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GeoIp2\Model\City Object
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                            [es] => Columbus
                            [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
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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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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                            [fr] => Amérique du Nord
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [validAttributes:protected] => Array
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                    [4] => names
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    [locales: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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            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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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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            [validAttributes:protected] => Array
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [2] => isInEuropeanUnion
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                    [4] => names
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    [traits:protected] => GeoIp2\Record\Traits Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [ip_address] => 216.73.216.110
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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                    [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
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                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
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                )

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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
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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            [validAttributes:protected] => Array
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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
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            [validAttributes:protected] => Array
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                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [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
                    [1] => confidence
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        )

    [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
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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
)
Navratri Mega Sale Price Tracking

About the Client

Location: Boulder, United States

Goal: To develop a scalable, full-stack web scraping solution that can collect real-time market trend data from multiple US retail business websites.

The client wanted to monitor pricing, product availability, store contact details, and location data for competitive and market trend analysis. The objective was to generate actionable insights and deliver the final dataset in clean, structured CSV format, suitable for analytics tools like Power BI and Tableau.

Challenges

Collecting accurate and fresh retail data from multiple sources can be tricky due to:

  • Dynamic page structures – Retail sites often use JavaScript-heavy rendering (React/Vue-based frontends).
  • Varying data formats – Price, availability, and store information differ widely between websites.
  • Data freshness – Pricing and stock data change frequently; daily updates were essential.
  • Legal compliance – Ensuring ethical and compliant scraping following each site's terms.

The client's internal team lacked full-stack scraping expertise, particularly for automating multi-site collection and managing data validation. They partnered with Actowiz Solutions to architect, build, and deploy a robust scraping framework from the ground up.

Project Objectives

Actowiz Solutions was tasked to:

  • Build a Python/Node.js-based web scraping engine for US retail businesses.
  • Extract pricing, product availability, store details (address, contact number, website).
  • Automate daily updates and export structured data in .csv format.
  • Implement error handling, rate limiting, and logging for reliability.
  • Maintain full compliance with data and privacy standards.

Solution Overview

Actowiz Solutions implemented a modular scraping system built on Python (Scrapy + Selenium) for dynamic websites, and Node.js (Puppeteer) for JavaScript-heavy pages.

The architecture allowed multiple websites to be scraped simultaneously, normalized into a single dataset, and updated daily.

Technical Architecture

Navratri Mega Sale Price Tracking
1. Data Collection Layer

Tools Used: Scrapy, Selenium, BeautifulSoup, Puppeteer

Function: Crawlers built per domain to extract data fields:

  • Product Name
  • Price
  • Availability
  • Website URL
  • Address & Phone Number

Each scraper was tuned to respect site load limits (delays and proxy rotation).

2. Data Normalization Layer

Python Scripts: Cleaned raw text into standardized units.

Parsing Logic: Extracted prices using regex and normalized currency (USD).

Availability Mapping: Converted terms like "In stock," "Available soon," "Limited stock" into binary 1/0 indicators.

3. Storage & Output Layer

Data Stored As: CSV and JSON formats

Cloud Integration: AWS S3 for daily file storage, plus optional API delivery.

Schema:

Field Description
Product Name Item title or description
Price (USD) Extracted numeric price
Availability In Stock / Out of Stock
Store Name Retailer Name
Address Store location
Phone Number Contact number
Website URL Direct link to product or store
Last Updated Timestamp for freshness
4. Validation & Quality Control

Actowiz Solutions ensured >97% accuracy through:

  • Duplicate Detection: URL-based de-duplication.
  • Regex Validation: For phone, URL, and numeric fields.
  • Cross-checking: Against store API or Google Business listings (where available).
5. Automation & Monitoring

Daily automated runs using cron jobs on a cloud VM.

Logging pipeline via Elastic Stack to monitor errors and request volumes.

Email alerts for failed tasks or site structure changes.

Infographic

Navratri Mega Sale Price Tracking

Sample Dataset (Simulated Example)

Product Name Price (USD) Availability Store Address Phone Website URL
Organic Avocado (2 pcs) 4.99 In Stock Whole Foods 2320 Pearl St, Boulder, CO +1-303-545-6611 wholefoodsmarket.com
12-Pack Sparkling Water 6.49 In Stock Target 2800 Pearl St, Boulder, CO +1-303-938-1600 target.com
Baby Diapers Size 4 24.99 Out of Stock Walmart 2285 23rd St, Boulder, CO +1-303-444-0500 walmart.com
Men's Running Shoes 79.00 In Stock Dick's Sporting Goods 1845 29th St, Boulder, CO +1-303-245-1122 dickssportinggoods.com
LED Desk Lamp 29.95 In Stock Best Buy 1740 28th St, Boulder, CO +1-303-938-2889 bestbuy.com

Key Metrics (Sample Chart)

A sample visualization summarizing the pilot scrape results:

Metric Result
Total SKUs Collected 2,600+
Average Price Accuracy 98.7%
Availability Detection 96% Correct
Data Freshness 24-hour update cycle
File Delivery Format CSV & JSON
Client Integration Time < 2 Weeks

Implementation Highlights

Dynamic Page Handling

Implemented headless Chrome using Selenium/Puppeteer for sites with heavy JavaScript rendering.

Managed scrolling, lazy-loading, and cookie modals.

Full Compliance

Actowiz's solution adhered to each website's robots.txt and ethical scraping norms.

Limited requests per second, avoided blocked endpoints, and scraped only public data.

Data Enrichment

Integrated Google Maps API to verify addresses and zip codes for accuracy.

Parsed phone numbers with country-code standardization using Python's phonenumbers library.

Front-End Interface (Optional Add-On)

Basic web dashboard using Flask (Python) showing category filters, recent crawls, and CSV download options.

Results & Insights

a. Market Coverage:

Data captured from 50+ retail businesses across the United States, including categories like grocery, electronics, apparel, and home goods.

b. Accuracy & Freshness:

Daily updates ensured live visibility of market shifts.

Price accuracy validated at >98% through random sampling.

Missing data flagged automatically for re-crawl.

c. Operational Impact:

Reduced manual market research hours by >85%.

Enabled real-time trend dashboards for the client's internal analysts.

Delivered actionable insights like price fluctuations, regional stock shortages, and contact mapping for supplier expansion.

Business Impact

After deployment, the client gained:

  • Faster Decision Making: Real-time CSV exports enabled analysts to compare competitors' prices instantly.
  • Retail Network Expansion: Verified address and contact data helped identify 120+ potential partnership stores.
  • Improved Forecasting: Weekly datasets revealed pricing patterns by region and product category.
  • Lower Operational Costs: Automation replaced 10+ manual research hours daily.

Example Analytical Insights

Category Avg Price Avg Discount In-Stock % City Coverage
Grocery $5.75 8% 95% 28
Apparel $43.20 14% 91% 32
Electronics $185.60 11% 88% 24
Home Goods $27.40 9% 93% 26

Insight: Apparel and Electronics had the highest fluctuation trends week-over-week, signaling promotion-based volatility in urban US stores.

Tools & Technologies Used

Function Tools
Web Scraping Scrapy, BeautifulSoup, Selenium, Puppeteer
Backend Logic Python, Node.js
Scheduling Cron, AWS Lambda
Storage PostgreSQL, AWS S3
Data Export CSV, JSON
Validation Pandas, Regex, phonenumbers
Visualization Power BI, Google Data Studio

Compliance & Ethical Framework

Actowiz Solutions follows global best practices:

  • Only publicly available data collected.
  • Transparent with clients about legal and ethical constraints.
  • Complies with US FTC data usage norms and GDPR standards where applicable.
  • Maintains audit logs of each crawl for accountability.

Why Choose Actowiz Solutions

  • Full-stack expertise in Python, Node.js, and data engineering.
  • Experience across 25+ industries – retail, FMCG, travel, healthcare, finance, and automotive.
  • Scalable infrastructure supporting millions of URLs daily.
  • End-to-end service: from scraping to analysis dashboards.
  • Focus on compliance, performance, and quality.

Client Testimonial

“Actowiz Solutions delivered exactly what we needed — accurate, fresh market data in a clean format. Their team managed compliance, scaling, and validation seamlessly. The automation has completely changed how we analyze retail trends.”

— Operations Head, Boulder, USA

Future Scope

  • Add Real-Time API Feeds – Streaming retail trend data directly into the client's analytics engine.
  • Sentiment Analysis Integration – Combine scraped review data with price movements.
  • Predictive Modeling – Use historical trend data to forecast market price shifts.
  • Geo-based Insights – Map heat zones for pricing competitiveness in the US retail landscape.

Conclusion

This case study demonstrates how Actowiz Solutions engineered a full-stack, compliant, and automated web scraping system to collect real-time market trend data across the US retail ecosystem.

From raw web pages to analytics-ready datasets, the client now benefits from structured CSV outputs, accurate store-level insights, and scalable technology designed for future growth.

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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