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GeoIp2\Model\City Object
(
    [city:protected] => GeoIp2\Record\City Object
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                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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    [location:protected] => GeoIp2\Record\Location Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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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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            [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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                            [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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                    [2] => names
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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
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                            [fr] => Amérique du Nord
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                            [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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                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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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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                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

        )

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

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

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [validAttributes:protected] => Array
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                    [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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                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                )

        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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                    [3] => isoCode
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
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                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
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                    [4] => ipAddress
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                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
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                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [prefix_len] => 22
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        )

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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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                )

            [continent] => Array
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                        (
                            [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] => Array
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                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

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            [location] => Array
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                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [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
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                            [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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                            [geoname_id] => 5165418
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                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

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                )

            [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
)
Case-Study-Optimizing-Grocery-Delivery-in-Tier-2-Cities-Using-Hyperlocal-Data-Extraction

Introduction

In India's bustling quick-commerce landscape, urban metros like Mumbai, Delhi, and Bangalore often receive the lion’s share of tech-driven innovation and delivery optimization. But what about Tier-2 cities like Jaipur and Indore? A rising quick-commerce startup decided to tackle this challenge by partnering with Actowiz Solutions, a leader in hyperlocal data scraping services. The goal? Enhance delivery efficiency and inventory management by extracting real-time grocery data from platforms like Blinkit and BigBasket.

The Challenge: Scaling Delivery in Tier-2 Markets

The-Challenge-Delivery-in-Tier-2-Markets

The startup had already established reliable operations in major cities but noticed growing customer complaints and inconsistent service quality in emerging markets like Jaipur and Indore. Key challenges included:

  • Stockouts and inventory mismatches at local warehouses.

  • Delivery delays due to incorrect forecasting and route planning.

  • Lack of localized pricing intelligence compared to metros.

  • Supplier inconsistencies due to poor visibility into demand trends.

With growth ambitions tied closely to Tier-2 market penetration, the team needed a scalable and automated data solution to drive smarter decisions.

Actowiz Solutions’ Role: Hyperlocal Grocery Data Scraping

Actowiz Solutions brought in its expertise in Quick Commerce Data Scraping Services. The team focused on pulling structured, hyperlocal data from platforms like Blinkit and BigBasket that operate robustly in Tier-2 cities.

Key Data Extracted:

  • Product availability by PIN code

  • Real-time pricing variations

  • Stock levels by location

  • Popular product trends

  • Delivery time estimates

  • Vendor reliability metrics

The Process: From Data to Actionable Insights

The-Process-From-Data-to-Actionable-Insights
1. Geo-Targeted Web Scraping Engine

Actowiz set up custom crawlers to extract data based on city-specific ZIP codes. For instance, grocery product availability in Pink City (Jaipur) could differ drastically from Rajwada (Indore). This allowed the client to differentiate strategies per location.

2. Real-Time Inventory Dashboards

Data from BigBasket and Blinkit was fed into a live dashboard to create:

  • SKU-level warehouse alerts

  • Product popularity heat maps

  • Stock mismatch predictions

3. Dynamic Pricing Intelligence

Using scraped data, Actowiz enabled the startup to track competitor pricing per locality. This helped in crafting localized promotional offers, increasing customer loyalty.

4. Demand Prediction Models

By layering scraped data with historical order logs, Actowiz helped design ML-powered models that improved:

  • Order fulfilment time

  • Vendor allocation

  • Product replenishment cycles

The Results: A Quantifiable Transformation

The-Results-A-Quantifiable-Transformation
🚚 On-Time Deliveries Improved by 18%

Before the implementation, average on-time delivery hovered around 70-72% in Jaipur and Indore. Post implementation, it rose to 85% in Jaipur and 83% in Indore.

🏬 Inventory Efficiency Jumped by 22%

Warehouse-level alerts and predictive stocking improved average inventory efficiency from 65-68% to 79-83%, reducing wastage and stockouts.

💸 Localized Offers Increased Conversion by 27%

Localized pricing and stock availability let the marketing team design region-specific discounts, improving conversion rates significantly.

📊 Visual Insights: Before vs After Metrics

![Chart displayed above showing delivery improvements in Jaipur and Indore]

Additional Benefits

1. Improved Vendor SLA Monitoring

Actowiz’s extraction of estimated delivery and vendor timelines helped the company renegotiate better terms with unreliable suppliers.

2. Real-time Stock Comparison with Competitors

Knowing when Blinkit or BigBasket had stockouts allowed the client to capitalize on competitor weaknesses in real-time.

3. Hyperlocal Personalization

With product preference trends available city-wise, the startup launched personalized grocery bundles, resulting in higher average order values.

Infographic: How Actowiz Streamlined Tier-2 Grocery Delivery

Infographic-How-Actowiz-Streamlined-Tier-2-Grocery-Delivery

Why Choose Actowiz Solutions?

✅ Expertise in Hyperlocal & Real-Time Data Scraping

Whether it’s grocery delivery, restaurant data, or ride-sharing insights, Actowiz offers industry-grade scraping solutions with custom filtering capabilities.

✅ Scalable for Multi-City Operations

From metros to Tier-3 towns, Actowiz’s scrapers can handle multi-layered, location-specific datasets, crucial for quick-commerce scale-ups.

✅ Integration with BI Tools

Actowiz offers seamless integrations into analytics tools, allowing instant visualization and strategy deployment.

Conclusion

The collaboration between the quick-commerce startup and Actowiz Solutions highlights how hyperlocal data scraping can make or break grocery delivery efficiency in underserved cities. With actionable insights derived from Blinkit and BigBasket data, the startup:

  • Boosted delivery performance,
  • Enhanced warehouse efficiency,
  • And unlocked localized growth potential in Tier-2 cities.

In an era where speed, personalization, and accuracy define customer satisfaction, hyperlocal data intelligence is no longer a luxury—it’s a necessity. And Actowiz Solutions is at the forefront of delivering it.

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
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