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GeoIp2\Model\City Object
(
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                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                            [ru] => США
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            [traits] => Array
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    [continent:protected] => GeoIp2\Record\Continent Object
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                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
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                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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            [validAttributes:protected] => Array
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    [country:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [3] => isoCode
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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
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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
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [0] => confidence
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        )

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

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
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                    [ip_address] => 216.73.216.141
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                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
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                    [8] => isHostingProvider
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
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                )

        )

    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
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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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            [validAttributes:protected] => Array
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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
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                    [1] => accuracyRadius
                    [2] => latitude
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                    [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
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            [validAttributes:protected] => Array
                (
                    [0] => code
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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
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                    [validAttributes:protected] => Array
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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
)
Revolutionizing-Global-Tire-Business-with-Tyre-Pricing-and-Market-Intelligence

Introduction

In the evolving world of e-commerce, grocery delivery apps have become a vital tool for consumers seeking convenience and variety. Mobile app grocery scraping is at the forefront of this transformation, offering businesses unparalleled insights into consumer behavior, product trends, and pricing structures.

Actowiz Solutions undertook a project to apply Indian grocery app scraping techniques to a popular Indian mobile application, extracting detailed and categorized grocery data. This case study explores the methodology, challenges, and results, emphasizing how mobile app data scraping can transform inventory management and customer experience.

Objectives

Objective

The project’s primary goal was to extract structured data from a leading Indian grocery app using advanced web scraping grocery lists techniques. Specific objectives included:

  • 1. Categorizing grocery data into hierarchical groups:

    • Baby Care

    • Beauty & Hygiene

    • Bakery, Cakes & Dairy

    • Beverages

    • Eggs, Meat & Fish

    • Cleaning and Household

    • Fruits & Vegetables

    • Food Grains, Oil & Masala

    • Kitchen, Garden & Pets

    • Gourmet & World Food

    • Snacks & Branded Foods

  • 2. Capturing metadata, including price, weight, brand, and product images.

  • 3. Delivering insights into grocery inventory data management and consumer preferences.

Methodology

Methodology
Platform Selection

Actowiz Solutions identified a top Indian grocery app known for its extensive product catalog and diverse user base to perform web scraping Indian grocery apps effectively.

Web Scraping Framework

Advanced grocery app scraping techniques were deployed using tools like Python, Selenium, and BeautifulSoup:

  • Selenium: Facilitated real-time interaction with dynamic content.

  • BeautifulSoup: Streamlined HTML parsing for data extraction.

  • Mobile app data extraction tools: Enabled seamless access to app content.

Data Points Captured
  • 1. Categories: Extracted main sections such as Fruits & Vegetables.

  • 2. Sub-Categories: Further refined groups like Citrus Fruits or Leafy Greens.

  • 3. Item Groups and Items Detailed breakdowns with metadata (name, brand, price, weight, images).

Image Extraction
  • Images were automatically downloaded via direct links.

  • Images were resized and labeled for database compatibility.

Data Storage
  • Data was structured into a scalable SQL database to facilitate ongoing grocery list data analysis and reporting.

Challenges and Solutions

Challenges-and-Solutions
Dynamic Content Loading
  • Challenge: Content was loaded dynamically via JavaScript.

  • Solution: Selenium enabled real-time interaction with the DOM, ensuring accurate extraction.

Anti-Scraping Mechanisms
  • Challenge: Encountered captchas and IP blocks during web scraping grocery lists.

  • Solution: Implemented IP rotation and human-like browsing behaviors to bypass restrictions.

Complex Categorization
  • Challenge: Overlapping items across sub-categories.

  • Solution: NLP algorithms standardized categorization, ensuring consistent data segmentation.

Results

Results
Comprehensive Data Coverage
  • Successfully extracted over 10,000 items across 11 categories.

  • Captured detailed metadata for every product, including pricing, weight, and brand information.

Hierarchical Structure

Delivered a structured dataset with clear categorization, supporting advanced grocery list data extraction:

  • Fruits & Vegetables: Citrus Fruits, Root Vegetables, Exotic Produce.

  • Food Grains, Oil & Masala: Rice, Lentils, Cooking Oils, Spices.

  • Bakery, Cakes & Dairy: Breads, Pastries, Dairy Products.

  • Beverages: Juices, Soft Drinks, Teas.

  • Snacks & Branded Foods: Chips, Chocolates, Instant Foods.

  • Beauty & Hygiene: Skincare, Haircare, Bath Products.

  • Cleaning and Household: Detergents, Cleaners, Utility Items.

  • Kitchen, Garden & Pets: Kitchen Tools, Gardening Supplies, Pet Food.

  • Eggs, Meat & Fish: Poultry, Seafood, Red Meat.

  • Gourmet & World Food: Imported Snacks, Specialty Foods.

  • Baby Care: Baby Foods, Diapers, Toys.

Visual Assets
  • Captured and categorized over 5,000 product images.

  • Enhanced marketing and inventory management systems with visually rich data.

Applications and Insights

Market Trends
  • Identified high-demand categories such as Fruits & Vegetables and Snacks & Branded Foods.

Consumer Preferences
  • Analyzed pricing patterns and product availability to understand demand better.

Competitive Edge
  • Leveraged Indian grocery app data to provide actionable recommendations for businesses aiming to enhance their online presence.

Testimonial

"Actowiz Solutions has been an invaluable partner in our data extraction efforts for the grocery retail sector. Their expertise in mobile app data scraping allowed us to seamlessly extract detailed product information from one of the leading Indian grocery apps, enabling us to better understand market trends and consumer behavior. The structured dataset they provided, with clear categorization and rich metadata, has transformed our inventory management and marketing strategies. The insights derived from their work helped us identify high-demand categories and optimize our product offerings to meet customer needs more effectively. Actowiz Solutions' professionalism, dedication, and innovative approach made this project a huge success, and we look forward to collaborating with them on future initiatives."

– Mark Thompson, Chief Data Officer

Conclusion

This project highlights the transformative potential of e-commerce grocery data extraction. By applying innovative grocery data mining techniques, Actowiz Solutions was able to extract, categorize, and analyze comprehensive grocery lists from a leading Indian mobile app.

The structured data enabled stakeholders to refine marketing strategies, streamline inventory management, and enhance customer satisfaction. This case study underscores the power of app-based grocery data solutions in driving growth and operational efficiency for businesses in the retail sector.

If your business seeks to capitalize on the benefits of Indian grocery app analysis, Actowiz Solutions is ready to deliver tailored, scalable solutions to meet your unique needs!

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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