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Actowiz Metrics Now Live!
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Actowiz Metrics Now Live!
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Actowiz Metrics Now Live!
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Actowiz Metrics Now Live!
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Actowiz Metrics Now Live!
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Actowiz Metrics Now Live!
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Actowiz Metrics Now Live!
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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
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                    [geoname_id] => 4509177
                    [names] => Array
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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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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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            [registered_country] => Array
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                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
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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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                    [ip_address] => 216.73.216.213
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    [continent:protected] => GeoIp2\Record\Continent Object
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                    [geoname_id] => 6255149
                    [names] => Array
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                            [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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    [country: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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                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [3] => isoCode
                    [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
                (
                    [0] => queriesRemaining
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        )

    [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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                    [0] => confidence
                    [1] => geonameId
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                    [4] => names
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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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                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.213
                    [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
                    [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
                (
                    [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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                )

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            [validAttributes:protected] => Array
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                    [1] => geonameId
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        )

    [location:protected] => GeoIp2\Record\Location Object
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            [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
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            [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
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                    [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

Navratri Mega Sale Price Tracking

In online travel, reviews and ratings decide bookings.

For hotel chains, property managers, and travel-tech companies, platforms like MakeMyTrip (MMT) are critical. Guests often check MMT reviews before confirming a stay, which means:

  • A few negative reviews can impact conversion
  • Ratings trends can signal service issues
  • Recent reviews reflect real-time guest experiences

One of Actowiz Solutions’ clients wanted a web scraping API service focused only on MakeMyTrip property reviews and ratings.

Important scope clarification from day one:

“We don’t need flight-related data. Just hotels / properties – their reviews and ratings.”

This case study explains how Actowiz Solutions designed a MakeMyTrip Reviews & Ratings API that delivers clean, structured, and ready-to-analyze data for thousands of properties across India.

Client Background

Navratri Mega Sale Price Tracking

The client is an Indian travel / hospitality analytics and operations team that:

  • Tracks customer experience (CX) across multiple OTAs
  • Benchmarks their partner hotels against competitors
  • Wants to feed reviews into internal dashboards & BI tools

They were heavily dependent on:

  • Manual checks of MakeMyTrip reviews
  • Inconsistent, limited exports
  • Third-party tools that did not support property-level, India-focused customization

So they approached Actowiz Solutions for a web scraping API that can:

  • Focus specifically on MMT hotel/property pages
  • Fetch reviews + ratings only
  • Work at scale across multiple cities and states

Challenges

3.1 No Direct, Flexible API for Reviews

They needed raw review data with:

  • Full review text
  • Overall rating
  • Sub-ratings (if available)
  • Date of review
  • Reviewer details (where public)

Most third-party APIs didn’t give them:

  • Full control on which properties to track
  • The flexibility to add/remove property URLs
  • Custom field-level mapping
3.2 High Volume & Pagination

Each property page on MakeMyTrip can have:

  • Hundreds or thousands of reviews
  • Multiple pages of review content
  • Filters (recent / older / rating filter etc.)

Manually handling pagination and collecting all reviews was time-consuming and error-prone.

3.3 Data Consistency Across Many Hotels

The client wanted to scale from:

  • A few dozen to
  • Hundreds or thousands of hotels / properties

That meant the solution had to:

  • Maintain consistent structure
  • Handle different layouts / templates
  • Support continuous additions of new property URLs
3.4 Need for Continuous Refresh

Reviews are dynamic:

  • New reviews added every day
  • Ratings change over time
  • Recent reviews matter more than old ones

They wanted regular updates (daily / weekly) depending on property importance.

3.5 Clean, Analysis-Ready Outputs

The client didn’t just want raw HTML dumps. They needed:

  • Structured JSON / CSV
  • Clean fields for direct dashboard use
  • Easy exports into Excel / Google Sheets / BI tools

Actowiz Solutions – Approach

Actowiz Solutions proposed a dedicated MakeMyTrip Reviews & Ratings Scraping API that:

  • Focuses ONLY on properties (hotels, stays, resorts, etc.)
  • Allows client to input property URLs
  • Returns clean, structured review and rating data

Solution Design

5.1 URL-Based Property Input

The system lets the client:

  • Paste one or more MakeMyTrip property URLs
  • Upload bulk lists (CSV/Excel)
  • Add/remove properties over time

For each URL, Actowiz:

  • Detects property ID / slug
  • Identifies the reviews section
  • Handles all pagination automatically
5.2 Data Points Collected per Review

For each property, the scraper captures:

  • Property ID / Name
  • Location (city, state, area if available)
  • Overall Rating (average rating on MMT)
  • Total Number of Reviews

For each individual review:

  • Review ID (if available)
  • Reviewer Name (or masked identifier, as shown)
  • Stay Type (e.g., Family, Couple, Solo, Business – when available)
  • Rating (out of 5)
  • Review Title (if present)
  • Detailed Review Text
  • Check-in / Stay Date (if visible)
  • Review Posted Date
  • Room Type (if indicated)
  • Sub-ratings (e.g., Cleanliness, Location, Service, Value – where visible)
  • Helpful / likes count (if MMT shows it)
5.3 Reviews & Ratings Summary per Property

For each property, Actowiz also generates a summary block, including:

  • Average rating
  • Total reviews count
  • Distribution of ratings (5-star / 4-star / 3-star / etc.)
  • Latest review date
  • Earliest review date captured

This gives the client an instant view of:

  • How a property is performing overall
  • How fresh the feedback is
  • Whether rating trends are improving or declining
5.4 Output Format & Delivery

The MakeMyTrip reviews and ratings data is provided in multiple formats:

  • JSON (for API integrations)
  • CSV / Excel (for manual analysis and sharing)
  • Direct integration into dashboards such as Power BI, Tableau, Looker, etc.

Delivery options:

  • REST API
  • Secure cloud folder / S3 bucket drops
  • Email / SFTP-based periodic data dumps
5.5 Update Frequency

Actowiz configured flexible refresh options:

  • Daily fetch for priority hotels
  • Weekly fetch for broader coverage
  • On-demand refresh option for specific audits or campaigns

The client can choose frequency property-wise or segment-wise.

Sample Data Output (Illustration)

6.1 Property Summary Sample
Property Name City Avg Rating Total Reviews Last Review Date URL
Grand Hills Hotel & Resort New Delhi 4.3 512 2025-11-28 makemytrip.com/hotels/grand-hills...
Sea Breeze Beach Retreat Goa 4.6 298 2025-11-27 makemytrip.com/hotels/sea-breeze...
Mountain View Escape Manali 4.1 184 2025-11-26 makemytrip.com/hotels/mountain-view…
6.2 Review-Level Sample
Property Name Reviewer Name Rating Stay Type Review Date Review Title Review Text (short)
Grand Hills Hotel & Resort R***v 5.0 Family 2025-11-20 Excellent Stay Rooms were clean and staff was very helpful...
Grand Hills Hotel & Resort P***a 3.0 Couple 2025-11-15 Average Experience Location is good but service was slow at check-in...
Sea Breeze Beach Retreat N***h 4.0 Friends 2025-11-16 Great for groups Beach is nearby, food was decent, rooms spacious...

(Note: This is illustrative sample data, not live MakeMyTrip output.)

Use Cases Enabled for the Client

With this MakeMyTrip reviews and ratings API, the client could:

7.1 CX & Reputation Monitoring
  • Track hotel performance over time
  • Identify hotels with sudden drops in ratings
  • Monitor impact of operational changes on reviews
7.2 Hotel Partner Benchmarks

For a chain or aggregator:

  • Compare multiple properties within the same city
  • Benchmark own properties vs competing hotels nearby
  • Use review data in quarterly business reviews (QBRs)
7.3 Insights for Operations & Training

By mining review text, they could identify:

  • Common complaints: cleanliness, check-in delays, food quality
  • Frequently praised aspects: staff friendliness, location, views
  • Property-specific issues that need escalation
7.4 Marketing & Campaign Impact Measurement

Before and after a:

  • Renovation
  • New manager joining
  • Special package launch

…review trends could be compared to measure impact.

Technical Highlights

8.1 Robust Scraper Architecture
  • Handles pagination for all review pages
  • Automatically skips duplicates on re-runs
  • Detects layout changes and flags anomalies for quick fixes
8.2 Scalability
  • Supports few properties → thousands of properties
  • Can be extended to other OTAs (Booking.com, Goibibo, etc.) when needed
8.3 Error Handling & Monitoring
  • Retry logic for failed requests
  • Health checks and alerts
  • Logging for debugging and SLA tracking

Business Impact

After integrating Actowiz’s MakeMyTrip reviews & ratings API, the client achieved:

9.1 Centralized Visibility

All property reviews across multiple cities were visible in:

  • One internal dashboard
  • With filters for city, property, rating band, date range
9.2 Faster Decision-Making
  • Issues are spotted early
  • Negative trends trigger quick interventions
  • Good reviews are used in marketing & branding
9.3 Productivity Gains

Manual review copying / tracking time dropped drastically:

  • From many hours per week
  • To a fully automated feed that just needs monitoring
9.4 Stronger Hotel Partnerships

The client could share data-backed insights with hotels:

  • “Guests are consistently complaining about breakfast variety.”
  • “Your location rating is strong, but cleanliness is pulling the total rating down.”

This improved discussions and helped hotels act on concrete feedback.

Why Actowiz Solutions for MakeMyTrip Scraping?

  • Deep experience with OTA, travel, and hotel data scraping
  • Ability to customize per platform & per client use case
  • Flexible data delivery: APIs, files, dashboards
  • Support for only property reviews/ratings as requested (no flights, no irrelevant noise)
  • Scalable, monitored, and SLA-backed systems

Conclusion

For any company that needs MakeMyTrip property reviews and ratings at scale, manual tracking is not sustainable.

Actowiz Solutions built a focused, API-driven web scraping solution that:

  • Tracks only hotel/property pages
  • Extracts detailed reviews & ratings
  • Delivers clean, structured, analysis-ready data
  • Updates daily or weekly as required

Whether you are:

  • A hotel chain
  • A travel-tech startup
  • A CX analytics company
  • A consulting or insights agency

…Actowiz can power your review intelligence across MakeMyTrip and other major travel platforms.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

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

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

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

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

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

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

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

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

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

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

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

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

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

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

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

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

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

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★
'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
Product Image
2 min
★★★★★
“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
Product Image
1 min
★★★★★
“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
1 min

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

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

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

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

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

✔ Scraped Data: Price Insights Top-selling SKUs

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

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

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

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

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

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

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

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

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

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

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

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

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

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

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

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