Why Category Selection Is the Most Important ASO Decision

Most App Store Optimization advice focuses on what happens after you pick a category: optimize your title, refine your keywords, improve your screenshots. But the category you choose determines the ceiling for all of that work. A perfectly optimized listing in a saturated category with 50 established competitors will always struggle against a decent listing in an underserved category where users are desperate for a better option.

Finding underserved categories is not about guessing or following your gut. It is a systematic, data-driven process. The App Store and Google Play contain millions of apps, but user demand is not evenly distributed across them. There are categories where dozens of well-funded apps compete for the same users, and there are categories where search volume is high but no app adequately serves the need. Your job is to find the second kind.

This guide walks through a repeatable process for identifying those gaps using app intelligence data, keyword research, review analysis, and external demand signals. Whether you are deciding what to build next or looking for a better category positioning for an existing app, these steps will help you find opportunities that your competitors overlook.

Step 1: Analyze Category Saturation

Start with a bird’s-eye view of the entire category landscape. You need to understand where the crowds are and where the open spaces exist.

Pulling Category-Level Data

For every category and subcategory in both the App Store and Google Play, collect:

Total number of apps: Raw supply in the category
Number of apps updated in the last 6 months: Active supply (many listed apps are abandoned)
Average rating of top 50 apps: Quality floor the user expects
Average number of ratings for top 50 apps: Engagement level and market maturity
Revenue estimates for top 10 apps: Category earning potential

ASODOG and similar app intelligence platforms can pull this data programmatically. If you are doing this manually, sample the top 100 apps in each category from both stores and extrapolate.

Reading the Saturation Map

Plot categories on a two-axis chart:

X-axis: Number of actively maintained apps (supply)
Y-axis: Average rating of top apps (quality)

This gives you four quadrants:

Quadrant	Supply	Quality	What it means
High supply, high quality	Many apps	High ratings	Saturated. Hard to compete without major differentiation
High supply, low quality	Many apps	Low ratings	Opportunity. Many apps exist but none satisfy users well
Low supply, high quality	Few apps	High ratings	Niche dominated. A few good apps own the space
Low supply, low quality	Few apps	Low ratings	Underserved or non-existent demand. Investigate further

The most attractive quadrant for new entrants is high supply, low quality. Users are actively looking for solutions (supply proves demand exists) but the current options disappoint them. A well-built app with strong ASO in this quadrant can grow fast.

The low supply, low quality quadrant requires caution. It could mean a genuinely underserved market, or it could mean there is no real demand. You need external validation (Step 6) before committing.

Subcategory Granularity

Don’t stop at top-level categories. The “Health & Fitness” category contains everything from calorie trackers to meditation apps to physical therapy tools. The market saturation and quality levels vary dramatically across these subcategories.

Break each category into functional subcategories based on what the apps actually do, not how the store labels them. Group apps by primary use case and analyze each group separately. You will often find that a seemingly saturated top-level category contains underserved subcategories.

Step 2: Study Keyword Supply and Demand Gaps

Category-level analysis tells you where to look. Keyword analysis tells you exactly what users are searching for and whether the existing results meet their needs.

Finding High-Volume, Low-Competition Keywords

Use ASODOG or another keyword intelligence tool to pull:

Search volume: How many users search for this term monthly
Number of ranked apps: How many apps appear in results for this term
Quality of ranked apps: Average rating, update recency, and download volume of apps on page one
Keyword difficulty score: Competitive difficulty of ranking for this term

The sweet spot is keywords with high search volume and low keyword difficulty. These exist because not enough quality apps target them. Every such keyword represents an audience that the current app supply underserves.

Analyzing the Search Results Page

For each promising keyword, examine the top 10 results in detail:

Relevance: Are the top results actually relevant to the search term, or is the store showing loosely related apps because nothing better exists?
Recency: When were the top apps last updated? Outdated apps signal a neglected category
Ratings: Do the top results have 3 stars or lower? Users are settling, not choosing
Screenshots and metadata: Are the listings professionally optimized, or do they look like side projects from 2019?
Reviews: What do users say about the top results? (More on this in Step 3)

If the top search results for a high-volume keyword are irrelevant, outdated, or poorly rated, you have found an underserved keyword that maps to an underserved category need.

Long-Tail Keyword Clusters

Individual keywords tell part of the story. Keyword clusters tell the full picture.

Group related keywords into clusters that represent a single user intent:

Cluster: "Budget tracking for couples"
- shared budget app (1,200 searches/month)
- couples finance tracker (800 searches/month)
- budget app for two people (600 searches/month)
- joint expense tracker (500 searches/month)
- partner budget planner (400 searches/month)
Total cluster volume: 3,500 searches/month

A single keyword with 1,200 monthly searches might not seem worth building an app around. But a cluster of related keywords totaling 3,500 searches represents a meaningful, focused audience. If no existing app specifically targets this cluster, the entire audience is underserved.

Some keyword gaps are temporary. Use trend data to distinguish between:

Evergreen gaps: Consistently high search volume with consistently poor results (genuine underserved category)
Seasonal gaps: High volume at certain times of year (tax season, back to school, holiday shopping) with limited competition during peak periods
Emerging gaps: Search volume growing month-over-month for a relatively new term (new technology, cultural trend, regulatory change)

Emerging gaps are especially valuable. If search volume for a keyword is growing 20%+ month over month and no quality app exists yet, you have a window to establish yourself before competitors notice.

Step 3: Mine User Reviews for Unmet Needs

Keyword data shows what users search for. Review data shows what users actually experience and where they feel underserved. Reviews are the most honest signal of unmet demand because users write them in moments of frustration or delight.

Where to Look

Focus on reviews of the top 10-20 apps in your candidate categories. Read:

One-star reviews: What makes users angry enough to leave the lowest rating?
Two-star reviews: What almost works but falls short?
Three-star reviews with text: What do users like but wish was different?
Feature request reviews: Any review that says “I wish this app could…” or “If only it had…”

Extracting Patterns

Don’t read reviews one by one and try to remember patterns. Extract and categorize them systematically:

Complaint category	Frequency	Example quotes	Opportunity
Missing feature X	47 mentions	”No way to share with my partner”	Build shared/collaborative version
Poor offline support	32 mentions	”Useless without internet”	Offline-first architecture
Complicated UI	28 mentions	”Too many screens to do one thing”	Simplified, focused UX
Expensive subscription	25 mentions	”Not worth $12/month for what it does”	Competitive pricing or freemium model
Privacy concerns	19 mentions	”Why does a calculator need my contacts?”	Privacy-focused alternative
No widget support	15 mentions	”Would love a home screen widget”	Modern iOS/Android integration

ASODOG can help you aggregate review sentiment across competitors, making it faster to spot recurring themes without manually reading thousands of reviews.

Validating Review-Based Hypotheses

A complaint appearing in 50 reviews does not automatically mean a viable product opportunity. Validate by asking:

Is this fixable? Some complaints are about fundamental trade-offs (e.g., “the free version has ads” is a business model choice, not a product gap)
Is the audience large enough? A niche complaint from power users may not represent broad demand
Are people willing to switch? Users who complain but have invested years of data in an app may not leave easily
Can you solve it significantly better? A marginal improvement over the existing solution is not enough to overcome switching costs

The strongest opportunities come from complaints that are frequent, fixable, represent a broad audience, and point to a fundamentally different approach rather than an incremental improvement.

Step 4: Track Category Ranking Volatility

Stable top charts mean entrenched incumbents. Volatile top charts mean the market is in flux and new apps can break through.

Measuring Volatility

Track the top 50 apps in each candidate category daily for 4-8 weeks. Calculate:

Churn rate: What percentage of apps in the top 50 are different from week to week?
Average tenure: How long does a new entrant stay in the top 50?
Position stability: How much do individual apps move in rank day to day?

Interpreting Volatility

High volatility (20%+ weekly churn in top 50):

No dominant player has locked up the category
Users are actively trying and abandoning apps, searching for something better
A new app with strong retention can climb quickly
Marketing spend has higher ROI because organic growth is more achievable

Low volatility (under 5% weekly churn):

A few apps dominate and users stick with them
Breaking into the top charts requires displacing an entrenched incumbent
You need either a significantly better product or a very different positioning angle
Marketing spend has lower ROI because organic momentum is harder to build

Moderate volatility with patterns:

Some categories show volatility only in certain rank ranges (e.g., positions 20-50 churn frequently while the top 10 are stable). This suggests the top tier is locked but there is room to establish yourself in the middle tier and work upward

Combining with Download Estimates

Category volatility alone doesn’t tell you whether the category is worth entering. A highly volatile category with 500 total daily downloads across all apps is not interesting. A highly volatile category with 50,000 daily downloads is very interesting.

Cross-reference volatility data with category-level download estimates to find the sweet spot: high volatility plus high total demand.

Step 5: Evaluate Monetization Signals

An underserved category is only valuable if users in that category spend money. Free apps with no monetization path are a hobby, not a business.

In-App Purchase Analysis

Study the in-app purchase offerings of top apps in your candidate categories:

Subscription pricing: What do users pay monthly/annually? Higher prices suggest higher willingness to pay
One-time purchase prices: What premium features do users buy?
Consumption purchases: Do users buy credits, coins, or consumable items?
Purchase volume: Estimate revenue from download numbers multiplied by estimated conversion rates

A category where the top apps charge $9.99/month and users don’t complain about pricing in reviews has demonstrated monetization potential. A category where every attempt at monetization gets flooded with “greedy developers” reviews has a pricing sensitivity problem.

Ad Monetization Indicators

For ad-supported categories, look at:

Advertiser demand: Are major brands advertising in apps in this category? (Indicates high CPMs)
User session length: Longer sessions mean more ad impressions per user
Session frequency: Daily-use apps generate more ad revenue than occasional-use apps

Revenue Concentration

Check whether revenue in the category is concentrated in the top 1-2 apps or distributed across many apps:

Concentrated: The category leader captures 60%+ of revenue. Competing for revenue requires displacing the leader
Distributed: Many apps earn moderate revenue. There is room for a new entrant to capture a meaningful share without taking on the leader directly

Distributed revenue categories are more attractive for new entrants because you can build a sustainable business without needing to become the category leader.

Pricing Gap Analysis

Compare what users pay in the category versus what they get:

Are users paying premium prices ($10+/month) for basic functionality? You could offer better value at the same price or equivalent functionality at a lower price
Are free apps with ads the only option in a category where users would clearly pay for an ad-free experience? A well-positioned premium alternative can capture that willingness to pay
Are there categories where users pay for individual features across multiple apps that could be unified into a single, comprehensive tool?

Step 6: Cross-Reference with External Demand Signals

App store data tells you what is happening inside the stores. External signals tell you what is happening in the broader market. The most underserved categories often show strong demand outside the app stores that has not yet been matched by quality app supply.

Google Trends and Web Search Volume

Search for terms related to your candidate categories in Google Trends:

Rising search volume: A topic gaining web search interest is likely to generate growing app store search volume soon
Steady high volume: A topic with consistently high web search volume that has no dominant app solution represents accumulated unmet demand
Geographic patterns: Some categories are underserved in specific regions. A fitness category well-served in the US might be completely underserved in Southeast Asia

Compare web search volume to app store keyword volume. A large gap (high web search, low app store search) suggests the category is emerging and users haven’t yet formed the habit of looking for app solutions.

Reddit and Forum Analysis

Reddit communities, specialized forums, and Facebook groups reveal unfiltered user demand:

Search for subreddits related to your category. How many subscribers? How active?
Look for recurring “Is there an app for…” posts. These are direct demand signals
Read threads where users discuss existing apps. What do they wish was different?
Check if users share workarounds (spreadsheets, manual processes, cobbled-together tool chains) for problems that an app could solve better

A subreddit with 100,000 members discussing manual workarounds for a problem that could be solved by an app is a clear indicator of underserved demand.

Monitor Twitter/X, TikTok, and Instagram for:

Hashtags related to your category
Influencers discussing the topic and what tools they use
Complaints about existing tools in the space
“Day in my life” content that reveals workflows an app could improve

Industry Reports and Market Data

Look for market research reports that cover your candidate categories:

Total addressable market size and growth rate
User demographics and spending patterns
Regulatory changes that create new needs (privacy laws, accessibility requirements, financial regulations)
Technology shifts that enable new app capabilities (AR, on-device ML, health sensors)

Industry data is especially useful for categories that are emerging due to regulatory or technology changes. If a new law requires businesses to do something they’ve never done before, there will be app demand before there is app supply.

Adjacent Market Signals

Sometimes the strongest signal comes from adjacent markets:

A booming SaaS category on desktop that lacks a mobile counterpart
A physical product category going through rapid digitization
A professional workflow moving from enterprise software to consumer apps
A service industry shifting from in-person to app-based delivery

These adjacencies represent categories that are about to be underserved in the app stores, even if they don’t appear that way yet based on current data.

Step 7: Assess Competitive Moats and Barriers

Not every underserved category is easy to enter. Before committing resources, evaluate what it takes to build a competitive product and whether incumbents have structural advantages you need to overcome.

Technical Complexity

Some categories require deep technical capability:

Machine learning models: Apps that rely on computer vision, NLP, or prediction models need ML expertise and training data
Real-time infrastructure: Messaging, collaboration, and live-tracking apps need backend systems that scale
Hardware integration: Health, fitness, and IoT apps need to work with sensors, wearables, and external devices
Data sources: Apps that aggregate data from multiple sources need API access, partnerships, or web scraping infrastructure

High technical complexity is a barrier but also a moat. If the reason a category is underserved is that the problem is hard to solve technically, your solution (once built) will be harder for others to replicate.

Data Network Effects

Some categories become harder to enter over time because the incumbent’s product improves with more users:

Social apps where the value comes from the user base
Marketplace apps where more buyers attract more sellers
Review apps where content density drives utility
Mapping apps where user-contributed data improves accuracy

If the incumbent has strong data network effects, being underserved may not matter - users cannot switch even if they want to because the alternative lacks the data that makes the product useful.

Regulatory and Compliance Requirements

Certain categories have regulatory barriers:

Finance: Banking licenses, PCI compliance, KYC/AML requirements
Health: HIPAA compliance, medical device certifications, clinical validation
Education: COPPA compliance for children’s apps, accessibility standards
Government: Security certifications, data residency requirements

Regulatory barriers slow you down but also protect you once you clear them. An underserved category with high regulatory requirements is often underserved precisely because the barriers keep casual competitors out.

Switching Costs

Evaluate how easy it is for users to switch from an incumbent to your app:

Low switching cost: Utility apps, content consumption, standalone tools (easy to gain users)
Medium switching cost: Apps with user-generated data that can be exported (need a migration path)
High switching cost: Apps with years of personal data, integrated into daily workflows, or linked to subscriptions and accounts (very hard to win users even if your product is better)

Target categories where switching costs are low to medium. High switching costs can make an underserved category feel underserved forever because users are trapped in inferior solutions.

Step 8: Score and Prioritize Opportunities

You should now have a list of candidate categories with data on saturation, keyword gaps, user complaints, volatility, monetization, external demand, and competitive barriers. The final step is to score each opportunity and pick the one that offers the best risk-adjusted return.

Scoring Framework

Rate each category on a 1-5 scale across these dimensions:

Dimension	Weight	What 5 means
Demand strength	25%	High search volume, growing trends, active communities
Competitive gap	25%	Top results are low-rated, outdated, or irrelevant
Monetization potential	20%	Users pay premium prices, distributed revenue
Technical feasibility	15%	Your team can build a competitive product in 3-6 months
Strategic alignment	15%	Fits your team’s expertise, existing audience, or portfolio

Calculate a weighted score for each category:

Score = (Demand x 0.25) + (Gap x 0.25) + (Monetization x 0.20)
      + (Feasibility x 0.15) + (Alignment x 0.15)

Ranking and Selection

Sort categories by weighted score. For your top 3 candidates:

Build a quick competitive analysis. Who are the top 5 current apps? What would your differentiation be?
Estimate time to market. How long to ship a competitive MVP?
Model the economics. What conversion rate and pricing do you need to break even?
Validate with real users. Talk to 10-20 people who match the target audience. Would they use the app you’re describing?

The category with the highest score, validated by real user conversations, and achievable within your resource constraints is your target.

When to Niche Down vs Go Broad

Your scoring may reveal a tension between large, moderately underserved categories and small, deeply underserved niches. The right choice depends on your stage and strategy:

Niche positioning works best when:

You are a solo developer or small team
The niche has passionate users willing to pay premium prices
You can dominate the niche’s keywords and become the default choice
The niche can serve as a beachhead to expand into adjacent categories later

Broad positioning works best when:

You have the resources to compete on multiple fronts
The category lacks any strong player (rare but very valuable)
Network effects or data advantages reward scale
Your monetization model benefits from large user volumes (ad-supported, marketplace)

For most independent developers and small teams, niche positioning is the higher-probability path. It is easier to rank first for 20 specific keywords than to rank in the top 10 for 5 broad keywords. And a niche audience that loves your app will drive word-of-mouth growth, positive reviews, and organic rankings that compound over time.

Revisiting the Analysis

Category dynamics change. Run this analysis quarterly:

New apps enter previously underserved categories
Incumbent apps get acquired, abandoned, or redesigned
User demand shifts due to cultural, regulatory, or technology changes
Keyword volumes fluctuate seasonally

Keep your category intelligence current and be ready to pivot your positioning if the landscape shifts. The developers who treat category selection as an ongoing strategic decision rather than a one-time choice are the ones who consistently find growth.

How to Find Underserved App Categories