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Mobile App Data Extraction Services – Android & iOS

Actowiz Solutions helps businesses unlock structured data from Android & iOS apps with 99.9% accuracy. Extract real-time insights from e-commerce, food delivery, travel, mobility, finance, and retail apps — delivered in clean, analytics-ready datasets.

  • Extract product, pricing, reviews & delivery insights directly from mobile apps
  • API & dashboard delivery in JSON/CSV/Excel, or real-time via REST APIs
  • Compliant extraction with device emulation, proxy rotation, and geo-targeting
  • Scale seamlessly — from thousands to millions of app data points per day
  • AI-driven data cleaning, deduplication & sentiment tagging
  • Global coverage: USA, UAE, UK, Europe, India, Singapore, Japan & more

99.9% uptime | Real-time app data feeds | Enterprise-grade SLAs

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Mobile App Data Extraction Services for Global Businesses

Actowiz Solutions provides mobile app data extraction services designed to unlock structured insights directly from Android & iOS apps with 99.9% accuracy and SLA-backed delivery. Trusted by global enterprises across the USA, UK, UAE, India, Germany, Canada, and Europe, we deliver compliant, real-time app datasets that power smarter business decisions.

From e-commerce and quick commerce to food delivery, travel, real estate, finance, and mobility, we help businesses capture competitor pricing, product availability, reviews, delivery SLAs, ride fares, hotel rates, property listings, and customer sentiment — all directly from app ecosystems. Our solutions work seamlessly across apps like Amazon, Walmart, Instacart, Blinkit, Zomato, Uber, Lyft, Booking.com, and Deliveroo.

With custom extraction pipelines, device emulation, proxy rotation, 24/7 monitoring, and AI-powered data cleaning, Actowiz ensures accurate, structured outputs delivered in JSON, CSV, Excel, or via REST APIs — so your business never misses a critical app-driven data point.

Why Mobile App Data Extraction Matters

Mobile app data extraction goes beyond website scraping. With 90% of user activity happening inside apps, it ensures brands don’t miss critical insights locked in Android & iOS ecosystems.

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Apps Dominate Digital Behavior

90% of smartphone time is spent in apps, making them the richest source of customer data and engagement signals.

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Exclusive In-App Data

Many offers, promotions, and user reviews exist only within apps and never surface on websites.

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Competitive Intelligence

Benchmark pricing, delivery times, and in-app features across rival apps to stay ahead in the market.

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Market Entry Insights

Understand hyper-local demand, basket sizes, and regional preferences before expanding into new cities or countries.

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Real-Time Monitoring

Capture flash discounts, surge pricing, or availability changes as they happen, with real-time feeds.

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Seamless Integrations

Clean, structured outputs (JSON, CSV, Excel, or API) delivered directly into your BI dashboards and workflows.

What We Extract from Android & iOS Apps

Mobile apps contain a wealth of hidden data that never makes it to websites. At Actowiz Solutions, we specialize in extracting structured, analytics-ready datasets from Android and iOS apps across industries. Our data pipelines ensure accuracy, scalability, and compliance, so businesses can rely on us for mission-critical insights. Here’s what we help you capture:

1. Product Listings

Mobile apps often display exclusive product details not found on websites. We extract titles, SKUs, categories, product descriptions, specifications, images, and metadata directly from apps. This helps retailers, brands, and research firms maintain updated catalogs, track new product launches, and benchmark competitors. Whether you need grocery SKUs from Instacart, electronics from Amazon, or fashion listings from Flipkart, our solutions ensure comprehensive, SKU-level coverage in real time.

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2. Pricing & Discounts

App-only offers are increasingly common, especially in e-commerce, grocery, and ride-hailing platforms. We capture real-time product pricing, time-bound deals, coupons, flash sales, and bundle offers to help businesses optimize their pricing strategies. For example, FMCG brands use our data to benchmark discounts on Zepto or Talabat, while retailers leverage pricing insights from Walmart and Target apps. By tracking competitive pricing at scale, you can stay ahead of price wars and avoid revenue leakage.

3. Reviews & Ratings

Customer feedback inside apps offers unfiltered sentiment that drives purchase decisions. We extract reviews, star ratings, comments, and reviewer metadata to help businesses understand user satisfaction and pain points. Using AI-powered sentiment analysis, we categorize reviews into positive, neutral, or negative to deliver actionable intelligence. For example, QSR brands monitor Zomato and Uber Eats app reviews to refine menus and service, while fintech companies analyze app store feedback for feature improvements.

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4. Delivery SLAs & Availability

Delivery speed has become a key differentiator in quick commerce and food delivery apps. We capture delivery windows, average speed promises, surge pricing during peak hours, and stock availability by region. This data helps logistics teams benchmark competitors and improve their own SLA compliance. For instance, tracking Instacart or Blinkit delivery times in different cities can highlight gaps in fulfillment strategies and reveal hyper-local performance insights.

5. Promotions & Ads

In-app banners, seasonal offers, loyalty points, and ad placements are highly targeted but often invisible to non-users. We extract promotions and ad creatives across apps to help businesses track competitor campaigns and assess ROI. Retailers can monitor Ramadan or Diwali offers on Carrefour and BigBasket apps, while travel brands benchmark airline fare ads on Skyscanner or Booking.com apps. With this data, marketing teams gain a competitive edge in campaign planning.

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6. Search Results & Rankings

Search visibility inside apps is critical for discoverability. We capture in-app search results, keyword rankings, sponsored placements, and filters to help brands measure their visibility against competitors. For instance, tracking how a brand appears in Amazon or Noon app search results reveals share-of-shelf insights. Similarly, monitoring Uber ride availability by pin code helps identify demand hotspots. These insights are invaluable for SEO, ASO, and merchandising strategies.

7. User Engagement Metrics

Where accessible, we collect app-level engagement data such as downloads, session frequency, activity stats, and usage trends. This helps businesses understand adoption patterns and regional popularity. For example, app usage metrics from mobility apps like Lyft or Careem indicate demand shifts during events or festivals, while e-commerce brands track activity surges during Black Friday. With this data, businesses can align growth strategies with real-world user behavior.

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By extracting structured datasets across product, pricing, promotions, reviews, delivery, and user engagement, Actowiz ensures that no critical insight locked inside apps goes unnoticed. Whether you’re in retail, FMCG, travel, or fintech, our mobile app data extraction services provide the intelligence needed to stay ahead of competition and respond to market shifts in real time.

Use Cases of Mobile App Data Extraction by Industry

Mobile apps have become the front door for most consumer interactions — from ordering groceries to booking flights. Extracting structured data from Android & iOS apps unlocks actionable insights that help businesses improve pricing, enhance customer experience, and stay ahead of competition. Here’s how different industries benefit:

E-commerce & Quick Commerce

Apps like Amazon, Walmart, Flipkart, Instacart, Blinkit, Zepto, and Noon drive the majority of retail sales. With our app data extraction, businesses can track product availability, basket pricing, SKU-level discounts, reviews, and delivery SLAs in real time. This data enables dynamic pricing, demand forecasting, and promotion benchmarking. FMCG brands can monitor how products are priced differently across regions, while retailers can detect competitor assortment gaps. By combining this with user sentiment insights, companies can refine assortment strategies and boost conversions.

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Food Delivery & Restaurants

Food delivery apps such as Zomato, Swiggy, Uber Eats, DoorDash, and Deliveroo hold exclusive data on menus, pricing, delivery charges, reviews, and surge timings. By extracting this data, QSR brands and restaurant chains can monitor competitor menus, analyze customer reviews, and optimize delivery partnerships. For example, a pizza chain can benchmark delivery times across Uber Eats and DoorDash or track trending menu items on Zomato. This helps restaurants enhance customer loyalty, identify demand trends, and refine marketing campaigns.

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Travel & Mobility

Travel booking and ride-hailing apps like Uber, Lyft, Careem, Ola, Skyscanner, and Booking.com provide real-time pricing, fare surges, availability, and seat/room data. We extract structured datasets covering ride costs, hotel rates, flight fares, and seasonal demand fluctuations. Airlines and OTAs use this data to benchmark ticket prices, while mobility companies monitor competitor surge pricing. For instance, tracking Uber vs Lyft pricing in New York during peak hours helps ride-hailing startups adjust fares dynamically. With this intelligence, businesses can optimize inventory, pricing, and market expansion.

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Retail & FMCG

Retail apps like Tesco, Carrefour, Lulu, Costco, Target, and BestBuy publish store-level promotions and hyper-local availability data that rarely shows up on websites. By extracting this information, FMCG companies can measure how competitors run regional promotions, compare shelf prices, and monitor bundle offers. For example, during Ramadan or Christmas sales, tracking Carrefour or Tesco app promotions helps FMCG brands align their trade marketing and discounting strategies. This ensures better placement, optimized offers, and improved ROI on marketing spend.

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Finance & Banking

Banking and fintech apps host critical data such as loan rates, credit card offers, transaction fees, and customer feedback. By extracting app data, financial institutions can benchmark their services against competitors, identify trending financial products, and analyze customer sentiment. For example, tracking credit card offers on Paytm or Revolut apps helps a bank identify gaps in its reward programs. This intelligence helps financial companies design better products, improve user experience, and stay competitive in a rapidly evolving industry.

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Real Estate & Housing

Real estate apps like Zillow, Realtor, Bayut, 99Acres, and MagicBricks showcase property listings, pricing, amenities, and availability data. We help property developers, brokers, and consultants extract this data to analyze market demand, pricing benchmarks, and regional trends. For instance, tracking property prices in Downtown Dubai vs Sharjah on Bayut can highlight investment opportunities. Similarly, U.S. developers can monitor Zillow listings to study suburban housing demand. These insights help real estate players maximize ROI, plan projects, and optimize pricing.

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Healthcare & Pharmaceuticals

Healthcare apps list doctors, clinics, hospitals, and pharmacies with ratings, appointment availability, and reviews. Extracting this data enables healthcare providers and pharma companies to monitor patient sentiment, competitor pricing, and availability of medicines or services. For example, scraping Practo or ZocDoc app reviews helps hospitals improve patient experience. Pharma companies can track medicine availability and pricing in online pharmacy apps. This intelligence ensures better market positioning and improved healthcare delivery.

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Market Research & Consulting

Consulting firms and research agencies rely heavily on app-driven data for consumer behavior analysis and trend forecasting. By extracting data from retail, travel, finance, and healthcare apps, consultants gain access to real-time, multi-industry datasets. For example, analyzing quick commerce apps across different geographies reveals basket size trends and delivery time expectations. This intelligence powers strategic recommendations for global clients, helping them stay ahead of shifting consumer preferences.

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Across industries, mobile app data extraction enables real-time decision-making, competitor benchmarking, and customer sentiment analysis. With Actowiz Solutions, businesses get accurate, scalable, and compliant app data feeds tailored to their industry, ensuring they never miss a critical opportunity.

Benefits of Choosing Actowiz for Mobile App Data Extraction

Extracting app data requires far more than basic scraping — it demands scalability, compliance, and precision. At Actowiz Solutions, we combine enterprise-grade infrastructure with real-time delivery and global coverage to help businesses capture mission-critical app insights.

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Scalable Infrastructure

From tracking a few thousand app SKUs to extracting millions of data points across grocery, food delivery, and travel apps, our infrastructure is built to handle it all. Using distributed systems, cloud pipelines, and advanced device emulation, we ensure seamless scaling without downtime. Whether you need hourly delivery data from Blinkit or monthly property listings from Zillow, Actowiz provides a future-ready architecture that grows with your business needs.

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99.9% Accuracy & Data Quality

We know app data is only valuable if it’s accurate. Our automated cleaning, deduplication, and validation pipelines guarantee 99.9% data accuracy. With AI-driven error detection, duplicate removal, and metadata normalization, your teams get analytics-ready datasets. For example, e-commerce clients receive clean SKU listings across Amazon and Walmart apps without missing fields, ensuring reliable insights for decision-making.

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Compliance-First Approach

Mobile app scraping can be complex due to region-specific restrictions and compliance standards. At Actowiz, we follow ethical and compliant practices with IP rotation, throttling, and geo-specific device emulation. Our frameworks respect platform rules and ensure safe, regulation-aligned data collection. Businesses can confidently scale insights across USA, UAE, EU, and APAC markets without worrying about compliance risks.

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Real-Time Data Feeds

Markets move fast, and apps update even faster. We provide real-time APIs and scheduled crawlers that deliver fresh datasets when you need them. For example, retailers monitor hourly flash sales in Walmart’s app, while mobility companies track Uber surge pricing by the minute. With live feeds, push APIs, or scheduled reports, Actowiz ensures you never miss a critical market shift.

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Global Coverage

Our mobile app data extraction services cover 50+ countries, spanning North America, Europe, Middle East, Asia-Pacific, and Latin America. From hyper-local grocery insights in Mumbai to property listings in Dubai or ride-hailing trends in New York, we adapt to regional app ecosystems. With multi-country support and geo-specific targeting, global enterprises get consistent, comparable datasets to expand into new markets confidently.

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Seamless Integrations

We deliver structured datasets in JSON, CSV, Excel, or through custom APIs directly into your BI tools and cloud platforms like AWS, GCP, Azure, or S3. Our goal is to reduce friction — so instead of raw files, you get ready-to-use intelligence plugged into your workflows. Whether you’re running Power BI dashboards or AI models, our data flows seamlessly into your ecosystem.

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24/7 Monitoring & Dedicated Support

App environments change frequently, and crawlers need constant optimization. Our 24/7 monitoring systems detect issues, retries failed jobs, and ensure uninterrupted data delivery. Plus, with a dedicated account manager and support team, you get proactive assistance for scaling projects, troubleshooting, and new data requests. Actowiz works as an extension of your data team, ensuring consistency and reliability.

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AI-Powered Insights

Beyond raw data, we enhance outputs with AI-driven enrichment — sentiment analysis, entity extraction, categorization, and trend detection. For example, review data scraped from Zomato is automatically tagged as positive/negative, helping restaurant chains act on real customer feedback without extra processing. This gives businesses actionable intelligence rather than just raw data dumps.

Geographic Coverage

Mobile app ecosystems vary widely across regions — from U.S. quick commerce leaders like Instacart to UAE-based delivery giants like Talabat. At Actowiz Solutions, we provide global coverage with hyper-local accuracy, helping businesses capture app data in the regions that matter most. Whether you need city-specific ride fares, country-level grocery insights, or worldwide app reviews, we adapt our pipelines to your target geography.

North America

We extract structured app data across the USA and Canada, covering leading platforms like Amazon, Walmart Grocery, Instacart, DoorDash, Uber, Lyft, and Zillow. Businesses leverage our data for competitive benchmarking, dynamic pricing, and expansion strategies across major cities like New York, Los Angeles, Chicago, Toronto, and Vancouver.

Europe

In Europe, our coverage spans UK, Germany, France, Italy, Spain, and the Nordics. From retail apps like Tesco, ASDA, Carrefour, and Aldi to travel and ride-hailing platforms like Bolt, FlixBus, and Booking.com, we help businesses monitor country-specific trends, promotions, and customer behavior.

Middle East & Africa

We provide deep coverage across UAE, Saudi Arabia, Qatar, and South Africa, extracting data from apps such as Noon, Carrefour, Talabat, Careem, and Bayut. For FMCG, retail, and real estate clients, this region-specific intelligence is vital for aligning pricing, promotions, and investment decisions with local demand patterns.

Asia-Pacific

With the world’s fastest-growing app economies, APAC coverage includes India, Singapore, Japan, Korea, and Australia. We capture insights from Flipkart, BigBasket, Zomato, Swiggy, Grab, Rakuten, and Agoda. From monitoring Diwali promotions in India to ride-hailing trends in Singapore, we deliver city- and pin-code level insights that guide growth strategies in this dynamic market.

Latin America

In Brazil, Mexico, and Argentina, we track apps like Mercado Libre, Rappi, Cornershop, and Buser. These insights help businesses understand emerging quick commerce and mobility trends, benchmark against local competitors, and tap into one of the fastest-evolving digital commerce regions in the world.

With coverage across 50+ countries and 200+ cities, Actowiz Solutions ensures your business has access to both global intelligence and hyper-local insights. No matter where your customers are, we help you monitor apps in real time, so you can make informed decisions with confidence and precision.

Sample Data Table

At Actowiz Solutions, we deliver structured, analytics-ready datasets extracted directly from Android & iOS apps. Data is provided in your preferred format — JSON, CSV, Excel, or via API — with frequency customized to your business needs (real-time, hourly, daily, or weekly). Below is an example of what mobile app data extraction looks like:

App Name Data Extracted Format Region Frequency
Instacart Basket pricing, delivery SLAs, promotions CSV USA Hourly
Zomato Menus, ratings, reviews, delivery times JSON India Daily
Uber Ride fares, surge pricing, ETA tracking Excel UAE Real-time
Booking.com Hotel pricing, room availability, reviews API Global Daily
Carrefour Product SKUs, discounts, bundle offers CSV Middle East Weekly
Zillow Property listings, amenities, pricing JSON USA Daily

Key Highlights:

  • Flexible formats: JSON, CSV, Excel, or REST API
  • Custom frequency: Real-time, hourly, daily, or scheduled
  • Industry-ready: Retail, travel, food delivery, real estate, finance, and more
  • Geo-coverage: Pin-code, city, country, or multi-region datasets
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FAQs – Mobile App Data Extraction Services

We cover a wide range of apps across industries like e-commerce, grocery, food delivery, travel, finance, healthcare, real estate, and mobility. Popular platforms include Amazon, Walmart, Instacart, Blinkit, Zepto, Swiggy, Zomato, Uber, Lyft, Booking.com, Expedia, Zillow, Paytm, and Revolut. In addition, we also handle region-specific apps such as Talabat (UAE), BigBasket (India), Deliveroo (UK), and Mercado Libre (Brazil). Our infrastructure is designed to adapt to new or custom apps on request.
Yes. We offer both real-time APIs and scheduled crawlers depending on your use case. For time-sensitive insights such as Uber surge pricing, flash sales on Walmart apps, or room availability on Booking.com, our real-time feeds deliver instant updates. For long-term monitoring like monthly grocery promotions or quarterly property listings, scheduled crawlers provide consistent, reliable datasets at daily, weekly, or monthly intervals.
We follow a compliance-first approach. Our practices include proxy rotation, device emulation, throttling, and adherence to regional data privacy standards (GDPR, CCPA, etc.). We extract publicly available data for market research, business intelligence, and competitive benchmarking purposes, ensuring ethical and regulation-aligned processes.
Many apps deploy anti-scraping measures, but our team uses advanced techniques like dynamic rendering, device fingerprinting, IP rotation, and geotargeting to ensure uninterrupted data access. Even for geo-restricted or region-locked apps, we can emulate local devices and proxies to extract accurate datasets without triggering blocks.
We provide structured outputs in JSON, CSV, Excel, or via REST APIs. Businesses can choose their preferred delivery format based on workflows. For enterprises, we also support direct integration with cloud storage (AWS S3, GCP, Azure) and BI dashboards (Power BI, Tableau, Looker) for seamless data utilization.
Yes. In addition to real-time and forward-looking monitoring, we can build historical datasets by scraping and storing app data over time. This enables businesses to track pricing evolution, demand shifts, promotional campaigns, and sentiment trends. Historical data is especially valuable for market research firms, consultants, and enterprises entering new markets.
We guarantee 99.9% accuracy through multiple validation layers. Our pipelines include AI-driven error detection, deduplication, and normalization, ensuring that businesses receive clean, ready-to-use datasets. For example, product listings from Walmart and Instacart apps are automatically cross-checked for completeness before delivery.
Absolutely. We specialize in granular geo-level extraction, whether it’s tracking Uber fares in Manhattan vs Brooklyn, monitoring Blinkit delivery timelines in Delhi vs Gurugram, or analyzing Carrefour promotions across Dubai vs Abu Dhabi. This hyper-local precision helps businesses benchmark performance and capture micro-market opportunities.
Our solutions are fully customizable. Clients can define apps, data fields, regions, formats, and delivery frequency. For example, a retailer may request only SKU titles, prices, and reviews from Instacart (USA), while a travel startup may need hotel rates and availability from Booking.com (UAE, Germany, Singapore). We build tailored pipelines to fit specific business goals.
We encourage new clients to begin with a pilot program. You can request a sample dataset from target apps to validate data quality and coverage. Based on results, we scale up to larger, long-term projects. Pilots typically last 1–4 weeks, allowing businesses to evaluate data accuracy, timeliness, and ROI before committing to full-scale extraction.
Mobile app scraping benefits a wide range of industries:
  • Retail & FMCG – competitor pricing, promotions, and product visibility.
  • Food Delivery – menu tracking, customer reviews, and delivery times.
  • Travel & Mobility – ride fares, hotel pricing, and availability.
  • Finance & Banking – credit card offers, loan rates, and reviews.
  • Real Estate – property listings, pricing, and amenities.
  • Healthcare – doctor listings, hospital reviews, and pharmacy pricing.
  • Consulting & Research Firms – multi-sector data for analysis and reports.
Actowiz stands out due to scalable infrastructure, global coverage, real-time APIs, and AI-powered enrichment. Unlike basic scrapers, we provide enterprise-grade solutions with SLAs, 24/7 monitoring, and dedicated support. Our ability to extract data from hyper-local, region-specific apps across 50+ countries ensures unmatched depth, accuracy, and reliability.

These FAQs address the most common queries about our Mobile App Data Extraction Services. Still have questions? Our team is ready to provide custom demos and sample datasets to help you evaluate our capabilities.

Case Studies – Mobile App Data Extraction in Action

Case Study 1: Quick Commerce Insights – UAE

A leading FMCG brand in Dubai wanted to monitor real-time pricing, delivery timelines, and promotions across Talabat, Carrefour, and Noon apps. Using Actowiz’s mobile app scraping solutions, they extracted structured data on basket pricing, discounts, delivery SLAs, and in-app campaigns. Within three months, the company identified a 25% delivery time gap compared to competitors and discovered that certain SKUs were consistently under-promoted. By adjusting logistics operations and aligning promotions with Carrefour’s Ramadan campaigns, the brand achieved a 17% improvement in delivery efficiency and a 12% increase in app-driven sales.

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Case Study 2: Grocery Data Extraction – USA

A national retailer in California needed to monitor Instacart and Amazon Fresh app data for SKU availability, basket pricing, and delivery slots. Actowiz implemented real-time app crawlers that tracked 50,000+ grocery SKUs daily across Los Angeles and San Francisco. The data revealed that competitors were offering aggressive bundle discounts on organic products — a trend the client had overlooked. By replicating these offers and improving delivery speed guarantees, the retailer recorded a 20% increase in conversions and a 15% uplift in average basket value within a single quarter.

Case Study 3: Ride-Hailing Price Intelligence – Global

A mobility startup operating across India, UAE, and the UK wanted to understand how Uber, Lyft, and Careem apps handled surge pricing and regional fare fluctuations. Actowiz built a multi-country app monitoring solution that captured real-time ride fares, ETA tracking, and surge multipliers. Insights showed that Uber’s surge pricing in London and Dubai peaked during weekend evenings, while Careem maintained relatively stable rates in Riyadh. Using these insights, the client adjusted its pricing algorithm dynamically and rolled out localized promotions. The result: a 30% boost in rider acquisition and improved competitive positioning in three major markets.

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These case studies demonstrate how mobile app data extraction transforms raw information into actionable intelligence across industries. From improving delivery performance to optimizing pricing strategies, Actowiz Solutions delivers measurable business outcomes worldwide.

Get Started with Mobile App Data Extraction Today

Unlock the hidden insights inside Android & iOS apps with Actowiz Solutions. Whether you need real-time grocery pricing from Instacart, restaurant reviews from Zomato, ride fares from Uber, or property listings from Zillow, our solutions deliver clean, structured, and analytics-ready datasets at scale.

With 99.9% uptime, SLA-backed delivery, global geo coverage, and enterprise-grade support, we ensure your business never misses a critical app-driven opportunity.

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Trusted by Global Leaders Across Q-Commerce, Travel, Retail, and FoodTech

Our web scraping expertise is relied on by 3,000+ global enterprises including Zomato, Tata Consumer, Subway, and Expedia — helping them turn web data into growth.

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Why Global Leaders Trust Actowiz

Backed by automation, data volume, and enterprise-grade scale — we help businesses from startups to Fortune 500s extract competitive insights across the USA, UK, UAE, and beyond.

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7+
Years of Experience
Proven track record delivering enterprise-grade web scraping and data intelligence solutions.
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4,000+
Projects Delivered
Serving startups to Fortune 500 companies across 50+ countries worldwide.
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200+
In-House Experts
Dedicated engineers across scrapers, AI/ML models, APIs, and data quality assurance.
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Running weekly across eCommerce, Quick Commerce, Travel, Real Estate, and Food industries.
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270+ TB
Data Transferred
Real-time and batch data scraping at massive scale, across industries globally.
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380M+
Pages Crawled Weekly
Scaled infrastructure for comprehensive global data coverage with 99% accuracy.

AI Solutions Engineered
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LLM-Powered Attribute Extraction: High-precision product matching using large language models for accurate data classification.
Advanced Computer Vision: Fine-grained object detection for precise product classification using text and image embeddings.
GPT-Based Analytics Layer: Natural language query-based reporting and visualization for business intelligence.
Human-in-the-Loop AI: Continuous feedback loop to improve AI model accuracy over time.
🎯 Product Matching 🏷️ Attribute Tagging 📝 Content Optimization 💬 Sentiment Analysis 📊 Prompt-Based Reporting

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How a top-10 UK grocery retailer used Actowiz grocery price scraping to achieve 300% promotional ROI and reduce competitive response time from 5 days to same-day.

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Report

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Track UK Grocery Products Daily Using Automated Data Scraping across Morrisons, Asda, Tesco, Sainsbury’s, Iceland, Co-op, Waitrose, and Ocado for insights.

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                            [zh-CN] => 美国
                        )

                )

            [location] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [postal] => Array
                (
                    [code] => 43215
                )

            [registered_country] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [subdivisions] => Array
                (
                    [0] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                )

            [traits] => Array
                (
                    [ip_address] => 216.73.216.153
                    [prefix_len] => 22
                )

        )

    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => geonameId
                    [2] => names
                )

        )

    [country:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

    [locales:protected] => Array
        (
            [0] => en
        )

    [maxmind:protected] => GeoIp2\Record\MaxMind Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [validAttributes:protected] => Array
                (
                    [0] => queriesRemaining
                )

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
                )

        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.153
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
                (
                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
                    [5] => isAnonymous
                    [6] => isAnonymousProxy
                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
                    [21] => userType
                )

        )

    [city:protected] => GeoIp2\Record\City Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
                )

        )

    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
                )

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                        (
                            [0] => en
                        )

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
                        )

                )

        )

)
 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)
Array
(
    [city] => Columbus
    [country] => United States
    [countryCode] => +1
    [currencyCode] => USD
)
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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [continent] => Array
                (
                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

            [country] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [location] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [postal] => Array
                (
                    [code] => 43215
                )

            [registered_country] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [subdivisions] => Array
                (
                    [0] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                )

            [traits] => Array
                (
                    [ip_address] => 216.73.216.153
                    [prefix_len] => 22
                )

        )

    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => geonameId
                    [2] => names
                )

        )

    [country:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

    [locales:protected] => Array
        (
            [0] => en
        )

    [maxmind:protected] => GeoIp2\Record\MaxMind Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [validAttributes:protected] => Array
                (
                    [0] => queriesRemaining
                )

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
                )

        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.153
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
                (
                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
                    [5] => isAnonymous
                    [6] => isAnonymousProxy
                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
                    [21] => userType
                )

        )

    [city:protected] => GeoIp2\Record\City Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
                )

        )

    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
                )

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                        (
                            [0] => en
                        )

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
                        )

                )

        )

)
 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

Request Free Sample Data

Our team will reach out within 2 hours with 500 rows of real data — no credit card required.

+1
Free 500-row sample · No credit card · Response within 2 hours