Case Study : Start-Up Tracker

Background A startup tracking platform required a solution to track new startups and their products. They aimed to determine customer sentiment about the startups and their products.

Problem Statement The client needed a solution to scrape data and analyze it to gauge customer sentiment about new startups and their products.

Solution Approach We created a Python-based solution that involved building scrapers for 10 websites. The scraped text was then analyzed for specific keywords and overall customer sentiment about the products. We developed a dashboard that reported daily customer engagement for all startups.

Our solution involved the following steps

Building Scrapers: We built Python-based scrapers for 10 websites that tracked new startups and their products.

Text Analysis: We analyzed the scraped text to determine customer sentiment about the products. We used natural language processing (NLP) techniques to identify specific keywords related to the products and determine overall sentiment about them.

Dashboard Creation: We created a customizable dashboard that reported daily customer engagement for all startups. The dashboard provided detailed insights into customer sentiment about each startup and their products.

Results Our solution provided the client with a comprehensive view of customer sentiment about new startups and their products. The client could make informed investment decisions based on the sentiment analysis provided by our solution. The dashboard also enabled the client to track daily customer engagement and identify trends in customer sentiment over time.

Conclusion Our solution provided the client with a powerful tool for tracking new startups and their products. By analyzing customer sentiment, the client could make informed investment decisions and stay ahead of the competition. Our customizable dashboard provided daily insights into customer engagement and sentiment, enabling the client to adapt quickly to changing market conditions.