How to Read ASO Tool Discrepancies and Still Make Good Decisions

You pull up three ASO tools to research a keyword. Sensor Tower says the search volume score is 32. AppFollow says 58. MobileAction says 44. All three are looking at the same keyword, the same store, the same market. None of them agree.

This is not a bug. It's the normal state of app keyword research, and if you don't understand why it happens, you'll either freeze up and make no decision — or worse, anchor to whichever number feels most convenient.

Here's how to think through tool discrepancies correctly and still ship keyword decisions you can defend.

Why ASO Tools Disagree in the First Place

No ASO platform has direct access to Apple or Google's internal search data. Both stores treat query volume as proprietary. What the tools do instead is model volume using a combination of signals: keyword ranking data scraped from the store, download velocity estimates for ranked apps, panel data from opted-in users, and historical trends.

Every tool uses a different methodology, weights signals differently, and refreshes their models on a different cadence. That's the root cause of discrepancies — it's not that one tool is lying and others are telling the truth. They're all estimates built from different proxies.

A few specific factors make the gaps larger:

Panel size and geography. A tool with a smaller user panel will have noisier estimates, especially for niche or regional keywords.
Scraping frequency. If one platform refreshes rankings daily and another weekly, you'll see divergence during periods of algorithm volatility.
Volume score normalization. Some tools use a 0–100 index relative to the highest-volume keyword in the store. Others use raw install-per-day estimates. Comparing a score of 32 to a score of 44 is meaningless if they're on different scales.
Keyword clustering logic. Singular vs. plural forms, common misspellings, and close variants are sometimes rolled into one keyword entry, sometimes split into separate ones — depending on the tool.

Once you understand these, you stop trying to find the "right" number and start asking a better question: what can I reliably learn across tools despite the noise?

The Signal vs. Score Distinction

This is the most important mental reframe for anyone doing serious app keyword research.

A volume score is a single number. A signal is a pattern across multiple inputs. Volume scores are noisy. Signals are more durable.

Here are signals you can actually trust:

Relative ranking within a tool. If Tool A scores keyword X at 55 and keyword Y at 20, that relative relationship — X gets more search traffic than Y — is usually reliable even if the absolute numbers are off. The relative order is more trustworthy than the absolute values.
Ranking difficulty tiers. When multiple tools independently agree that a keyword is "hard" to rank for, that consensus is meaningful. When they disagree on difficulty, dig deeper before committing budget or metadata real estate.
Trending direction. If a keyword has been climbing in estimated volume across tools for 3+ months, that trend signal is more reliable than any single month's score.
Competitor presence. Which apps rank for this keyword? If your top three competitors in the category are all visibly optimizing for it, that's independent corroborating evidence that the keyword matters — regardless of what any score says.

A Practical Reconciliation Framework

When you're staring at conflicting numbers, run through this process before making a metadata decision.

Step 1: Normalize to a common scale. Check whether the tools you're comparing use indexed scores (0–100) or raw estimate ranges. If one uses 0–100 and another uses estimated daily installs, you can't compare the numbers at all. Normalize first.

Step 2: Check for relative agreement. Pull your full keyword candidate list into a spreadsheet. Score each keyword in every tool you're using. Then rank-order by each tool's score separately. Look for keywords that consistently appear in the top tier across all tools. Those are your high-confidence targets.

Step 3: Apply a consensus threshold. A keyword that ranks in the top 25% in two out of three tools is a stronger candidate than one that looks excellent in one tool and mediocre in the others. We apply this pattern in our engagements to build a shortlist before touching any metadata.

Step 4: Layer in competitor evidence. For every keyword on your shortlist, manually check the App Store or Google Play search results. Are strong, well-reviewed competitors ranking there? Is the results page crowded or thin? Actual store behavior is ground truth that no tool can fake.

Step 5: Weight difficulty against opportunity. A keyword where all tools agree volume is high but difficulty scores diverge wildly deserves extra scrutiny. The divergence on difficulty often means the ranking landscape is in flux — which is either an opportunity or a risk depending on your current app authority.

Tool Comparison Cheat Sheet

The table below summarizes what each major platform does well and where its estimates are weakest. Use this when deciding which tools to trust for which decisions.

Tool	Strength	Weakness	Best Used For
Sensor Tower	Large panel, strong US iOS data	Thinner data outside US/UK, expensive	US market keyword validation, competitor benchmarking
AppFollow	Good review and rating data integration	Volume estimates weaker for niche keywords	Reputation monitoring alongside keyword research
MobileAction	Strong paid UA + organic keyword overlap	UI can conflate search ads data with organic	Identifying keywords that work in both paid and organic
data.ai (formerly App Annie)	Good download estimate modeling	Volume scores can lag trend changes by weeks	Long-term trend analysis, not real-time decisions
AppTweak	Excellent keyword density and metadata analysis	Smaller panel than Sensor Tower in some regions	Metadata gap analysis, localization research

No single tool wins on every dimension. In practice, using two tools — one with strong panel data for volume, one with strong metadata analysis — gets you most of the value without paying for five subscriptions.

What This Means for Your Metadata Decisions

Keyword research in ASO ultimately feeds three metadata fields on iOS: title, subtitle, and keyword field — and on Google Play: title, short description, and long description. These are finite fields with character limits. Every character is a trade-off.

Given that, here's the decision rule we use: only put a keyword in your highest-authority metadata positions (title, subtitle) when you have multi-source confidence. If only one tool shows a keyword as high-value, it goes to the keyword field or the long description first. Let it prove itself in the rankings before you give it prime real estate.

For a deeper look at how this connects to broader search visibility across platforms, the post on deep linking and strategic marketing covers how in-app discovery intersects with external traffic — which changes how you prioritize some of these keyword positions.

Semnexus runs full ASO audits and keyword strategy as part of our mobile app marketing services. If your installs have plateaued and you're not sure whether the problem is keyword coverage or conversion rate, that's exactly where we start.

Common Mistakes to Avoid

Anchoring to the highest score. Whichever tool shows the largest volume number tends to win by default in meetings. This is selection bias. A high score from one tool with no corroboration is a hypothesis, not a strategy.

Ignoring keyword difficulty because you're early. New apps with few ratings and installs cannot rank for high-difficulty terms, period. No amount of metadata optimization changes that. Targeting high-volume, high-difficulty keywords with a brand-new app wastes your metadata real estate for months before you gather any signal.

Updating metadata too frequently. Apple's algorithm needs time to index and test new metadata. Changing your keyword field every two weeks means you never accumulate enough impression data to know what's actually working. Typically, a minimum of 4–6 weeks per test cycle is needed to get readable data.

Treating volume as the only axis. A keyword with moderate volume and low competition is often more valuable than a high-volume keyword where you'll rank on page three. Opportunity — which combines volume, competition, and your current app authority — is the real metric. Volume alone is not.

FAQ

Do I need to use multiple ASO tools, or can I pick one and stick with it?

You can build a functional keyword strategy with a single well-chosen tool. But using two tools for cross-validation — especially when you're making decisions about high-stakes metadata like your app title — significantly reduces the risk of optimizing for a keyword that turns out to have inflated estimates in your primary tool.

How often should I refresh my keyword research?

Approximately every 60–90 days for a steady-state app. More frequently right after a major algorithm update, a competitor launch in your category, or a new product feature that opens new keyword angles.

Which tool is most accurate for Google Play specifically?

AppTweak and MobileAction both have decent Google Play coverage. Sensor Tower's Google Play data has improved, but its panel remains stronger on iOS. In our engagements, we've found that cross-checking two sources — one tool's volume estimate plus direct inspection of Play Console's keyword suggestion data inside your own account — gives the best picture.

Should I target the same keywords on both iOS and Android?

Not necessarily. Search behavior differs between App Store and Google Play users, and the algorithm mechanics are different enough that your highest-opportunity keywords may not overlap cleanly. Treat them as related but separate research tracks.

Can I use Google Keyword Planner data to inform ASO research?

Directionally, yes. Google Search volume data can help you validate whether a concept has search demand in the broader web ecosystem, which often correlates with app store demand. But web search intent and app search intent aren't the same thing — people searching "fitness tracking app" on the web and people searching it inside the App Store are in different stages of the funnel. Use web data as context, not as a substitute for dedicated ASO tools.

How do I know when a discrepancy is big enough to worry about?

If two tools disagree by more than one tier — for example, one scores a keyword as low-volume and another scores it as high-volume, not just medium vs. high — treat it as unvalidated until you find a tiebreaker. That tiebreaker might be competitor ranking data, Play Console suggestions, or Apple Search Ads impression data if you're running paid campaigns.

The goal of app keyword research isn't to find certainty in inherently uncertain data — it's to build a defensible shortlist of targets where the weight of evidence points in the same direction. Tools give you inputs. Your job is to synthesize them.

If you want a second opinion on your current ASO strategy or a full keyword audit from a team that has shipped 15+ apps across iOS and Android, book a 30-minute call or explore our mobile app marketing services to see how we approach it end-to-end.