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GeoIp2\Model\City Object
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    [raw:protected] => Array
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            [city] => Array
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                    [geoname_id] => 4509177
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                            [ja] => コロンバス
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                            [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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                            [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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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [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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                )

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            [validAttributes:protected] => Array
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    [country:protected] => GeoIp2\Record\Country Object
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

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

    [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
                            [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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                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [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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                    [0] => en
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            [validAttributes:protected] => Array
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                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.141
                    [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
                        )

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

    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
                (
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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
        (
            [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
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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
)
Real-Time Regional Insights with Customizable E-commerce Dashboards

Introduction

Introduction

In India’s highly fragmented FMCG retail landscape, pricing varies not only across platforms but also across regions—even down to individual pin codes. Brands that operate without granular price visibility risk losing market share to more agile competitors.

Actowiz Solutions helped a leading FMCG company unlock pin code-level pricing intelligence across e-commerce platforms like Amazon India, Flipkart, and BigBasket, enabling real-time competitive benchmarking and smarter pricing strategies across 50,000+ SKUs.

Business Challenge

The FMCG brand faced three major challenges:

  • Hyperlocal Price Variation: Product prices differed across cities and pin codes based on logistics, local taxes, and competition.
  • Platform-Level Discrepancy: Prices for the same product differed across Amazon, Flipkart, and BigBasket.
  • Lack of Real-Time Visibility: Weekly manual checks couldn’t keep up with dynamic pricing changes and flash discounts.

They needed a robust, automated pin code-based pricing scraper to:

  • Extract SKU-wise product pricing
  • Track price history and discount trends
  • Benchmark against competitor products
  • Generate regional pricing heatmaps

Actowiz Solutions Approach

Our approach was divided into four phases:

Phase 1: Requirement Mapping
  • Identified priority pin codes: Tier-1 & Tier-2 cities across India
  • Defined product taxonomy: Pack size, variant, SKU code
  • Integrated client SKU mapping with competitor SKUs
Phase 2: Web Scraping Infrastructure
  • Created custom scrapers for Amazon.in, Flipkart.com, and BigBasket.com
  • Configured location-switching modules to simulate pin code-level browsing
  • Implemented anti-bot evasion (headers, delays, headless browsers)
  • Scheduled scrapes every 6 hours for dynamic updates
Phase 3: Data Normalization & Intelligence Layer
  • Parsed structured data:
    • Product Name
    • MRP
    • Selling Price
    • Discount %
    • Availability
    • Product Rating
    • Delivery Time
    • Seller/Marketplace info
  • Mapped variations across regions and platforms
  • Created automated dashboards for brand, SKU, and region-level analytics
Phase 4: Delivery & Integration
  • Delivered data via:
    • API for internal tools
    • Excel & CSV exports for sales teams
    • Power BI dashboards for strategy teams
  • Setup alerts for abnormal price drops and competitor undercuts

Sample Data Output

Field Description
Platform Amazon, Flipkart, BigBasket
Pin Code 110001 (Amazon), 560034 (Flipkart), 400001 (BigBasket)
Product Name Dettol Liquid 500ml
SKU Code DET500
MRP (₹) 99.00
Selling Price (₹) 83.00 (Amazon), 86.00 (Flipkart), 89.00 (BigBasket)
Discount (%) 16.16% (Amazon), 13.13% (Flipkart), 10.10% (BigBasket)
Availability In Stock
Delivery Time 1 Day (Amazon, BigBasket), 2 Days (Flipkart)
Seller Cloudtail India (Amazon), RetailNet (Flipkart), BB Super Saver (BigBasket)

Key Features Delivered

  • Pin Code Switching: Automated user-agent simulation for 100+ pin codes daily
  • Real-Time Price Drop Monitoring: Alerts on 10%+ price changes per SKU
  • Inventory Insights: Regional availability detection to identify stockouts
  • Price Heatmaps: Visualize high/low price zones across India

Outcomes Achieved

1. 22% Boost in Promo Efficiency

By understanding which pin codes had the highest price gaps, the brand launched localized discounts in regions with strong competitor presence.

2. 35% Faster Price Matching

The brand’s pricing team was able to adjust MOPs (Market Operating Prices) in near real-time, maintaining parity with Amazon and Flipkart.

3. Improved Seller Negotiations

Using platform-level pricing visibility, the brand could push resellers on Flipkart to maintain agreed pricing norms.

4. SKU Rationalization

Products that were constantly undercut or underperforming in certain regions were flagged for promotional rethinking or delisting.

Dashboard Snapshots (Illustrative)

[Dashboard View – BigBasket Mumbai vs Bangalore]

Field Description
City Mumbai, Bangalore
Avg. Price (₹) 84.5 (Mumbai), 79.8 (Bangalore)
Stock % Available 92% (Mumbai), 88% (Bangalore)
Best Seller BB Super Saver (Mumbai), GNS Retail (Bangalore)

[Pin Code Heatmap - Price Gaps on Amazon]

  • Red Zones = Above MOP
  • Green Zones = Below MOP
  • Blue Zones = Discounted zones by competitors

Outcomes Achieved

1. 22% Boost in Promo Efficiency

By understanding which pin codes had the highest price gaps, the brand launched localized discounts in regions with strong competitor presence.

2. 35% Faster Price Matching

The brand’s pricing team was able to adjust MOPs (Market Operating Prices) in near real-time, maintaining parity with Amazon and Flipkart.

3. Improved Seller Negotiations

Using platform-level pricing visibility, the brand could push resellers on Flipkart to maintain agreed pricing norms.

4. SKU Rationalization

Products that were constantly undercut or underperforming in certain regions were flagged for promotional rethinking or delisting.

Technologies Used

  • Python (Scrapy, Selenium, Requests)
  • MongoDBfor raw storage
  • PostgreSQLfor clean product-price joins
  • Power BI & Tableaufor visualization
  • AWS EC2for scheduling crawlers

SEO Keywords Used

  • FMCG product pricing insights
  • Pin code-level price tracking
  • E-commerce pricing scraper India
  • Amazon and Flipkart price monitoring
  • Real-time pricing data BigBasket
  • SKU-level pricing intelligence
  • FMCG competitive benchmarking tool
  • Hyperlocal pricing strategy India

Why Actowiz Solutions?

Actowiz Solutions specializes in real-time pricing intelligence and retail analytics. With a proven track record in web data extraction and a scalable platform that adapts to business rules, we offer:

  • Customizable scraping infrastructure
  • Real-time and batch delivery options
  • Location-aware data extraction
  • Continuous monitoring and alerting

What’s Next for the Client?

  • Expansion of SKU monitoring to D2C websites
  • Tracking offline-to-online price leakage using partner tools
  • Building an AI-powered pricing recommendation engine

Conclusion

The partnership between Actowiz Solutions and the FMCG brand proved the power of hyperlocal intelligence. With over 500+ pin codes analyzed daily, price visibility went from anecdotal to actionable. The result? Precision marketing, smarter pricing, and increased profitability.

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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