| Challenge | Power retail platform with business intelligence |
| Solution | BI tool for a retail platform |
| Technologies and tools | Python, Scikit-learn, Implicit, Docker |
The client is a top tech vendor that develops high-quality solutions for international trade and retail. They help businesses uncover market insights and automate review collection and analysis. Their key goal is to help retailers better market their products and generate more revenue.
The company contacted us for machine learning consulting services. They needed a helping hand in developing an effective BI solution for market research and sentiment analysis of product reviews. The customized solution should provide market insights and in-depth trend analysis.
The client challenged ud to create an intelligent BI solution for retail. Enabling NLP algorithmic trading functionality in the platform can make the right decisions under any market circumstances.
Another challenge was to apply natural language processing for the client’s trading and retail platform. Using NLP in trading and retail, they wanted to estimate the market sentiment through sources like Twitter, Facebook, Instagram and online press releases. NLP trading and retail algorithms can scrape and summarize information from all of these sources.
Our team started the work from analyzing the best practices on BI tool development, business intelligence use cases in trading and retail, and the ways users interact with the platform. We also paid much attention to proactive communication. We were taking the lead and upholding the contact, pitching ideas on how to best apply business intelligence in trading and retail industries.
We created a tool that combines all product reviews in a database where a retailer can find all the necessary information on specific products, sentiment and prices on them and export it in CSV. format. The tool enabled automated product review gathering and its analysis. Our solution helps retailers better understand the niche they operate in and avoid the pitfalls.
Next step was to create a robust NLP model for trading and retail. We had to work with both structured and unstructured data. Structured data includes press releases, blog posts, notes, etc. It is usually massive, clear and coherent. As for unstructured data, it’s heavy, incoherent, size-limited and hard to interpret. It’s social media posts, feeds, logs and recordings. To analyze these types of data, we used our natural language processing API. Our solution enabled sentiment analysis that helps retailers understand the expediency of promoting this or that product.
We’ve built a robust business intelligence solution that caters to the client’s business needs and enhances the decision-making process of a retailer. We’ve also implemented natural language processing in the trading platform. The support on data science, machine learning and analytics was provided by our tech team.
The system brings the following benefits to the client:
Since the client was planning to better market their platform, they needed more insights on how to promote it. They asked us to provide predictive analytics for B2B sales & marketing. Our tech team also continued the work on leveraging the benefits of NLP in trading and retail.
