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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
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                        (
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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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                            [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
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                )

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                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
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            [location] => Array
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                    [longitude] => -83.0061
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            [postal] => Array
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            [registered_country] => Array
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                    [iso_code] => US
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                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
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                                    [fr] => Ohio
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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
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            [traits] => Array
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                    [ip_address] => 216.73.216.150
                    [prefix_len] => 22
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        )

    [continent:protected] => GeoIp2\Record\Continent Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
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                            [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
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                )

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    [country:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [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
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            [validAttributes:protected] => Array
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                    [4] => names
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    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
                )

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [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
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            [validAttributes:protected] => Array
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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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                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.150
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [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
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                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
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                )

        )

    [city:protected] => GeoIp2\Record\City Object
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            [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
                        )

                )

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            [validAttributes:protected] => Array
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        )

    [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
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                            [0] => en
                        )

                    [validAttributes:protected] => Array
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                            [0] => confidence
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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
)
Navratri Mega Sale Price Tracking

Introduction

Online Travel Agencies (OTAs) such as Ctrip, Agoda, and Booking.com dominate global hotel discovery and bookings. These platforms publish vast amounts of lodging data covering hotels, resorts, serviced apartments, and alternative stays across all major travel markets.

Travel-tech companies, hospitality analytics firms, investors, and brands increasingly require structured, continuously updated hotel data from OTAs to support:

  • Price intelligence and benchmarking
  • Market and supply analysis
  • Demand and availability tracking
  • Review and reputation monitoring
  • Content aggregation and discovery platforms

A client approached Actowiz Solutions seeking a scalable hotel/lodging data extraction solution from multiple OTAs, with clean outputs suitable for analytics, dashboards, and APIs.

Client Requirement Overview

Navratri Mega Sale Price Tracking

Industry: Travel, Hospitality, Market Intelligence

Data Sources: Ctrip, Agoda, Booking.com

Geographic Coverage: Global (Asia, Europe, Americas, Middle East)

Key Requirements:
  • Hotel and lodging listings from major OTAs
  • Room-level pricing and availability
  • Future-date calendars
  • Reviews and ratings
  • Consistent schema across OTAs
  • Scalable refresh cycles

Business Challenges

3.1 Different Data Structures Across OTAs

Each OTA presents:

  • Different room naming conventions
  • Different tax and fee structures
  • Varying cancellation and meal-plan rules

Normalizing this data into a single schema was critical.

3.2 Dynamic Pricing & Availability

Hotel prices and availability change frequently due to:

  • Demand fluctuations
  • Events and seasonality
  • Cancellation policies
  • Inventory updates

The system needed reliable refresh and monitoring.

3.3 Room-Level Complexity

The same property may have:

  • Multiple room categories
  • Different inclusions (breakfast, cancellation)
  • Different prices per OTA

Room-level matching and normalization were required.

3.4 Large-Scale Global Coverage

The client required a solution that could scale from:

  • A few cities
  • To thousands of hotels across multiple countries

Actowiz Solutions – Approach

Actowiz Solutions built a Multi-OTA Hotel & Lodging Intelligence Platform that:

  • Extracts property and room-level data from multiple OTAs
  • Normalizes pricing, availability, and content
  • Supports future-date availability
  • Delivers analytics-ready datasets
  • Scales globally with monitoring and QA

Scope of Data Extracted

For each hotel or lodging property, Actowiz extracts (where available):

Data Attributes Captured

Property-Level Data
  • Hotel name
  • Property type (hotel, resort, apartment, villa)
  • Brand / chain (if applicable)
  • Address, city, country
  • Latitude / longitude
  • Star rating
  • Property description
  • Amenities list
  • Check-in / check-out rules
Room-Level Data
  • Room name / category
  • Bed configuration
  • Max occupancy
  • Room size (if available)
  • Inclusions (breakfast, cancellation)
Pricing & Availability
  • Nightly rate
  • Taxes & fees
  • Total out-the-door price
  • Availability status
  • Minimum stay rules
  • 30 / 90 / 365-day calendar (configurable)
Reviews & Ratings
  • Average rating
  • Total review count
  • Recent review indicators
  • Rating distribution (where visible)

Sample Output (Tabular Format)

Sample – OTA Hotel & Lodging Dataset

OTA Hotel Name City Room Type Nightly Price Total Price Rating Reviews Availability
Booking.com Grand City Hotel Paris Deluxe Double Room €145 €168 8.6 2,134 Available
Agoda Ocean View Resort Phuket Sea View Suite $182 $205 8.9 1,487 Limited
Ctrip Skyline Business Hotel Shanghai Executive King ¥820 ¥910 4.5 3,205 Available
Booking.com Alpine Lodge Zermatt Standard Twin CHF 165 CHF 185 8.2 964 Sold Out
Agoda Downtown Apartments Singapore Studio Apartment SGD 138 SGD 152 8.4 1,120 Available

Sample data shown for illustration purposes only.

Output Format & Delivery

Supported Formats:

  • JSON
  • CSV
  • Excel
Delivery Methods:

  • Secure SFTP / FTP
  • Cloud storage (S3 / Blob)
  • Direct database ingestion
  • API endpoints (optional)

Update Frequency

  • Daily refresh for active markets
  • Weekly refresh for long-tail properties
  • On-demand refresh supported

Implementation Timeline

Phase Duration
Requirement Mapping & Schema Design 1–2 days
OTA Scraper Setup & QA 4–5 days
Sample Review & Validation 1 day
Production Deployment 1 day
Total Timeline 7–9 Working Days

Business Impact

11.1 Unified View Across OTAs

The client gained a single dataset combining Ctrip, Agoda, and Booking.com hotel data.

11.2 Better Pricing & Market Insights

Enabled:

11.3 Faster Product & Analytics Development

Structured data fed directly into:

  • BI dashboards
  • Market intelligence tools
  • Travel discovery platforms
11.4 Reduced Manual Effort

Eliminated manual OTA checks and spreadsheet-based tracking.

Why Actowiz Solutions?

  • Deep expertise in OTA & travel data scraping
  • Proven ability to handle room-level complexity
  • Global coverage and scalability
  • Clean, schema-consistent outputs
  • Secure delivery and monitoring
  • Fast turnaround

Actowiz supports hotel and lodging data extraction from:

  • Booking.com
  • Agoda
  • Ctrip
  • Expedia
  • Hotels.com
  • Regional OTAs

Conclusion

Actowiz Solutions delivered a scalable, multi-OTA hotel and lodging intelligence platform that aggregates pricing, availability, room details, and reviews from leading global OTAs.

This solution empowers travel-tech companies, analytics firms, hospitality brands, and investors to make data-driven decisions using accurate, up-to-date lodging intelligence.t regards,

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:

Fintech / Digital Payments

Result

Accurate daily voucher &

cashback visibility across platforms

★★★★★

“Actowiz Solutions helped us automate daily voucher and cashback data collection across PhonePe, Paytm, Flipkart, and Hubble. The API-driven delivery significantly improved offer accuracy and operational efficiency.”

Product Manager, Fintech Platform (India)

✓ Daily voucher & cashback tracking via Push & Pull APIs

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

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