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GeoIp2\Model\City Object
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    [continent:protected] => GeoIp2\Record\Continent Object
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                            [de] => Nordamerika
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                    [iso_code] => US
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                            [de] => USA
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                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
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            [validAttributes:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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    [registeredCountry:protected] => GeoIp2\Record\Country Object
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
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                            [de] => USA
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                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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    [traits:protected] => GeoIp2\Record\Traits Object
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                    [ip_address] => 216.73.216.157
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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                    [1] => autonomousSystemOrganization
                    [2] => connectionType
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                    [8] => isHostingProvider
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
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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
                        (
                            [de] => Columbus
                            [en] => Columbus
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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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    [location:protected] => GeoIp2\Record\Location Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
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            [validAttributes:protected] => Array
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                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
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                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [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
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                    [record:GeoIp2\Record\AbstractRecord:private] => Array
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                            [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
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                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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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
)
Real-Time Regional Insights with Customizable E-commerce Dashboards

Client Overview

  • Client Name: Confidential (U.S.-based mid-sized omnichannel retailer)
  • Industry: Retail – Electronics & Home Goods
  • Location: Operations across Illinois, Indiana, and Michigan
  • Channels: Online store, Amazon Seller Central, Walmart Marketplace, 6 physical stores
  • Duration: 6 months (2024 Q3–Q4)
  • Data Partner: Actowiz Solutions

Business Challenge

The-Client

The client faced three major operational bottlenecks:

1. Inventory sync issues between their online platforms (Amazon, Walmart, internal eCommerce) and physical stores.

2. Unpredictable delivery times leading to low customer satisfaction and frequent returns.

3. Inefficient warehouse utilization, with either overstock or unfulfilled demand in key cities like Chicago and Indianapolis.

They needed a real-time, automated solution to monitor inventory levels and delivery performance across Amazon Fulfillment Centers and Walmart store networks.

Actowiz Solutions Approach

Actowiz Solutions deployed an integrated data scraping system to collect and process:

  • SKU-level inventory data from Amazon.com (FBA) and Walmart.com
  • ZIP-code-based delivery time estimates
  • Warehouse restock timelines
  • Localized stock availability in nearby cities
  • This data was synced via a custom dashboard and API into the client’s ERP and warehouse management systems for automated decision-making.

    Step 1: Inventory Sync with Amazon & Walmart

    We built custom SKU-matching crawlers to extract the following data from Amazon and Walmart daily:

    Platform SKU Stock Status Fulfilled By Price (USD) ETA (ZIP 60601)
    Amazon ELEC-BX101 In Stock Amazon FBA 49.99 1 Day
    Walmart ELEC-BX101 Low in Store Walmart DC 47.99 2 Days
    Amazon HOM-DT230 Out of Stock Merchant N/A N/A

    This real-time feed allowed the client to:

    • Instantly reflect stock-outs or low inventory on their own site.
    • Auto-deactivate products if Walmart or Amazon ran out.
    • Sync availability across Shopify, Amazon Seller Central, and internal POS systems.

    Result:

    Order cancellations dropped by 18%, and cart abandonment due to “out of stock” errors reduced by 23% in 60 days.

    Step 2: Delivery Time Optimization

    By scraping estimated delivery times from Amazon.com and Walmart.com for over 500 ZIP codes in Illinois, Indiana, and Michigan, Actowiz Solutions created a delivery-time intelligence dashboard.

    Sample Delivery Time Dataset:

    Platform SKU ZIP Code Delivery ETA Fulfillment Center Prime Eligible
    Amazon HOM-DT230 60601 2 Days Joliet, IL Yes
    Walmart HOM-DT230 60601 3 Days Store #456 – Cicero No

    Insights uncovered:

    • Amazon’s Joliet, IL FBA warehouse delivered 30% faster than client’s in-house fulfillment.
    • Walmart stores showed delayed dispatch on weekends.

    Using this data, the client:

    • Redirected high-demand SKUs to Amazon FBA to meet Prime delivery expectations.
    • Scheduled same-day dispatch for ZIPs with historically delayed Walmart deliveries.
    • Re-routed orders to alternative fulfillment centers during peak traffic or storms.

    Result:

    Average delivery time reduced from 3.5 days to 1.9 days, and delivery SLA compliance improved by 34%.

    Step 3: Warehouse Planning Using Stock Trends

    Actowiz Solutions implemented stock frequency analysis using scraped historical inventory data for 6 months.

    Insights Derived:

    • SKU ELEC-BX101 ran out at Walmart every Saturday evening, indicating high weekend demand.
    • Amazon restocked SKU HOM-DT230 every Tuesday, enabling predictive planning for Tuesday-Wednesday shipping.
    • 70% of customer complaints came from ZIPs that lacked localized inventory.

    Warehouse Allocation Dashboard View:

    SKU Location Avg Weekly Demand Overstock % Suggested Action
    ELEC-BX101 Chicago IL 1,200 units 5% Keep steady replenishment
    HOM-DT230 Peoria IL 300 units 42% Divert stock to Chicago
    ACC-CBL999 Joliet IL 50 units 3% Move to clearance stock

    Based on these insights, the client revised their warehouse plans:

    • Consolidated underperforming SKUs in 2 of 6 warehouses.
    • Increased forward stocking for high-turnover SKUs in Joliet and Naperville.
    • Automated replenishment triggers based on competitor restock cycles.

    Result:

    Warehouse storage costs dropped by 21%, and order fulfillment rates improved by 26%.

    Key Features Delivered by Actowiz Solutions

    Feature Description
    Amazon & Walmart Scraper Near real-time scraping with SKU/ZIP targeting
    Historical Price & Stock Trends 90-day view for restock pattern detection
    Fulfillment Heatmap Visualization of Amazon/Walmart delivery speeds per ZIP
    ERP/API Integration Seamless feed into internal inventory & WMS systems
    Anomaly Detection Alerts Notifications for unusual price/stock behavior

    Tech Stack Used

    • Python + Scrapy for scalable crawling
    • Rotating proxies + CAPTCHA bypassing
    • PostgreSQL for structured inventory and trend data
    • Power BI / Tableau connectors for data visualization
    • Custom REST API for ERP integration

    Client Feedback

    “Actowiz Solutions transformed our operations. Their scraping engine made our data actionable—we now make restocking decisions in real-time, not weeks later. From delivery SLAs to shelf planning, we’re running smarter than ever”

    — Director of Operations, Midwest Retailer

    Business Impact Summary

    Metric Before After (6 Months)
    Avg Delivery Time 3.5 days 1.9 days
    Inventory Stockouts 12.4% 6.1%
    Order SLA Compliance 61% 95%
    Warehouse Utilization Efficiency 64% 87%
    Cart Abandonment (Stock Issue) 18.7% 5.2%
    Start Your Retail Intelligence Journey
    Contact Us Today!

    Conclusion

    With Actowiz Solution's web scraping and data intelligence tools, the client gained a synchronized inventory, predictive delivery insights, and optimized warehouse workflows across three major U.S. states.

    This case proves how data scraping, when implemented strategically, bridges the operational gap between digital platforms and physical logistics—leading to measurable business outcomes.

    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

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