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GeoIp2\Model\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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                            [pt-BR] => América do Norte
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                            [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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                    [geoname_id] => 6255149
                    [names] => Array
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                            [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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        )

    [registeredCountry: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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                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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                    [8] => isHostingProvider
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 4509177
                    [names] => Array
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                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
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                            [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
                (
                    [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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                    [7] => postalConfidence
                    [8] => timeZone
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        )

    [postal:protected] => GeoIp2\Record\Postal Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => 43215
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            [validAttributes:protected] => Array
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                    [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
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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
)
Case Study Naver Store Seasonal Sales Analysis – Discount Trends During Korean Chuseok Festival-0

Introduction: Why NYC is the Quick Commerce Capital

New York City is one of the most competitive retail environments in the world. With a dense population, fast-paced lifestyle, and high reliance on delivery services, NYC has become a proving ground for quick commerce innovation.

Platforms like Walmart Grocery and Uber Eats dominate grocery delivery in the city, offering speed, variety, and convenience. For local retailers, the challenge lies in competing with these giants while keeping margins intact.

This case study highlights how Actowiz Solutions helped NYC retailers harness real-time grocery data from Walmart and Uber Eats to stay competitive. By leveraging insights into pricing, stock availability, promotions, and delivery speed, these businesses unlocked significant growth in their quick commerce operations.

The Challenge: Retailers Struggling in NYC’s Competitive Market

Introduction

Retailers in New York face unique challenges:

  • High Customer Expectations – NYC consumers expect same-day or even 1-hour deliveries.
  • Price Sensitivity – With Walmart’s competitive pricing, local stores risk losing loyal customers.
  • Promotional Overload – Uber Eats frequently launches seasonal and bundle campaigns, making it tough for smaller retailers to match.
  • Stock Management Pressure – Delivering on-time without stockouts is difficult in a dense urban market.

Without access to real-time competitor data, many NYC retailers were either over-discounting (losing margins) or underperforming in promotions (losing customers). They needed reliable intelligence to keep pace with Walmart and Uber Eats.

Solution: Real-Time Grocery Data from Walmart & Uber Eats

Introduction

Actowiz Solutions implemented a customized web scraping framework for NYC retailers to capture live competitor intelligence:

  • Dynamic Pricing Data
    • Hourly updates on Walmart’s grocery SKU prices.
    • Uber Eats delivery fees, surge pricing, and promotional discounts tracked continuously.
  • Inventory Tracking
    • Real-time monitoring of in-stock and out-of-stock products across categories like fresh produce, beverages, and packaged goods.
    • Visibility into substitutions and replacements during shortages.
  • Promotional Campaigns
    • Tracking Uber Eats coupon codes, bundle offers, and time-sensitive flash sales.
    • Walmart’s seasonal discounts (e.g., holiday grocery packs, back-to-school bundles).
  • Delivery Performance Data
    • Estimated delivery times, minimum order values, and geographic availability.
  • Customer Reviews & Ratings
    • Scraped reviews helped identify consumer sentiment trends—what customers liked or disliked about Walmart vs. Uber Eats.

Data was delivered in dashboards, Excel reports, and API feeds so retailers’ operations, marketing, and pricing teams could act immediately.

Case Study Results: NYC Retail Growth Impact

After six months of implementing Actowiz Solutions’ scraping and data delivery system, NYC retailers saw major gains:

  • Pricing Competitiveness
    • Real-time Walmart pricing allowed automated repricing.
    • Grocery categories like dairy and snacks saw 15% higher conversions.
  • Stock Optimization
    • Out-of-stock cases dropped by 28%.
    • Retailers pre-emptively restocked based on trending SKUs scraped from Uber Eats.
  • Promotional Effectiveness
    • Local campaigns aligned with Uber Eats’ bundles boosted sales by 35%.
    • Weekend delivery promotions outperformed previous campaigns by 22%.
  • Customer Loyalty
    • Improved on-time delivery and competitive pricing increased repeat order frequency by 20%.
  • Revenue Growth
    • Collectively, participating retailers achieved a 23% revenue uplift in NYC quick commerce.

Sample Data Insights

Such insights gave retailers the ability to undercut Walmart or Uber Eats pricing strategically while ensuring faster local delivery.

Product Walmart Price Uber Eats Price Stock Status Avg. Rating Delivery ETA
Milk 1 Gallon $3.19 $3.39 In Stock 4.6/5 1 hr
Organic Apples 1lb $2.49 $2.59 Low Stock 4.8/5 45 min
Cereal Family Pack $4.89 $5.29 In Stock 4.5/5 1 hr
Bread Loaf $2.29 $2.79 In Stock 4.7/5 30 min

Why This Worked in New York

The turning point was data-driven agility. Retailers weren’t just collecting competitor information—they were acting on it immediately:

  • Integrating data into POS systems for repricing.
  • Adjusting promotional calendars in line with Walmart & Uber Eats.
  • Optimizing warehouse and delivery scheduling for high-demand areas like Manhattan and Brooklyn.

This gave local retailers the ability to compete toe-to-toe with national giants.

Benefits of Walmart & Uber Eats Data Scraping

Introduction
  • Faster Competitive Response – Match or beat pricing in real time.
  • Local Demand Forecasting – Identify trending groceries in NYC neighborhoods.
  • Promotion Matching – Ensure campaigns remain relevant and effective.
  • Delivery Optimization – Benchmark against Uber Eats’ speed for consumer expectations.
  • Profit Protection – Prevent margin erosion while staying competitive.

Industries Benefiting in NYC

  • Independent Grocery Stores – Remain competitive against Walmart’s aggressive pricing.
  • D2C Food Brands – Track how Uber Eats lists and promotes products.
  • FMCG Suppliers – Align their retailer promotions with Walmart’s campaigns.
  • Quick Commerce Startups – Use data to improve delivery efficiency in Manhattan & Brooklyn.

Future of Quick Commerce in NYC

Introduction

New York’s quick commerce is expected to grow by 20% annually over the next five years. Retailers who continue to harness Walmart and Uber Eats scraping data will lead the way in:

  • Personalized neighborhood bundles (e.g., “NYC Breakfast Packs”).
  • Price matching as a standard offering.
  • AI-driven forecasting to anticipate demand spikes.

Actowiz Solutions is already helping NYC businesses integrate predictive analytics into their scraping workflows, ensuring they don’t just respond to trends—they stay ahead of them.

FAQs

Q1. How is Walmart & Uber Eats data collected?

Through ethical scraping and APIs that capture prices, promotions, reviews, and delivery metrics.

Q2. Can small NYC retailers use this data?

Yes—independent shops benefit the most by gaining insights into Walmart’s pricing strategy.

Q3. Is this scraping legal?

Yes, when done responsibly—only public data is extracted.

Q4. What formats are supported?

CSV, Excel, JSON, and APIs for real-time integration.

Q5. How quickly is data refreshed?

Retailers usually receive hourly or daily updates.

Q6. What ROI is possible?

Most NYC retailers saw 23% growth in revenue within two quarters.

Q7. Which boroughs benefit most?

Manhattan, Brooklyn, and Queens where quick commerce adoption is highest.

Q8. Can Uber Eats delivery data be used for forecasting?

Yes—tracking delivery times helps benchmark against competitors.

Q9. How does Actowiz Solutions add value?

We don’t just provide raw data—we deliver structured, actionable intelligence tailored for quick commerce.

Q10. What’s next for NYC retail data scraping?

Integration with AI demand prediction and expansion into hyper-local (ZIP-code level) dashboards.

Final CTA

Are you a retailer in New York struggling to compete with Walmart and Uber Eats? With Actowiz Solutions’ real-time data scraping services, you can optimize pricing, promotions, and stock availability to drive quick commerce growth in NYC.

👉 Request a Free NYC Data Sample Today!
Contact Us Today!

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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Quick Commerce in Texas – Competitive Grocery & E-Commerce Intelligence in Dallas & Houston

Discover how Dallas & Houston retailers used real-time grocery data from Walmart, Instacart, and Uber Eats with Actowiz Solutions to grow revenue by 22%.

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