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GeoIp2\Model\City Object
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                    [names] => Array
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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
                            [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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                            [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
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                    [longitude] => -83.0061
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            [postal] => Array
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            [registered_country] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
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                            [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
                        )

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                            [iso_code] => OH
                            [names] => Array
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                                    [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] => 216.73.216.115
                    [prefix_len] => 22
                )

        )

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

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [validAttributes:protected] => Array
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                    [0] => code
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                    [2] => names
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        )

    [country:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
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                            [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
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                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [validAttributes:protected] => Array
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                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

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

    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [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
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [validAttributes:protected] => Array
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                    [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
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                    [0] => en
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            [validAttributes:protected] => Array
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                    [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] => 216.73.216.115
                    [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
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                    [0] => en
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            [validAttributes:protected] => Array
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                    [2] => names
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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
                )

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

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

    [subdivisions:protected] => Array
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            [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
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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
)

Introduction: Data Becomes the New Compass for Diwali Travel

Every Diwali season, Indian travelers light up airports, train stations, and booking portals across the world. But in 2025, festive travel is no longer about guesswork — it’s about data-backed decision-making.

Using Travel Price Scraping in India, Actowiz Solutions analyzed millions of real-time flight and hotel data points to uncover where Indians are traveling this Diwali and how pricing patterns are shaping consumer behavior.

With the help of Actowiz travel data insights, airlines, OTAs, and tour operators can now predict demand surges, detect hidden fare drops, and optimize pricing strategies before the festive rush peaks.

Let’s explore how web scraping for travel sites is rewriting the rules of festive travel analytics — and why data is now the most powerful ticket to understanding traveler intent.

Where Indians Are Flying This Diwali 2025

Based on Actowiz’s festive travel demand analysis, Indian travelers are booking flights earlier than ever before. Demand has risen sharply for short-haul international destinations and select luxury domestic hotspots.

Top Diwali Destinations from India – 2025 (Based on Scraped OTA Data)

Rank	Destination	Type	YOY Demand Growth	Avg Fare (INR)	Booking Lead Time
1	Dubai	International	+41%	₹29,500	42 days
2	Singapore	International	+38%	₹32,200	39 days
3	Bangkok	International	+35%	₹28,700	36 days
4	London	International	+31%	₹62,800	51 days
5	Goa	Domestic	+26%	₹8,900	29 days
6	Jaipur	Domestic	+22%	₹7,400	27 days

Actowiz travel data insights reveal that this surge is driven by a combination of school holidays, long weekends, and early festive promotions launched by OTAs in September.

Data Tip from Actowiz: “Travelers booking 35–40 days before Diwali saved an average of ₹2,800 per ticket compared to last-minute buyers.”

Scraping Festive Travel Demand Analysis

Actowiz’s Travel Price Intelligence platform used Flight and Hotel Data Scraping from 40+ booking sites and airline portals between August 20 – October 5, 2025.

Data Points Collected:

• Flight fares (economy, business, premium)
• Hotel rates (3★, 4★, 5★)
• Package bundle pricing
• Occupancy and availability by region
• Search query trends and booking timestamps

Data Source	Records Scraped	Frequency	Use Case
MakeMyTrip	2.4M	3× daily	Domestic route price tracking
Cleartrip	1.9M	Hourly	International flight monitoring
Booking.com	3.2M	Hourly	Hotel rate volatility
Agoda	1.1M	2× daily	Cross-border rate comparison
Skyscanner	2.8M	Real-time	Fare prediction model training

This web scraping for travel sites allows Actowiz to visualize and predict demand based on live market fluctuations — not static reports.

Hotel Pricing Trends – The Hidden Festive Surge

Diwali is also a peak period for hotels across India and neighboring countries. According to Actowiz’s hotel data scraping analysis, price surges begin roughly 10–12 days before Diwali.

Hotel Pricing Trends (Actowiz Sample Dataset)

Destination	Avg 4★ Rate (₹)	Rate Surge %	Avg Occupancy	Booking Window
Dubai	₹18,200	+29%	91%	40 days
Singapore	₹14,800	+25%	87%	37 days
Goa	₹10,400	+21%	89%	32 days
Jaipur	₹8,600	+18%	83%	29 days

Actowiz travel data insights: Hotel occupancy and price surges show a correlation index of 0.78, meaning rates begin rising once occupancy hits 70%.

This kind of flight and hotel data scraping empowers OTAs and hotels to identify early opportunities for pricing optimization and dynamic promotional campaigns.

Flight Price Scraping for Diwali Travel Planning

Actowiz’s Live Crawler API continuously extracts flight price data across major Indian and international routes. By analyzing over 4.5 million fare variations, the system maps how prices shift as booking windows shrink.

Sample Flight Price Dataset (Actowiz API Feed)

Route	45 Days Before Diwali	30 Days Before	15 Days Before	5 Days Before	Surge %
DEL–DXB	₹27,400	₹29,800	₹33,600	₹36,200	+32%
MUM–BKK	₹28,000	₹30,100	₹33,900	₹35,800	+28%
BLR–SIN	₹26,700	₹28,500	₹31,200	₹33,000	+24%
DEL–GOI	₹8,200	₹9,000	₹9,800	₹10,700	+30%

Data Tip from Actowiz: “Peak airfare surge begins 12–15 days before Diwali. The ideal booking window is 28–35 days in advance.”

Price vs. Booking Correlation Analysis

To visualize how booking intent affects pricing, Actowiz plotted real-time scraped data showing a near-linear correlation between fare hikes and search spikes.

Simplified Correlation Sample

Date	Search Volume Index (100 = peak)	Avg Fare (₹)	% Fare Increase
Sept 15	42	₹25,600	—
Sept 25	58	₹27,100	+6%
Oct 1	76	₹29,300	+13%
Oct 10	94	₹32,400	+23%

This correlation shows how predictive analytics and Travel Price Scraping in India can help brands anticipate when prices will peak and travelers will convert.

Regional Insights – Domestic vs. International Travel

Domestic Insights

• Tier-2 cities like Ahmedabad, Surat, and Indore show 40% higher travel intent for festive breaks.
• Rail-to-air migration continues — first-time flyers up 18% YoY.
• Popular short-haul circuits: Goa–Pune–Bangalore, Jaipur–Udaipur–Delhi.

International Insights

• 3-day and 5-day itineraries dominate search patterns.
• Family group bookings (3–5 travelers) represent 61% of traffic.
• Dubai, Singapore, and Bangkok lead festive international traffic.

Actowiz’s India travel data insights reveal that Indian travelers prefer “quick luxury escapes” during Diwali 2025 — blending short itineraries with higher spend per booking.

How Actowiz Travel Data Insights Predict Demand

Actowiz Solutions has built its proprietary Travel Price Intelligence System that analyzes:

• Scraped flight and hotel prices
• Real-time search data
• Demand spikes across OTAs
• Competitor promotional activity

By blending this data, the platform forecasts booking surges and fare plateaus with up to 91% accuracy.

Demand Forecast Model Output (Sample)

Destination	Forecast Period	Predicted Fare Surge	Peak Demand Window
Dubai	Oct 15–22	+14%	Oct 7–12
Singapore	Oct 12–18	+12%	Oct 5–10
London	Oct 10–17	+10%	Sept 30–Oct 6
Goa	Oct 18–23	+11%	Oct 8–13

Insight: “Festive travel demand starts surging roughly 10–12 days earlier than airlines expect. Early scraper alerts help brands prepare targeted campaigns.”

Technology Behind the Actowiz Live Crawler API

The Actowiz Live Crawler API enables real-time travel data scraping across thousands of sources, including OTAs, metasearch engines, and airline websites.

Technical Highlights

• Average scrape latency: <3 seconds per page
• Monitors 100+ travel sites simultaneously
• Detects fare changes within 90 seconds of update
• Integrates with pricing tools and dashboards via RESTful APIs

Module	Function	Frequency
Flight Scraper	Extracts fare and class data	Every 10 min
Hotel Scraper	Tracks room rates & occupancy	Hourly
Promotions Crawler	Detects banner & coupon codes	Continuous
Price Comparison API	Generates cross-market visualizations	Real-time

This infrastructure supports flight price scraping for Diwali travel planning and year-round competitive analysis.

Real-World Case Study Insight

A leading Indian OTA integrated Actowiz’s Travel Price Intelligence Platform ahead of Diwali 2024.

Results:

• 27% faster response to competitor fare changes
• 19% improvement in early booking conversions
• ₹1.8 crore incremental revenue gain from optimized dynamic pricing

“Actowiz’s data helped us predict fare hikes before they hit. We could run flash offers proactively instead of reactively.” — Revenue Manager, Indian OTA

Business Impact of Travel Price Scraping in India

Business Metric	Before Actowiz	After Actowiz	Change
Fare Prediction Accuracy	73%	91%	+18%
Missed Flash Sales	28/month	9/month	-68%
Average Booking Window	18 days	28 days	+55%
Revenue Per Booking	₹12,400	₹15,700	+26%

These measurable gains demonstrate how Travel Price Scraping transforms travel decision-making and margin control during peak seasons.

Visual – Diwali Price Trend Curve

(for infographic use)

• X-axis: Days before Diwali
• Y-axis: Avg flight price (₹)
• Curve shows gradual rise → surge → peak → post-Diwali drop
• Highlight “Best Booking Window” (28–35 days prior) with label
• Caption: Data Source: Actowiz Travel Data Insights Dashboard, 2025

The Future of Festive Data Analytics

The festive travel ecosystem is becoming more predictive, integrated, and automated. In the next two years, Actowiz expects:

• AI-driven dynamic fare modeling across OTAs
• Region-specific travel demand scraping for localized campaigns
• Hyper-personalized deal alerts powered by real-time traveler intent data

As competition intensifies, Actowiz Solutions continues to lead the way by helping travel brands turn raw web data into predictive insights and revenue action.

Call-to-Action: Get a Live Diwali Travel Price Dashboard Demo

Don’t wait for travel trends to reveal themselves — predict them. Actowiz Solutions invites you to experience the power of real-time Travel Price Intelligence.

See flight and hotel prices update live, track demand surges, and plan campaigns proactively.

Contact Us Today!

Summary Highlights

Key Insight	Actowiz Finding
Peak Diwali Destinations	Dubai, Singapore, Bangkok, London, Goa
Fare Surge Window	12–15 days before Diwali
Ideal Booking Time	28–35 days before
Avg Hotel Rate Increase	+25%
Predictive Accuracy	91%
Booking Conversions	+19% improvement
Primary Source	Actowiz Live Crawler API

Final Word

The Diwali 2025 travel season proves one thing: data doesn’t just report demand — it predicts it.

With Actowiz Solutions’ Flight and Hotel Data Scraping, Travel Price Intelligence, and Live Crawler API, travel brands now see market movements before competitors even notice.

This Diwali, the smartest travel strategies aren’t built on discounts — they’re built on data-driven foresight powered by Actowiz Solutions.

You can also reach us for all your mobile app scraping, data collection, web scraping , and instant data scraper service requirements!

GeoIp2\Model\City Object
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                            [zh-CN] => 哥伦布
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                            [ru] => США
                            [zh-CN] => 美国
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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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            [traits] => Array
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    [continent:protected] => GeoIp2\Record\Continent Object
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            [validAttributes:protected] => Array
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    [country:protected] => GeoIp2\Record\Country Object
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                            [zh-CN] => 美国
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            [validAttributes:protected] => Array
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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
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                )

        )

    [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] => 216.73.216.115
                    [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

+1 ▼

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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Monitoring Flash Sales Across 15 OTAs with Travel Price Scraping – Actowiz Solutions Data Reduced Missed Opportunities by 68%

Discover how Actowiz Solutions used real-time OTA price scraping and travel data intelligence to reduce missed flash-sale opportunities by 68%.

Festive Price Surge Tracker: Amazon Fresh vs BigBasket vs JioMart in India

Track how prices of sweets, snacks, and groceries surged across Amazon Fresh, BigBasket, and JioMart during Diwali & Navratri in India with Actowiz festive price insights.

Competitive Product Pricing on Tesco & Argos Using Data Scraping to Uncover 30% Weekly Price Fluctuations in the UK Market

Discover how Competitive Product Pricing on Tesco & Argos using data scraping uncovers 30% weekly price fluctuations in UK market for smarter retail decisions.

Oct 17, 2025

How Travel Agencies in Italy Use Trenitalia Data Scraping for Route Optimization to Enhance Customer Experience?

Discover how Italian travel agencies use Trenitalia Data Scraping for Route Optimization to improve scheduling, efficiency, and enhance the overall customer experience.

Oct 16, 2025

Diwali 2025 Travel Trends & Price Insights – Where Indians Are Flying and How Data Predicts Demand

Discover where Indians are flying this Diwali 2025. Actowiz Solutions shares real travel data, price scraping insights, and booking predictions for top festive destinations.

Oct 15, 2025

How BevMo Best-Selling Spirits Data Scraping Reveals 35% Yearly Sales Trends in the USA?

Discover how BevMo Best-Selling Spirits Data Scraping uncovers 35% yearly sales trends, helping brands analyze demand, pricing, and consumer preferences across the USA.

Read More

Monitoring Flash Sales Across 15 OTAs with Travel Price Scraping – Actowiz Solutions Data Reduced Missed Opportunities by 68%

Discover how Actowiz Solutions used real-time OTA price scraping and travel data intelligence to reduce missed flash-sale opportunities by 68%.

Dynamic Pricing Analysis of Hotels in India Using Web Scraping from Booking.com, Agoda, and MakeMyTrip

A case study on dynamic pricing analysis of hotels in India using web scraping from Booking.com, Agoda, and MakeMyTrip for market insights and pricing strategy.

Building a Comprehensive Global Google Maps Business Dataset for Market Intelligence and Competitive Analysis

A case study on building a global Google Maps Business Dataset to unlock market intelligence, analyze competitors, and drive data-driven business insights.

Read More

Festive Price Surge Tracker: Amazon Fresh vs BigBasket vs JioMart in India

Track how prices of sweets, snacks, and groceries surged across Amazon Fresh, BigBasket, and JioMart during Diwali & Navratri in India with Actowiz festive price insights.

Top 5 Brands Offering Deepest Discounts on Clothes This Navratri

Score big this Navratri 2025! Discover the top 5 brands offering the biggest clothing discounts and grab stylish festive outfits at unbeatable prices.

Top 10 Most Ordered Grocery Items During Navratri 2025

Discover the top 10 most ordered grocery items during Navratri 2025. Explore popular festive essentials for fasting, cooking, and celebrations.

Read More

Competitive Product Pricing on Tesco & Argos Using Data Scraping to Uncover 30% Weekly Price Fluctuations in the UK Market

Discover how Competitive Product Pricing on Tesco & Argos using data scraping uncovers 30% weekly price fluctuations in UK market for smarter retail decisions.

Airline Ticket Price Trends - Scrape Airline Ticket Price Trend and Track 20–35% Market Volatility in U.S. & EU

Discover how Scrape Airline Ticket Price Trend uncovers 20–35% market volatility in U.S. & EU, helping airlines analyze seasonal fare fluctuations effectively.

Quick Commerce Trend Analysis Using Data Scraping - Insights from Nana Direct & HungerStation in Saudi Arabia

Quick Commerce Trend Analysis Using Data Scraping reveals insights from Nana Direct & HungerStation in Saudi Arabia for market growth and strategy.

Read More

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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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