App Store Optimization Examples: One Project Walked Through Each Stage
App store optimization examples from one real project: competitor mining, scored keywords, listing fields, localisation, rank checks, and icon review, with honest limits.
**App store optimization examples** are more useful when they show the work, not just the result. This guide walks one real project through each stage of ASO, from the first competitor list to the final icon review, so you can see what each step produced.
The example is BloodSnap, a blood pressure and glucose tracker used as NewASO's public example. It is one app's data. The numbers are not a benchmark, and your category will look different.
Key takeaways
- A good ASO example shows the inputs and decisions, not only the final listing.
- Competitor mining and scoring come before writing. Copy written from guesswork is hard to defend later.
- Each store field has its own job and its own limit. Validate them separately.
- Rank and icon checks close the loop, because they show what the market responds to.
- Use any example as a method, not a target. Your phrases and competitors will differ.
The project and the starting point
BloodSnap is a health tracker that records readings and presents them clearly. It is the public example inside NewASO, and it is used here only to show a workflow. This article describes the store work only and makes no claim about medical measurement.
The starting question was simple: which phrases should the listing own, and which should it leave alone? Without a shortlist, the team would have guessed at titles and hoped for the best.
Stage 1: mine the competitors
NewASO reads competitor titles, subtitles, and descriptions, then finds the 2 to 4 word phrases that repeat across several apps. In this project, the mining pass covered 35 competitors. Phrases that appeared across many apps became roots. Phrases that appeared once were dropped.
Stage 2: expand with store autocomplete
Each root was expanded with Google Play and App Store autocomplete. The research pass produced about 1,200 keywords, and the quality gate removed one-word noise. Autocomplete is a signal of what people type, not a volume count, so it guides the list without claiming exact demand.
Stage 3: score before you write
KW Scoring turned the list into 268 scored keywords across five signals: volume index, competition, difficulty, relevance, and opportunity. Each score carries a confidence label. The result sorted into Focus, Primary, Secondary, Maybe, and Skip, which gave the writer a clear priority order.
| Recommendation | How it was used |
|---|---|
| Focus | Candidates for the title and first-line subtitle |
| Primary | Candidates for the subtitle, description, or keyword field |
| Secondary | Supporting copy and localisation |
| Maybe | Tested later, not written into fields yet |
| Skip | Left out of the listing |
Stage 4: write and validate the listing
Listing Studio wrote title options with character counts, Play and App Store fields, and validation checks. The team chose copy from the Focus and Primary groups and checked each field against its limit. The final words were still reviewed by a person for tone and accuracy.
Stage 5: localise, then check rank
Localization adapted the Play listing into 10 locales, including right-to-left Arabic and Urdu, and kept each locale inside its own limits. Live Rank then checked keywords by market. In this project, the checks found live ranks in 13 countries. Those results are a snapshot on the day of the check.
Stage 6: review the icon
App Icon Studio placed the BloodSnap icon beside eight competitor icons and returned a rank of 3 of 9 with a score of 76. Vision review produced written strengths and weaknesses, and prompts for image tools. The team treated the result as a structured second opinion.
What to copy from this example
- Write down the competitor set before you look at your own keywords.
- Score the list and keep the confidence labels visible.
- Give each store field one job, and validate it against its limit.
- Check rank by market on a schedule, and save each check.
- Review the icon against the category before you commit to new art.
Where NewASO fits
NewASO keeps this trail in one local project, from competitor roots to rank checks, so the reasoning stays attached to the listing. It does not guarantee the outcome, and it does not replace a product people want to keep. For the workflow in order, read how to do ASO. For the case study angle, read ASO case study.
Frequently asked questions
Are these numbers a benchmark for my app?
No. They describe one app and one run. Use the method, then collect your own competitor set, scores, and rank data.
Why does the example start with competitors?
Competitor phrases show which language the category already uses. Starting there keeps the keyword list grounded in the market rather than in your own assumptions.
Can I follow this without a paid tool?
You can do the competitor review and autocomplete steps by hand. Scoring and validation are faster in a workspace, but the logic is the same: research, score, write, check.