Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
 
GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => 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
                        )

                )

            [continent] => Array
                (
                    [code] => NA
                    [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
                        )

                )

            [country] => 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
                        )

                )

            [location] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [postal] => Array
                (
                    [code] => 43215
                )

            [registered_country] => 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
                        )

                )

            [subdivisions] => Array
                (
                    [0] => 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
                                )

                        )

                )

            [traits] => Array
                (
                    [ip_address] => 216.73.216.155
                    [prefix_len] => 22
                )

        )

    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => NA
                    [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
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => geonameId
                    [2] => names
                )

        )

    [country: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
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

    [locales:protected] => Array
        (
            [0] => en
        )

    [maxmind:protected] => GeoIp2\Record\MaxMind Object
        (
            [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
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

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

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

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

        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.155
                    [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
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
                )

        )

    [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
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [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
                        (
                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
                        )

                )

        )

)

 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
)

Start Your Project

+1 ▼
  • Home
  • Resources
  • Amazon vs Walmart Holiday Pricing Benchmark 2025
Report

Amazon vs Walmart Pricing Benchmark – Holiday Season 2025

A real-time Actowiz Solutions report analyzing Amazon vs Walmart pricing, discounts, volatility, stock-outs, and category trends during the 2025 holiday season.

Download Research Report

+1 ▼

Note: You’ll receive it via email shortly after submitting the form.

Introduction

The 2025 holiday season delivered record-breaking online traffic, aggressive discounting, and unprecedented pricing volatility across the U.S. retail ecosystem. Amazon and Walmart — the two largest retail players — competed intensely across top categories including electronics, toys, grocery, beauty, appliances, home goods, and seasonal gifting.

Actowiz Solutions monitored millions of pricing events across both platforms from November to December 2025. This report reveals how the two giants adjusted their discounts, delivery promises, and inventory strategy hour by hour — and what this means for brands entering the 2026 retail cycle.

Why the 2025 Holiday Season Was Different

The holiday season behaved differently compared to earlier years:

Inflation slowed but consumer sensitivity increased

Shoppers waited longer for deeper discounts.

Q-commerce influenced retail pricing

Instant delivery of giftables reshaped expectations for speed.

Flash sales overtook long-duration holiday campaigns

Amazon’s hourly “Lightning Deals” disrupted Walmart’s price-match strategy.

Inventory pressure changed hourly

Popular toys and electronics experienced rapid stock-outs.

Retailers used algorithmic price adjustments

Dynamic repricing happened up to 15–20 times per SKU per day in key categories.

Understanding this behavior is essential for brand pricing, promotions, and forecasting.

Amazon & Walmart Holiday Season Homepage Screens

Navratri Mega Sale Price Tracking

Pricing Benchmark Overview (Amazon vs Walmart)

Actowiz captured real-time pricing snapshots every 15 minutes across 10 major categories.

Below are the biggest takeaways:

1. Amazon Offered Deeper Discounts in Electronics

Across laptops, smartwatches, and headphones:

  • Amazon average discount: 22–38%
  • Walmart average discount: 15–28%

Amazon leveraged Black Friday + hourly Lightning Deals to dominate.

2. Walmart Outperformed Amazon in Grocery & Essentials

Walmart positioned itself as the “holiday savings” store:

  • Lower prices on coffee, snacks, pantry essentials
  • 5–12% average lower pricing vs Amazon
  • Higher stock reliability in household goods
3. Toys Category Was the Most Volatile

Both platforms adjusted prices aggressively due to massive demand.

  • Amazon had sharper hourly drops
  • Walmart had consistent price-matching behavior
4. Beauty & Personal Care Saw Platform Split
  • Amazon pushed premium brands with bundle pricing
  • Walmart offered stronger deals on mass retail beauty SKUs

Amazon vs Walmart Price Trend Charts

Navratri Mega Sale Price Tracking

Category-Wise Breakdown

Actowiz segmented the analysis across 10 critical retail categories.

A. Electronics (Laptops, Tablets, Headphones)

Amazon Strategy:

  • Heavy Lightning Deals
  • Brand-exclusive promotions (Sony, Apple accessories, Samsung)
  • Large number of micro price drops

Walmart Strategy:

  • Lower baseline prices
  • Bundle-heavy promotions (TV + Soundbar, Laptop + Accessories)

Winner: Amazon (Aggressiveness + volume of discounts)

B. Toys & Games

Amazon:

  • Highest volatility during Black Friday week
  • Instant stock watch triggered rapid price changes

Walmart:

  • Better availability
  • Lower effective pricing on bestsellers

Winner: Walmart (Stock + reliability)

C. Grocery & Beverages

Amazon:

  • Higher price variability
  • Strong deals on coffee & packaged snacks

Walmart:

  • Best pricing stability
  • Stronger omnichannel fulfillment
  • Lower average grocery pricing by 5–12%

Winner: Walmart

D. Beauty & Personal Care

Amazon:

  • Broader catalog
  • Premium beauty discounts up to 35%
  • Multiple third-party sellers driving competition

Walmart:

  • Less volatility
  • Better deals in mass-market skincare

Winner: Amazon (Premium growth), Walmart (Mass retail)

Category-Level Price Benchmark Graphs

Navratri Mega Sale Price Tracking

Discounting Behavior (Deep Dive)

Actowiz studied hourly discount swings across both platforms.

Amazon Discount Patterns
  • Highest volatility
  • Flash sales every 1–3 hours
  • Algorithmic repricing based on competitor signals
  • Fastest reaction to Walmart price changes

Categories with biggest swings:

  • Electronics
  • Toys
  • Beauty
Walmart Discount Patterns
  • Stable discounting
  • Strong price-match behavior
  • Higher predictability for shoppers
  • Better grocery positioning

Categories with biggest impact:

  • Grocery
  • Household
  • Toys

Flash Sale & Repricing Visualization

Navratri Mega Sale Price Tracking

Stock Availability & Out-of-Stock (OOS) Impact

The most telling difference between the platforms:

Amazon OOS Behavior:
  • OOS triggered instant price spikes on alternate sellers
  • Some products shifted to higher-priced third-party vendors
Walmart OOS Behavior:
  • OOS caused category re-ranking
  • Substitutes were shown aggressively
  • Price-matching paused temporarily

Most affected OOS categories:

  • Toys
  • Gaming accessories
  • Small kitchen appliances
  • Beauty gift sets

OOS Indicators on Amazon & Walmart

Navratri Mega Sale Price Tracking

Delivery & Fulfillment Benchmark

Holiday-season delivery speed is a competitive factor.

Amazon Prime:
  • Consistently fastest
  • Heavily promoted same-day & overnight shipping
  • Stronger fulfillment scores
Walmart:
  • Excellent 1–2 day delivery in urban areas
  • Outperformed Amazon on in-store pickup options
  • More stable estimated-delivery accuracy

Delivery Speed Comparison Amazon vs Walmart

Navratri Mega Sale Price Tracking

2026 Predictions Based on 2025 Data

Actowiz forecasts the following trends shaping next year's holiday season:

  • Amazon will invest heavily in faster automated repricing
  • Walmart will expand grocery + household category dominance
  • Toys will continue being the most price-volatile category
  • Premium beauty will see deeper discounting on Amazon
  • Walmart will focus on aggressive in-store pickup + omnichannel bundling
  • Q-commerce apps will influence gifting & beverage categories

Brands must prepare for faster, AI-driven pricing strategies from both retailers.

Conclusion: Who Won Holiday Season 2025?

Amazon dominated:

  • Electronics
  • Beauty (premium)
  • Flash-sale-driven conversions

Walmart dominated:

  • Grocery
  • Toys
  • Household essentials
  • Fulfillment reliability

Both platforms behaved differently — and brands require real-time, SKU-level intelligence to react and price competitively.

Actowiz Solutions delivers exactly this capability with:

Hourly price monitoring

Inventory & OOS tracking

Promotion & discount intelligence

Category-level benchmarking<< /strong>/p>

Multi-retailer competitive analysis

Holiday success is no longer about planning.

It is about live responsiveness powered by data.

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:

Fintech / Digital Payments

Result

Accurate daily voucher &

cashback visibility across platforms

★★★★★

“Actowiz Solutions helped us automate daily voucher and cashback data collection across PhonePe, Paytm, Flipkart, and Hubble. The API-driven delivery significantly improved offer accuracy and operational efficiency.”

Product Manager, Fintech Platform (India)

✓ Daily voucher & cashback tracking via Push & Pull APIs

View Case Studies

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

View Case Studies

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×

View Case Studies

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

View Case Studies

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

View Case Studies

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

View Case Studies

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

View Case Studies

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

View Case Studies

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

Request Demo Access icon

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

Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

All
Blog
Case Studies
Infographics
Report
thumb
Feb 19, 2026

Scraping Aqualite UK with Python - BeautifulSoup vs Selenium - Choosing the Right Approach for Data Extraction

Scraping Aqualite UK with Python - BeautifulSoup vs Selenium compares speed, accuracy, and scalability to choose the best scraping method.

thumb

Costco Walmart Grocery Pricing Scraping – Geo-Based Real-Time Houston

Geo-based real-time grocery pricing scraping for Costco and Walmart in Houston. Boost retail intelligence with Actowiz Solutions data APIs.

thumb

Manual Data Collection vs Automated Scraping — Which Saves More Time & Cost?

Discover the key differences between manual data collection and automated web scraping. Learn which method saves more time, reduces costs, and improves efficiency for your business in 2026.

thumb

Amazon Sports And Fitness Market Analysis - Key Insights From Our Analysis Of 1896 Products

Amazon Sports And Fitness Market Analysis covering pricing trends, competition insights, and key findings from 1896 products.
thumb
Feb 19, 2026

Scraping Aqualite UK with Python - BeautifulSoup vs Selenium - Choosing the Right Approach for Data Extraction

Scraping Aqualite UK with Python - BeautifulSoup vs Selenium compares speed, accuracy, and scalability to choose the best scraping method.

thumb
Feb 18, 2026

How Pedidosya Food Data Extraction Covers All Cities and Categories for Smarter Market Insights

Boost insights with Pedidosya Food Data Extraction. Access all cities and categories to track trends, menus, and customer preferences efficiently.

thumb
Feb 17, 2026

How to Solve Stock and Pricing Challenges with Zara Price & Inventory Monitoring with AI-Based Web Scraping

Track Zara product prices and stock efficiently with Zara Price & Inventory Monitoring with AI-Based Web Scraping, ensuring accurate, real-time retail insights.

Read More
thumb

Costco Walmart Grocery Pricing Scraping – Geo-Based Real-Time Houston

Geo-based real-time grocery pricing scraping for Costco and Walmart in Houston. Boost retail intelligence with Actowiz Solutions data APIs.

thumb

Apartments.com Inventory Scraping Houston 2026 – Lead Generation for Real Estate

Houston apartment inventory scraping from Apartments.com for 2026. Generate verified rental leads with structured data and real-time insights by Actowiz Solutions.

thumb

How We Solved Real-Time Competitive Monitoring Challenges with AI-Based Zara Fashion Product Scraping for a Retail Client

AI-powered Zara product scraping enabled real-time competitive insights, automated tracking, and improved pricing decisions for our retail client.

thumb

Comparing Medicine Prices in Mumbai vs Delhi Using Real-Time 1mg Data

Comparing medicine prices in Mumbai vs Delhi using real-time 1mg data to analyze price differences, availability, and savings across cities.

Read More
thumb

Manual Data Collection vs Automated Scraping — Which Saves More Time & Cost?

Discover the key differences between manual data collection and automated web scraping. Learn which method saves more time, reduces costs, and improves efficiency for your business in 2026.

thumb

10 Ways Data Scraping Drives Business Growth & Market Intelligence

Discover 10 powerful ways data scraping boosts business growth, from competitive price intelligence and demand forecasting to inventory tracking and market monitoring.

thumb

Grocery Price Movement Tracker – Walmart, Instacart & Target

Real-time grocery price changes across Walmart, Instacart and Target. Track top SKU drops, increases and hourly volatility with Actowiz Solutions.

Read More
thumb

Amazon Sports And Fitness Market Analysis - Key Insights From Our Analysis Of 1896 Products

Amazon Sports And Fitness Market Analysis covering pricing trends, competition insights, and key findings from 1896 products.
thumb

Gap vs American Eagle Data Scraping – The Financial Giant vs. The Physical Leader Divide

Gap vs American Eagle Data Scraping reveals e-commerce trends, sales performance, and market gaps between the financial giant and physical leader.
thumb

Sephora vs Ulta Beauty Data Scraping Comparison - Extracting Prices, Ratings & Trends at Scale

In-depth research report comparing Sephora and Ulta Beauty data scraping, analyzing prices, ratings, product trends, and competitive insights at scale.
Read More
phone
Quick Connect
phone
Quick Connect