Mobile Growth Glossary

User-generated store feedback expressed through star ratings and written reviews, often segmented by storefront, app version, and device context.

Why it matters: They influence trust and conversion and can surface product defects, but teams should analyze themes and timing rather than optimizing only the average score.

Marketing or product activity designed to bring an existing or inactive user back to a meaningful app experience.

Why it matters: Success should be measured with incremental return, deep-link completion, renewed value, and cannibalization, not reopened sessions alone.

The assignment of a new eligible acquisition source to an inactive existing user who engages and returns under configured inactivity and reattribution rules.

Why it matters: It changes dynamic source reporting and must be separated from new installs, organic returns, and permanent first-source attribution.

The first session after an app has previously been deleted and downloaded again on a device, detected according to platform and MMP rules.

Why it matters: A redownload may or may not qualify as a new attributed install depending on exclusion and inactivity settings, so cohort treatment must be explicit.

The percentage of a defined cohort that remains active or returns during a specified period after a starting event such as install or subscription.

Why it matters: Meaningful retention requires a clear cohort, qualifying activity, interval method, and denominator; D1, D7, rolling, and classic retention are not interchangeable.

A tracked purchase, subscription, ad-revenue, or other monetization event carrying validated value and currency information.

Why it matters: Revenue events need deduplication, refund handling, store-fee logic, and backend reconciliation before they become a bidding or ROAS source of truth.

Revenue attributed to advertising divided by advertising spend for a defined cohort, source, event basis, and time horizon.

Why it matters: ROAS can overstate profitability when it ignores margin, refunds, organic cannibalization, attribution bias, and incomplete revenue maturity.

A statistically meaningful difference between expected and observed experiment allocation across control and variants.

Why it matters: SRM is a health failure that can indicate broken assignment, eligibility, tracking, or identity; results should not be trusted until it is resolved.

An advertising network that determines attribution claims using its own engagement data and shares the result with an MMP through an integration.

Why it matters: SAN reporting must be reconciled with MMP rules, event sharing, windows, privacy paths, and date logic rather than treated as directly comparable totals.

The controlled evaluation of App Store or Google Play screenshot concepts, sequences, captions, and visual narratives against a defined conversion hypothesis.

Why it matters: The first frames carry disproportionate impact, but a winner should also preserve user quality and message continuity with acquisition traffic.

Apple Ads automation that matches an app to relevant App Store searches using listing metadata, category, similar apps, and other contextual signals.

Why it matters: It is best used as a discovery source, separated from exact keywords and regularly mined for terms to promote or exclude.

The actual query a user entered before seeing or engaging with an organic result or paid search ad.

Why it matters: Search terms reveal real intent and should be distinguished from advertiser-selected keywords, which are targeting inputs rather than observed queries.

A discovery system that interprets meaning, entities, use cases, and relationships beyond literal keyword matching.

Why it matters: ASO content should communicate coherent topics and product capabilities rather than relying on repetitive keyword insertion.

A bounded period of app activity grouped according to analytics rules, commonly ending after a configured inactivity timeout.

Why it matters: Session definitions vary by tool and are less reliable than meaningful events for comparing engagement unless timeout and background behavior are aligned.

Apple's delayed, aggregated, privacy-preserving framework for measuring eligible iOS advertising outcomes without unrestricted user-level attribution.

Why it matters: SKAN data has timing, crowd-anonymity, conversion-value, and campaign-granularity constraints and cannot be joined back to ATT users at individual level.

An Apple Ads structure that isolates one important keyword in an ad group for dedicated bid, audience, creative, and reporting control.

Why it matters: SKAGs are useful for high-value or high-volume terms but create unnecessary operational fragmentation when applied to every low-volume keyword.

Evidence that other people or credible organizations use, trust, recommend, or benefit from a product, including ratings, testimonials, usage, outcomes, and recognizable partners.

Why it matters: Specific, verifiable proof reduces perceived risk more effectively than generic popularity claims and should match the audience's main objection.

Evidence, under a specified statistical model and threshold, that an observed experiment difference is unlikely to be explained by random variation alone.

Why it matters: Significance does not guarantee practical value, correct implementation, adequate power, or safe guardrails; those conditions must be evaluated separately.

A frequency ratio such as DAU divided by MAU used to approximate how regularly monthly users return on an average day.

Why it matters: It is useful for trend comparison when active-user definitions are stable, but expected cadence differs greatly across product categories.

Google Play's native experimentation feature for testing selected listing assets or text against the current store listing.

Why it matters: A valid decision requires sufficient traffic, stable acquisition mix, one clear hypothesis, and review of both conversion and downstream quality.

The percentage of eligible subscriptions that successfully renew at a billing point after accounting for cancellation, billing failure, grace period, and recovery rules.

Why it matters: Renewal is a core driver of subscription LTV and should be analyzed by plan, tenure, market, acquisition cohort, and offer.

The percentage of Apple Ads impressions that produce a tap, calculated within the selected placement, keyword, audience, and reporting scope.

Why it matters: Low TTR often signals weak relevance, ratings, visible screenshots, or message alignment; raising bids does not repair poor user response.

A bidding or planning target representing the desired average cost for a specified conversion action.

Why it matters: The target must reflect conversion lag, event quality, margin, volume, and attribution maturity; setting it too aggressively can restrict delivery.

A model that credits activity to a campaign only for a limited post-engagement period before returning later activity to a fallback source.

Why it matters: It is useful for CRM and short-term re-engagement when the campaign should receive immediate impact credit without permanently owning the user source.