ASO in 2026: How App Store and Google Play Search Is Changing
ASO is moving into a phase where “classic” keyword work is no longer enough on its own. Both Apple and Google are leaning harder into AI-assisted discovery, stronger personalisation, and quality signals tied to what users actually do after install. If you’re planning growth for 2026, you need to treat search visibility as the output of your full funnel: acquisition quality, activation, retention, and store page conversion all feed into how stores decide what deserves to be shown.
Ranking signals that matter more: retention, engagement, and page conversion
Across both stores, behavioural metrics have become more valuable because they are harder to fake at scale and directly correlate with long-term user satisfaction. In practical terms, apps that keep users active (especially after day 1 and day 7) tend to build a stronger organic baseline than apps that spike installs but quickly churn. This is why many ASO teams now track retention alongside rankings as a single system, not separate dashboards.
Store page conversion rate is another signal that increasingly behaves like a “ranking multiplier”. If your impressions are stable but installs per impression are weak, it becomes difficult to hold positions, especially for competitive generic queries. Google Play has made this link more explicit through Store Listing Experiments (A/B testing) and by surfacing retention and acquisition quality metrics directly inside Play Console. The implication is simple: listing optimisation is no longer just branding work; it’s tightly connected to organic growth mechanics.
User sentiment is also gaining weight, but not in a simplistic “more stars = higher rank” way. Ratings and reviews influence conversion, and conversion influences ranking. On top of that, both stores use review text and complaint patterns to detect quality issues (crashes, missing features, misleading claims). In 2026, the teams that win are the ones that treat reviews as product feedback and risk management, not as a reputation checkbox.
What ASO teams should measure weekly (and why it changes priorities)
A useful weekly scorecard in 2026 should include: impression share by query cluster, store page conversion by traffic source, and at least two retention cuts (D1 and D7) split by acquisition channel. This sounds product-heavy, but it prevents a very common mistake: optimising metadata to attract the wrong audience. When that happens, conversion drops, churn rises, and organic visibility becomes unstable.
You also need to monitor “ranking velocity” rather than just positions. The important question is how fast you gain or lose ranks after changes, because that often indicates how the system is interpreting your quality signals. For example, if you improve screenshots and see conversion lift but rankings stay flat, the issue may be semantic relevance. If rankings lift briefly then drop, the store may be testing your app and then demoting it after weak engagement signals.
Finally, treat paid search visibility as part of the ecosystem. Apple has confirmed that App Store Search Results will show more ads starting in 2026, which can change what users see even when your organic rank stays the same. That means you’ll need a clearer split between “organic visibility” and “share of voice” on important queries, because paid placements can push organic results further down the screen.
Personalised results: country, language, and user history are shaping discovery
Personalisation is no longer limited to the “Today” tab or recommendation feeds. Store search itself is gradually becoming more contextual: what the user already installed, what they kept, what they uninstalled quickly, and which categories they engage with. In 2026, two users can type the same query and see meaningfully different results, especially in broad categories like fitness, finance, shopping, or games.
Country and language signals remain foundational, but the nuance is increasing. Local competition, local seasonal behaviour, and local conversion norms matter more than ever. This is why localisation is moving beyond translation: it’s about adjusting value propositions, screenshots, and even feature emphasis for each market. A “one listing for all English-speaking markets” approach often underperforms because UK, US, and AU users can respond differently to the same messaging and visual style.
User history is also changing the way branded vs generic discovery works. If a user has shown interest in a category, stores may prioritise apps that match their behaviour even when the query is generic. This means that being strong in a niche can help you appear for broader terms for the right audience segment. From an ASO perspective, it increases the value of building consistent category signals through metadata, creative assets, and high-quality engagement after install.
How to approach localisation when results are increasingly personalised
Start by mapping your markets into “behaviour clusters”, not just languages. For example, two countries might share a language but have different payment preferences, trust signals, or dominant competitors. Your store page should reflect what users in that market consider proof of credibility: local awards, relevant integrations, or support options that match local expectations.
Next, treat semantic work as market-specific. Keyword popularity and intent can shift dramatically by country, and in 2026 you’ll see more AI-driven matching where the system interprets meaning rather than exact phrases. That makes it risky to rely on direct translations of your core keyword set. Instead, build local clusters: problems users describe, features they expect, and the language they use in reviews.
Finally, align localisation with product instrumentation. If you see weak retention in a particular market, you may be attracting the wrong intent. Sometimes the fix is not in the product but in the listing: clearer messaging, better expectation-setting, and screenshots that demonstrate the actual flow. This reduces refund requests, negative reviews, and early churn — all of which feed back into visibility.
Rebuilding the ASO process for 2026: testing, analytics, content, and reviews
In 2026, ASO needs a tighter operational loop. The winning workflow looks more like continuous optimisation than periodic “keyword updates”. Teams plan changes as hypotheses, run controlled tests, and connect results to downstream metrics (activation and retention), not just installs. Google Play already supports structured experimentation via Store Listing Experiments, and more teams are building similar disciplined testing frameworks for iOS using Apple’s available tools and analytics stacks.
Analytics also needs to become less siloed. ASO teams that only watch rankings will miss the reasons those rankings change. The strongest setups combine store analytics (impressions, conversion, acquisition sources), product analytics (activation, engagement, retention cohorts), and review intelligence (topic clustering, sentiment changes, issue detection). This gives you the ability to diagnose declines early — for example, spotting that a conversion drop is driven by new competitors, not by your own metadata.
Content and review management are also shifting. With AI and personalisation, store algorithms look for clearer intent matching and more reliable quality cues. That pushes teams to keep listings fresh, accurate, and aligned with what the app actually delivers. Reviews need a structured response plan: prioritise issues that affect trust and conversion (billing confusion, crashes, missing features), and route them into product backlogs. The goal is not “more replies”, but measurable improvement in user sentiment and retention.
A practical 90-day ASO reset plan (built for 2026 realities)
Weeks 1–4 should focus on measurement and alignment. Audit your current query portfolio, identify which clusters drive high-retention users, and separate them from high-churn traffic. Refresh your store page to match your strongest retention audience, then establish a baseline: conversion rate by source, D1/D7 retention by channel, and review themes. This becomes your “truth set” for decisions.
Weeks 5–8 should focus on structured testing. Run at least one creative experiment (icon or screenshot order) and one messaging experiment (short description or feature framing) per major market. Keep tests clean: one variable at a time, run long enough to cover weekday and weekend behaviour, and validate not only installs but retention. A lift that creates lower-quality users is not a win in 2026 — it can weaken organic visibility over time.
Weeks 9–12 should focus on scaling what works and improving trust signals. Expand successful creatives and messaging across similar markets, strengthen localisation where conversion lags, and implement a review and ratings playbook tied to real user moments (after successful actions, not at random). At the same time, coordinate with product and UA teams so acquisition sources reinforce the same audience profile your ASO is targeting. When all of that aligns, personalisation starts working in your favour instead of diluting results.
