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Client Background

The client needed a streamlined solution to collect and process part number data from multiple websites. Their existing method relied on manual data entry, which was slow, prone to errors, and increasingly difficult to manage as the volume of information grew. Extracting, analyzing, and organizing this data required significant time and effort, limiting their ability to make timely decisions. To address these challenges, they required automated web scraping tools capable of mechanizing these tasks while ensuring accuracy and adaptability.

Challenges & Goals

The following were some of the challenges and goals of the project:

Challenges

Collecting part number data manually was time-consuming and required a considerable amount of effort. This method not only slowed down the process but also led to inconsistencies, making it difficult to maintain accuracy.

Many websites posed additional challenges, such as requiring logins, incorporating captchas, and using dynamically loaded content, all of which complicated data extraction. These barriers made it difficult to gather information efficiently and required constant manual adjustments.

Even when data was successfully retrieved, it often lacked a structured format. This made it challenging to compare and analyze, further slowing down decision-making processes. As the need for data grew, the limitations of manual collection became even more apparent, highlighting the necessity for a more effective and scalable approach.

Goals

The first goal of the project was to create a system for web scraping using Selenium, SerpApi, and Python to collect part number data from multiple websites. By automating this process, the aim was to reduce reliance on manual entry and improve the reliability of data collection.

Another key objective was to apply AI-based processing to analyze and organize the extracted data. The system needed to identify alternate and equivalent part numbers, allowing for a more comprehensive understanding of available components and their relationships.

Ensuring data retrieval remained accurate and consistent despite website restrictions was also a priority. The question was: How to bypass captchas in web scraping? The solution also had to navigate logins and dynamically loaded content without disrupting the flow of information.

Finally, the extracted data needed to be presented in structured formats, such as CSV and Google Sheets. This would allow for seamless integration into the client’s existing workflows, making the information easily accessible and actionable.

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Conclusion

This project improved how the client collects and processes data, replacing manual methods with an automated system that organizes and structures information effectively. By combining web scraping with AI, Relu Consultancy provided a reliable solution tailored to the client’s needs. The result was a more accessible, accurate, and manageable data collection process, allowing for better decision-making and reduced workload.

Implementation & Results

A custom web scraping workflow was built using SerpApi, Selenium, and Python. The system was designed to handle various website structures, extract part numbers accurately, and minimize errors. With this approach, data retrieval became faster and required less manual input.

AI-Powered Data Structuring

Once the data was collected, Gemini 1.5 Pro AI processed and structured the information. This AI-powered data extraction:

• Identified alternate and equivalent part numbers, ensuring a broader scope of data.
• Formatted the extracted information into structured files for better usability.
• Generated reports in CSV and Google Sheets, making data more accessible for analysis.

Reliable System for Long-Term Use

To maintain accuracy and consistency, the system was built to:

• Adjust to changing website structures, reducing the need for constant manual updates.
• Bypass obstacles like logins, captchas, and dynamic content without compromising reliability.
• Require minimal manual intervention while being adaptable to increasing data demands.

Business Impact

By implementing this system, the client saw significant improvements in their workflow:

• Reduced manual data collection, lowering errors and saving valuable time.
• Faster data retrieval, enabling quicker responses to business needs.
• Structured insights made data easier to analyze, improving decision-making.
• A system built to handle growing data needs, ensuring continued usability.

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Key Insights

• Reducing manual processes saves time and minimizes errors.
• AI-powered structuring makes data more practical for analysis.
• Addressing website restrictions ensures reliable data extraction over time.
• Systems that adapt to growing data requirements remain useful in the long run.

The Client Background: What We’re Working With

The client operates in the retail sector, where real-time access to structured competitor data plays a critical role in pricing strategy and catalog decisions. For businesses aiming to stay competitive, using web scraping for e-commerce market research offers a distinct advantage.

Previously, product data was gathered manually, which led to inefficiencies, outdated insights, and incomplete records. Team members had to revisit product pages repeatedly to record changing prices and availability. This approach was both labour-intensive and error-prone, creating delays in internal processes and limiting visibility into competitor activity.

A high-volume, automated solution was required to scale efforts and extract timely market insights from web scraping, not just once, but on a recurring basis.

Challenges & Objectives

The following were the challenges we identified during the execution of the project:

Challenges

• Navigating deep and paginated category hierarchies
• Dealing with inconsistent templates and missing fields
• Bypassing anti-bot mechanisms without site bans or CAPTCHAs

Objectives

• Develop a reliable ecommerce scraper for over 650,000 records
• Store outputs in a clean CSV format for immediate integration
• Maintain structural integrity even when fields (like price or currency) are missing
• Enable scalable, undetected scraping using rotating proxies and headers
• Prepare data in a format suitable for catalog updates and price monitoring dashboards

Conclusion

Relu Consultancy’s solution demonstrated how a purpose-built ecommerce scraper can support high-volume e-commerce data extraction without triggering anti-scraping systems. With clean, structured outputs and built-in flexibility, the system enables businesses to generate accurate, up-to-date product datasets for intelligence and operational needs.

By adopting e-commerce product scraping for market insights, the client now has the infrastructure for smarter catalog management, competitive price tracking, and ongoing business intelligence. The system is already prepared for recurring runs and can scale to support additional categories or new platforms in the future.

Approach & Implementation

Let’s get into the approach we crafted for the client:

Website Analysis

The team first analyzed the platform’s structure to identify key product and category URLs. Pagination rules were documented, and field-level consistency was assessed. Field mapping allowed the developers to account for variations in the placement of prices, descriptions, and availability statuses. This groundwork ensured accurate e-commerce product scraping for market insights.

Scraping Development

The scraping tool was built in Python using BeautifulSoup for parsing HTML. It extracted:

• Product names
• Prices and currencies
• Availability
• Product descriptions

Each page was processed with logic that handled deep pagination and multiple templates. Custom handling was added for fields that were sometimes missing or placed inconsistently across product listings. This allowed the system to deliver structured e-commerce data extraction output without redundancy or confusion during downstream analysis.

Anti-Scraping Strategy

To achieve real-time e-commerce data scraping without interruption, the system used:

• Rotating user agents, headers, and IPs via proxy services
• Incidental delays between requests to simulate human browsing patterns
• Graceful error handling and retry logic to account for intermittent blocks or broken links

These steps helped avoid blocks while maximizing throughput. Additionally, session tracking was omitted to prevent the scraper from being flagged as a bot due to repetitive access patterns.

Execution & Delivery

The system successfully scraped 642,000+ unique product listings. Any records missing essential data, like price or currency, were clearly marked for transparency. The CSV output included flags for null or inconsistent fields, enabling downstream teams to filter or correct records as needed.

The final data was structured and shared via a Google Drive link for immediate access and review. The format supported direct import into analytics dashboards and catalog systems.

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Benefits

Implementation of the e-commerce product scraping system gave the client many clear advantages:

Time Savings: It eliminated the need for manual data collection leaving the team free to focus on analysis instead of repetitive tasks.

Real-time Insights: The system enabled faster price comparisons and inventory checks against rivals.

Higher Accuracy: The system ensured higher accuracy by reducing the errors that happened due to manual entry and inconsistent data gathering.

Scalability. The system can handle thousands of product records while requiring minimum manual intervention.

Structured Output: It delivers clean CSV files that are ready for dashboards, analytics, and catalog systems.

Operational Efficiency: Apart from freeing up internal resources, it led to improvement in decision making speed.

Automated Catalog Management: Products could be listed, updated, or removed automatically without requiring any human input.

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Valuable Market Insights

The system did not just extract data. Instead, it created a foundation for actionable insights. Some of the valuable market intelligence gained included the following:

• Price Benchmarking: The ability to compare own product prices with competitors across various categories.
• Inventory tracking: Monitoring products that competitors frequently ran out of to reveal demand surges.
• Trend Identification: Spotting new product launches early and tracking how fast they gain traction.
• Promotional Analysis: Detecting discount cycles, seasonal offers, and bundles used by competitors.
• Category gaps: Identifying product categories that had high demand but limited competitor presence.
• Consumer demand signals: Frequent stockouts and price increases gave indirect signals about fast-moving items.

Besides helping the client adjust pricing strategies, these insights empowered them to improve catalog decisions and identify untapped market opportunities.

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Use Cases for Various Industries

While this project was created for the retail sector, the principles of automated large-scale data extraction may be applied to other industries as well. This solution can address diverse business needs by adapting the scraping logic, data fields, and antiblocking strategies:

Ecommerce & Retail: Scraping competitor product catalogs, tracking daily or seasonal price changes, and identifying new entrants. Apart from allowing retailers to adjust prices quickly, it lets them manage inventory more efficiently, and launch targeted campaigns.

Travel & Hospitality: Gathering hotel rates, flight prices, package details, and seasonal availability from multiple booking websites. It allows agencies to offer competitive pricing, optimize packages, and respond to changing demand patterns in real time.

Real Estate: Extracting property listings, rental prices, neighborhood trends, and project availability from real estate websites. This can be used by agencies and investors to track market fluctuations, analyse demand, and generate comparative reports for clients.

Automotive: Monitoring car dealership websites for new and used vehicle listings, spare parts availability, and latest offers. Besides competitive analysis, it supports pricing intelligence, and stock management for dealers and resellers.

Consumer Electronics: Tracking online availability, price drops, and reviews for gadgets and appliances. It can be used by brands to identify new launches by competitors, adjust promotional campaigns, or ensure price parity across marketplaces. 

Market Research & Analytics Firms: Aggregating data from multiple sectors for creating market reports, forecasts, and performance benchmarks. Automated scraping ensures that insights are based on the most recent and accurate information.

Healthcare & Medical Supplies: Monitoring online suppliers for essential medical equipment, pharmaceuticals, and consumables. It can be used by hospitals and procurement teams to secure better deals and maintain uninterrupted supply.

Key Takeaways

At the end of the project, the following were the main lessons relevant for businesses and decision makers:

• Time and efforts saved: Automating product data collection eliminates the need for manual research. It also allows the team to focus on smarter tasks.
• Better Market Awareness: Businesses can stay ahead with regular access to competitor prices, stock levels, and promotions.
• Accurate Insights: While clean and accurate data reduces errors, it also supports confident decision making. 
• Scalability Matters: A system which can handle hundreds of thousands of products ensures growth without any extra effort.
• Actionable Outcomes: Beyond information collection, the solution provided practical insights for pricing, catalog updates, and market opportunities.

Client Background

HP is a well-recognized technology company that makes and sells computers and printers. Its business is divided into two main segments: Personal Systems and Printing. Through its personal systems segment, the company sells desktops, laptops, and work stations, catering to both individual consumers and businesses.

In 2015, the original Hewlett Packard company split into two entities: HP and HPE. HP Inc. retained the computer and printer business. The company serves millions of customers globally and has established a solid distribution network. It offers its products through brand stores, online, and directly to businesses.   

As a global technology brand, HP continually looks for ways to extend its brand reach through authentic initiatives. Employees and channel partners are often the most credible advocates of a brand, and HP aimed to leverage this network to amplify its message organically. 

However, tracking social media participation across multiple platforms manually was proving to be unsustainable and inefficient. HP needed a scalable employee advocacy platform that could automate data collection, standardize scoring, and visualize performance in real time to maintain momentum throughout the campaign period. 

Challenges & Objectives

Before diving into the solutions, let’s understand the key challenges we faced and the main objectives we aimed to achieve through this project:

Challenges

Multi-Platform Complexity: The project required capturing posts from three different social media platforms, each with its own set of data structures, APIs and privacy restrictions. Consistent data collection and validation became technically challenging due to this.

Content Quality Control: Filtering out irrelevant posts, duplicates, and unrelated hashtags became challenging at scale. Users experimented with different content formats making the task all the more complex.  

Motivation and Fairness: It was critical to maintain trust in the scoring system. Participation and overall engagement could be reduced by any perceptions of bias or due to errors.

Scalability Issues: Besides being time consuming, manual monitoring was not sustainable for the campaign’s global reach. An automated system was required to handle large volumes of content daily without errors.

Engagement Fatigue: Another major concern was to sustain participant interest over the entire campaign duration. Employees and partners could lose motivation without visible recognition and timely updates.

Data Transparency for Stakeholders: The marketing team needed accurate and real-time insights into campaign performance. Manual spreadsheets could cause delays and also lacked visualisation for quick decision making.

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Objectives

Automate Campaign Monitoring: The project required the creation of a system that could seamlessly capture, validate, and track hashtag based posts across the three social media platforms: Facebook, Instagram, and LinkedIn.

Standardise Scoring: A transparent, rules-based scoring matrix was needed to ensure fairness and encourage quality over quantity.

Promote Healthy Competition: A leaderboard , which is updated in real time, motivating employees and partners with gamification and recognition.

Deliver Actionable Insights: Another important objective was to provide the HP marketing team with a comprehensive dashboard that offered data visualization, performance tracking, and campaign reporting. 

Ensure Scalability and Reusability: The project also required designing a framework, which could be adapted for future campaigns, other regions, or extended to various internal initiatives.

Strengthen Advocacy Culture: Another critical objective was to encourage employees and channel partners to become brand advocates. This would amplify HP’s reach through their trusted networks.

Improve Efficiency: We needed to minimize manual effort and reduce dependency on human intervention such that the focus remained on strategy and engagement. 

Conclusion

Relu Consultancy’s end-to-end solution enabled HP to run a high-impact social media engagement campaign that combined automation, transparency, and user motivation. The system successfully tracked and rewarded internal and partner contributions across multiple platforms, transforming what would have been a manual and error-prone task into a streamlined, data-driven campaign.

By promoting brand content through trusted networks and recognizing active contributors in real time, HP was able to increase its organic visibility and foster stronger community involvement. The campaign set a benchmark of social media gamification for future employee and partner-driven marketing initiatives.

Approach & Implementation

Here is the step-by-step strategy and actions taken to successfully plan, execute, and complete the project:

Hashtag & Platform Criteria

The project began by working closely with HP to define the list of approved campaign hashtags. These would serve as the primary filters for identifying eligible content. The system was designed to monitor posts on Facebook, Instagram, and LinkedIn, using keyword-matching logic to verify inclusion of valid hashtags.

Automated Scraper System

Relu Consultancy developed a platform-specific scraping system that collected social media posts once every 24 hours. The scraper was customized to handle each platform’s formatting and privacy constraints. It extracted relevant data, including:

• Date and time of the post
• Post content
• Associated hashtags
• Poster identity (when available)

The automation eliminated the need for manual tracking and reduced the chances of human oversight, functioning as a social media automation dashboard for enterprise brands.

Scoring Logic & Matrix

A flexible point system was introduced to quantify participant contributions. For instance, a valid post on Instagram might earn a specific number of points, while an additional post on LinkedIn would be rewarded similarly. 

Points were calculated based on the number of valid posts per participant and were automatically updated in the system. This operated as a part of an employee advocacy tracking system with real-time scoring.

Only posts that met predefined criteria were awarded points, ensuring quality control and preventing exploitation through repetitive or unrelated content.

Dashboard & Leaderboard

Relu Consultancy delivered two key visual components:

1. Admin Dashboard for HP: This interface allowed HP’s marketing team to review participation statistics, post volumes, and platform-specific breakdowns.

2. Leaderboard for Participants: A public-facing interface ranked contributors by total score, helping build excitement and encouraging healthy competition. The leaderboard was refreshed daily, reflecting the latest validated activity.

Together, these tools supported HP’s vision for brand engagement automation with as much transparency as possible.

Maintenance & Updates

Daily monitoring and updates were central to the campaign’s success. The system was configured to scrape new data every morning, validate it against scoring rules, and refresh both dashboards. Feedback loops from HP’s team were used to refine filtering logic and address edge cases, such as duplicate hashtags or platform-specific anomalies.

Results & Outcomes

The following results highlight the key achievements and impact of the project

• Over the course of the campaign, the system tracked thousands of valid posts across the three platforms, all without manual intervention
• Participation among internal employees and channel partners increased significantly due to real-time recognition and visibility.
• The gamified structure and transparent leaderboard fostered sustained engagement, with several participants competing actively until the campaign’s end.
• HP was able to access a clean, structured dataset for performance reporting and future campaign planning.

Benefits 

Here are the main benefits of the HP Social Tracker system: 

• Real-time engagement tracking: Daily updates incentivize participation and keep momentum high. 
• Scalable framework: The system can be reused for future campaigns or adapted for other internal initiatives. 
• Custom dashboards: Tailored interfaces for admins and participants enable transparency and control. 
• Automation reduces overhead: Manual effort is minimized, freeing teams to focus on strategy and community building. 
• Data-driven insights: Structured reporting enables better evaluation of campaign effectiveness and future planning. 

These advantages collectively ensured that HP's campaign was not only successful in the short term, but also established a scalable model for future engagement initiatives.

Use Cases by Industry

The HP Social Tracker can support different industries based on their specific communication and engagement goals:

Technology & IT Services:

• Track employee advocacy and engagement during product launches or internal branding initiatives.
• Encouraging partners and resellers to share case studies, client success stories with the aim to strengthen market credibility.

Retail:

• Motivate in-store employees and franchise partners to share promotional campaigns, seasonal sales, and store opening updates. 
• Showcase customer experiences and product demonstrations through employee generated content.

Education:

• Boost visibility for campus events, admission campaigns, and alumni stories by engaging students and staff.
• Encouraging student ambassadors to share experiences during placement drives or academic competitions. 

Healthcare: 

• Promoting awareness campaigns for health checkups, vaccination drives, or hospital initiatives through employee and doctor advocacy. 
• Encouraging staff to highlight success stories, CSR activities, or patient education content.

Finance:

• Strengthening internal communications and brand image by gamifying content sharing during key business quarters.
• Encouraging employees to share updates about financial literacy programs, investment products, or corporate achievements.

Hospitality & Travel:

• Encouraging hotel staff, travel agents, and tour operators to share guest experiences, property highlights, and cultural activities. 
• Promoting seasonal offers, new packages, and local experiences through employee advocacy.

Manufacturing & Industrial:

• Showcasing factory operations, safety initiatives, and CSR activities with the aim to build transparency and trust.
• Enabling distributors and channel partners to share updates on product launches and training workshops.

Non-Profit & NGOs:

• Mobilize volunteers and staff to share stories from fieldwork and fundraising events.
• Using the leaderboard concept to motivate communities to amplify awareness drives.

Government & Public Sector:

• Encouraging employees to share updates related to national campaigns, digital initiatives, or public outreach programs.
• Use advocacy tracking during awareness weeks to measure citizen engagement.

These examples show how the tracker can be adapted for real-world engagement, regardless of sector or campaign focus. 

Key Takeaways

Here are the main insights and lessons from the project:

• Gamification Works: Introducing a leaderboard and reward structure was effective in motivating participants and sustaining activity.
• Automation Was Essential: Without an automated scraper and scoring engine, real-time tracking at this scale would have been unmanageable.
• Visibility Increases Engagement: Participants were more likely to contribute consistently when they could track their ranking and receive immediate feedback.
• Scalability Is Now Possible: The system built by Relu Consultancy can be reused and scaled for future campaigns or extended to other regions and user groups.

The Client Background

The client manages property inquiries for real estate listings. Each incoming inquiry had to be responded to in a timely, relevant, and personalized manner. Before this project, lead data was manually extracted and sorted, a process prone to delays, errors, and duplicate follow-ups.
To scale their outreach while maintaining a personal touch, the client needed a way to automate lead follow-up emails using Zapier while minimizing manual intervention.

Challenges and Objectives with the Project

The following are some challenges that we ran into during execution:

Challenges

-The default Zapier email parser struggled with consistently extracting data from semi-structured inquiry emails.
Without a validation layer, follow-up emails risked being sent multiple times to the same lead.
The process had to maintain personalization while scaling automated responses.

Objectives

Improve parsing logic to extract leads from emails with Zapier Email Parser
Validate each new lead against a live database before sending communication
Use Zapier lead automation to deliver booking links and tailored messages without delays

Conclusion

Relu Consultancy delivered a reliable, scalable solution for Zapier lead automation that improved how the client responded to property inquiries. The system combined email parsing, lead validation, and tailored communication to offer fast, professional responses without human oversight.

By using email automation tools and enhancing the Zapier email parser, the client was able to reduce turnaround times, prevent errors, and improve lead conversion outcomes. The project stands as a model for how to automate lead follow-up emails using Zapier in a structured, error-free, and scalable manner.

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Our Tailored Approach & Implementation

Here’s how we structured our approach:

Email Parsing & Extraction

The first step involved identifying emails with the subject line “New Interest.” Relu Consultancy developed a Python script that extended the Zapier email parser's functionality by applying pattern recognition to extract:

• Lead name
• Phone number
• Email address
• Property or building of interest

This combination of email automation tools and custom scripting offered more control over how the lead details were captured. It also provided a clear example of how to use Zapier Email Parser for lead management in real-world applications.

Filtering & Validation

Once lead details were extracted, the system checked whether a valid email address was present. If missing, the process was halted to avoid incomplete follow-ups. When an email was found, it was cross-checked with the client’s lead database.

This ensured that follow-up emails were only sent to new leads, preventing duplicate outreach and protecting the client’s credibility.

Automated Email Sending

After validation, the system triggered a personalized follow-up email containing a property tour booking link. The message used the extracted details to address the lead by name and reference the property of interest, keeping the tone relevant and human.

All new lead data was logged into the database to support future validation, analytics, or CRM usage. This process showed how to automate lead follow-up emails using Zapier while preserving a professional tone.

Results & Outcomes

Upon implementation, we observed the following key results:

• Lead data was accurately extracted using enhanced logic layered over the Zapier email parser.
• Real-time follow-up emails were delivered without delay or duplication.
• The manual effort was drastically reduced, freeing up internal resources.
• The client improved their response time and credibility with prospective buyers.
• The project demonstrated the effective use of Zapier lead automation to manage high volumes of inquiries.

Benefits:

Implementation of this automated lead follow up system led to several measurable and strategic benefits:

Faster Lead Response: The client could respond to inquiries instantly by automating parsing and follow up. It significantly improved the chances of engaging qualified leads

Reduced Manual Effort: The team did not need to spend hours extracting and validating lead data. It freed them up for higher value tasks, such as client relationship building. 

Improved Accuracy: Errors while extracting contact details were minimised with enhanced parsing logic. It ensured messages reached the right people with the right context.

Elimination of Duplicate Outreach: The validation layer made sure that each lead received only one relevant follow-up. It helped to protect brand reputation and avoid customer frustration.

Personalized at Scale: The system maintained a human tone in all communications. It helps create trust and rapport without manual intervention.  

Scalability: The automated process can handle growing enquiry volumes without affecting speed or quality, which supports the client’s long term growth. 

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Use Cases Across Industries:

This project was designed for a real estate business. However, the same approach can be adapted to multiple sectors:

E-Commerce: Extract customer inquiries automatically from order-related emails for sending personalized product recommendations or status updates.

Travel and Hospitality: Parse booking inquiries from email to send instant confirmations, itineraries, or upsell offers for tours and activities.

Education: For processing student admission or course inquiries from email to send tailored program details with application links.

Healthcare: Extract patient queries from email and send appointment booking links or relevant resources.

Professional Services: Capture potential client inquiries to automate responses with service details, booking links, or case studies.

Event Management: Parse event registration emails for sending immediate confirmation, schedules, and ticket details.

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Key Takeaways

The following were our core learnings and takeaways from the undertaking:

• Using email automation tools alongside custom Python scripting extends the value of the Zapier email parser.
• Real-time validation protects against redundant outreach and maintains a consistent lead experience.
• This project is a clear example of how to use Zapier Email Parser for lead management in real estate or similar industries.
• By combining automation with personalization, it’s possible to extract leads from emails with Zapier Email Parser and act on them intelligently.

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Project Overview

This project focused on building an e-commerce scraper capable of extracting over 650,000 product records from a large online retail platform. The solution was designed to support catalog management, pricing intelligence, and market insights from web scraping.

By automating data collection, the client moved away from manual efforts to a scalable system tailored for e-commerce data extraction.

The Client Background: What We’re Working With

The client operates in the retail sector, where real-time access to structured competitor data plays a critical role in pricing strategy and catalog decisions. For businesses aiming to stay competitive, using web scraping for e-commerce market research offers a distinct advantage.

Previously, product data was gathered manually, which led to inefficiencies, outdated insights, and incomplete records. Team members had to revisit product pages repeatedly to record changing prices and availability. This approach was both labour-intensive and error-prone, creating delays in internal processes and limiting visibility into competitor activity.

A high-volume, automated solution was required to scale efforts and extract timely market insights from web scraping, not just once, but on a recurring basis.

Challenges & Objectives

The following were the challenges we identified during the execution of the project:

Challenges

• Navigating deep and paginated category hierarchies
• Dealing with inconsistent templates and missing fields
• Bypassing anti-bot mechanisms without site bans or CAPTCHAs

Objectives

• Develop a reliable ecommerce scraper for over 650,000 records
• Store outputs in a clean CSV format for immediate integration
• Maintain structural integrity even when fields (like price or currency) are missing
• Enable scalable, undetected scraping using rotating proxies and headers
• Prepare data in a format suitable for catalog updates and price monitoring dashboards

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Conclusion

Relu Consultancy’s solution demonstrated how a purpose-built ecommerce scraper can support high-volume e-commerce data extraction without triggering anti-scraping systems. With clean, structured outputs and built-in flexibility, the system enables businesses to generate accurate, up-to-date product datasets for intelligence and operational needs.

By adopting e-commerce product scraping for market insights, the client now has the infrastructure for smarter catalog management, competitive price tracking, and ongoing business intelligence. The system is already prepared for recurring runs and can scale to support additional categories or new platforms in the future.

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Approach & Implementation

Let’s get into the approach we crafted for the client:

Website Analysis

The team first analyzed the platform’s structure to identify key product and category URLs. Pagination rules were documented, and field-level consistency was assessed. Field mapping allowed the developers to account for variations in the placement of prices, descriptions, and availability statuses. This groundwork ensured accurate e-commerce product scraping for market insights.

Scraping Development

The scraping tool was built in Python using BeautifulSoup for parsing HTML. It extracted:

• Product names
• Prices and currencies
• Availability
• Product descriptions

Each page was processed with logic that handled deep pagination and multiple templates. Custom handling was added for fields that were sometimes missing or placed inconsistently across product listings. This allowed the system to deliver structured e-commerce data extraction output without redundancy or confusion during downstream analysis.

Anti-Scraping Strategy

To achieve real-time e-commerce data scraping without interruption, the system used:

• Rotating user agents, headers, and IPs via proxy services
• Incidental delays between requests to simulate human browsing patterns
• Graceful error handling and retry logic to account for intermittent blocks or broken links

These steps helped avoid blocks while maximizing throughput. Additionally, session tracking was omitted to prevent the scraper from being flagged as a bot due to repetitive access patterns.

Execution & Delivery

The system successfully scraped 642,000+ unique product listings. Any records missing essential data, like price or currency, were clearly marked for transparency. The CSV output included flags for null or inconsistent fields, enabling downstream teams to filter or correct records as needed.

The final data was structured and shared via a Google Drive link for immediate access and review. The format supported direct import into analytics dashboards and catalog systems.

Benefits

Implementation of the e-commerce product scraping system gave the client many clear advantages:

Time Savings: It eliminated the need for manual data collection leaving the team free to focus on analysis instead of repetitive tasks.

Real-time Insights: The system enabled faster price comparisons and inventory checks against rivals.

Higher Accuracy: The system ensured higher accuracy by reducing the errors that happened due to manual entry and inconsistent data gathering.

Scalability. The system can handle thousands of product records while requiring minimum manual intervention.

Structured Output: It delivers clean CSV files that are ready for dashboards, analytics, and catalog systems.

Operational Efficiency: Apart from freeing up internal resources, it led to improvement in decision making speed.

Automated Catalog Management: Products could be listed, updated, or removed automatically without requiring any human input.

Valuable Market Insights

The system did not just extract data. Instead, it created a foundation for actionable insights. Some of the valuable market intelligence gained included the following:

• Price Benchmarking: The ability to compare own product prices with competitors across various categories.
• Inventory tracking: Monitoring products that competitors frequently ran out of to reveal demand surges.
• Trend Identification: Spotting new product launches early and tracking how fast they gain traction.
• Promotional Analysis: Detecting discount cycles, seasonal offers, and bundles used by competitors.
• Category gaps: Identifying product categories that had high demand but limited competitor presence.
• Consumer demand signals: Frequent stockouts and price increases gave indirect signals about fast-moving items.

Besides helping the client adjust pricing strategies, these insights empowered them to improve catalog decisions and identify untapped market opportunities.

Use Cases for Various Industries

While this project was created for the retail sector, the principles of automated large-scale data extraction may be applied to other industries as well. This solution can address diverse business needs by adapting the scraping logic, data fields, and antiblocking strategies:

Ecommerce & Retail: Scraping competitor product catalogs, tracking daily or seasonal price changes, and identifying new entrants. Apart from allowing retailers to adjust prices quickly, it lets them manage inventory more efficiently, and launch targeted campaigns

Travel & Hospitality: Gathering hotel rates, flight prices, package details, and seasonal availability from multiple booking websites. It allows agencies to offer competitive pricing, optimize packages, and respond to changing demand patterns in real time.

Real Estate: Extracting property listings, rental prices, neighborhood trends, and project availability from real estate websites. This can be used by agencies and investors to track market fluctuations, analyse demand, and generate comparative reports for clients.

Automotive: Monitoring car dealership websites for new and used vehicle listings, spare parts availability, and latest offers. Besides competitive analysis, it supports pricing intelligence, and stock management for dealers and resellers.

Consumer Electronics: Tracking online availability, price drops, and reviews for gadgets and appliances. It can be used by brands to identify new launches by competitors, adjust promotional campaigns, or ensure price parity across marketplaces.

Market Research & Analytics Firms: Aggregating data from multiple sectors for creating market reports, forecasts, and performance benchmarks. Automated scraping ensures that insights are based on the most recent and accurate information.

Healthcare & Medical Supplies: Monitoring online suppliers for essential medical equipment, pharmaceuticals, and consumables. It can be used by hospitals and procurement teams to secure better deals and maintain uninterrupted supply.

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Key Takeaways

At the end of the project, the following were the main lessons relevant for businesses and decision makers:

• Time and efforts saved: Automating product data collection eliminates the need for manual research. It also allows the team to focus on smarter tasks.
• Better Market Awareness: Businesses can stay ahead with regular access to competitor prices, stock levels, and promotions.
• Accurate Insights: While clean and accurate data reduces errors, it also supports confident decision making.
• Scalability Matters: A system which can handle hundreds of thousands of products ensures growth without any extra effort.
• Actionable Outcomes: Beyond information collection, the solution provided practical insights for pricing, catalog updates, and market opportunities.

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Project Overview

Did any online review confuse you? That’s what we encountered on Amazon to identify as a fake review. These reviews mislead buyers, damage seller reputations, and distort product rankings. It’s a serious threat to sellers relying on authentic customer feedback.

Our client, a mid-sized Amazon seller with listings across multiple categories, noticed a sudden drop in product credibility. Their listings were receiving suspicious reviews that didn’t match actual customer experiences. Some reviews were irrelevant, while others promoted unrelated products. The client wanted to comprehend the problem, put a full stop to the damage and prevent future attacks.

We took on the challenge with a clear goal: identify fake reviews, analyse manipulation prototypes, and build a scalable system for reporting and prevention.

The Challenges

The client faced several interconnected problems:

• Suspicious Review Volume: A sharp increase in 1-star reviews with vague or irrelevant content. Many reviews appeared automated or copied from other listings.
  
  
• Manual Detection Limitations: The client’s internal team was unable to keep up the pace. Identifying coordinated attacks manually was slow and often inaccurate.
  
  
• Lack of Structured Evidence: Even when fake reviews were spotted, there was no organised data to present to Amazon for escalation.
  
  
• Hidden or Missing Reviews: Some reviews were visible only in certain regions or devices. Others disappeared after being flagged, making it hard to track patterns.
  
  
• Scalability Issues: The problem wasn’t limited to one product. Multiple listings across categories were affected, raising a doubt about a competitor’s involvement.

It was evident that the client needed a robust, data-driven approach to detect and respond to review manipulation.

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To Sum Up

This project highlights the power of structured review analysis. Fake reviews are more than dangerous. They damage brand credibility and customer trust. By combining manual insights with automation planning, we helped the client uncover manipulation, protect their listings, and prepare for future challenges.

Relu’s Promise: We don’t just solve problems! We build systems that protect your brand, scale with your growth, and earn customer trust.

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Our Approach

We broke the solution into five key components:

Review Pattern Analysis

We started by analysing 1-star reviews. Many had repeated phrases, poor grammar, or promoted adult products. These weren’t relevant to the client’s listings. These reviews were likely generated by bots or coordinated groups.

We looked for patterns in timing, language, and reviewer behaviour. Reviews that appeared in bursts, used similar wording, or came from accounts with no purchase history were flagged.

Reviewer Profiling

Next, we studied the reviewer accounts. Some exhibited bot-like activity—posting multiple reviews in a short time, across unrelated categories. Others had conflicting review histories, praising one product while criticising a similar one from the client.

We also found profiles linked to competitor products. These reviewers often left negative feedback on the client’s listings and positive reviews on rival items. We flagged these accounts and documented their activity.

Impact Mapping

We tracked how fake reviews affected the client’s listings. Products with high volumes of suspicious reviews ended in poor visibility, conversion rates, and even lost Amazon badges like “Best Seller.”

We mapped these impacts across categories to understand how manipulation spread. This helped us identify which products were most vulnerable and which competitors were likely involved.

Structured Reporting

To support escalation, we built a detailed dataset. It included:

• Reviewer metadata (account age, review history, purchase status)
• Behavioural flags (repetition, timing, sentiment anomalies)
• Product segmentation (affected listings, categories, and review clusters)
• Hyperlinked references to each flagged review and profile

We created a clear and organised report that Amazon’s support team could easily review. It helped the client raise complaints and request the removal of fake reviews.

Automation Exploration

Finally, we proposed an AI-based detection framework. This system would:

• Analyse keywords and sentiment in reviews
• Detect overlaps in reviewer behaviour across listings
• Identify hidden or region-specific reviews
• Send real-time alerts if suspicious patterns appear
• This blueprint laid the foundation for future automation, making review monitoring faster and more reliable.

What Made Our Solution Different

Our approach stood out for four key reasons:

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The Results We Achieved

The impact was clear and measurable:

• The client could flag and report fake reviews with confident.

• Amazon accepted the escalation and began investigating the flagged profiles

• Product credibility improved, and buyer trust returned
• Revenue loss from manipulated listings was reduced
• The client gained insights into competitor tactics and targeting patterns
• Reviewing reliability scores helped guide catalogue decisions and future product launches

These results proved that structured analysis and smart reporting could transform a complex problem into a manageable process.

Key Benefits & Impact

• • Detects fake reviews and suspicious reviewer behaviour.
  • Spots harmful content patterns across product listings.
  • Helps sellers report fake reviews to Amazon with solid evidence.
  • Builds a system to monitor future review attacks.
  • Protects brand reputation from coordinated review manipulation.
  • Reduces revenue loss caused by misleading reviews.
  • Improves buyer trust by removing harmful content early.
  • Reveals competitor tactics and targeting strategies.
  • Supports smarter catalogue decisions using review reliability scores.
  • Prepares for AI tools that detect fake reviews in real time.

Use Cases Across Industries & Domains

This solution works beyond Amazon sellers. It helps many types of businesses:

E-commerce Brands

• Protect product listings from fake reviews.
• Improve customer trust and sales performance.

Hospitality & Travel

• Detect fake hotel or tour reviews.
• Maintain reputation across booking platforms.

App Developers

• Spot fake ratings and reviews on app stores.

• Prevent manipulation of app rankings.

Healthcare Providers

• Monitor patient feedback for authenticity.

• Avoid reputational damage from false reviews.

Online Education Platforms

• Ensure course reviews are genuine.
• Build trust with students and educators.

SaaS & B2B Services

• Track client feedback across review sites.

• Use insights to improve service quality and retention.

Business Owners

• Understand how competitors use fake reviews.

• Make informed decisions using review data.

Final Delivery & Next Steps

We delivered two key assets:

• A Google Sheet with reviewer profiles, flagged reviews, and behavioural insights
• A PDF report summarising findings, patterns, and escalation-ready documentation

The client is now equipped to take action, either by escalating to Amazon or making internal decisions about product listings.

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