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Actowiz Metrics Now Live!
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Actowiz Metrics Now Live!
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Actowiz Metrics Now Live!
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Food Delivery Data Scraping Services – Real-Time Menus, Prices, Reviews & Promotions

Stay ahead in the competitive food delivery and online dining industry with Actowiz Solutions. Our food delivery data scraping services help businesses extract menus, pricing, promotions, reviews, ratings, and delivery insights in real time from leading platforms like Zomato, Swiggy, Uber Eats, DoorDash, Grubhub, Deliveroo, and Talabat.

We serve enterprises in the USA and UAE, as well as high-demand regions including the UK, India, Europe, Japan, Canada, Singapore, and Australia, delivering >99% accurate datasets in JSON, CSV, or Excel, seamlessly integrated into your systems.

With Actowiz, you can:

  • Benchmark restaurant pricing & delivery fees
  • Monitor menu changes and item availability
  • Analyze customer reviews, ratings & sentiment
  • Track promotions, offers & seasonal discounts
  • Access city-level & hyperlocal food delivery intelligence

Turn raw data into actionable insights that optimize pricing strategies, improve delivery performance, and strengthen customer loyalty across the USA, UAE, and global food delivery markets.

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Expanded Use Cases of Food Delivery Data Scraping

Scraper Development 1

Dynamic Menu & Pricing Intelligence

In food delivery, menus and prices change frequently — sometimes several times a day. Restaurants introduce limited-time offers, aggregators adjust delivery fees based on demand, and hidden costs like packaging charges or surge pricing impact the customer’s final bill. For restaurant chains, cloud kitchens, and consultancies, manual tracking of these changes across multiple platforms is inefficient and error-prone. Without accurate, up-to-date data, businesses risk underpricing or overpricing, losing customers to competitors, and missing revenue opportunities.

Actowiz Solutions helps businesses track real-time menu and pricing data at item and outlet level. From base prices and add-ons to taxes, surcharges, and delivery fees, we deliver structured datasets that allow you to run competitive benchmarking, dynamic pricing strategies, and profitability analysis. With automated scraping, you no longer rely on guesswork or outdated screenshots — you gain reliable intelligence to make smarter, data-driven pricing decisions.

What We Do:
  • Extract restaurant menus with item names, categories, and variants.
  • Capture item-level prices, discounts, add-ons, and combo deals.
  • Track platform-specific charges: service fees, delivery fees, packaging charges, surge pricing.
  • Log price fluctuations by day, time, and city.
  • Normalize data across platforms (Zomato, Swiggy, Uber Eats, DoorDash, Deliveroo, etc.).
  • Deliver datasets in JSON, CSV, or Excel with full timestamping.
Impact:
  • Enable dynamic pricing strategies to stay competitive.
  • Detect hidden costs and analyze their effect on conversions.
  • Benchmark competitors’ menus and price elasticity.
  • Improve profit margins by aligning with market demand.
  • Provide reliable datasets for BI dashboards and predictive modeling.
Example (mini case):

A leading QSR chain wanted to understand competitor pricing across 5,000 outlets in India. Actowiz set up automated crawlers for Swiggy and Zomato to track menu updates, hidden charges, and real-time price shifts. Within three months, the chain identified that small packaging fees were hurting conversions in metro cities. By introducing combo deals and adjusting delivery charges dynamically, they improved basket size by 15% and increased order conversions by 12%.

Read More
Flash Sale Tracking

Promotion & Coupon Monitoring

Promotions and coupons are among the biggest drivers of customer acquisition in the food delivery space. Platforms like Zomato, Swiggy, Uber Eats, and DoorDash launch daily offers — from “Buy One Get One Free” (BOGO) to “Free Delivery Above ₹299” — to attract customers. Restaurants often run their own offers in parallel, creating a layered promotional ecosystem. However, tracking these promotions across hundreds of outlets and multiple platforms manually is nearly impossible.

Without visibility into what discounts competitors are offering, businesses risk overspending on ineffective promotions or losing market share by not offering competitive deals. Actowiz Solutions enables automated monitoring of all types of coupons, discounts, banners, and subscription offers in real time. This ensures that brands, cloud kitchens, and restaurant chains can assess promotion effectiveness, benchmark competitors, and adjust campaigns to maximize ROI.

What We Do:
  • Scrape banners, promotional tags, and discount listings on food delivery apps.
  • Capture coupon codes, validity dates, terms, and usage restrictions.
  • Track subscription offers like Zomato Gold, Swiggy One, DashPass, Deliveroo Plus, etc.
  • Log the frequency and placement of promotions across restaurants and regions.
  • Provide structured datasets linking promo exposure with menu items and prices.
  • Deliver daily or hourly feeds for ongoing promo benchmarking.
Impact:
  • Identify high-performing promotions that drive real order lift.
  • Eliminate wasted spend on discounts that don’t improve conversions.
  • Benchmark competitors’ promotional intensity across apps and cities.
  • Improve ROI of marketing campaigns with data-driven adjustments.
  • Strengthen brand positioning with smarter, targeted offers.
Example (mini case):

A cloud kitchen group operating in India and UAE was spending heavily on discount campaigns but struggling to see ROI. Actowiz built a promo-tracking pipeline that logged competitor offers across Swiggy, Zomato, and Talabat hourly. The analysis revealed that competitors focused promotions during lunch hours, while the client’s discounts were spread thin throughout the day. By shifting 70% of their promo spend to peak lunch windows and removing underperforming evening offers, the client achieved a 20% increase in conversions while reducing promotional costs by 18% in just two months.

Read More
Delivery Fee Benchmarking

Delivery SLA & Surge Analytics

Delivery time is one of the biggest factors influencing customer satisfaction on food delivery platforms. Customers expect fast, reliable delivery — and platforms constantly adjust SLAs (Service Level Agreements) and fees to balance demand and driver supply. Surge pricing often kicks in during peak hours, festivals, or bad weather, leading to fluctuating delivery charges. For restaurants and aggregators, not tracking these shifts results in poor forecasting, higher costs, and missed opportunities to optimize staffing and delivery strategies.

Actowiz Solutions enables automated tracking of delivery SLAs, surge pricing patterns, and prep time across locations, hours, and events. With structured, time-stamped datasets, businesses can analyze how ETAs change during lunch vs dinner, weekdays vs weekends, and normal vs high-demand conditions. This visibility helps optimize driver allocation, improve customer satisfaction, and identify hidden opportunities for competitive advantage.

What We Do:
  • Capture real-time delivery ETAs quoted by apps.
  • Monitor surge pricing patterns across hours and events.
  • Track prep times and order processing times for restaurants.
  • Analyze SLA compliance and on-time delivery performance.
  • Provide historical surge and SLA trend data.
  • Deliver datasets in JSON/CSV/Excel with timestamps.
Impact:
  • Improve staffing and driver allocation.
  • Reduce late deliveries and SLA breaches.
  • Benchmark competitor ETAs and surge charges.
  • Identify service gaps by city, time, or event.
  • Increase customer satisfaction with optimized delivery times.
Example (mini case):

A delivery aggregator in the Middle East wanted to understand why order cancellations spiked during weekends. Actowiz tracked ETAs and surge fees across Talabat and Careem. The data revealed that average ETAs jumped from 30 minutes to 55 minutes during weekend evenings due to surge shortages. By incentivizing drivers with higher payouts during these slots and adjusting customer communication, cancellations dropped by 22%, and customer satisfaction scores improved within two months.

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Stock & Substitution Monitoring

Review & Sentiment Analysis

Customer reviews are gold for understanding brand perception. Food delivery apps display ratings, written reviews, and feedback tags that directly influence purchase decisions. A one-star drop in ratings can reduce order volume significantly. Manually scanning thousands of reviews across platforms is not scalable, and businesses risk missing key feedback themes like “late delivery,” “poor packaging,” or “excellent taste.”

Actowiz Solutions automates the extraction of ratings, review counts, sentiment trends, and keyword themes. With NLP-based enrichment, businesses can go beyond star ratings to identify patterns in customer complaints or compliments. These insights help restaurants improve service quality, aggregators refine delivery standards, and brands monitor reputation at scale.

What We Do:
  • Scrape star ratings, review counts, and written feedback.
  • Extract sentiment keywords like “cold,” “tasty,” or “expensive.”
  • Track review recency to gauge ongoing performance.
  • Normalize reviews across multiple apps.
  • Provide structured sentiment datasets.
Impact:
  • Spot recurring issues before they damage reputation.
  • Improve menu, packaging, or delivery processes.
  • Benchmark competitors’ customer sentiment.
  • Enhance brand trust and visibility on delivery apps.
  • Feed structured data into AI/BI dashboards for trend tracking.
Example (mini case):

A QSR chain in Europe wanted to analyze why one of its outlets performed poorly compared to others. Actowiz extracted reviews from Deliveroo and Just Eat. Sentiment analysis revealed “cold food” was mentioned 40% more at that outlet compared to others. The brand implemented new packaging solutions and retrained staff. Within three months, ratings improved from 3.5★ to 4.3★, and weekly orders increased by 25%.

Read More
Private Label vs National Brand Pricing

Marketplace Visibility & Sponsored Rankings

Visibility is everything in food delivery apps. Restaurants that appear higher in search results or are featured in sponsored sections enjoy significantly higher order volumes. However, tracking organic vs sponsored placement across platforms and geographies is complex. Without this data, restaurants cannot understand why competitors are winning visibility or whether their ad spend is effective.

Actowiz Solutions monitors marketplace rankings for your outlets and competitors. We track keyword searches, sponsored placement tags, carousel positions, and overall category visibility. This allows businesses to analyze discoverability, evaluate paid vs organic performance, and optimize ad campaigns to improve ROI.

What We Do:
  • Track organic vs sponsored restaurant listings.
  • Capture keyword search rankings for restaurants/cuisines.
  • Monitor carousel and featured placement frequency.
  • Benchmark competitor ad spend intensity.
  • Deliver structured ranking datasets across platforms.
Impact:
  • Improve brand discoverability across apps.
  • Reduce wasted ad spend on ineffective placements.
  • Benchmark competitors’ visibility strategies.
  • Optimize keyword targeting and promotions.
  • Increase order volumes through smarter visibility.
Example (mini case):

A beverage brand on Uber Eats noticed declining impressions despite consistent ad spend. Actowiz tracked category rankings and found competitors were buying sponsored slots for “healthy drinks” during lunch hours. By reallocating ad spend to peak hours and adding targeted keywords, the brand increased impressions by 22% and boosted orders by 15% within six weeks.

Read More
Seasonal Demand Forecasting

Assortment & Menu Gap Analysis

One of the biggest competitive advantages in food delivery is having the right menu assortment. Customers often compare not only prices but also the variety of items offered. Competitors may introduce new cuisines, portion sizes, or combos that appeal to evolving tastes, leaving others behind. Without visibility into competitor assortments, restaurants risk losing out on customer demand or launching items that don’t resonate.

Actowiz Solutions provides item-level assortment analysis across multiple food delivery platforms. By scraping menus, add-ons, and bestsellers, we help identify what competitors are offering and where your menu might have gaps. Whether it’s discovering a new trending cuisine, adding family-size portions, or bundling popular add-ons, these insights ensure your menu stays competitive and relevant.

What We Do:
  • Scrape restaurant menus with items, categories, and add-ons.
  • Track bestseller tags and trending cuisines.
  • Compare assortments across outlets and competitors.
  • Identify missing items or portion sizes in your menu.
  • Deliver normalized menu datasets for analysis.
Impact:
  • Launch menu items that match customer demand.
  • Avoid missed sales opportunities due to menu gaps.
  • Benchmark assortment variety against competitors.
  • Design profitable combos and bundles.
  • Increase customer retention with better offerings.
Example (mini case):

A cloud kitchen in Singapore wanted to identify gaps in its menu. Actowiz scraped competitor menus across GrabFood and Foodpanda. The data revealed that vegetarian and gluten-free options were underrepresented in the client’s catalog compared to competitors. By introducing five new vegetarian dishes and two gluten-free variants, the kitchen increased order volumes by 17% and captured a loyal new customer segment within three months.

Read More
Regional Price Disparity

Franchise Compliance Monitoring

For franchise chains operating across multiple cities or countries, consistency is critical. Variations in menu pricing, missing items, or incorrect brand visuals can harm reputation and customer trust. Manually auditing hundreds of outlets is time-consuming and prone to errors.

Actowiz Solutions enables automated monitoring of franchise compliance across food delivery platforms. From checking mandatory menu items and prices to verifying allergen disclosures and branding, our system ensures consistency at scale. With regular scraping, franchise managers can identify compliance violations quickly and enforce standards across outlets.

What We Do:
  • Verify menu consistency across outlets.
  • Track pricing parity for franchise-wide promotions.
  • Capture allergen and dietary information disclosure.
  • Monitor brand creatives, logos, and descriptions.
  • Deliver compliance reports with timestamped evidence.
Impact:
  • Ensure consistent brand experience across outlets.
  • Reduce customer complaints about pricing differences.
  • Enforce franchise agreements effectively.
  • Lower compliance risks and penalties.
  • Build stronger brand trust with uniform service.
Example (mini case):

A pizza chain with 400 outlets in India used Actowiz to monitor compliance across Zomato and Swiggy. The data showed that 12% of outlets had missing “family meal” combos and inconsistent prices. After enforcing compliance, customer complaints about price mismatches dropped 30%, and the chain improved brand perception ratings on delivery apps.

Read More
Subscriptions & Membership Tracking

New Market Expansion & Site Selection

Choosing the right city or neighborhood to expand is a critical decision for food brands. Traditional market research is costly and slow, while food delivery apps already provide live demand signals in the form of restaurant density, cuisine popularity, delivery times, and pricing. Without this data, expansions risk failing due to poor location choices.

Actowiz Solutions delivers structured datasets for territory expansion and site selection. By analyzing delivery coverage, cuisine gaps, and performance of competitors, we help brands identify high-potential areas for launching new outlets or cloud kitchens.

What We Do:
  • Map restaurant density by location and cuisine.
  • Track delivery radius and ETA coverage.
  • Benchmark competitor presence in target areas.
  • Analyze customer demand signals by cuisine.
  • Deliver geo-tagged datasets for expansion analysis.
Impact:
  • Launch in markets with proven demand.
  • Reduce expansion risks with data-driven site selection.
  • Identify underserved cuisines or price points.
  • Shorten payback period for new outlets.
  • Improve investor confidence in expansion plans.
Example (mini case):

A US-based QSR chain wanted to expand into UAE. Actowiz analyzed Talabat and Deliveroo data across Dubai neighborhoods. The analysis revealed a shortage of Mexican cuisine options in key high-income areas. By opening outlets in those micro-markets, the chain achieved profitability within six months and beat ROI projections by 20%.

Read More
Assortment Intelligence

Cloud Kitchen Menu Optimization

Cloud kitchens depend heavily on food delivery apps for visibility and sales. Unlike dine-in restaurants, they cannot rely on ambiance or foot traffic. Success depends entirely on having the right menu mix, optimized pricing, and high visibility on delivery apps. Without data, cloud kitchens struggle to adapt quickly to trends and miss out on revenue opportunities.

Actowiz Solutions enables cloud kitchens to track competitor menus, bestseller trends, add-on attachment rates, and price variations. With automated scraping, kitchens can test new items, bundle profitable combos, and respond quickly to customer demand.

What We Do:
  • Capture competitor menu performance and bestseller tags.
  • Track add-on usage and combo design.
  • Monitor pricing variations across time and location.
  • Deliver item-level datasets for A/B testing.
  • Provide insights into customer demand by cuisine.
Impact:
  • Improve profitability with optimized combos.
  • Launch items that resonate with customers faster.
  • Reduce trial-and-error risks in menu design.
  • Benchmark against top-performing competitors.
  • Grow order volumes consistently.
Example (mini case):

A Dubai-based multi-brand cloud kitchen used Actowiz to analyze bestseller trends across Talabat. The insights showed that rice bowls were trending among office lunch orders. The kitchen introduced rice bowl combos with customizable add-ons, which increased basket size by 19% and improved daily order volume by 22%.

Read More
Dynamic Pricing Wars

Seasonal Trend Tracking

Food delivery demand fluctuates significantly with seasons, festivals, and holidays. Promotions, bestselling cuisines, and delivery fees all change during these periods. Without tracking these shifts, brands miss opportunities to capitalize on peak demand or misallocate resources during low-demand phases.

Actowiz Solutions enables seasonal trend tracking by scraping delivery apps during key events like Ramadan, Diwali, Christmas, or Super Bowl weekend. We deliver datasets on menu specials, surge pricing, and promo intensity during these times, helping businesses plan better.

What We Do:
  • Track special menus and seasonal items.
  • Monitor surge pricing during peak demand.
  • Analyze competitor promotions during events.
  • Deliver time-stamped datasets for trend analysis.
  • Provide historical seasonal insights for planning.
Impact:
  • Launch timely seasonal menus.
  • Optimize promotions for peak demand.
  • Staff delivery fleets more effectively.
  • Increase revenue during high-demand events.
  • Avoid losses during low-demand periods.
Example (mini case):

A restaurant group in India used Actowiz to track Diwali specials across Zomato and Swiggy. Insights showed vegetarian thali combos dominated festive orders. By launching their own festive combos, the group increased sales by 26% during Diwali week compared to the previous year.

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Price Elasticity Analysis

Food Packaging & Fee Benchmarking

Packaging quality and fees significantly influence customer experience and profitability in food delivery. Many customers abandon carts when they see excessive packaging or hidden fees. Without tracking these charges across competitors, brands risk poor conversions and negative reviews.

Actowiz Solutions captures packaging charges, small-order fees, and final basket totals from delivery apps. We also help track packaging types and keywords in reviews related to quality. This gives restaurants visibility into how competitors price packaging and how it impacts customer satisfaction.

What We Do:
  • Track packaging and handling charges.
  • Capture small-order and hidden fees.
  • Extract review mentions of packaging quality.
  • Deliver final bill breakdown datasets.
  • Benchmark competitors’ fee structures.
Impact:
  • Improve transparency in pricing.
  • Reduce cart abandonment from hidden fees.
  • Optimize packaging cost without hurting perception.
  • Benchmark packaging strategies by cuisine and outlet.
  • Increase customer trust and retention.
Example (mini case):

A fast-casual chain in the UK saw poor reviews citing “excessive packaging.” Actowiz tracked competitor reviews on Deliveroo, revealing that minimal but eco-friendly packaging scored higher ratings. The chain adopted sustainable packaging and reduced packaging fees by 10%. Within three months, ratings improved, and cart abandonment dropped by 15%.

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Loyalty Program Benchmarking

Loyalty Program & Subscription Tracking

Food delivery platforms increasingly use loyalty and subscription models like Zomato Gold, Swiggy One, DashPass, and Deliveroo Plus. These programs offer free delivery, discounts, or exclusive items. Restaurants and competitors who don’t track these programs risk losing loyal customers.

Actowiz Solutions helps track loyalty program presence, offers, and impact on customer behavior. With structured data, businesses can assess whether joining or competing against such programs makes sense.

What We Do:
  • Scrape loyalty program benefits and discounts.
  • Track exclusive menu items for subscribers.
  • Benchmark subscription adoption across competitors.
  • Deliver datasets linking loyalty perks with customer orders.
  • Provide insights into subscription ROI.
Impact:
  • Decide whether to join or compete with subscription models.
  • Benchmark competitor adoption rates.
  • Reduce churn by offering competitive benefits.
  • Strengthen customer loyalty with targeted perks.
  • Improve ROI of loyalty campaigns.
Example (mini case):

A QSR brand in the US analyzed DashPass discounts on DoorDash using Actowiz data. They discovered competitors offering exclusive “subscriber-only” items that boosted loyalty. By launching their own subscriber-exclusive menu, they improved repeat order rates by 21% within two months.

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Competitor Assortment Gap Analysis

Competitor Benchmarking Across Aggregators

Restaurants often list across multiple apps like Swiggy, Zomato, Uber Eats, and DoorDash. But performance can vary widely due to fees, visibility, and promotions. Without cross-platform benchmarking, restaurants cannot identify which platform drives the best ROI.

Actowiz Solutions enables automated benchmarking of competitors across multiple aggregators. From menu prices and fees to ratings and promotions, we provide cross-platform visibility that helps optimize channel strategy.

What We Do:
  • Scrape competitor listings across multiple apps.
  • Track price and fee variations across platforms.
  • Benchmark visibility and promotions by platform.
  • Deliver normalized datasets for analysis.
  • Provide insights into platform-specific performance.
Impact:
  • Optimize partnerships with delivery aggregators.
  • Improve ROI by focusing on high-performing apps.
  • Reduce dependency on underperforming platforms.
  • Benchmark competitor presence across channels.
  • Enhance channel negotiation power.
Example (mini case):

A restaurant brand in India compared performance across Zomato and Swiggy using Actowiz. The data revealed higher delivery fees on Swiggy, leading to lower conversions. The brand shifted marketing budgets towards Zomato, improving ROI by 25% in one quarter.

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Scraper Development 1

Driver/Delivery Fee Analysis

Delivery driver payouts and customer delivery fees play a huge role in food delivery economics. Platforms adjust them dynamically, but restaurants and cloud kitchens rarely have visibility into how they affect orders. Without tracking these fees, restaurants may lose orders due to high costs.

Actowiz Solutions scrapes delivery fee data across platforms, tracking variations by city, time, and surge. This helps restaurants benchmark competitor delivery charges and optimize pricing strategies.

What We Do:
  • Track real-time delivery fees per order.
  • Monitor surge-based delivery charges.
  • Benchmark fees across apps and locations.
  • Deliver structured datasets for fee analysis.
  • Provide insights into fee elasticity.
Impact:
  • Reduce order drop-offs due to high fees.
  • Benchmark competitor delivery costs.
  • Improve pricing and promotions to offset fees.
  • Enhance negotiations with aggregators.
  • Increase customer satisfaction with fair pricing.
Example:

A café chain in the Middle East used Actowiz to analyze delivery fees on Talabat. Insights showed that weekend evening surcharges added 15–20% extra costs, leading to drop-offs. By offering free delivery during these hours (absorbing costs strategically), the chain boosted order volumes by 19% while maintaining profit margins through larger basket sizes.

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Competitor New Product Launch Tracking

Cuisine Popularity Trends

Food preferences shift rapidly — sushi may trend one month, vegan bowls the next. For restaurants and cloud kitchens, staying ahead of these cuisine trends is critical. Without structured data, it’s hard to know which cuisines are gaining popularity and which are declining. Guesswork often results in misaligned investments and wasted menu launches.

Actowiz Solutions scrapes delivery platforms to identify cuisine tags, bestseller rankings, and order trends across regions. By monitoring these signals, businesses can forecast demand shifts and adapt menus accordingly. This prevents missed opportunities while keeping offerings relevant and profitable.

What We Do:
  • Scrape cuisine tags from restaurant profiles.
  • Track bestseller cuisines across apps.
  • Monitor rising and declining cuisine demand.
  • Deliver trend datasets with time-stamped history.
  • Provide competitive benchmarking by cuisine.
Impact:
  • Identify fast-growing cuisines before competitors.
  • Launch relevant dishes for higher ROI.
  • Reduce risks in new menu development.
  • Attract new customer segments with timely offerings.
  • Increase order volumes by staying aligned with demand.
Example:

A food-tech investor in Europe used Actowiz to track cuisine trends across Deliveroo and Glovo. Data showed a 40% rise in demand for plant-based dishes in London. They invested in a vegan cloud kitchen brand, which grew orders by 28% within six months, proving the predictive power of trend data.

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Promotion ROI Benchmarking

Festival & Event Demand Monitoring

Demand for food delivery spikes dramatically during festivals, holidays, and major events. Super Bowl weekend in the US, Diwali in India, or Ramadan evenings in the Middle East all see unique patterns in orders, menus, and promotions. Without this data, brands miss opportunities to maximize sales during high-demand windows.

Actowiz Solutions enables detailed monitoring of food delivery platforms during these events. From festive menu specials to surge pricing and promotional intensity, we capture the complete picture so businesses can prepare ahead of time.

What We Do:
  • Track festive menus and seasonal combos.
  • Scrape special promotions tied to events.
  • Monitor surge pricing during high-demand hours.
  • Benchmark competitor performance during events.
  • Deliver historical and real-time event datasets.
Impact:
  • Align menus and promotions with festive demand.
  • Maximize ROI during peak ordering times.
  • Anticipate surge and allocate staff accordingly.
  • Benchmark festive performance vs competitors.
  • Strengthen brand presence during cultural moments.
Example:

A QSR chain in the US leveraged Actowiz during the Super Bowl. Data revealed that pizza competitors doubled promo intensity and reduced ETAs with extra staffing. By replicating this playbook, the brand grew orders by 31% during the event compared to the previous year.

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Category Share Monitoring

Basket Size & Combo Optimization

Average Order Value (AOV) is a key metric in food delivery. Smart combos and add-ons often push customers to spend more. Without visibility into competitor bundles and basket-building strategies, restaurants may leave money on the table.

Actowiz Solutions helps analyze basket size patterns by tracking combos, add-on options, and bestseller bundles. We provide structured datasets to identify which add-ons are most effective and how combos are designed across competitors.

What We Do:
  • Scrape combo deals and bundled menu items.
  • Track add-on attachment rates.
  • Monitor bestseller baskets by cuisine.
  • Deliver structured datasets for AOV optimization.
  • Provide comparative analysis of combo economics.
Impact:
  • Increase AOV with smarter combos.
  • Improve profitability with add-on upselling.
  • Benchmark competitor combo strategies.
  • Reduce customer churn by offering value bundles.
  • Strengthen menu design with proven tactics.
Example:

A cloud kitchen in India used Actowiz to analyze combos on Swiggy. Insights showed that burger + fries combos had 30% higher AOV than standalone burgers. By introducing similar value meals, the kitchen improved basket size by 18% within a quarter.

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Shelf Visibility & Digital Placement

Discount Elasticity Analysis

Not all discounts drive equal results. Some promos boost conversions, while others erode margins without ROI. Without structured testing and analysis, brands waste promo budgets.

Actowiz Solutions captures discount data, coupon uptake, and order lift across restaurants and platforms. By analyzing discount elasticity, we help identify the sweet spot between conversion and profitability.

What We Do:
  • Track discount levels by item and outlet.
  • Measure impact of % discounts vs flat offers.
  • Analyze promo-driven order lift.
  • Deliver structured elasticity datasets.
  • Provide insights into margin-optimized discounts.
Impact:
  • Maximize ROI from discount campaigns.
  • Avoid overspending on low-performing offers.
  • Identify price thresholds that boost conversions.
  • Improve customer loyalty with fair pricing.
  • Optimize marketing budgets.
Example:

A pizza brand in the UK tested 20% vs 30% discounts using Actowiz data from Deliveroo. Analysis revealed 20% discounts drove nearly equal conversions but protected margins better. By standardizing on 20% offers, the brand saved 12% in promo costs annually.

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Review & Sentiment Analytics

Brand Visibility & Placement Tracking

Competition for top placement on delivery apps is fierce. Sponsored ads, featured listings, and keyword targeting drive visibility. Without data, brands cannot know how competitors secure top positions or whether their own ad spend is effective.

Actowiz Solutions tracks restaurant placements across search results, featured sections, and carousels. We distinguish between organic and sponsored listings and provide insights into share of voice by cuisine or keyword.

What We Do:
  • Scrape search result rankings for restaurants.
  • Identify organic vs paid placements.
  • Track carousel/featured listings.
  • Benchmark share of voice by category.
  • Deliver structured placement datasets.
Impact:
  • Improve visibility strategy with real data.
  • Optimize ad spend to high-ROI placements.
  • Benchmark competitor placement tactics.
  • Increase impressions and order volume.
  • Strengthen discoverability across apps.
Example:

A beverage brand on DoorDash noticed poor visibility in “healthy drinks.” Actowiz revealed competitors dominated sponsored placements during morning hours. By reallocating spend to breakfast hours, the brand boosted impressions by 20% and sales by 14%.

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Regional Demand Mapping

Regional Cuisine Performance Mapping

Not all cuisines perform equally across regions. Sushi may thrive in metro areas, while biryani dominates smaller cities. Without structured mapping, restaurants miss the chance to tailor offerings regionally.

Actowiz Solutions provides geo-tagged datasets of cuisine performance across delivery apps. We track bestseller cuisines, pricing trends, and customer sentiment by region, giving brands a data-backed roadmap for regional strategies.

What We Do:
  • Scrape cuisine tags and bestseller rankings.
  • Deliver geo-tagged sales trend datasets.
  • Benchmark regional cuisine popularity.
  • Track price-performance variations by location.
  • Provide historical performance analysis.
Impact:
  • Launch region-specific menus for better fit.
  • Improve regional sales with tailored assortments.
  • Reduce menu complexity where demand is low.
  • Benchmark competitor strategies by geography.
  • Strengthen localization efforts.
Example:

A QSR brand in India used Actowiz to track regional cuisine demand. Insights revealed biryani dominated Tier-2 cities, while pizzas thrived in metros. By adjusting assortments regionally, the brand boosted orders by 23% across its portfolio.

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Delivery Partner Benchmarking

Delivery Radius & Coverage Insights

Delivery coverage determines how many customers a restaurant can reach. However, coverage varies by platform, city, and even time of day. Without data, brands cannot identify coverage gaps or optimize delivery strategies.

Actowiz Solutions maps delivery radii, ETAs, and coverage zones for restaurants across platforms. This provides visibility into competitor reach and opportunities for optimizing delivery logistics.

What We Do:
  • Scrape delivery radius and ETA data.
  • Benchmark competitor coverage across regions.
  • Track changes in coverage during peak times.
  • Deliver geo-tagged coverage maps.
  • Provide datasets for logistics optimization.
Impact:
  • Expand reach by identifying underserved zones.
  • Benchmark competitor delivery coverage.
  • Improve logistics with targeted adjustments.
  • Increase customer acquisition in new areas.
  • Strengthen partnerships with aggregators.
Example:

A pizza chain in the UAE used Actowiz to analyze delivery coverage on Talabat. Data revealed competitors extended radius during weekends. By matching this, the brand added 18% more orders without significant delivery delays.

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Competitor Expansion Tracking

New Product Launch Benchmarking

Launching new products without competitive benchmarking risks failure. Restaurants need to know how new items are priced, promoted, and received on delivery apps.

Actowiz Solutions enables real-time tracking of new product launches across platforms. We capture item listings, promo intensity, pricing, and review sentiment to provide benchmarks for successful launches.

What We Do:
  • Track new menu item launches by competitors.
  • Scrape pricing and promotions for new items.
  • Monitor customer reviews of new launches.
  • Deliver structured launch performance datasets.
  • Provide insights for faster iteration.
Impact:
  • Improve success rates for new launches.
  • Benchmark competitors’ launch strategies.
  • Reduce risks in menu innovation.
  • Increase customer engagement with timely items.
  • Shorten time-to-market for new products.
Example:

A dessert brand in Singapore benchmarked new ice cream flavors launched on GrabFood. Actowiz data revealed that “salted caramel” received 30% higher ratings than others. The brand adopted the trend quickly, boosting new launch sales by 19%.

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Delivery Partner Benchmarking

Cross-Platform Menu Consistency

Many restaurants list on multiple delivery platforms. However, inconsistent menus, prices, or item availability create confusion and harm customer trust. Manually auditing across platforms is impossible at scale.

Actowiz Solutions automates cross-platform consistency monitoring. From menu items and pricing to imagery and branding, we ensure your presence is uniform across apps.

What We Do:
  • Scrape menu data across multiple apps.
  • Compare item availability and pricing.
  • Track brand creatives and descriptions.
  • Deliver consistency audit reports.
  • Provide alerts for discrepancies.
Impact:
  • Improve customer trust with uniform menus.
  • Avoid price mismatches across platforms.
  • Strengthen brand reputation.
  • Reduce operational inefficiencies.
  • Enhance aggregator relationships.
Example:

A US restaurant chain found inconsistent pricing between Uber Eats and DoorDash. Actowiz flagged the discrepancy, enabling corrections that improved customer satisfaction scores by 12% and reduced complaints.

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Competitor Expansion Tracking

Consumer Behavior & Cart Abandonment Analysis

Cart abandonment is a major pain point in food delivery. Hidden fees, long ETAs, or unattractive pricing cause customers to drop out. Without visibility, restaurants cannot reduce lost sales.

Actowiz Solutions tracks cart abandonment triggers by monitoring final basket breakdowns, fees, and ETAs. Combined with sentiment data, this reveals why customers don’t complete orders.

What We Do:
  • Scrape basket totals including hidden fees.
  • Track surge and delivery time at checkout.
  • Monitor promo code application success.
  • Deliver structured cart abandonment datasets.
  • Provide actionable triggers for improvement.
Impact:
  • Reduce abandonment rates with data-driven fixes.
  • Improve pricing transparency for customers.
  • Benchmark competitor basket experiences.
  • Increase conversions with optimized promos.
  • Boost overall order volumes.
Example:

A cloud kitchen in India used Actowiz to track cart abandonment triggers on Swiggy. Insights revealed packaging fees caused 15% drop-offs. After reducing fees and introducing free delivery promos, abandonment fell by 21%.

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Delivery Partner Benchmarking

Food Tech & Investment Market Scans

Investors, consultants, and food-tech firms need reliable market data for decision-making. Traditional research is costly and outdated compared to live signals from food delivery platforms.

Actowiz Solutions provides large-scale market scans across geographies. By analyzing restaurant density, cuisine performance, fee structures, and customer sentiment, we help stakeholders identify opportunities and risks.

What We Do:
  • Scrape restaurant density and performance datasets.
  • Track cuisine and pricing trends across markets.
  • Capture aggregator fee structures and promotions.
  • Provide geo-tagged, normalized datasets.
  • Deliver insights for investment or market entry.
Impact:
  • Reduce risks in investment decisions.
  • Identify high-growth niches in food delivery.
  • Benchmark markets for global expansion.
  • Provide reliable due diligence datasets.
  • Support consultants with real-time insights.
Example:

A PE firm evaluating investment in a Southeast Asian food-tech startup used Actowiz to scan GrabFood and Foodpanda data. Insights revealed strong growth in healthy bowls and premium coffee, guiding a $15M investment with confidence.

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List of Popular Retail Websites

Popular retail websites are essential for online shopping, offering a wide variety of products from electronics and clothing to home goods and groceries. These platforms provide competitive prices, customer reviews, and convenient delivery options, making them go-to destinations for consumers looking to make informed purchasing decisions and enjoy a seamless shopping experience. Here is the list of popular e-commerce websites:

City-wise Popular Websites

Benefits of Food Delivery Data Scraping with Actowiz

The food delivery industry evolves by the hour — menus update daily, surge pricing shifts with demand, and promotions change weekly. For restaurants, cloud kitchens, aggregators, and brands, manual tracking is simply not enough. Actowiz Solutions delivers structured, accurate, and real-time data that helps you keep up with these changes. From menu pricing and delivery SLAs to reviews and competitor promotions, our scraping solutions give you a 360° view of the market. With this intelligence, you can optimize pricing, improve customer experience, ensure compliance, and make better business decisions powered by real insights rather than guesswork.

Discovery & Setup

360° Menu & Pricing Intelligence

Stay ahead of the competition with real-time tracking of menu updates, item-level prices, hidden charges, and surge fees across delivery apps. This enables you to benchmark against competitors and adopt dynamic pricing strategies that maximize revenue without losing market share.

Example: A QSR chain used Actowiz to monitor menu pricing across 1,200 outlets. Insights revealed packaging fees were driving cart abandonment. By adjusting fees and launching bundle offers, the chain increased conversion rates by 12% in three months.

Discovery & Setup

Delivery SLA & Availability Insights

Delivery time is one of the top drivers of customer satisfaction. With scraping, you can track estimated delivery times, surge pricing windows, and prep times across platforms. This helps optimize staffing and driver allocation to reduce cancellations and SLA breaches.

Example: A delivery aggregator in the Middle East used Actowiz data to analyze ETAs during weekends. By reallocating drivers during surge hours, they reduced late deliveries by 18%.

Discovery & Setup

Review & Sentiment Analytics

Customer reviews and ratings are direct indicators of brand performance. Actowiz extracts star ratings, review counts, and sentiment keywords, providing actionable insights into customer experience. This helps you spot recurring issues like “cold food” or “poor packaging” and fix them quickly.

Example: A restaurant brand in Europe discovered that negative reviews frequently mentioned “cold pizza.” After switching to new packaging, ratings improved from 3.8★ to 4.4★, boosting weekly orders by 20%.

Discovery & Setup

Global Marketplace Coverage

Food delivery is a global game. Actowiz covers platforms like Zomato, Swiggy, Uber Eats, DoorDash, Deliveroo, Talabat, Foodpanda, and more — across the USA, Europe, Middle East, and Asia. This ensures that no matter where you operate, you always have visibility into competitor activity.

Example: A global F&B brand used Actowiz to monitor its presence across five countries. By standardizing pricing and promotions, it improved brand consistency and saw a 15% uplift in cross-country sales.

Discovery & Setup

Compliance & Franchise Monitoring

Franchise chains must ensure consistency in menu, pricing, and brand representation across outlets. Our scraping solutions monitor compliance, flag inconsistencies, and provide timestamped evidence to enforce standards.

Example: A global burger chain used Actowiz to check menu uniformity across 400 outlets. Insights showed 10% had missing mandatory combos. After corrections, customer complaints about price mismatches dropped by 30%.

Discovery & Setup

Smarter Promotions & Marketing ROI

Not all discounts work equally. With promo monitoring, you can track competitor coupons, BOGO deals, and banner placements, while analyzing ROI on your own campaigns. This ensures you spend smarter, not more./p>

Example: A cloud kitchen shifted its promo spend from evening slots to lunch hours after Actowiz data showed competitors concentrated offers at midday. Result: a 20% lift in conversions with 18% lower promo spend.

Discovery & Setup

Ready-to-Use Data for AI/BI Tools

Actowiz delivers structured, timestamped datasets that plug directly into AI, BI, or analytics dashboards. From predictive pricing to demand forecasting, these insights power data-driven decision-making at scale.

Example: A consultancy integrated Actowiz data into Power BI for its client. This enabled real-time promo benchmarking and competitor analysis, reducing reporting time by 40% while improving decision accuracy.

Industries We Serve

Food delivery data is a valuable resource for a wide variety of industries. From QSR chains and cloud kitchens to aggregators, CPG brands, consultancies, and investors, each stakeholder in the food delivery ecosystem needs structured, accurate insights. Actowiz Solutions delivers industry-specific datasets that help optimize pricing, streamline operations, monitor customer sentiment, and identify new growth opportunities. Whether you want to benchmark competitors, design smarter promotions, or evaluate market expansion, our scraping solutions adapt to your business needs with precision and scale.

Discovery & Setup

Quick Service Restaurants (QSRs)

QSR brands like pizza, burger, and fried chicken chains depend on food delivery apps for a large share of their revenue. They need to ensure consistent pricing across outlets, track competitor promotions, and optimize delivery performance. With Actowiz, QSRs can monitor menu pricing, bestseller trends, and delivery SLAs in real time, ensuring they remain competitive and customer-friendly.

Example: A global pizza chain used Actowiz to harmonize menu prices across 1,000+ outlets. By adjusting fees and aligning promotions, they improved order conversions by 12% in three months.

Discovery & Setup

Cloud Kitchens & Virtual Brands

Cloud kitchens operate entirely through delivery platforms, making data visibility critical for survival. They need to identify trending cuisines, bundle high-margin combos, and track add-on attachment rates. Our solutions provide structured insights into competitor menus and bestseller trends, helping cloud kitchens adapt quickly and stay ahead of demand.

Example: A multi-brand cloud kitchen in Dubai discovered growing demand for rice bowls. By launching customizable rice bowl combos, it increased basket size by 19% and daily orders by 22%.

Discovery & Setup

Food Delivery Aggregators & Marketplaces

Aggregators like Swiggy, Zomato, DoorDash, and Deliveroo must constantly benchmark competitors to improve customer satisfaction. With Actowiz, they can track fees, SLAs, subscription programs, and promotional intensity. This helps optimize platform economics and reduce cancellations.

Example: A Middle Eastern aggregator analyzed competitor ETAs and surge fees. By reallocating driver incentives during peak hours, they reduced order cancellations by 22%.

Discovery & Setup

CPG & Beverage Brands

Packaged food and beverage brands increasingly list directly on delivery apps or through restaurant tie-ups. They must track visibility, promo execution, and pricing consistency to maximize ROI. Our scraping feeds allow CPGs to benchmark competitor placement and measure promotion effectiveness.

Example: A beverage brand on Uber Eats identified that “healthy drinks” were trending during morning hours. By targeting this window, impressions rose by 20% and sales by 14%.

Discovery & Setup

Consultancies & Market Research Firms

Consultancies and research agencies require real-time datasets to deliver accurate recommendations to clients. Traditional market surveys lag behind live market dynamics. Actowiz delivers structured delivery app data that helps consultants advise clients on pricing, promotions, and market entry.

Example: A consultancy in Singapore used Actowiz to identify gaps in vegan cuisine offerings across GrabFood. Their client launched vegan options and saw a 17% sales increase within two quarters.

Discovery & Setup

Ad-Tech & Analytics Firms

Ad-tech companies and analytics providers need structured datasets to enrich BI dashboards, measure campaign ROI, and run predictive models. Actowiz delivers normalized, API-ready data for direct integration into analytics systems.

Example: An analytics firm integrated Actowiz datasets into Power BI to track promo intensity across multiple platforms. Reporting time dropped by 40%, while insights became more actionable for clients.

Discovery & Setup

Investors & Financial Analysts

Private equity firms, VCs, and financial analysts require robust data to evaluate opportunities in food delivery and food-tech. With structured datasets, they can benchmark markets, assess aggregator economics, and validate cuisine demand.

Example: A PE firm assessing a Southeast Asian food-tech startup used Actowiz’s datasets to confirm rapid growth in healthy bowls and premium coffee, supporting a $15M investment with confidence.

Geo Coverage

Food delivery trends vary across regions — what works in New York may not resonate in Dubai or Singapore. Actowiz Solutions provides global coverage with localized datasets, helping businesses understand menus, prices, promotions, and delivery dynamics across diverse markets. Whether you’re a QSR chain expanding internationally, a cloud kitchen testing new cuisines, or an investor evaluating opportunities, our geo-tagged insights ensure you have accurate, region-specific intelligence. We cover leading food delivery platforms in North America, Europe, the Middle East, and Asia-Pacific, giving you a unified yet localized view of the food delivery landscape.

Regions & Marketplaces:
  • North America: Uber Eats, DoorDash, Grubhub, SkipTheDishes
  • Europe: Deliveroo, Just Eat, Glovo, Wolt
  • Middle East & Africa: Talabat, Careem, HungerStation, Jahez, Mrsool
  • Asia-Pacific: Zomato, Swiggy, Foodpanda, GrabFood, GoFood, ShopeeFood
  • Latin America: iFood, Rappi, PedidosYa
Crawlers & Scheduled Crawlers 01

Case Studies

Case Study 1: Optimizing Pricing & Fees for a QSR Chain

  • A global pizza chain operating across 1,200 outlets in India struggled with inconsistent pricing and hidden packaging charges across Swiggy and Zomato. Actowiz implemented an automated pipeline that tracked menu prices, packaging fees, and basket totals daily. Insights revealed that small packaging charges were driving high cart abandonment in metro cities. By harmonizing prices and adjusting packaging fees, the chain reduced cart abandonment by 18% and increased conversions by 12% within three months.

Case Study 2: Boosting Cloud Kitchen Performance with Menu Insights

  • A multi-brand cloud kitchen in Dubai needed to identify growth opportunities in a saturated delivery market. Actowiz scraped competitor menus on Talabat and Foodpanda, highlighting bestseller items and trending cuisines. Data revealed growing demand for customizable rice bowls among office-goers. The kitchen launched its own rice bowl range, coupled with targeted lunchtime promotions. Result: 22% increase in daily orders and 19% growth in basket size within one quarter.

Case Study 3: Reducing Cancellations for a Delivery Aggregator

  • A leading Middle Eastern delivery aggregator faced high cancellations during weekend evenings. Actowiz tracked surge pricing and delivery ETAs across competitors. Data showed that Talabat and Careem incentivized drivers with higher payouts during these peak hours, while the client did not. By replicating the incentive model and adjusting customer ETA communication, cancellations dropped by 22%, while on-time deliveries improved by 15%. This strengthened customer trust and improved retention.

FAQs

We support all major food delivery apps globally, including Zomato, Swiggy, Uber Eats, DoorDash, Grubhub, Deliveroo, Talabat, Careem, Foodpanda, Glovo, Wolt, iFood, and Rappi. Our scrapers are adaptable, so if your target platform isn’t listed, we can add it during scoping. Coverage extends across North America, Europe, the Middle East, Asia-Pacific, and Latin America. We also provide multi-platform benchmarking, ensuring you can compare performance across apps within the same market. Whether you’re tracking menus, promotions, or delivery fees, our coverage ensures no blind spots in your strategy.
Data delivery is highly flexible. You can opt for live (on-demand), hourly, daily, weekly, or custom intervals depending on your needs. For time-sensitive insights like surge pricing or promo monitoring, many clients choose hourly feeds. For broader market benchmarking, daily or weekly snapshots may be sufficient. Actowiz pipelines are scalable, capable of delivering millions of data points daily while maintaining accuracy and timeliness. Each dataset comes with timestamps, making trend analysis and historical comparisons straightforward.
We deliver structured data in JSON, CSV, or Excel formats. For enterprise clients, we also provide direct delivery to cloud storage (AWS S3, Google Cloud, Azure) or via REST APIs and webhooks. This makes integration seamless with your existing analytics stack, BI tools, or AI models. We also provide schema definitions and data dictionaries to ensure teams can onboard quickly and use the data without extra formatting. Our goal is to deliver not just raw data, but ready-to-use intelligence.
Yes. We extract customer ratings, review counts, and written feedback from delivery platforms. Beyond simple scraping, we can also enrich this data with Natural Language Processing (NLP) to categorize sentiment (positive, negative, neutral) and identify recurring keywords like “cold food,” “late delivery,” or “excellent taste.” This gives you deeper insights into customer perception. By monitoring sentiment trends, restaurants and brands can address issues proactively, improving customer experience and boosting app rankings. Sentiment insights are also useful for brand monitoring and competitive benchmarking.
Absolutely. We capture banners, discount offers, coupon codes, loyalty deals, and subscription perks across platforms. Each record includes details like terms, validity periods, and promo intensity. For example, we can show you whether a competitor runs lunch-only discounts or blanket evening offers. Tracking promotions is one of the most requested services, as it directly impacts sales strategy. By analyzing promo data, you can optimize campaign spend, benchmark ROI against competitors, and identify underperforming discounts that drain budgets.
Food delivery platforms frequently update layouts, APIs, and anti-bot mechanisms. Our scrapers use adaptive parsing, IP rotation, geo-distributed proxies, and device/browser simulation to stay resilient. We respect platform rate limits and follow responsible collection practices. In case of sudden platform changes, our team provides fast adjustments with SLA-backed support. This ensures uninterrupted data flow for clients. You don’t need to worry about parser maintenance or delivery disruptions — we handle all the technical complexity in the background.
Accuracy is one of our core commitments. We employ multi-level validation — including timestamp checks, schema consistency, deduplication, and anomaly detection — before delivering datasets. We also run manual spot audits and provide audit trails. For clients with high sensitivity (like compliance monitoring), we can add custom validation layers. On average, our clients report 99.9% data accuracy across millions of rows. Reliable data means confident decision-making without second-guessing numbers.
Yes. Many clients choose to integrate data feeds directly into BI dashboards (Power BI, Tableau, Looker Studio), databases, or analytics platforms. We deliver through APIs, cloud storage, or SFTP, depending on your IT setup. We also provide starter dashboards or dbt models on request, helping teams visualize data quickly without heavy setup. Our integration-first approach means you spend less time formatting and more time analyzing insights.
Yes, wherever available. In some cases, we maintain historical datasets of menu, pricing, and review data. In other cases, we can build history progressively once scraping begins. Clients often use historical data for trend analysis, seasonal comparisons, or demand forecasting. By combining live and historical datasets, you get both immediate insights and long-term intelligence.
Food delivery data serves multiple stakeholders — QSR chains, cloud kitchens, delivery aggregators, CPG brands, consultancies, ad-tech firms, and investors. Each industry leverages different slices of the data, from menu optimization and promo ROI to compliance and market entry analysis. Our flexible approach ensures that we deliver datasets customized to your use case, whether you’re a restaurant chain benchmarking fees or a PE firm running due diligence.
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] => 216.73.216.24
                    [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] => 216.73.216.24
                    [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
)

Start Your Project

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💬 "Average Response Time: Under 12 hours"

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

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Real results from real businesses using Actowiz Solutions

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Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
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★★★★★
“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
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1 min
★★★★★
“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
1 min

See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price Insights Top-selling SKUs

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

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Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

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Case Studies
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Oct 28, 2025

Scraping Consumer Preferences on Dan Murphy’s Australia - Unveiling 5-Year Trends Across 50,000+ Alcohol Listings (2020–2025)

Discover how Scraping Consumer Preferences on Dan Murphy’s Australia reveals 5-year trends (2020–2025) across 50,000+ vodka and whiskey listings for data-driven insights.

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Web Scraping Whole Foods Promotions and Discounts Data to Optimize Grocery Pricing Strategies

Discover how Web Scraping Whole Foods Promotions and Discounts Data helps retailers optimize pricing strategies and gain competitive insights in grocery markets.

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Scrape USA E-Commerce Platforms for Inventory Monitoring - Tracking 5-Year Stock Trends Across 50,000+ Online SKUs (2020–2025)

Scrape USA E-Commerce Platforms for Inventory Monitoring to uncover 5-year stock trends, product availability, and supply chain efficiency insights.

Oct 28, 2025

Scraping Consumer Preferences on Dan Murphy’s Australia - Unveiling 5-Year Trends Across 50,000+ Alcohol Listings (2020–2025)

Discover how Scraping Consumer Preferences on Dan Murphy’s Australia reveals 5-year trends (2020–2025) across 50,000+ vodka and whiskey listings for data-driven insights.

Oct 27, 2025

Scraping APIs for Grocery Store Price Matching - Comparing Walmart, Kroger, Aldi & Target Prices Across 10,000+ Products

Discover how Scraping APIs for Grocery Store Price Matching helps track and compare prices across Walmart, Kroger, Aldi, and Target for 10,000+ products efficiently.

Oct 26, 2025

How to Scrape The Whisky Exchange UK Discount Data to Track 95% of Real-Time Whiskey Deals Efficiently?

Learn how to Scrape The Whisky Exchange UK Discount Data to monitor 95% of real-time whiskey deals, track price changes, and maximize savings efficiently.

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Web Scraping Whole Foods Promotions and Discounts Data to Optimize Grocery Pricing Strategies

Discover how Web Scraping Whole Foods Promotions and Discounts Data helps retailers optimize pricing strategies and gain competitive insights in grocery markets.

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AI-Powered Real Estate Data Extraction from NoBroker to Track Property Trends and Market Dynamics

Discover how AI-Powered Real Estate Data Extraction from NoBroker tracks property trends, pricing, and market dynamics for data-driven investment decisions.

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How Automated Data Extraction from Sainsbury’s for Stock Monitoring Improved Product Availability & Supply Chain Efficiency

Discover how Automated Data Extraction from Sainsbury’s for Stock Monitoring enhanced product availability, reduced stockouts, and optimized supply chain efficiency.

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Scrape USA E-Commerce Platforms for Inventory Monitoring - Tracking 5-Year Stock Trends Across 50,000+ Online SKUs (2020–2025)

Scrape USA E-Commerce Platforms for Inventory Monitoring to uncover 5-year stock trends, product availability, and supply chain efficiency insights.

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Maximizing Margins - Scraping Online Liquor Stores for Competitor Price Intelligence to Monitor Competitor Pricing in the Online Liquor Market

Explore how Scraping Online Liquor Stores for Competitor Price Intelligence helps monitor competitor pricing, optimize margins, and gain actionable market insights.

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Real-Time Price Monitoring and Trend Analysis of Amazon and Walmart Using Web Scraping Techniques

This research report explores real-time price monitoring of Amazon and Walmart using web scraping techniques to analyze trends, pricing strategies, and market dynamics.

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