Mobile App API Scraping: A Key to Competitive Intelligence

Background

• Scraping has evolved from screen/HTML scraping into API harvesting. Mobile apps are often the freshest source of pricing and availability signals, and are increasingly under target.
• The broader web scraping market is already ~$1B and is forecast to roughly double by 2030 in at least one major model.
• “Mobile app scraping” is rarely examined outside web scraping more broadly, but it’s strategically important due to mobile first applications and the long amount of time users spend in mobile apps, compared to browsers/web apps.
• The capability is openly sold, increasingly wrapped in AI language, and targeted at the exact business flows that encode strategy: quotes, availability, routes, and promos.

History: From Screen Scraping to API Harvesting

Web scraping isn’t new. What's changed is its interface. What began as pulling text from rendered screens, then parsing HTML pages, has increasingly shifted toward structured extraction from APIs. Mobile apps accelerate this trend because they’re typically API-first clients. That shift matters for competitive intelligence. API responses are already structured, real-time, and easy to turn into a time series, which is the raw ingredient for automated competitor monitoring.

Your app holds many of the ingredients of your competitive advantage. Backend endpoints host your prices, inventory/availability, routes/ETAs, and promotions where they can be retrieved for your customers. Once breached, this information can become weaponized by your competition.

Market Size: How Big is This, and How Fast is it Growing?

How big of a problem is mobile app web scraping for app builders? While increasingly visible, it can be hard to estimate. Most market research folds mobile/API harvesting into broader web scraping / web data extraction categories. Those broader markets are already substantial and consistently forecast to grow exponentially:

• Mordor Intelligence estimated the web scraping market at $1.03B in 2025, reaching $2.00B by 2030.
• A software-only framing from Market Research Future estimates $1.131B in 2024, projecting $6.848B by 2035.

Even though mobile/API scraping is a smaller fraction of this market, it is an important and significant one.

Why? A large share of the freshest pricing/availability signal lives in native apps, and data.ai intelligence cited by DataReportal reports users spend less than 6% of smartphone time in browsers and search engine apps, an indicator of how “app-native” the competitive surface has become.

The “Openness” Datapoint: Providers Advertise This Capability out Loud

What makes this threat model feel immediate is how plainly the market sells it. Providers publicly market mobile app scraping / app data extraction as packaged services. Providers use competitive-intelligence language (pricing, promotions, competitor monitoring) and increasingly highlight “AI-powered” workflows.

For example, DoubleData pitches “fully managed mobile app scraping services” that “unlock…data locked within the world’s leading mobile apps” for “actionable intelligence,” Actowiz Solutions promotes Android/iOS app data extraction for “real-time datasets on pricing, reviews, delivery, and promotions,” scaling “to millions of app data points per day” with “device emulation, proxy rotation, and geo-targeting,” plus “AI-driven data cleaning,” APISCRAPY markets “AI web scraping” and “Mobile App Scraping” that “convert[s] any mobile…app data into structured data,” X-Byte explicitly claims its mobile app scraping helps “track product prices…to do price comparisons,” and RealDataAPI sells a “mobile data scraping API” for “competitor tracking” using extracted “reviews, ratings, product info, and pricing.” Even tool ecosystems like Oxylabs AI Studio advertise “AI-powered” scraping and extraction “using natural language,” reducing the friction to produce structured datasets.

What’s Targeted: Pricing, Availability, Routes, and Sensitive Business Flows

Scrapers aiming at competitive intelligence typically focus on endpoints that encode strategy:

• Search / quote / fare calculation (pricing logic)
• Availability / inventory / coverage (supply footprint)
• Routes / ETAs / service areas (operational reality)
• Promotions / eligibility / loyalty (conversion levers)
• Ranking / offer ordering (marketplace positioning)

This aligns with OWASP’s API Security Top 10 (2023), which calls out “Unrestricted Access to Sensitive Business Flows”, business processes that may be “valid” for users but harmful if accessed excessively via automation.

The following examples show how “availability + pricing + location” becomes an actionable strategy for your competitors when collected at scale.

Threat Models: Mobile App API Scraping for Competitive Intelligence

Assets at risk

• Prices, fees, surge/dynamic pricing signals
• Availability/inventory, capacity, route coverage, ETAs
• Promotions, eligibility rules, localized offers
• Rankings and offer placement logic

Likely adversaries

• Competitors building internal pricing/availability dashboards
• Data brokers reselling industry “intelligence feeds”
• Aggregators optimizing price matching and offer positioning
• Specialized scraping operators-for-hire

Common patterns

• High-frequency polling of quote/search/availability endpoints
• Geographic fan-out (many cities/regions)
• Time-series change detection (what changed, where, when)
• Use of consumer-like traffic sources to blend in

Why Detection is Hard, and Attribution is Even Harder

Even when a team strongly suspects scraping, three realities slow response:

1. The requests can look legitimate because they target flows real customers use.
2. IP blocking is unreliable when adversaries route through residential proxy infrastructure. Cloudflare describes bot operators moving through IP space “until they blend in…mimicking real users’ behavior,” and notes IP blocking can trigger false positives.

What to Protect First

With more tools available to your competitors than ever, what can app builders do? To maintain competitive advantage, you'll need to do more than blocking bot traffic.

Start by Protecting Competitive Signals:

1. List the flows that encode your competitive advantage: quote/search, availability, routes/ETAs, promos, ranking.
2. Classify what’s truly sensitive: what reveals pricing rules, supply footprint, or promo mechanics.
3. Define acceptable access: human-scale rates, partner access models, and normal geo patterns.
4. Apply layered controls to business flows: especially where “valid” requests still cause business harm (OWASP’s “sensitive business flows” lens).
5. Self assess your app with Approov's self assessment worksheet.

Symptoms to Watch:

• Look for quote/search/availability endpoints that dominate app traffic
• Be skeptical of perfectly regular polling intervals
• Keep an eye on extreme geo fan-out with repeated identical queries
• Record mismatch between app analytics and API call volume spikes