Consumer Behaviour
App discovery
Also known as: mobile app discovery, how users find apps
App discovery is the set of ways people find and decide to install mobile apps, including store search and browse, editorial placements, web search, social feeds, advertising, AI assistants and word of mouth. It covers the whole path from first awareness to tapping install. Growth teams study it to work out which surfaces create demand and which only capture it.
What it is
App discovery describes the routes a potential user takes before installing, which may start inside an app store or entirely outside it. Common entry points include store search for a category or brand, top charts and curated collections, search engine results, creator and community recommendations, paid campaigns, and links from a company's own website or emails. The journey is rarely a single step, so a user may hear about an app socially and then search for it by name in the store.
Why it matters
Store search is heavily brand-led in many categories, so demand generated off-store often shows up as branded store queries, which complicates attribution. Understanding the real mix stops teams over-crediting the last touch and under-investing in the content, PR and community work that created the demand. It also tells you which assets matter most, because a listing that wins browse traffic may need different creative from one that converts branded searches.
How it works
Practitioners map discovery surfaces, then instrument each one with deep links, campaign parameters and store referrer data so installs can be traced back. They combine store console reports, mobile measurement partner data, surveys that ask how users heard about the app, and keyword research to see which terms drive organic store traffic. The findings guide where to spend, what to name features, and how to write listings that match the language users actually search with.
When it applies
App discovery analysis applies at launch, when entering a new market, and whenever install volume shifts without an obvious campaign change. It is also useful before a rebrand or renaming, because changes to naming can break existing discovery paths.
Examples
- A productivity app finds that most installs follow branded store searches, traces the demand to a popular YouTube review, and builds a creator programme off the back of it.
- A travel brand adds Android App Links and iOS Universal Links so people arriving from search results open the app rather than the mobile website.
- A fitness app adds a how did you hear about us question to onboarding and discovers that podcast sponsorship is driving more installs than its paid social reports suggested.
How it is measured
- Share of installs from organic store search versus browse, referrals and paid campaigns
- Branded versus non-branded store search impressions and installs
- Self-reported attribution from onboarding surveys, tracked by channel over time
- Assisted installs from web and deep link traffic, measured through install referrer and link attribution data
Insights on App discovery
Related terms in Consumer Behaviour
- Agentic browserA browser (or browsing layer) that uses an LLM agent to interpret pages, summarise content, and take actions on behalf of the user. Arc Search, Perplexity Comet, Browser Company's Dia, Dia browser, and similar.
- Agentic commerceAgentic commerce is buying and selling in which an AI agent completes part or all of the purchase on a person's behalf, from finding products to placing the order. Instead of browsing a site, the shopper states an intent and the agent searches, compares, fills the cart and sometimes pays. It shifts the point of decision away from the merchant's own interface and towards the assistant.
- Brand demandSearch volume for branded terms. In an AI-search world, brand demand is the single strongest moat, generic queries are absorbed by AI Overviews and ChatGPT, while branded queries route users directly to brand properties.
- Brand discoveryBrand discovery is the process by which someone encounters a brand for the first time, or becomes newly aware it can solve their problem. It happens across search results, AI assistants, social feeds, communities, creators, reviews, marketplaces and word of mouth. Marketers study it to work out which surfaces introduce them to future customers.
- Consumer behaviour signalObservable patterns in how users phrase queries, refine searches, and choose answers, used by both ranking systems and generative models to infer intent and quality.
- Conversational searchConversational search is the pattern of finding information through a back and forth dialogue rather than a single keyword query, with each follow-up interpreted in the context of what came before. Users ask broad questions, then narrow with refinements such as cheaper, nearby or without the subscription. It shifts discovery from ranked link lists towards answers that must survive several rounds of questioning.