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GeoIp2\Model\City Object
(
    [city:protected] => GeoIp2\Record\City Object
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            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 4509177
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                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

        )

    [location:protected] => GeoIp2\Record\Location Object
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                    [0] => averageIncome
                    [1] => accuracyRadius
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                    [3] => longitude
                    [4] => metroCode
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                    [latitude] => 39.9625
                    [longitude] => -83.0061
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                    [time_zone] => America/New_York
                )

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

    [subdivisions:protected] => Array
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            [0] => GeoIp2\Record\Subdivision Object
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                    [record:GeoIp2\Record\AbstractRecord:private] => Array
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                            [iso_code] => OH
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                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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    [continent:protected] => GeoIp2\Record\Continent Object
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            [validAttributes:protected] => Array
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                    [0] => code
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                )

        )

    [country:protected] => GeoIp2\Record\Country Object
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            [validAttributes:protected] => Array
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                    [3] => isoCode
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [iso_code] => US
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                            [de] => USA
                            [en] => United States
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                        )

                )

        )

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

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
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                            [es] => Estados Unidos
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                )

        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
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                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
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                    [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
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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        )

    [raw:protected] => Array
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                            [fr] => Columbus
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                            [pt-BR] => Columbus
                            [ru] => Колумбус
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                )

            [continent] => Array
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                            [es] => Norteamérica
                            [fr] => Amérique du Nord
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                )

            [country] => Array
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                            [fr] => États Unis
                            [ja] => アメリカ
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                            [ru] => США
                            [zh-CN] => 美国
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            [location] => Array
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                    [latitude] => 39.9625
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                    [time_zone] => America/New_York
                )

            [postal] => Array
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            [registered_country] => Array
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                    [geoname_id] => 6252001
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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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                )

            [subdivisions] => Array
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                    [0] => Array
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                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                )

            [traits] => Array
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                    [ip_address] => 216.73.216.110
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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
)
Building-a-Multi-Lingual-Grocery-Database

Introduction

India’s diverse linguistic landscape poses unique challenges for businesses aiming to serve the entire country. For Actowiz Solutions, a leader in data intelligence services, this diversity presented an opportunity to showcase their expertise by building a multi-lingual grocery database tailored for Pan-India coverage. This case study highlights the process, challenges, and outcomes of creating a localized and inclusive database for the Indian grocery market.

Background

Background

India is home to 22 officially recognized languages and hundreds of regional dialects. In the grocery industry, regional variations in product names, units of measurement, and preferences can significantly impact customer engagement. Actowiz Solutions recognized the growing demand for a database that could bridge linguistic divides while ensuring accurate and culturally relevant information for diverse audiences.

The goal was to create a comprehensive grocery database that:

  • 1. Supported multiple languages.

  • 2. Addressed regional variations in product details.

  • 3. Delivered a user-friendly interface tailored to different linguistic groups.

Project Objectives

Project-Objectives-0
  • Localization of Product Details: Include regional names, unit preferences, and culturally relevant information for grocery items.

  • Multi-Lingual Support: Enable seamless access to the database in major Indian languages such as Hindi, Tamil, Telugu, Bengali, and Marathi.

  • Scalable Framework: Ensure the database could easily accommodate additional languages and regions in the future.

  • Enhanced User Interfaces: Provide interfaces optimized for regional audiences, focusing on usability and cultural preferences.

Challenges Faced

Challenges

Building a multi-lingual grocery database came with its own set of challenges:

    1. Data Collection and Standardization:
    • Gathering product information from diverse sources across India was a monumental task. Regional variations in product names and categorizations added complexity.

    • Standardizing the data while retaining regional nuances required careful planning.

    2. Linguistic Ambiguities:
    • Translating product names accurately into multiple languages often led to ambiguities. For instance, the word “flour” has different regional variations (“atta” in Hindi, “maida” in Tamil).

    3. Cultural Relevance:
    • Ensuring that the database catered to cultural differences, such as units of measurement (grams versus “pav”), was crucial for user acceptance.

    4. Technical Scalability:
    • Designing a database that could handle large volumes of data while being scalable for future growth was critical.

    5. User Interface Adaptations:
    • Tailoring the UI for audiences with varying levels of digital literacy presented design challenges.

Solution Design

Solution

Actowiz Solutions adopted a systematic approach to address these challenges:

    1. Comprehensive Data Collection Framework:
    • Partnered with local vendors, retailers, and market experts to gather authentic data.

    • Used web scraping tools to collect information from online grocery platforms, ensuring data accuracy and relevance.

    2. Linguistic Expertise:
    • Employed native language experts and translators to ensure accurate translations of product names and descriptions.

    • Used AI-driven language tools to manage large-scale translations and maintain consistency.

    3. Cultural Adaptations:
    • Incorporated region-specific data such as commonly used units (liters, kilograms, or local measures).

    • Included localized product images and descriptions to enhance cultural relevance.

    4. Technological Framework:
    • Developed a scalable and modular database architecture capable of supporting additional languages and products.

    • Integrated Natural Language Processing (NLP) to handle queries in multiple languages efficiently.

    5. User Interface Design:
    • Conducted user research to identify preferences across regions.

    • Designed interfaces with language toggles and intuitive navigation to cater to users with varying digital proficiency.

Implementation Process

    1. Phase 1: Data Aggregation
    • Collected over 500,000 product records from urban and rural markets.

    • Standardized the data structure while retaining region-specific nuances.

    2. Phase 2: Language Integration
    • Added support for 10 major Indian languages, ensuring accurate translations and context-aware adaptations.

    • Used feedback loops with native speakers to refine translations.

    3. Phase 3: Localization Features
    • Introduced regional filters to allow users to view products relevant to their location.

    • Enabled dynamic units of measurement based on user preferences.

    4. Phase 4: Testing and Deployment
    • Conducted extensive testing with focus groups representing different linguistic regions.

    • Optimized the database for performance and usability based on user feedback.

Key Outcomes

Key-Outcomes
    1. Enhanced User Engagement:
    • The multi-lingual support led to a 40% increase in user interactions across regions.

    • Localization features improved customer satisfaction, with a significant reduction in drop-off rates.

    2. Market Expansion:
    • Retailers using the database reported a 25% growth in sales in Tier-2 and Tier-3 cities.

    • The database enabled businesses to tap into previously underserved linguistic markets.

    3. Scalability and Flexibility:
    • The modular architecture allowed for seamless integration of additional languages and products.

    • The database was adopted by multiple e-commerce platforms and grocery retailers.

    4. Industry Recognition:
    • The project was recognized as a pioneering effort in leveraging technology for inclusivity in the grocery sector.

Lessons Learned

    1. Localization is Key:
    • Tailoring the database to regional needs was critical for success. One-size-fits-all solutions do not work in linguistically diverse markets.

    2. Collaboration with Local Experts:
    • Partnering with native speakers and cultural experts ensured the accuracy and relevance of the database.

    3. Scalable Architecture is Essential:
    • A robust technological framework enabled Actowiz Solutions to adapt quickly to changing requirements.

    4. User-Centric Design:
    • Focusing on usability and cultural preferences drove higher adoption rates.

Testimonial

"Partnering with Actowiz Solutions on the multi-lingual grocery database project has been a transformative experience for our company. Their in-depth understanding of data intelligence and expertise in handling India's diverse linguistic and cultural nuances allowed us to build a powerful, localized database that has significantly boosted our reach. The attention to detail in localizing product information, translating accurately, and adapting to regional preferences was invaluable. As a result, we've seen an impressive increase in user interaction and sales growth in previously underserved markets. Actowiz Solutions has not only helped us enhance our offerings but also positioned us for long-term success in India’s competitive grocery landscape. We look forward to working with them on future endeavors."

– John Mitchell, CEO

Future Prospects

Future-Prospects

    Actowiz Solutions aims to further enhance the database by:

    • Adding support for more Indian languages and dialects.

    • Incorporating AI-driven personalization to recommend region-specific grocery items.

    • Expanding the database’s reach to neighboring countries with similar linguistic diversity.

Conclusion

The multi-lingual grocery database developed by Actowiz Solutions stands as a testament to the power of localization in addressing India’s diverse needs. By combining linguistic expertise, technological innovation, and user-centric design, Actowiz Solutions successfully created a platform that bridges linguistic and cultural divides. This case study underscores the importance of inclusivity and localization in building solutions for diverse markets.

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 & palniring

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 inights Top-slling SKUs

Our Data Drives Impact - Real Client Stories

Blinkit | India (Relail Partner)

"Actow's helped us reduce out of ststack 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

"Actow's helped us reduce out of ststack 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
Aug 08, 2025

Discounted Devotion? Janmashtami Offer Mapping Across Quick Commerce Platforms

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Discounted Devotion? Janmashtami Offer Mapping Across Quick Commerce Platforms

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Aug 08, 2025

Grocery Discount Trends from Toters, JOKR, and Getir – Regional Analysis

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Aug 07, 2025

How to Track Weekly Flipkart Electronics Prices for Smarter Pricing Decisions & Competitive Edge?

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Track Janmashtami Quick Commerce Banner Leaders – Dairy, Mithai & Puja Brands Insights

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Price Tracking of Rakhi Gift Hampers – Did Discounts Really Deliver Value?

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Real-Time Ride Fare Comparison: Uber vs DiDi vs Bolt Across 7 Countries

Compare Uber, DiDi & Bolt ride fares across 7 countries with real-time scraping insights. Discover surge patterns, price differences & platform efficiency globally.

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🇮🇳 India: Independence Day Sale Price Mapping – Flipkart vs Amazon

Actowiz Solutions compares Flipkart & Amazon prices during India’s Independence Day Sale 2025. Discover top deals, price drops & brand discount trends.

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Lazada Grocery App Dataset Analysis - Market Intelligence & Grocery Delivery Trends for American Startups

Explore Lazada grocery App dataset insights to uncover grocery delivery trends, pricing, and market gaps for American startups entering Southeast Asian markets.

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Raksha Bandhan & Independence Day 2025: How Holiday Travel Surges Impacted Flight and Hotel Pricing in India

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