Scalable and Instant Data Extraction and Automation Services

How to boost your E-Commerce Sales with Automated Product Listings?‍

The project is about to achieve the goal of automating the product listing process. The user has a Shopify store and the challenge is to enhance the efficiency with data enrichment and automation. By using advanced data scraping techniques, Relu has provided a streamlined solution that saves time and increases client listing accuracy and quality.

Challenge

The client has a growing e-commerce business on Shopify. They struggled to keep the manual product data entry as the process was time-consuming and faced a lot of errors. The team was challenged by maintaining accurate and updated product listings, especially during the inventory expansion.  

Moreover, while enriching product data they have to add details like specifications, pricing, and description for each which is again a tedious process. A solution was needed to streamline the product listing and ensure consistency across the catalog.

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Universal support

Relu eCommerce automation solution is flexible and can be used in various e-commerce platforms. This approach is beneficial to other businesses facing similar challenges like catalog accuracy, and data management.

Relu Solution: Automated Product listing with Shopify data scraping

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Overview

The team implemented a Shopify data scraping solution that automated collecting, organizing, and enriching product data from multiple resources. We also built a custom scraper that extracts the essential product information and structures to match Shopify’s format. With this, we have also integrated data enrichment while adding details of products like descriptions, pricing, and tags. This gave a completed and cohesive product catalog and was ready for launch.

Custom data scraper development: A custom scraper was put into place to capture critical product information. it is then formatted according to Shopify unique product listing structure.

The scraper is used to integrate multiple data sources for a more holistic view of product details.

Enhanced product details: To improve customer experience, Relu has also incorporated data enrichment into the scraping process. This system will automatically add valuable information to the product like in-depth descriptions, comprehensive specification and optimized tags. This process enhances the product visibility on Shopify and also in search engines.

Results

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Overview:

Our eCommerce automation solution reduced the time and effort the client spent on product listing. The automated product listing also ensured the sharing of details of new products to the consumer at the right time. This automation ensures data accuracy, and up-to-date listings with minimal oversight.  

The demonstrated results of the product data scraping solution are adaptable to any e-commerce business facing the mentioned challenges. With the help of e-commerce data scraping by Relu, any business can benefit from increased efficiency and improved sales points.  

Reduced manual entry: With the automation of product listing, the client found a significant reduction in time and effort. This saving helped clients to focus on other critical areas of business.

Increased data accuracy and consistency: The automated scraping solution reduced the human error and led to preparation of accurate product catalog. This consistent listing gained customer trust and contributed significantly in effective inventory management.

Better customer experience: The enriched data helped the customers to view comprehensive product information. This made shopping more informed and enjoyable. Moreover, automation ensures that new products are listed in real time giving immediate access to customers.

Project Overview‍

Over 75% of Americans face challenges in effectively managing their personal finances.  

Now more than ever, Americans want to gain greater and more detailed insights into their spending habits.

This case study explores how we helped mastercard  track and monitor daily credit card expenses, providing them with more visibility and control over their financial habits.

Objectives and Goals

Here are the key objectives and goals mastercard  wished to achieve with an advanced budget control solution:

• Our client wanted a clear picture of daily and monthly spending.  

• It was essential for them to understand their financial habits better and identify areas where they could save more.

• They wanted to reduce the time they spent manually tracking expenses. It would also free up more time to focus on their financial goals.  

Challenges and Pain Points

The client faced several challenges that highlighted the need for a more efficient budgeting tool. Some of these were:

• Inconsistent Expense Tracking: Manually tracking credit card expenses often led to missed entries and incomplete and incorrect financial records.  

• Complexity in Financial Reporting: The client couldn’t clearly understand their spending habits and how they aligned with their monthly budget.  

• Time-intensive Manual Processes: Our client’s ability to maintain an accurate budget was significantly impacted by manual recording.

Conclusion and Future Plans

Implementing an advanced and automated budget control and expense tracking system proved quite beneficial for the client. It helped them gain control over their finances and make proactive financial decisions. With the reduction in manual tracking tasks, they could focus on more important aspects of financial planning.  

Though we implemented this tool for an individual client, we can also tailor it for different organizational needs.  

Solutions Provided

To address these issues, we provided the following solutions:

• Automated Expense Tracking
  The client provided us secure access to their credit card expense data, giving us accurate insights into their financial habits and enabling the setup of an automated expense tracker. This automation was essential, as the client, a business owner with frequent travel, had varied spending across locations using a single card for both personal and business transactions. With automation, each transaction was recorded instantly, eliminating the risk of missing data and ensuring the client had a complete, accurate, and continuously updated expense record.
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• AI-Driven, Daily Expense Categorization
  We asked ourselves: How could we simplify this for the client? To make financial reporting more accessible, we implemented an AI-powered system to categorize transactions by expense type. Categories like ‘Entertainment,’ ‘Groceries,’ ‘Utilities,’ and ‘Travel’ were automatically generated, allowing the client to see a clear spending breakdown. This categorization also provided a detailed financial profile, helping the client understand their spending patterns and quickly spot high-expenditure areas, ultimately supporting their goal of informed budgeting and greater visibility into their habits.
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• Automated, Insightful Report Generation and Analysis
  Our system went beyond categorization, generating insights by analyzing spending patterns and pinpointing high-expenditure areas. The client wanted to eliminate manual tracking, so we introduced an automated daily email report, offering a concise, clear overview of spending patterns. This routine report allowed the client to passively monitor transactions, while our automation continued to track spending trends and identify emerging patterns, supporting their long-term financial planning goals.
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• Multi-Frequency Report Alerts
  To keep the client consistently aware of their spending, we implemented personalized daily, weekly, and monthly reports with alert notifications. These prompts made it easy to track short-term spending and observe broader trends, enabling the client to adjust spending as needed and supporting their long-term financial planning goals.

Results and Outcomes

The client achieved the following outcomes:

• Through the daily report, they noticed an average daily spend of $50 in the first month. This was broken down into different categories, such as groceries ($20), entertainment ($5), dining out ($10), etc. The client also made some occasional larger expenses, like $100 on weekends.  

• Our advanced budgeting helped them realize that by the end of the month, they had spent $1500 on their credit card. Out of this amount, $400 was spent on dining and entertainment when they had originally planned to spend $300 on these categories.

• Eventually, the client could adjust their budget and cut back on discretionary expenses the following month. It helped them save an additional $150. They also gained a clear understanding of how to reach their goal of saving $500 monthly.

Project Overview

Businesses like comic books are very niche markets. They are required to have access to up-to-date and accurate data. This keeps them competitive in the market and approaches the challenge better. Our client is a comic book retailer. He approached us with the challenge of streamlining the data sourcing.

The challenge area also includes poor inventory management and meeting customer demand. We implemented a comprehensive data scraping solution that allowed them to collect and organize the comic book data in real-time automatically. The challenges in detail and the solution by Relu are mentioned further for better understanding.

Challenges faced by the client

The challenges faced by the client are scattered to different areas of their business operations. The impact area is inventory, meeting customer demand levels, and competition. To come up with the solution we needed a present process scenario.

Firstly, the team was manually gathering the data from multiple distribution websites. This was both time-intensive and error-driven.

Secondly, the new comic book issue and special editions were constantly released. This created a hurdle to keep the inventory updated and make informed stocking decisions.

Thirdly, they manually extracted data, which gave them outdated and incomplete information.

Lastly, the lack of automation in the process made the entire transformation process slow for them in the event of any changes in comic book availability, reprints, or limited-edition releases.

Our Solution to their problems

To solve the challenges we designed a custom data scraping system that looked into the client’s needs first. This solution involved creating a web scraping tool that can gather live comic book data from different sources.

The solution also caters to the release date, pricing, availability, and special edition information. Relu also configured the tool to handle high-frequency updates. This allowed the client to access in real-time at the time of new releases and stock changes.

We equipped the system with useful filters that will capture relevant information and eliminate unnecessary data. This streamlined the process of inventory management.

In the end, we implemented an easy-to-use interface that helped the client to extract data in one structured format. This made the data analysis simpler especially at the time of identifying the trends and adjusting the inventory.

Results and Impact

Relu data scraping solution has measurable results for the client's problem. With live updates and accurate data on book availability to customer response, we reduced the missed sales opportunities.

This enhanced customer satisfaction rate and the client could offer new and in-demand comic book titles to them.

Moreover, the client noticed a reduction in the time spent on manual data entry. This gave time to most of their resources who can now focus on other strategic aspects of the business like marketing and customer engagement.

The solution also helped the client to prepare a tool that could adapt to future changes in the comic market, proving efficient data extraction is a powerful asset to any business.

Why can this solution work for any business?

The data scraping approach extends beyond the comic book industry. Businesses with fast-changing and updated products frequently need inventory updates and accurate information.

This solution is the blueprint for using data scraping to solve inventory issues, and customer management issues as well. With the list of solutions, businesses of all types can stay competitive by leveraging the data in their decision-making process.

Project Overview

Extraction and analysis of multiple reports is time-consuming, especially for 19 locations. Cameron Ray, the COO and Co-founder of Sparkle Express Car Wash Company, faced this issue. Relu stepped in to provide a solution that streamlined the extraction and analysis of operational and financial data, reducing Cameron’s workload.  

Company Background

Sparkle Express Car Wash Company offers top-notch car wash services in 19 different locations across the USA via their three different websites. The company relied on a manual process of data collection, extraction, and analysis.  

Challenges Faced By The Client

Sparkle Express Car Wash's distribution to 19 locations increased the number of reports to manage and analyze. The manual approach of recording and analyzing the data challenged the team with the compilation of revenue, labor count, and conversion from different locations. This not only took a toll on time but also introduced potential errors. Moreover, the key members couldn’t get the data at the right time to process as the company doesn’t have a dashboard.

Our Solution To The Problems

Relu Consultancy developed a custom script solution that automated data extraction from the three different websites used by Sparkle Express Car Wash. The solution then catered to three areas:

Data extraction scripts- The customized scripts pulled raw data from all the websites.

Data processing function- We introduced the functions to process the extracted data by generating it as per the metrics Cameron suggested such as ledgers, each location performance, and weekly reports from Monday to Sunday.

Automated report- We introduced an email function as well to send automated reports to all key members on Sunday. This clear approach helped key members with a clear overview of which location was performing well and which needed attention.

Results And Impact

The implementation of these solutions resulted in significant time savings and better data accuracy for Sparkle Express Car Wash Company.

The real-time data generation and weekly report helped them analyze each location's profit, performance, and areas of improvement. This solution is not only for streamlined operations but also a template that can benefit any business facing similar challenges in data management and reporting.

Relu Consultancy's solution gave Cameron and Sparkle Express Car Wash Company more time to focus on operations rather than report management and analysis. 

The challenge

The job market is huge and ever changing. Thousands of jobs are listed online, on various job portals every day. To list out these job alerts manually from job portals according to the requisite keywords, is extremely time consuming and tiring. In addition to this, it requires human resources exclusively dedicated to this work, which puts more financial strain on a firm.

Our client, suffering from the same issue, was looking for options to automate job listing based on keywords and organizations across different job search sites, primarily Google Job Search and welcometothejungle.com. The client wanted a job scraping tool, to scrap three main data points, the job title, the job URL, and the date when the job was posted, from these job search platforms.

The Solution

So, to simplify their search for employment opportunities, we came through with a job scraping software which would undertake web scraping on the websites mentioned by the clients in order to gather data on job listings in a simple, time and cost-efficient way.

Firstly, we created a job scraper bot to perform all the manual processes from searching for the companies and keywords on the listed job portals. We also built an API, which acts as a trigger that initiates the process.

Along with that, we integrated an n8n automation tool to give the process and environment a smooth and uninterrupted run. When the client clicks start in the n8n tool, it will initiate the process, and the scraper bot will run through the website and gather the required data.

When the scraper set is ready, the web crawlers start providing the data in the client’s required format. If the client provides the company name and keyword, the scraper will collect the job title, URL and data posted. If the company is not found, then it will give the result otherwise.

Advantages

• Swift Work Ethic: within a week we designed the technical aspects and set up the web crawlers, allowing the client to gather data in a shorter time.
• Industry Expertise: our hands-on experience in web scraping helped us design a solution that can quickly perform all the manual processes and control a vast amount of data.
• Affordable Alternative: the job scraper will be more affordable in terms of cost and time than the manual listing.

The challenge

If all the seller profiles are opened and checked manually, the task seems to be nearly impossible because of the huge number of sellers selling their products on bol.com.

The Solution

So, to fulfill their needs we developed a tool called ‘Bol scraper’ which automates the whole process of going through all the pages of the e-commerce website and extracting the details of the seller according to the client’s need. Bol Scraper is a GUI-based tool which means after the tool is delivered to the client, even a user without much technical knowledge can make changes to the parameters (such as the number of reviews, SKUs, and rating) for filtering out the sellers and operate it without any hassle. The client can either select the category through the UI which is to be scraped otherwise, he also has the option to scrape all the categories at once.

We use scrapy, a python-based framework, to scrape through all the pages of the e-commerce website. Along with that, we have integrated various extensions in the module to avoid getting blocked by the bol servers that may happen after making repeated requests for data within a small amount of time.

The scraper shows the details of the sellers meeting all the criteria in real-time as they are scraped through a table in the UI and the user has the option to export all the scraped data to a CSV file at any point during the scraping process.

Using this scraper, we were able to scrape more than 1000 subcategories from bol.com.

Advantages

• Thousands of pages can be scraped at once, allowing the client to gather data in a shorter time.
• The scraper can be used for lead generation and approaching different sellers according to the different requirements of the client.

The Challenge

There was a requirement of the details of golf courses which was all available on a website. If that data was to be used for any process, it was first required to be exported in a proper format from that website. This could’ve not been manually possible to do as there were hundreds of golf courses.

The Solution

So, this process was automated and details was extracted from the website and an approach was used to also include the latitudinal and longitudinal coordinates of the location of the golf course. It takes few minutes to extract the details of hundreds of golf courses in a format required by the client.

Advantages

• A lot of time and human effort is saved through this process.
• Another similar site can be scraped with minimum changes in the main script.
• The client can get or update existing data within minutes through this process.

The challenge

The client requested that we generate leads from a Taiwan-based e-commerce website called “Shopee.” Our client wanted to expand their business by providing their delivery service to the sellers selling their products on this particular website. Therefore, they asked us to extract a list of all retailers and their details so that they can extend their services to them. They also asked us to find the e-mail addresses and phone numbers to contact the sellers.

Because of the large number of sellers selling their products on Shopee, manually opening, and checking all of the seller profiles appears to be nearly impossible.

The solution

As a result, we used web scraping and web crawling technologies to automate this process. Our data processing technology assisted in extracting the data much faster and without making the targeted website suspicious. To find the contact information we used our in house email and phone number finder code so that our client can contact his customers easily. When the process was completed, we provided a list of seller names, along with their number of followers, joining history, rating, page URL, usernames, product category, number of products sold, number of items sold, email addresses, and phone numbers. We provided this information in an Excel file that the client could easily access.

The outcome

We were able to extract information from that website from around 700+ sellers thanks to this scraper. EZIE can now directly contact the potential sellers and broaden their client base with the scraped data. Web scraping saved a lot of time, money, and effort for all the parties involved by searching and analyzing every detail of the sellers. Given that the entire procedure is automated, this method also produced accurate data, which also makes it more reliable.

This web scraping service can be extended to any website or application. If you want to gather such information from any online business or website, just let us know and we’ll be happy to assist.

Why to use our web scraping service?

1. To enable dynamic pricing models in real-time, compare the costs of comparable items across all competitors.
2. Utilize competitive knowledge and expertise to completely transform your pricing approach.
3. Find the list price, selling price, and discount for every product at each rival’s current price.
4. For every SKU, find the exact match, and keep an eye on price changes. Chart each product’s price evolution.
5. Be informed of fresh discounts and offers.
6. Set the appropriate pricing for each SKU that is neither too high nor too cheap and applies to all channels and times.
7. Utilize real-time matching and product discovery to maximise your inventory.
8. Keep up with your own precise product profiles.
9. Find new markets for your items or categories.
10. Know as soon as your suppliers launch a new brand line so you can instantly add the SKUs to your website.
11. Extract all product information and gain access to the competitor’s product catalogue and inventory status
12. Measure consumers ’ opinions
13. Recognize changes in customer demand and rapidly pinpoint products that are becoming more or less popular with consumers.
14. Find out which products and sectors are popular in each country.
15. Verify design, variety, and merchandising choices to make sure the commercial offer is appropriate.
16. Recognize the obstacles potential clients confront by understanding their path.
17. Concentrate your marketing initiatives on top sales.

The challenge

The client wanted the list of courses provided by various universities containing information such as course code, department code, and course name.

The Solution

Most universities have a web interface or an online catalog for the students to check the information of all the courses. We took advantage of this interface/online catalog and scraped the catalogs of various universities to deliver the required content to the client.

The whole catalog of any university can be exported to a CSV file within a few minutes at the click of a button.

Advantages

• A lot of time and human effort is saved through this bot.
• The process is fast, reliable, and cost-friendly.

The challenge

The world is going at a very fast pace, any business that doesn’t speeds up its working cannot survive profitably. It is really hard to think of doing things manually anymore. Accessing essential web data efficiently is vital for businesses. However, extracting relevant information from websites can be time-consuming and complex without the right tool. WebHarvest Pro addresses this challenge by providing the fastest, safest, and highly automated web scraping solution, revolutionizing data extraction for users across all technical backgrounds including Sales, Marketing, Recruitment & Hiring, and possibly all other business function.

We have the Solution in form of WebHarvest Pro

Welcome to WebHarvest Pro, the finest web scraping tool that effortlessly gathers critical business data, including names, email addresses, phone numbers, physical addresses, and contact us page URLs. With just a few clicks, you can unlock valuable insights from the vast internet and that too at a lightning fast speed and with 100% accuracy.

Multi-Industry and Multi-Functional Use Cases:

• Lead Generation: Identify potential clients and acquire their contact information for targeted marketing campaigns.
• Market Research: Uncover competitor insights, industry trends, and market dynamics for informed decision-making.
• Sales Prospecting: Access qualified leads for effective outreach and personalized communication.
• Geographic Analysis: Analyse business presence and opportunities in specific geographic regions for strategic expansion.
• Data-Driven Decision Making: Utilize scraped data to enhance data-driven decision-making across your organization.
• Finding Suppliers & Vendors: It can easily be used to identify new suppliers and vendors for any business
• Finding Distributors: It can be used to find distributors of products and services, further helping in downward business expansion.

How one of our E-Commerce Client effectively used this tool:

A leading e-commerce retailer wanted to enhance its competitive edge by identifying new product suppliers. WebHarvest Pro came to the rescue with its multi-keyword input feature. The retailer entered multiple keywords related to their industry and locations of interest.
WebHarvest Pro intelligently crawled through thousands of websites, quickly extracting valuable supplier information. It scraped the names, email addresses, phone numbers, and physical addresses of potential suppliers, as well as URLs to their contact us pages. Using the Excel data export feature, the retailer seamlessly integrated the extracted data into their CRM and sales database. Armed with a comprehensive list of potential suppliers
and their contact details, the retailer streamlined their outreach efforts, resulting in valuable new partnerships and a strengthened supply chain.

How does this works?

WebHarvest Pro operates as a powerful web crawler, intelligently searching the internet for specified keywords and locations. It swiftly gathers precise data while cap

• Input: Users of WebHarvest Pro can input specific keywords and locations relevant to their data requirements. Whether it’s searching for potential clients in a particular industry or scouting competitor information in specific regions, the tool accommodates diverse search criteria.
• Process: Once the user enters the desired keywords and locations,
  WebHarvest Pro initiates its web crawling process. It navigates through the vast expanse of the internet, intelligently searching for websites that match the specified criteria. The tool’s advanced algorithms ensure efficient and accurate data extraction.
• Output: WebHarvest Pro collects comprehensive data from the identified websites, including business names, email addresses, phone numbers, physical addresses, and URLs to the contact us pages. Moreover, the tool captures and securely stores screenshots of each website’s home page, providing users with visual references for their data.
• Utilizing the data: One of the most powerful features of WebHarvest Pro is its ability to export the extracted data in Excel format. This functionality opens up a world of possibilities for users, enabling seamless integration with various applications and enhancing data utilization in multiple ways including but not limited to Adding data in CRM, Email Marketing, Finding Suppliers & Vendors, Running Targeted Marketing Campaigns, Sales Strategies, Market Segmentation, Competitor Analysis, and so much more.

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The challenge

Our client takes into account, the charities registered at the Australian Charities and Not-for-profits Commission (ACNC), which are nearly 60,000, with the number fluctuating everyday as new charities are registered and others cease to exist. For each charity there is an assorted group of responsible people and the communication channels through which they can be contacted. Collecting all this nonprofit fundraising data manually, would be a tiresome process and would imply a significant drain on human resources, efficiency and profits. The client thus wanted a list of all the new charities, the people of concern and their contact details, all at one place.

The Solution

This is where we come in! Using our automation and python skills, we built a web scraper that would (in seconds) extract the relevant data of new charities, their heads/trustees as well as their contact information from the website and consolidate it all in a list. This list updates on a weekly basis and can also be customized to change at any preferred timespan.

Aside from this, we put HubSpot in place, which helps the client in generating and pushing leads. It also makes the email communication channel amidst employees, and with potential donors and charities, more effective and time saving by providing automation tools.

Advantages

• For quality web scraping solutions. We made the data mining process automated by building a web scraper which not only eased up a tedious data collecting process but also freed up manhours.
• With the introduction of HubSpot, leads were pushed as well as the communication channel was streamlined to ensure effective and efficient communication between employees and, employees and customers.

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The challenge

Sponsorscout faced the challenge of automating web crawling to find newsletters, podcasts, events, and other sponsored content from a diverse range of organizations. Turning thousands of newsletters, watching tons of videos, and keeping track ofcountless events would consume unimaginable man-hours and prove unsustainable. They sought an automated mechanism that could deliver exact results in minimal time, with reduced costs and efforts.

Process

• We initiated the content aggregation process using the Feedly API. This versatile API enabled the automatic extraction of a multitude of newsletters, podcasts, events, and digital content from various sources.
• With the content in hand, we introduced Google Vision API, a robust image analysis tool. It meticulously detected and interpreted elements within images and videos, enhancing our ability to identify sponsor mentions within visual content.
• Google OCR was employed to convert textual information from images and scanned documents into machine-readable text. This tool facilitated text-based analysis and the extraction of valuable information from visual content.
• Google Entity Recognition further enriched the extracted data. It intelligently recognized and categorized entities like names, dates, and locations within the text, enhancing the overall accuracy and structure of the information.
• To fortify the database, we integrated the Crunchbase API. This versatile API provided access to comprehensive information about companies, funding rounds, leadership teams, and more. It empowered us to incorporate accurate and up-to-date company data into the database.
• The n8n Workflow Automation platform allowed us to seamlessly connect and coordinate the various applications, services, and APIs involved in the workflow.
• The extracted and organized data found its home in Airtable, ensuring easy accessibility, storage, and collaboration on the amassed information.

Outcome

With the n8n and make.com automation, our client achieved a continuous and ever-growing list of sponsors from across the web. The data was stored in Airtable, making it universally applicable and allowing easy access and analysis

Conclusion

Using n8n combined with other powerful tools such as Feedly and Google OCR proved to be a game-changer for gosponsorscout.com. Complex and labor-intensive tasks were
effortlessly automated, providing a comprehensive and accurate database of sponsors. The capabilities of n8n and make.com are vast, empowering us to create tailored automations for
countless use cases, meeting the diverse needs of our clients. If you are looking forward to automating tasks involving an organized and structured approach to data, we can help you with our immense expertise with these tools.

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The Challenge

The client had a substantial volume of scanned financial documents from which specific data—Name, Date, and Amount—needed to be extracted accurately. The process was initially manual, proving to be time-consuming, prone to human error, and inefficient for the increasing workload. Furthermore, organizing the extracted data in a systematic manner for easy access and reference posed another major challenge.

For instance, in one month, our solution processed 10,000 documents, with an impressive data accuracy rate of 99.5%. This was a 75% reduction in processing time compared to their previous manual method.

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Conclusion

This case study demonstrates the potent efficiency and accuracy of our data scraping solution in handling large volumes of scanned financial documents. By automating data extraction and organization, we were able to significantly reduce processing time, increase data accuracy, and streamline the document retrieval process. Our solution provides a compelling answer to similar challenges faced by financial institutions and serves as a ready model for future scalability.

Solution

Our team developed and implemented a sophisticated data scraping solution tailored specifically for scanned financial documents. First, the client collected all the relevant documents and provided us with their scanned copies. We then used our solution to scrape the required data. Using advanced data recognition and extraction algorithms, our system was able to identify and extract the necessary information—Name, Date, and Amount—from the various documents.

Once the data was extracted, the solution’s next task was to sort the documents accordingly. We implemented an automated system to create specific folders based on the Date, allowing for systematic organization of the documents. Each scraped document was then saved in its designated folder.

Results

The results of implementing our data scraping and sorting solution were immediately evident and overwhelmingly positive. The client was able to process a significantly larger volume of documents within a short time, with a notable increase in the accuracy of data extraction, eliminating the possibility of human error.

Our solution’s organization feature also proved invaluable. With each document being automatically sorted and saved in a designated folder according to the Date, the client was able to easily access and reference the scraped documents, enhancing their operational efficiency.

The Challenge

NAME.com boasts an extensive set of filters, sub filters, and sub selections, making the process of reaching the final list of cars a multi-layered task. Users must navigate through a cascade of filter choices, from the basic options like make and model to complex decisions regarding annual mileage, lease length, upfront payments, and finance types. Manually extracting data from NAME.com’s intricate filter system consumed substantial time and resources for our client. They sought a custom-built tool that could scrape data swiftly, taking into account multiple sets of specific filter combinations.

About NAME.com: The platform from which data was to be scraped

NAME.com stands as a leading online platform in the United Kingdom, dedicated to transforming how consumers discover and lease vehicles. The platform’s mission revolves around simplifying the intricate world of car rental services, making it accessible and convenient for individuals across the UK. NAME.com empowers users with an array of filters, allowing them to  pinpoint their perfect vehicle. These filters include Make & Model, Monthly Budget, Lease Duration, Fuel Type, Body Type, Transmission, Features & Specifications, Colour Preferences, Lease Types, and more.

Specific Requirements

1. Streamline Data Extraction: Our client required a tool to retrieve car data without relying on external APIs or paid tools and wanted a tool that was custom coded from scratch.
2. Navigate Complex Filters: The scraper had to navigate through NAME.com’s intricate filter hierarchy and the tool to replicate the process of selecting filters as is done by normal users.
3. Speedy Results: Despite the vast data, the client needed quick scraping results.
4. User-Friendly Interface: Rather than code scripts, the client wanted a user-friendly web interface to access the tool and obtain data with a single click.

The Output & The Process

We delivered a user-friendly web page with a pre-filled table of filter values, aligning with the client’s frequently used selections. Client could simply click a button associated with each filter set to initiate data scraping. Our tool replicated the manual filter selection process in the background while swiftly presenting results in Excel format on the front end. Separate buttons allowed users to scrape data for the current date or the past 30 days. The final Excel sheet included a wealth of data about vehicles falling under the selected filter set. It encompassed details such as make, model, trim level, model derivative, finance type, pricing for the first, second, and third positions, and providers of the vehicle for the top three positions. This saved the client hours of manual scraping, streamlining the process of accessing vital data.

Conclusion

Our custom tool successfully tackled the complexities of multi-level, multi-filter data scraping, simplifying a formerly labour-intensive process. This achievement demonstrates our capacity to develop similar tools for diverse businesses, facilitating highly intricate scraping tasks within minutes. For businesses aiming to optimize data extraction, our expertise can pave the way for enhanced efficiency and productivity.

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The Challenge

The client required a targeted data extraction tool that could scrape a website listing all internet providers according to zip codes. Their focus was on three main data points: the state in which the internet providers operated, the population covered by each provider, and the maximum speed offered. In addition, they needed detailed information about the company’s size, revenue, and the number of employees. The challenge lay in accurately scraping the required information and organizing it in an accessible, clear, and useful manner.

Our Solution

To meet the client’s needs, we developed an advanced internet provider scraper tailored to their specific requirements. The tool was designed to search the targeted website, extract the relevant information as per the client’s filters, and present the data in an organized Excel sheet.

The scraper was built to capture key data points such as the state of operation, population covered, and maximum speed offered by each provider. Additionally, it was programmed to gather critical business intelligence, including the company’s size, revenue, and employee count.

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Results

The outcome of our solution was transformative for the client. Our scraper significantly reduced the time spent on manual data gathering, resulting in a 80% increase in efficiency. The scraper was able to systematically extract data for over 1,000 internet providers within a short period, presenting accurate, insightful data in an easy-to-analyze format.

By using the scraper, the client could now perform a comparative analysis of various internet providers. This detailed comparison allowed them to make informed business decisions based on data such as population coverage, maximum speed, company size, revenue, and employee count.

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Conclusion

This case study stands as a testament to our expertise in developing tailored data scraping solutions. Our tool empowered the client with data-driven insights, enhancing their operational efficiency and strategic planning. It is our commitment to continuously deliver innovative digital solutions that drive business growth and success. Let us help you unlock new opportunities and propel your business forward.

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The Challenge

• Diverse NGO Services: NGOs offer a myriad of services ranging from medical assessments, legal aid, language instruction, to programs related to gender-based violence. Understanding the breadth and specificity of these services was a challenge.
• Language Barriers: With programs offered in multiple languages like English,French, and Russian, it was essential to ensure the tool could cater to various linguistic groups.
• Effective Matching: Individuals seeking support often struggle to find the right NGO program, particularly if they lack resources. It was crucial to develop a tool that could accurately match a person’s needs with the right service.
• Data Compilation: With vast amounts of data scattered across different NGO websites, the client faced the challenge of extracting, compiling, and presenting this information in a user-friendly manner.

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The Process

• Data Extraction: The client’s tool was designed to crawl various NGO websites and extract pertinent information about the diverse programs they offer.
• Algorithm Development: An advanced matching algorithm was developed to efficiently pair individuals with suitable NGO programs based on their profiles.
• Feedback Loop: The tool incorporated a feedback mechanism to continually refine its matching process, ensuring greater accuracy over time.

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The Output

• Comprehensive Database: The tool successfully compiled a vast database of NGO programs, categorized by service type, language, eligibility criteria, and more.
• Efficient Matching: Individuals in need could now find the most suitable NGO programs in mere seconds, ensuring they receive the assistance they require.
• Community Benefits: By connecting individuals to free or low-cost programs, the tool ensured that more people could access essential services, leading to stronger, more resilient communities.
• Lead Generation: The tool also served as a lead generation platform, offering the compiled data at affordable rates for various stakeholders in the NGO sector.

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Conclusion

Our client’s innovative tool successfully addressed a significant gap in the NGO sector by efficiently connecting individuals in need with the right resources. By leveraging technology, the tool not only streamlined the process of finding appropriate NGO programs but also created a platform that could evolve and adapt based on feedback and changing societal needs. This case study underscores the immense potential of digital solutions in addressing complex societal challenges and paves the way for more such innovations in the future.

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The Challenge

Our client faced a significant hurdle in extracting valuable leads from vast amounts of multilingual data that they generate regularly. To overcome this challenge, they approached us with the need for a tool that could efficiently translate content from different languages, identify key entities, and then re-translate them into their original language for verification.

The Process

Our solution involved a comprehensive process that  seamlessly integrated with the client’s workflow:

1. Translation and Entity Extraction: The tool efficiently translated content from various languages into English, preserving the original meaning. It also systematically identified key entities from the data, making it highly
   adaptable.
2. Noun Extraction in English: Following translation, the tool systematically identified nouns in the English data. This step was crucial in extracting names and company information from the content.
3. Translation back to original language for Verification: The extracted
   names and company details were then translated back into it’s original language. This step served to verify the accuracy of the information in the original context.
4. Customization for Multilingual and Varied Data: The versatility of the
   tool was a key feature. It could be customized to function with any language, allowing the client to adapt it to various markets. Furthermore, the tool seamlessly processed data in different formats, providing flexibility in its application.
5. Information Extraction: Once verified, the tool efficiently extracted valuable information, including leads, from the processed data. This step ensured that the client could gather meaningful insights and potential business opportunities.

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Output

The output of our tool was twofold. Firstly, it successfully addressed the client’s immediate need by providing an efficient means of lead generation from multilingual data. Secondly, the tool’s customization feature opened up possibilities for its application in diverse linguistic and data environments, offering the client a scalable and adaptable solution for future challenges.

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Conclusion

In conclusion, our tailored tool not only met the client’s specific requirement for lead generation from multilingual data but also demonstrated its potential for roader applications. By leveraging systematic entity extraction and versatile language translation, we created a powerful tool that empowers our client to unlock valuable insights from a wide range of multilingual and varied data sources. This case study serves as a testament to our commitment to providing innovative solutions that align with our client’s evolving needs.

The Challenge

The challenge lay in the intricacies of LinkedIn’s security measures. LinkedIn is renowned for its stringent security protocols, akin to other prominent social media platforms like Facebook and Instagram. These platforms make scraping data from their backend APIs a formidable task. They employ a multitude of security checks and obstacles to prevent automated data extraction.

Additionally, the client had a specific set of requirements that included capturing data on the most recent posts from the target profile. This entailed recording critical details such as the post’s URL, date of post, the number of likes and reactions it received, the total number of comments, and the post’s caption. However, the client did not require the retrieval of images included in the posts. Furthermore, the tool needed to be capable of extracting data from the selected profile efficiently and quickly.

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While images were not included, this streamlined approach allowed for efficient and quick data
extraction. The tool operated seamlessly, collecting data from LinkedIn profiles for up to one year in a single run. This meant that users could access a year’s worth of posts from any profile, providing valuable insights for data analysis and sentiment assessment.

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Conclusion

Our client presented us with a distinctive challenge: to scrape LinkedIn posts from a specific profile spanning a year. Despite LinkedIn’s robust security measures, we successfully developed a custom scraping tool that efficiently navigated the platform’s backend API calls. By mimicking human behavior and employing login cookies, we ensured the tool’s effectiveness and compliance with the platform’s security checks. The output of our tool met the client’s requirements precisely. It provided a dataset containing essential post details, enabling data analysis and sentiment assessment. This case study showcases our ability to tackle complex scraping tasks, even on highly secured platforms, and deliver efficient, customized solutions to meet our client’s unique needs.

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The Process

Our approach involved the development of a custom scraping tool from scratch. This tool was designed to effectively navigate LinkedIn’s intricate backend API calls. It utilized login cookies for authentication, enabling it to access profiles and collect data.

The tool’s operation was based on the concept of mimicking human behavior, ensuring that its scraping activity appeared as genuine as possible to the platform’s security measures. This
approach enabled the tool to access and extract the required data without arousing suspicion.

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The Output

The output of our custom scraping tool was exactly aligned with the client’s requirements. For each post within the specified profile, the tool collected and compiled data.
This dataset included details such as the post’s publication date, its URL, the total number of likes and specific reactions (including empathy, praise, interest, entertainment, and appreciation), the total number of comments, and the post’s caption.

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