Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
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] => 216.73.216.213
                    [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] => 216.73.216.213
                    [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
)
Navratri Mega Sale Price Tracking

Overview

The hyperlocal grocery ecosystem in India has changed rapidly. Dark stores, fast delivery promises, and dynamic pricing now control demand, conversions, and brand competitiveness. Platforms like Blinkit, Zepto, and Swiggy Instamart are updating prices multiple times a day, adjusting stock status in real time, and reshuffling assortments based on neighborhood demand.

Brands need live visibility into:

  • Price changes
  • Stock-outs and replenishment
  • Delivery-time fluctuations
  • Competitor discounts
  • City-level availability
  • Platform-to-platform assortment differences
  • Dark store–level insights

Actowiz Solutions partnered with a major FMCG and D2C portfolio company to build a complete Dark Store Intelligence System. This case study explains the challenges, methodology, sample data, insights, outcomes, and how Actowiz Solutions helps brands win across hyperlocal delivery apps.

Client Challenge

Navratri Mega Sale Price Tracking

The client had over 250+ fast-moving SKUs across snacks, beverages, personal care, dairy, cleaning supplies, home care, and essentials. They were relying on manual monitoring and outdated dashboards that offered no real-time tracking.

The biggest problems:
1. Prices fluctuated multiple times a day

Blinkit and Zepto updated prices based on demand, stock, delivery slot load, and competition. Without automation, the client had no historical visibility or insight into price dependence.

2. Frequent stock-outs with no pattern visibility

Out-of-stock (OOS) events directly impacted sales, but the brand couldn't track:

  • Which SKUs were OOS
  • How long they stayed OOS
  • When they were replenished
  • Which cities were impacted the most
3. Platform-wise assortment mismatch

The brand wanted equal representation on all three apps, but assortment varied:

  • Blinkit: 27 SKUs listed
  • Zepto: 20 SKUs listed
  • Instamart: 18 SKUs listed

There was no city-wise, store-wise breakdown.

4. Competitors were discounting aggressively

Some competitors ran flash discounts, location-level offers, and product bundling. The client didn't have a structured benchmark to respond.

5. Delivery time unpredictability

Delivery time affects conversions. A 10-minute delivery promise often performed 4–5x better than a 25-minute ETA.

The client needed a single, unified intelligence layer to track all three apps in real time.

Actowiz Solutions: The Approach

Actowiz Solutions deployed a Dark Store Data Monitoring Engine to capture real-time, SKU-level data across all three apps.

The system covered:

  • Real-time price monitoring
  • Scheduled every 10–20 minutes for sensitive categories.
  • SKU-level stock availability
  • Track Low Stock, Limited Stock, Out of Stock, and Replenishment.
  • Multi-city coverage
  • Delhi, Mumbai, Bengaluru, Hyderabad, Chennai, Pune, Kolkata, Ahmedabad.
  • App-wide assortment comparison
  • Which platform lists which SKUs, in which city, at what stock depth.
  • Promotions & discounts tracking
  • BOGO, promo codes, wallet deals, flash discounts.
  • Delivery time intelligence
  • Exact ETA monitoring for every SKU or category.
  • Competitive positioning
  • Compare brand performance against top competitors in FMCG, CPG, beverages, snacks, and household categories.

Data was extracted through mobile app scraping, API-based crawlers, and structured pipelines using Actowiz's proprietary infrastructure.

Data Points Collected

For every SKU across all platforms:

  • Platform (Blinkit / Zepto / Instamart)
  • Product Name
  • Brand
  • Image URL
  • SKU Size
  • MRP
  • Selling Price
  • Discount %
  • Previous Price
  • Price Change Timestamp
  • Stock Status
  • OOS Duration
  • Stock Replenishment Timestamp
  • Delivery ETA
  • Category & Subcategory
  • City & Latitude/Longitude Coverage
  • Promo Codes
  • Combo Offers
  • Competitor Presence

This gave the client a complete single-source intelligence layer across hyperlocal marketplaces.

Sample Dataset Extracts

Blinkit – Price & Stock Snapshot
SKU Name Size MRP Selling Price Discount Stock Status Delivery Time City
Amul Taaza 1L ₹70 ₹64 9% In Stock 10 mins Delhi
Tropicana Orange 1L ₹130 ₹112 14% Low Stock 12 mins Mumbai
Lays Classic Salted 115g ₹50 ₹48 4% Out of Stock Bengaluru
Coca-Cola 750ml ₹45 ₹42 7% In Stock 8 mins Hyderabad
Zepto – SKU Sample
SKU Name Size MRP Selling Discount Stock ETA City
Nescafe Classic 50g ₹165 ₹150 9% In Stock 9 mins Mumbai
Amul Butter 500g ₹285 ₹260 8% Low Stock 11 mins Pune
Quaker Oats 1kg ₹180 ₹170 6% In Stock 13 mins Chennai
Pepsi 750ml ₹45 ₹43 4% Out of Stock Delhi
Instamart – SKU Sample
SKU Name Size MRP Selling Discount Stock Status ETA City
Britannia Good Day 600g ₹120 ₹109 9% In Stock 15 mins Kolkata
Real Mixed Fruit Juice 1L ₹130 ₹125 4% Limited Stock 17 mins Bengaluru
Surf Excel Matic 2kg ₹450 ₹420 7% In Stock 20 mins Delhi
Maggi Masala 70g ₹14 ₹14 0% Out of Stock Hyderabad

Key Insights Delivered

After collecting over 1.2 million data points, Actowiz Solutions delivered deep, actionable insights:

  • Blinkit had the fastest delivery times overall
    • Average Blinkit ETA: 10–15 minutes
    • Zepto: 12–18 minutes
    • Instamart: 15–22 minutes

    This was a major driver of higher conversions in metros.

  • Price volatility was highest on Zepto
  • Beverages, ready-to-drink, and household categories changed prices 2–4 times a day. Zepto ran the highest per-SKU promotions.

  • Instamart had the widest household category assortment
  • For detergents, cleaners, and home essentials, Instamart consistently had the widest options and highest in-stock percentage.

  • Weekend stock-outs peaked across all platforms
  • OOS issues increased by 40–60% on Fridays and Saturdays, especially in dairy, snacks, bread, and beverages.

  • Competitor discount strategy was aggressive
  • One major competitor ran:

    • Buy 1 Get 1
    • ₹10 instant-off
    • Location-based discounts

    Blinkit and Zepto matched discounts within 30–45 minutes.

  • SKU cannibalization was visible
  • Certain pack sizes overtook others due to:

    • Higher discount
    • Faster ETA
    • Better visibility
    • "Best Seller" label

    The client optimized supply accordingly.

Technology & Architecture

Actowiz Solutions built a real-time intelligence architecture that included:

  • Automated Mobile-App Crawlers
  • Custom scripts extracted structured data from:

    • Blinkit (Android + iOS)
    • Zepto (Android)
    • Instamart (Swiggy app module)
  • Distributed Proxy Network
  • Ensured:

    • Zero IP blocks
    • Seamless access to city-specific results
    • High-volume, 24/7 scraping stability
  • Scalable Cloud Pipelines
  • All data was processed through:

    • AWS
    • GCP
    • Actowiz's internal orchestration
    • Kafka queues for high-frequency SKU fetching
  • Normalization Layer
  • Converted unstructured data into:

    • Clean tables
    • Standardized fields
    • Comparable formats across the three platforms
  • Dashboard & Alerts
  • The client received:

    • Realtime stock-out alerts
    • Price change alerts
    • Delivery time change alerts
    • Daily summary reports
    • A custom BI dashboard

Business Impact

Within 30 days, the client saw measurable improvements:

  • 18% fewer stock-outs
  • Better coordination with Blinkit and Zepto supply teams.

  • 13% improvement in pricing competitiveness
  • Immediate actions on competitor discounts.

  • 21% higher SKU visibility across platforms
  • Ensured top SKUs appeared in all important cities.

  • 9% increase in hyperlocal revenue
  • Fast delivery + optimized prices improved conversions.

  • Better demand forecasting
  • Patterns showed:

    • Spike in dairy during evenings
    • Spike in snacks on weekends
    • Peak juice demand in warm months

    These helped optimize inventory placement.

Conclusion

Hyperlocal retail is now driven by speed, pricing optimization, and stock depth. Platforms like Blinkit, Zepto, and Swiggy Instamart run 24/7 automated pricing and stock algorithms. Without real-time intelligence, brands lose market share.

Actowiz Solutions helped the client build a powerful, real-time Dark Store Intelligence System that delivered:

  • Faster decisions
  • Clear price visibility
  • Real-time stock tracking
  • Competitive benchmarking
  • City-level insights
  • Predictive demand planning

This is now being used across all major FMCG and D2C product lines.

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

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★
'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
Product Image
2 min
★★★★★
“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
Product Image
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

Actowiz Insights Hub

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

All
Blog
Case Studies
Infographics
Report
thumb
Jan 07, 2026

Amazon India vs Flipkart vs Snapdeal Product Data Mapping – Comparing Prices, Seller Networks, and SKU Match Rates

Amazon India vs Flipkart vs Snapdeal Product Data Mapping helps compare pricing, seller networks, and SKU match rates to uncover marketplace trends and drive smarter ecommerce decisions.

thumb

Extracting GrabTaxi Fare & Availability Data to Improve Ride-Hailing Price Transparency

Discover how extracting GrabTaxi fare and availability data improved ride-hailing price transparency, enabling smarter pricing decisions and better rider trust.

thumb

Driving Smarter Marketplace Decisions with Seller Competition & Pricing Intelligence on Amazon India and Snapdeal

Seller Competition & Pricing Intelligence on Amazon India and Snapdeal helps brands optimize pricing, track rivals, and make smarter marketplace decisions.

thumb
Jan 07, 2026

Amazon India vs Flipkart vs Snapdeal Product Data Mapping – Comparing Prices, Seller Networks, and SKU Match Rates

Amazon India vs Flipkart vs Snapdeal Product Data Mapping helps compare pricing, seller networks, and SKU match rates to uncover marketplace trends and drive smarter ecommerce decisions.

thumb
Jan 07, 2026

How Web Scraping Grab Taxi Data Helps Brands Decode Real-Time Ride Prices, Routes & Demand Trends?

Learn how web scraping Grab Taxi data reveals real-time ride prices, popular routes, and demand trends to help brands make smarter mobility decisions.

thumb
Jan 06, 2026

How Daily Liquor Pricing & Availability Monitoring Fixes Inventory Blind Spots for Modern Beverage Brands?

Daily Liquor Pricing & Availability Monitoring helps brands track stock levels, spot price changes, and reduce revenue loss across competitive retail markets.

thumb

Extracting GrabTaxi Fare & Availability Data to Improve Ride-Hailing Price Transparency

Discover how extracting GrabTaxi fare and availability data improved ride-hailing price transparency, enabling smarter pricing decisions and better rider trust.

thumb

How We Helped a Hospitality Brand Track 700+ Properties by Scraping Booking.com Hotel Prices in France

Scraping Booking.com hotel prices in France helps brands track real-time rates across 700+ hotels to optimize pricing strategies and stay competitive.

thumb

Real-Time Rental Intelligence in London’s Prime Property Market How Actowiz Solutions Empowered a Real Estate Fund with Granular Market Data

See how Actowiz Solutions helped a London property fund track 10,000+ rental shifts daily using AI-driven web scraping for real-time market intelligence.

thumb

Driving Smarter Marketplace Decisions with Seller Competition & Pricing Intelligence on Amazon India and Snapdeal

Seller Competition & Pricing Intelligence on Amazon India and Snapdeal helps brands optimize pricing, track rivals, and make smarter marketplace decisions.

thumb

Scraping Top-Selling GrabMart Products - Top Categories & SKUs Across Singapore, Malaysia & Thailand

Detailed research on GrabMart’s top-selling products, highlighting leading categories and SKUs across Singapore, Malaysia, and Thailand for market insights

thumb

City-Wise Demand & Delivery Time Analysis for NIC Ice Cream - Solving Last-Mile Challenges in Quick Commerce

City-Wise Demand & Delivery Time Analysis for NIC Ice Cream reveals how data improves stock planning, delivery speed, and customer satisfaction across markets.

phone
Quick Connect
phone
Quick Connect