THE ASO PROFESSIONAL FIELD GUIDE / ASO keyword prioritisation
CHOOSE THE NEXT MOVE. NOT THE BIGGEST NUMBER.
A useful shortlist tells the team what to investigate or implement next, and why. It should make the reasoning easier to inspect rather than hide it behind a mysterious total score.
Prioritise ASO keywords by checking product fit first, then comparing audience value, evidence and implementation readiness. The output is a shortlist with explicit decisions and revisit conditions, not a ranking forecast disguised as a score.
Your deliverable: Choose keyword work using relevance, evidence, feasibility and business fit instead of a single score.
The product fits, but important assumptions remain. Gather the missing evidence before treating the term as a confident priority.
A transparent editorial decision aid based only on your selected categories. It is not a keyword score, statistical model or forecast. Selections are not uploaded or saved.
01
Exclude unsuitable candidates before ranking them.
Start with product fit and accuracy. A term that implies an unsupported capability should not win because it has strong estimated demand. Keep the exclusion and its reason in the research record so the decision is traceable.
Relevance, demand context, competitive evidence and implementation effort describe different things. Combining them into one weighted score can conceal a serious weakness behind an attractive total. If a scoring system is used internally, document the assumptions and sensitivity.
The decision aid here deliberately uses categories instead of a numerical prediction. It gives a suggested next action based on your inputs, not an objective measure of keyword quality.
03
Distinguish missing evidence from poor opportunity.
An unknown demand estimate is not zero demand. An unreviewed competitor set is not low competition. Mark the unknown and decide whether investigating it is worthwhile. The confidence of the conclusion should reflect the evidence actually available.
A candidate may fit the product but require a clearer screenshot narrative or native-language review before the listing can support it. Another may be ready for a straightforward copy change. Include those dependencies in the handoff.
A priority is not just a term. It is a decision with an owner, a reason and a feasible next step. Connect the research to the broader strategy.
05
Review priorities when context changes.
A new product capability, market shift or useful piece of evidence can change the order. Preserve earlier versions and explain the revision. Do not keep a candidate at the top simply because it looked promising in an old export.
For a working research document and metadata handoff, explore keyword research services. The goal is a defensible plan your team can use, with uncertainty left visible where it belongs.
WORKED EXAMPLE / FICTIONAL APP
A language app chooses a smaller, clearer promise
TalkTrip is a fictional language app built around short travel conversations. Its backlog contains “learn languages,” “travel Spanish” and “translate documents.” The first is broad, the second describes its core workflow, and the third implies a capability it does not offer. A stakeholder wants whichever term has the highest tool score.
How to reason through it
Apply product relevance as an entry condition. Then compare the remaining candidates on audience value, demand evidence, competitive context and the work required to support the promise. Use ordinal judgments such as strong, uncertain and weak when the evidence does not justify precise numbers. A weighted formula is useful only if the underlying inputs and weights have a defensible meaning.
The next practical move
Investigate travel-conversation terms first and draft a listing that demonstrates a realistic exchange. Keep broader language-learning terms as a separate research track rather than pretending one metadata change can own the entire category. Reject document translation. Note which assumptions could change the priority, such as a new supported workflow or stronger evidence of audience demand.
Swipe horizontally to compare the decision map →
A language app chooses a smaller, clearer promise: illustrative decision map
Decision area
What to inspect
What it means for the work
Travel Spanish
Strong fit with supported conversations
Research and draft now
Learn languages
Broad promise and mixed expectations
Investigate before expanding positioning
Translate documents
Unsupported product capability
Reject regardless of tool score
The trap to avoid
Avoid fake mathematical precision. Multiplying three subjective scores does not produce an objective opportunity forecast. Show the reasoning behind the priority and keep estimates visibly separate from observed results.
When the situation gets more complicated
Prioritisation should produce a portfolio, not only a winner. Identify a near-term cluster, a small number of research questions and explicit exclusions. Review it when evidence changes rather than on a fixed urge to refresh keywords. Link each selected cluster to a metadata owner and a change-log entry so the research survives the handoff into publishing.
FROM METHOD TO DELIVERABLE
A decision register for TalkTrip
This completed example separates eligibility from scheduling. Unknown demand remains unknown. The team can investigate a relevant phrase without committing to publish it, and can reject an irrelevant phrase without spending time scoring it.
Swipe horizontally to read the complete example →
Fictional priority register. No demand, competition or expected-uplift measurements are implied.
Check whether the audience expects translation or lessons
Learn languages
Hold: broader promise than the primary product experience
Revisit if supported learning scope expands
Translate documents
Exclude: unsupported workflow
Revisit only after a verified product capability change
Use gates before trade-offs
Ask whether a phrase describes something the current app does, whether the target audience is appropriate and whether the listing can substantiate the implied promise. A failed product-fit gate is a reason to exclude or investigate, not a weakness that high estimated demand can cancel out. Only compare effort and opportunity after those checks.
Break a tie with a smaller learning commitment
If two supported clusters remain plausible, identify the cheapest useful evidence that distinguishes them. For TalkTrip, asking relevant travellers what they expect from “restaurant Spanish” may expose a translation-versus-learning mismatch before copy production. Record what answer would lead you to keep, narrow or reject the cluster. This is research prioritisation, not a claim that a small interview estimates market demand.
Handle dependencies visibly
A priority can be strategically attractive and operationally blocked. Put native review, product verification and required creative beside the recommendation. Assign an owner and a review trigger. When a dependency clears, reconsider the evidence rather than automatically publishing a stale draft. The final shortlist should identify ready work, investigation work and exclusions separately.
One candidate has excellent relevance but little demand evidence. Another has strong estimated demand but uncertain relevance. Which should go first?
Reveal the reasoning
Resolve the relevance uncertainty before prioritising the second candidate. For the first, investigate whether the audience uses different language for the same job. Neither weak demand evidence nor a high estimate should be converted into certainty by a scoring formula.
Your working brief
Use your own app for this exercise. Fill in the six fields below and attach the evidence behind each answer. If you cannot complete a field, write the missing question and the person who can resolve it. Do not replace unknowns with optimistic assumptions.
Candidate or cluster
Product-fit gate and evidence
Audience/business value
Demand evidence with confidence
Competitive and implementation constraints
Decision, rationale and revisit trigger
For each field, distinguish an observation from an interpretation. Add the date and source where available, then identify the decision that follows. A colleague should be able to trace your recommendation back to the evidence without needing you to explain the entire project again.
Track selected terms consistently after implementation, without treating rankings as revenue.
The worked example is an original editorial framework. Its fictional app, decisions and scenarios are not an industry dataset or a claim of measured uplift.
