the work moved to the product; ASO stops.
- Band dry and the pool producing nothing: a new country or source is needed.
- Rank up, downloads flat: the problem is the page; keywords will not fix it.
- Three months with no measurable effect: check the measurement first, then the moves.
Frequently asked
- Can you give ASO's return as a single number?
- No, and anybody who does should not be trusted. The measurable part of the chain ends at visibility; the part running from there to installs and revenue is visible only in your own data.
- How much time a month should I put in?
- A systematic loop is about twelve hours: two and a half a week plus two and a half a month. Widening the scope raises that number faster than linearly.
- Which kind of move works best?
- There is no general answer, and a text claiming one is inventing data. The answer is in your records: split the moves into five types and count how many times each was tried and won.
- Can I compare it with advertising?
- Two units that cannot be divided into each other: cost per install on one side, first-page phrases won per month on the other. Tracking both over time and seeing which accelerates is a more honest comparison than one ratio.
- Why no percentages anywhere?
- Writing an average effect as a percentage requires long measurement across many apps, and that data has not accumulated. It will be published when it does; until then a percentage would be invention.
Explore our resources
The rankcusp guide: keyword strategy (the cusp band)
Discover rankcusp's new keyword strategy guide! This resource shows step by step which words to set realistic targets on and how to carry cusp band keywords into the top ten. Build your own ASO road map with rankcusp, raise your app's visibility in search and catch the searches hidden in competitor reviews before anyone else. We are with you at every step!
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