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] => 216.73.216.213
                    [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] => 216.73.216.213
                    [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
)
Navratri Mega Sale Price Tracking

Introduction

In today's hyper-competitive global marketplace, consumer packaged goods (CPG) brands face an unprecedented challenge: understanding and reacting to real-time market dynamics across diverse e-commerce platforms. For a leading multi-national CPG brand, let's call them "Global Innovations," this challenge was particularly acute. With a vast portfolio of products spanning multiple categories, Global Innovations needed a robust solution to monitor product listings, pricing, promotions, competitor activities, and customer sentiment across major online retailers like Amazon, Walmart, and Target. Their existing manual methods were proving insufficient, time-consuming, and prone to inaccuracies, leading to missed opportunities and suboptimal strategic decisions. This is where Actowiz Solutions, a leader in data scraping and web crawling services, stepped in to provide a transformative solution.

The Challenge: A Labyrinth of E-commerce Data

Global Innovations' core problem was the sheer volume and fragmentation of e-commerce data. They needed to:

  • Monitor Product Availability and Stock Levels: Ensure their products were consistently in stock and visible to customers across all platforms and regions.
  • Track Pricing Strategies: Understand their own pricing, as well as competitor pricing, to maintain competitiveness and profitability.
  • Analyze Promotional Activities: Keep abreast of all promotions, discounts, and bundles offered by themselves and their rivals.
  • Gather Product Review and Rating Insights: Understand customer sentiment, identify pain points, and leverage positive feedback for product development and marketing.
  • Identify New Product Launches and Trends: Stay ahead of the curve by monitoring new entrants and emerging product trends in their categories.
  • Benchmark Against Competitors: Gain a comprehensive view of competitor strategies, including their product offerings, pricing, promotions, and customer engagement.
  • Regional Variances: Account for differences in product availability, pricing, and promotions across various geographic regions served by Amazon, Walmart, and Target.

The manual collection of this data was not only inefficient but also introduced significant delays, rendering the insights outdated by the time they were compiled. Global Innovations needed an automated, scalable, and accurate data scraping solution that could provide real-time, actionable intelligence.

Actowiz Solutions: The Strategic Partner

Global Innovations engaged Actowiz Solutions due to their proven expertise in large-scale web scraping, commitment to data quality, and ability to handle complex, dynamic e-commerce websites. Actowiz Solutions proposed a comprehensive data scraping strategy tailored to Global Innovations' specific needs, focusing on:

  • Customizable Scraping Infrastructure: Building a flexible and robust scraping architecture capable of handling the unique structures and anti-scraping mechanisms of Amazon, Walmart, and Target.
  • Real-time Data Collection: Implementing systems for frequent, scheduled data collection to ensure the insights were always fresh and relevant.
  • Data Quality Assurance: Establishing rigorous validation processes to ensure the scraped data was accurate, complete, and free from anomalies.
  • Scalability: Designing a solution that could scale effortlessly to accommodate new product lines, additional e-commerce platforms, and increased data volume.
  • Data Delivery and Integration: Providing data in easily digestible formats (e.g., CSV, JSON) and ensuring seamless integration with Global Innovations' existing business intelligence (BI) tools and internal databases.
  • Ethical Scraping Practices: Adhering to all legal and ethical guidelines for data collection, including respecting robots.txt protocols and implementing responsible crawling patterns.

Implementation and Solution Architecture

Navratri Mega Sale Price Tracking

Actowiz Solutions deployed a multi-faceted approach to address Global Innovations' data requirements:

1. Advanced Web Scrapers

Utilizing a combination of custom-built Python-based scrapers with libraries like BeautifulSoup and Scrapy, alongside advanced proxy rotation networks and headless browser technologies (e.g., Selenium), Actowiz Solutions developed robust scrapers for each target platform:

  • Amazon Scraper: Designed to navigate Amazon's complex product pages, variations, and regional storefronts (e.g., Amazon.com, Amazon.co.uk, Amazon.de). It extracted ASINs, product titles, descriptions, bullet points, pricing (including deal prices), seller information, stock status, star ratings, review counts, and individual review text.
  • Walmart Scraper: Engineered to handle Walmart's dynamic product catalog, focusing on in-store vs. online availability, "rollback" pricing, and competitor comparisons. It captured product IDs, names, brands, categories, prices, stock levels, rating summaries, and customer reviews.
  • Target Scraper: Developed to extract data on Target's unique "RedCard" pricing, weekly ad promotions, and product bundles. Data points included product URLs, titles, images, regular prices, sale prices, availability, and customer feedback.
2. Intelligent Proxy Management

To circumvent IP blocking and rate limiting, Actowiz Solutions implemented a sophisticated proxy management system. This involved:

  • Residential Proxies: Utilizing a vast pool of residential proxies to mimic genuine user traffic, significantly reducing the chances of detection and blocking.
  • Proxy Rotation: Automatically rotating IPs at regular intervals or upon detection of a block.
  • Geolocation Targeting: Using proxies from specific geographic locations to ensure accurate pricing and availability data for different regions.
3. Scheduling and Orchestration

A centralized scheduling system was established to orchestrate the scraping jobs. This allowed for:

  • Daily/Hourly Runs: Critical data points like pricing and stock levels were updated multiple times a day, while less volatile data like product descriptions were scraped daily.
  • Error Handling and Retries: Automated mechanisms to detect scraping failures, log errors, and retry failed requests.
  • Monitoring and Alerts: A dashboard to monitor scraper performance, data volume, and identify potential issues, with alerts sent to the Actowiz team for immediate intervention.
4. Data Storage and Processing

Scraped data was initially stored in a temporary NoSQL database (e.g., MongoDB) for flexibility. Post-processing involved:

  • Data Cleansing: Removing inconsistencies, duplicates, and irrelevant characters.
  • Normalization: Structuring data into a consistent format for easier analysis.
  • De-duplication: Identifying and removing duplicate product entries across different scraping runs.
  • Sentiment Analysis (Optional but Recommended): Applying natural language processing (NLP) techniques to customer reviews to gauge overall sentiment and identify key themes.
5. Secure Data Delivery

Processed data was then delivered to Global Innovations via secure SFTP in their preferred JSON format, allowing for seamless integration into their existing data warehouses and BI tools (e.g., Tableau, Power BI).

Sample Data Examples

Here are illustrative examples of the kind of data Actowiz Solutions scraped for Global Innovations:

Amazon Product Data (JSON Format):
{
  "product_id": "B08KGVBY7K",
  "asin": "B08KGVBY7K",
  "product_url": "https://www.amazon.com/example-product-brand/dp/B08KGVBY7K",
  "title": "Global Innovations Super Cereal, Whole Grain, Family Size, 20oz",
  "brand": "Global Innovations",
  "category": "Grocery & Gourmet Food > Cereals & Breakfast Foods",
  "price": "$4.99",
  "prime_eligible": true,
  "in_stock": true,
  "seller_name": "Amazon.com",
  "average_rating": 4.5,
  "total_reviews": 1250,
  "bullet_points": [
    "Made with 100% whole grain oats",
    "Rich in fiber and essential vitamins",
    "Perfect for a healthy start to your day",
    "Family-size box for lasting enjoyment"
  ],
  "description": "Start your day right with Global Innovations Super Cereal...",
  "images": [
    "url_to_image_1.jpg",
    "url_to_image_2.jpg"
  ],
  "last_updated": "2023-10-26T10:30:00Z"
}
Walmart Product Data (CSV Format - Simplified):
product_id,product_name,brand,category,price,on_sale,sale_price,in_stock,average_rating,review_count,item_url,last_updated
123456789,Global Innovations Laundry Detergent Pods,Global Innovations,Household Essentials,12.97,TRUE,10.50,TRUE,4.7,2500,https://www.walmart.com/ip/global-innovations-detergent/123456789,2023-10-26
987654321,Global Innovations Coffee K-Cups 12-Pack,Global Innovations,Coffee,Tea & Cocoa,8.98,FALSE,,TRUE,4.3,800,https://www.walmart.com/ip/global-innovations-coffee/987654321,2023-10-26
Target Product Data (JSON Format - Pricing & Promotion Focus):
{
  "product_url": "https://www.target.com/p/global-innovations-shampoo-24oz",
  "product_title": "Global Innovations Revitalizing Shampoo - 24 fl oz",
  "brand": "Global Innovations",
  "regular_price": "$7.99",
  "sale_price": "$6.79",
  "on_sale": true,
  "redcard_discount_available": true,
  "redcard_price": "$6.45",
  "promotion_type": "Weekly Deal",
  "promotion_description": "Save 15% on all Global Innovations Hair Care products this week!",
  "stock_status": "In Stock",
  "last_checked": "2023-10-26T11:00:00Z"
}

The Impact: Transformative Business Outcomes

The partnership with Actowiz Solutions delivered profound and measurable benefits for Global Innovations:

  • Enhanced Pricing Strategy: With real-time pricing data from all three platforms, Global Innovations could dynamically adjust their prices, respond swiftly to competitor moves, and optimize their profit margins. They identified instances where their products were priced too high or too low relative to the market, allowing for immediate corrective action.
  • Optimized Promotional Campaigns: By tracking competitor promotions and identifying effective strategies, Global Innovations could design more impactful and timely promotional campaigns. They were able to capitalize on emerging trends and avoid costly, ineffective promotions.
  • Improved Product Visibility and Availability: Consistent monitoring of stock levels across Amazon, Walmart, and Target allowed Global Innovations to proactively manage their inventory, minimize out-of-stock situations, and ensure their products were always available to customers. This significantly reduced lost sales opportunities.
  • Deeper Customer Understanding: The analysis of millions of customer reviews provided invaluable insights into product strengths, weaknesses, and unmet customer needs. This intelligence directly fed into product development, marketing messaging, and customer service improvements. For example, by analyzing common complaints about a specific product feature, Global Innovations was able to iterate and release an improved version, leading to higher customer satisfaction.
  • Robust Competitive Intelligence: Global Innovations gained a 360-degree view of their competitive landscape. They could track new product launches by competitors, analyze their marketing tactics, and understand their overall market positioning, enabling them to refine their own strategies and maintain a competitive edge.
  • Data-Driven Decision Making: The shift from anecdotal evidence to comprehensive, real-time data empowered Global Innovations' marketing, sales, and product development teams to make informed decisions with confidence. This led to more agile responses to market changes and a more proactive approach to business growth.
  • Cost and Time Savings: Automating the data collection process drastically reduced the manual effort required, freeing up Global Innovations' internal teams to focus on analysis and strategy rather than tedious data gathering. This translated into significant operational cost savings and increased efficiency.

Conclusion

The collaboration between Global Innovations and Actowiz Solutions stands as a testament to the power of advanced e-commerce data scraping in today's digital economy. By providing accurate, scalable, and real-time insights from Amazon, Walmart, and Target, Actowiz Solutions enabled Global Innovations to overcome their data challenges and transform their market understanding. This strategic partnership allowed the multi-national CPG brand to optimize pricing, refine promotional strategies, enhance product development, and ultimately, solidify its position as a leader in a dynamic and competitive global marketplace. As e-commerce continues to evolve, the ability to harness vast amounts of public web data will remain a critical differentiator, and Actowiz Solutions is poised to be the indispensable partner for brands seeking to unlock this strategic advantage.

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

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

All
Blog
Case Studies
Infographics
Report
thumb
Jan 08, 2026

How a Scraping API for Lowes Product Data Solves Inventory and Pricing Challenges in Real Time?

Discover how a Scraping API for Lowes Product Data helps businesses track inventory, monitor pricing, and make real-time data-driven retail decisions.

thumb

How We Helped a Brand Scrape Woolworths Australia Data to Improve Pricing and Inventory Decisions

Discover how we helped a brand scrape Woolworths Australia to improve pricing accuracy, track inventory in real time, and make smarter retail decisions.

thumb

Driving Smarter Marketplace Decisions with Seller Competition & Pricing Intelligence on Amazon India and Snapdeal

Seller Competition & Pricing Intelligence on Amazon India and Snapdeal helps brands optimize pricing, track rivals, and make smarter marketplace decisions.

thumb
Jan 08, 2026

How a Scraping API for Lowes Product Data Solves Inventory and Pricing Challenges in Real Time?

Discover how a Scraping API for Lowes Product Data helps businesses track inventory, monitor pricing, and make real-time data-driven retail decisions.

thumb
Jan 07, 2026

Amazon India vs Flipkart vs Snapdeal Product Data Mapping – Comparing Prices, Seller Networks, and SKU Match Rates

Amazon India vs Flipkart vs Snapdeal Product Data Mapping helps compare pricing, seller networks, and SKU match rates to uncover marketplace trends and drive smarter ecommerce decisions.

thumb
Jan 07, 2026

How Web Scraping Grab Taxi Data Helps Brands Decode Real-Time Ride Prices, Routes & Demand Trends?

Learn how web scraping Grab Taxi data reveals real-time ride prices, popular routes, and demand trends to help brands make smarter mobility decisions.

thumb

How We Helped a Brand Scrape Woolworths Australia Data to Improve Pricing and Inventory Decisions

Discover how we helped a brand scrape Woolworths Australia to improve pricing accuracy, track inventory in real time, and make smarter retail decisions.

thumb

Extracting GrabTaxi Fare & Availability Data to Improve Ride-Hailing Price Transparency

Discover how extracting GrabTaxi fare and availability data improved ride-hailing price transparency, enabling smarter pricing decisions and better rider trust.

thumb

How We Helped a Hospitality Brand Track 700+ Properties by Scraping Booking.com Hotel Prices in France

Scraping Booking.com hotel prices in France helps brands track real-time rates across 700+ hotels to optimize pricing strategies and stay competitive.

thumb

Driving Smarter Marketplace Decisions with Seller Competition & Pricing Intelligence on Amazon India and Snapdeal

Seller Competition & Pricing Intelligence on Amazon India and Snapdeal helps brands optimize pricing, track rivals, and make smarter marketplace decisions.

thumb

Scraping Top-Selling GrabMart Products - Top Categories & SKUs Across Singapore, Malaysia & Thailand

Detailed research on GrabMart’s top-selling products, highlighting leading categories and SKUs across Singapore, Malaysia, and Thailand for market insights

thumb

City-Wise Demand & Delivery Time Analysis for NIC Ice Cream - Solving Last-Mile Challenges in Quick Commerce

City-Wise Demand & Delivery Time Analysis for NIC Ice Cream reveals how data improves stock planning, delivery speed, and customer satisfaction across markets.

phone
Quick Connect
phone
Quick Connect