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GeoIp2\Model\City Object
(
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                            [es] => Norteamérica
                            [fr] => Amérique du Nord
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                            [fr] => États Unis
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                            [ru] => США
                            [zh-CN] => 美国
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            [postal] => Array
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                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [ru] => США
                            [zh-CN] => 美国
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                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                    [iso_code] => US
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                            [ru] => США
                            [zh-CN] => 美国
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    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [validAttributes:protected] => Array
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    [registeredCountry:protected] => GeoIp2\Record\Country Object
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                    [iso_code] => US
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                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [4] => names
                    [5] => type
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
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                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
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                    [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
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 4509177
                    [names] => Array
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                            [en] => Columbus
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                )

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

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

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

                        )

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)

 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
)

• Home
• Services
• Ride-Hailing Data Scraping USA | Uber, Lyft & Mobility Intelligence

Ride-Hailing • USA

Real-Time Ride-Hailing & Mobility Data Scraping in the USA

The US ride-hailing and mobility industry is highly dynamic, with apps like Uber, Lyft, and Zipcar serving millions of daily rides. Prices fluctuate by minute, surge charges vary by event, and availability depends on time and city. Without real-time data, mobility operators, analysts, and investors risk missing critical insights.

Actowiz Solutions delivers ride-hailing and mobility data scraping services tailored for the US market. From Uber surge pricing in New York, Lyft wait times in San Francisco, to Zipcar availability in Chicago, we provide structured, reliable datasets. These insights empower ride-hailing startups, car rental firms, regulators, and consultants to monitor competition, optimize pricing, and design smarter strategies.

Stay ahead of the US mobility market with structured data intelligence.

Request Mobility Data Sample

USA • Nationwide

Top Countries:

Create your own

Why Mobility Data Matters in the USA

Uber & Lyft

dominate, but face local competition.

Zipcar & Car2Go

drive short-term rentals in cities.

Taxi apps

remain strong in metros like NYC, Chicago, Las Vegas.

Event-driven surges

(Super Bowl, Coachella, CES) impact fares & wait times.

Car rental companies

(Hertz, Avis, Enterprise) adjust prices daily.

Mobility data scraping allows US businesses to:

Benchmark competitor fares across cities.

Detect surge patterns during events.

Analyze wait times & driver availability.

Optimize car rental pricing.

Provide demand forecasts for investors and regulators.

High-Impact Use Cases

Surge Pricing Intelligence

Events like concerts, conferences, and holidays cause fare surges.

What we do: Scrape Uber/Lyft surge multipliers in real time across major cities.

Impact: Mobility startups predict demand, regulators track consumer fairness, investors spot growth patterns.

Example: During CES Las Vegas, we scraped hourly Uber fares → clients forecasted demand + launched promotions.

Competitive Fare Benchmarking

Comparing Uber vs Lyft vs Taxi apps ensures better positioning.

What we do: Capture fares across platforms for identical routes.

Impact: Firms identify undercutting & pricing opportunities.

Example: A Boston-based mobility operator used scraped fares to launch loyalty discounts → ridership rose 18%.

Driver & Wait Time Analytics

Driver ETAs affect booking decisions.

What we do: Track availability and wait times per city/ZIP.

Impact: OTAs & mobility firms improve fleet deployment.

Example: NYC data showed longer Lyft waits than Uber → operator improved dispatch algorithms.

Car Rental Pricing & Fleet Insights

Zipcar, Hertz, Avis, Enterprise adjust daily rates.

What we do: Scrape car rental sites for price, availability, add-ons.

Impact: Competitors optimize rental yields.

Example: A Chicago rental chain benchmarked Zipcar → adjusted weekend rates → bookings grew 12%.

Geo-Based Mobility Heatmaps

Mobility demand differs by city, ZIP, or even street.

What we do: Collect fare, wait, availability at city-block level.

Impact: Regulators plan transport infra, investors analyze demand pockets.

Example: San Francisco data → high surges in SoMa at night → clients deployed extra cars.

Consumer Review & Sentiment Scraping

App reviews shape perception of ride-hailing brands.

What we do: Scrape App Store, Google Play, Yelp reviews.

Impact: Detect service gaps like “driver cancellations” or “pricing complaints.”

Example: Analysis of 50K Uber reviews → flagged driver cancellations → policy changes improved CSAT.

Industries We Serve

Ride-Hailing Operators

Uber, Lyft, startups.

Car Rental Companies

Zipcar, Hertz, Avis.

Regulators & Transport Boards

Fair pricing, demand maps.

Market Research Firms

US mobility industry datasets.

Investors & Consultants

Market trend signals.

Urban Planners

Heatmaps of ride-hailing supply/demand.

Case Study

Industry:

Ride-Hailing & Mobility – USA

Result

Up to 24% Improvement in Pricing Accuracy & Customer Retention

★★★★★

“Actowiz Solutions enabled us to track Uber, Lyft, and local taxi fleets across major US cities like New York, Los Angeles, and Chicago. Their real-time data on fares, surge pricing, ETAs, car availability, and rider reviews helped us optimize pricing strategies and improve service allocation—leading to a 24% improvement in pricing accuracy, 17% better fleet utilization, and 11% higher customer retention.”

Head of Mobility Strategy, US Ride-Hailing Operator

✓ 24% improvement in pricing accuracy

✓ 17% better fleet utilization during peak hours

✓ 11% higher customer retention

GeoIp2\Model\City Object
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                    [geoname_id] => 4509177
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                            [de] => Columbus
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                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                            [zh-CN] => 北美洲
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                            [ru] => США
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                                    [fr] => Ohio
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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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    [continent:protected] => GeoIp2\Record\Continent Object
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    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [validAttributes:protected] => Array
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

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                    [3] => isoCode
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [3] => isoCode
                    [4] => names
                    [5] => type
                )

        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.160
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
                (
                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
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                    [3] => domain
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                    [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
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                )

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

    [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
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                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
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                )

        )

)

 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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Select Service Web Scraping Real-Time API Access Data Dashboards Datasets Review & Sentiment Analysis Price Monitoring Custom Solution

Select Industry E-Commerce Quick Commerce Food Delivery Travel & OTA Grocery & Retail Real Estate Others

Target Country Web Scraping Services Mobile App Scraping Web and App Scraping API Datasets Dashboard

A$лвR$C$CHF¥Kčkr€£HK$FtRp₪₹kr¥₩Mex$RMkrNZ$₱złleikrS$฿₺$R Budget Range $500 - $1,000 - Starter Budget (POC/Single Site) $1,000 - $2,500 - Basic Plan (Small Business Needs) $2,500 - $5,000 - Growth Phase (Multi-site/Data Volume Projects) $5,000 - $10,000 - Business-Scale (Automation/API Integrations) $10,000 - $25,000 - Enterprise Projects (Real-Time Systems, Global Data) $25,000 - $50,000 - Enterprise+ (Custom Dashboards, Ongoing Support) $50,000+ - Strategic Partnership (End-to-End Data Ops)

Additional Trust Elements

✨ "1000+ Projects Delivered Globally"

⭐ "Rated 4.9/5 on Google & G2"

🔒 "Your data is secure with us. NDA available."

💬 "Average Response Time: Under 12 hours"

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape → Structure → Analyze → Visualize

Explore Solutions Get a Custom Demo

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.

Ecommerce

Q-Commerce

Travel & OTA

Food Delivery

Real Estate

Social Media

Trusted by Global Leaders – Secured by International Standards

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 Galido

Co-Founder / Head of Product at Upright Data Inc.

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.”

Iulen Ibanez

CEO / Datacy.es

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!”

Febbin Chacko

-Fin, Small Business Owner

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

Request Demo Access

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

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How a Client Scrape Cocktail Trends From Zomato in Mumbai & Bangalore for Market Insights

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Top 10 Cities with the Most McDonald’s Locations in 2025

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Extract Festive Sale Data from Amazon, Flipkart & Reliance — 90% flash-sale alerts; 50+ brands analyzed

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Unlock Sephora’s Stock Secrets - Sephora Inventory & Stock Data Scraping API by Regions Tracks 90–98% Accuracy

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Sep 17, 2025

How Costs Change Weekly - Web Scraping weekly Delivery Fees Data From GrabFood for PH, SG, and MY

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How a Client Scrape Cocktail Trends From Zomato in Mumbai & Bangalore for Market Insights

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Web Crawlers for Grocery Coupon & Discount Tracking Across Walmart, Kroger & Safeway

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Tracking Hermès Birkin Availability & Resale Pricing via Web Scraping – Powered by Actowiz Solutions

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Top 10 Cities with the Most McDonald’s Locations in 2025

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Monthly Tourism Flow Analysis via Hotel Booking Scraping in Sharjah

Monthly tourism flow in Sharjah analyzed via hotel booking scraping reveals visitor trends, occupancy rates, and booking patterns for data-driven tourism insights.

India's Holiday Travel Surge Raksha Bandhan to Independence Day 2025

Explore India's holiday travel trends during the 2025 festive season—from Raksha Bandhan to Independence Day. Discover top destinations, travel tips, and booking insights.

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Extract Festive Sale Data from Amazon, Flipkart & Reliance — 90% flash-sale alerts; 50+ brands analyzed

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Web Scraping Services in UAE – Historical Navratri Sales Data – 2020–2025 Discount Trends

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Myntra vs Ajio Navratri discount scraping 2025

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Our perks are irreplaceable

Time Zone Flexibility

Benefit from the ease of collaboration with Actowiz Solutions, as our team is aligned with your preferred time zone, ensuring smooth communication and timely delivery.

Clear Communication

Our team focuses on clear, transparent communication to ensure that every project is aligned with your goals and that you’re always informed of progress.

Uncompromising Quality

Actowiz Solutions adheres to the highest global standards of development, delivering exceptional solutions that consistently exceed industry expectations

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