ANALYSIS PHASE · YOUR OWN DATA
Daily ingestion of App Store Connect and Google Play Console analytics. Score History tracks how your audit scores actually moved over time. Recommendation Impact tells you what to expect before you ship. Every recommendation, traceable to the conversion line it moved.
ASO is constant. So is Apptonomy.
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What it is and why it matters
ASO is only credible if you can tie what you changed to what your numbers moved. Without store-level conversion data, every recommendation is theoretical and every result is anecdotal. Buyers need to point at a real number and say "we shipped X, the line moved Y" — to themselves, to their CFO, to a client.
Two distinct surfaces serve this need: a forward-looking estimate of what a recommendation will move (Recommendation Impact), and a backward-looking timeline of what your scores have done (Score History). One helps you prioritize; the other proves the work landed.
How Apptonomy addresses it
Daily ingestion of App Store Connect and Google Play Console analytics flows into a unified four-pillar scoring rubric (Discoverability, Conversion, Retention, Trust). Audit history is side-by-side with score-trend tracking. Email digests on meaningful change.
The two impact surfaces serve different jobs: Recommendation Impact carries an estimated lift on the per-engine score (or conversion rate) for each recommendation, so you can prioritize before shipping. Score History is the timeline of your actual audit scores over time, with content-change reference lines marking every published revision — so you can see which change moved which delta.
Together they close the credibility loop. Recommendation Impact tells you what to expect; Score History tells you what happened. Both run on your own data, not modeled estimates.
In detail
Four in-product surfaces make the analytics claim real, not rhetorical.
A timeline of your ASO Readiness Score and pillar scores over time, with content-change reference lines marking each published revision. Click any change to see what shipped and what the score did next. Slice by locale and lookback window.
Every recommendation carries an estimated lift on the engine score (or conversion rate) it would move if shipped. Used to prioritize the Weekly Revision Plan — and used by the Fastest Win Spotlight to surface the single highest impact/effort recommendation on the Watchtower.
Four KPI cards summarizing current score, score change, net change, and total audits across your lookback window. The summary view a CMO or CFO will read first.
For asset-changing recommendations (icon, screenshots, store-text fields), see the proposed change next to the current asset with the score-delta the change is expected to move. Field-level diff before you publish.
Recommendation Impact tells you what to expect. Score History tells you what happened. Both run on your own data: the proof behind the promise of more organic installs, for fewer hours, and a lower blended CAC on the channel you own.
Customer example
A travel app ships three subtitle changes across two weeks. Recommendation Impact estimated a +9 / +4 / +6 score lift respectively. Score History, two audit cycles later, shows a 14% lift in store-listing conversion on the Saturday-to-Saturday window correlated with the first change, no significant movement on the second, and a 6% lift on the third. The team kills the second variant and ships further variants of the winners.
Illustrative scenario. Real customer-app outcomes are documented in case studies (forthcoming).
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