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GeoIp2\Model\City Object
(
    [city:protected] => GeoIp2\Record\City Object
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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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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [latitude] => 39.9625
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    [postal:protected] => GeoIp2\Record\Postal Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                            [iso_code] => OH
                            [names] => Array
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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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                            [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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                            [zh-CN] => 美国
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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    [traits:protected] => GeoIp2\Record\Traits Object
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            [validAttributes:protected] => Array
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                    [1] => autonomousSystemOrganization
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                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
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                            [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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                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                    [latitude] => 39.9625
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            [postal] => Array
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                    [code] => 43215
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            [registered_country] => Array
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                    [0] => Array
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                            [geoname_id] => 5165418
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                                    [fr] => Ohio
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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] => 216.73.216.110
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)

 country : United States
 city : Columbus
US
Array
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    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

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  • Zip Code-Level Grocery Price Insights USA

Case Study: Hyperlocal Pricing Intelligence Across U.S. Zip Codes Using Grocery APIs

Actowiz Solutions delivers hyperlocal grocery pricing intelligence using Walmart, Shipt & Target APIs to track zip code-level insights across U.S. cities.

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Real-Time Regional Insights with Customizable E-commerce Dashboards

Introduction

Grocery pricing in the United States isn’t uniform—it varies by city, neighborhood, and even ZIP code. This variability directly impacts FMCG brands that aim to stay competitively priced across all channels.

Actowiz Solutions partnered with a leading food & beverage company in the U.S. to develop a hyperlocal pricing intelligence system that aggregates data using Walmart, Shipt, and Target APIs. The goal: identify pricing gaps, avoid regional mismatches, and enable dynamic price adjustments at the zip code level.

Objectives

  • Extract daily product prices from Walmart, Shipt, and Target across 100+ ZIP codes.
  • Identify price disparities across neighboring zones.
  • Provide a unified dashboard for marketing and pricing teams.
  • Trigger alerts for high-variance SKUs and discount competition.

Challenges

The-Client
  • Zip-code level API access varied per platform.
  • Some platforms dynamically altered price per delivery location.
  • Data normalization was difficult due to SKU naming inconsistencies.
  • Shipt’s delivery-centric model had fewer APIs available.

Actowiz Solutions' Approach

The-Client
1. API Integration Strategy:
  • Integrated Walmart’s ZIP-specific API endpoint
  • Accessed Target’s public pricing API with ZIP parameter injection
  • Reverse-engineered Shipt’s delivery pricing module using mobile endpoints
2. ZIP-Code Data Mapping:
  • Curated a list of 120 ZIP codes across 12 metro areas (e.g., 10001, 90210, 60601, 77001)
  • Scheduled data extraction 4x daily
  • Created geo-intelligence layers to visualize city and neighborhood patterns
3. Product Matching Engine:

Used NLP-based fuzzy matching to unify SKUs with slight name or size differences, e.g.:

  • "Oreo Double Stuf 15.35 oz" vs. "OREO Double Stuffed Cookies 15.3oz"
4. Price Comparison Metrics:

Generated daily comparative analytics like:

  • % Deviation from National Average
  • Top 10 Price Volatile ZIPs
  • ZIPs with Lowest Average Price for Top 50 SKUs

Sample Data Snapshot

SKU ZIP Code Walmart Price Target Price Shipt Price Avg Price Variance %
Lay's Classic 8oz 10001 $3.89 $4.19 $4.45 $4.18 14.4%
Tropicana Orange 52oz 90210 $5.25 $5.00 $5.75 $5.33 13.9%
Cheerios Cereal 12oz 60601 $3.49 $3.79 $4.05 $3.77 15.6%

Key Deliverables

  • Real-time zip-code level price feed via REST API
  • Daily Excel/CSV exports for all ZIPs and SKUs
  • Custom-built Power BI dashboard for geo-filtering
  • Competitive variance report delivered every Monday

Impact & Results

The-Client
🔹 Regional Price Harmonization:
  • Reduced price mismatches across cities by 22% within 6 weeks.
🔹 Optimized Local Promotions:
  • Marketing targeted areas with higher competitor discounting.
🔹 Dynamic Pricing Engine Feed:
  • Data now used as input in client's AI-driven dynamic pricing module.
🔹 Expansion to 200 ZIP Codes:
  • Based on early success, coverage scaled to 2x more ZIPs.

Technology Stack

  • Languages: Python, Node.js
  • Databases: PostgreSQL, MongoDB
  • APIs Used: Walmart Open API, Target Store Price API, Shipt Mobile API (reverse-engineered)
  • Visualization: Power BI, Tableau integration
  • Hosting: AWS (Lambda, EC2, S3)

Why Actowiz Solutions?

  • 🌐 Advanced geo-based scraping & API integration
  • 🎉 NLP-powered SKU standardization
  • 🚀 Fast deployment: 10 days to MVP
  • ✅ Proven success across multiple U.S. metros

Client Feedback

"Actowiz Solutions gave us the visibility we never had before. Our teams now react to market changes in hours, not weeks."

— VP of Revenue Strategy, Grocery Client USA

What’s Next

  • Integrating Costco and Kroger ZIP-based pricing
  • Creating forecast dashboards based on price volatility
  • Real-time alerts via Slack + SMS for pricing managers

Conclusion

Hyperlocal grocery pricing is no longer a guessing game. With Actowiz Solutions' data infrastructure and U.S.-focused grocery API integrations, brands can take control of pricing strategy ZIP code by ZIP code. This leads to improved competitiveness, better ROI on promotions, and data-driven marketing at its best.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape → Structure → Analyze → Visualize
Explore Solutions Get a Custom Demo

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.

Ecommerce
Q-Commerce
Travel & OTA
Food Delivery
Real Estate
Social Media
Trusted by Global Leaders – Secured by International Standards

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

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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

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