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Product Reviews and Ratings Solution with Outlier Identification for a Leading PC manufacturer
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Natural Language Processing (NLP)
Applicable Functions
- Product Research & Development
- Sales & Marketing
Use Cases
- Predictive Quality Analytics
Services
- Data Science Services
The Challenge
A leading global PC manufacturer wanted to set up a completely automated system of analyzing reviews and ratings for self and competition, enabling actionable insights for product, marketing, and consumer research teams. The client wanted to understand top-rated products by different categories, create an automated solution to categorize unstructured customer reviews, and use NLP and sentiment analysis approach to create insights on behavior and brand perception.
About The Customer
The customer is a leading global PC manufacturer. They are a major player in the Information Technology industry and have a significant presence worldwide. The company is known for its innovative products and has a strong focus on customer satisfaction. They are constantly looking for ways to improve their product offerings and understand their customers better. To this end, they wanted to set up a system that could analyze reviews and ratings for their products as well as their competitors'. The goal was to gain actionable insights that could be used by their product, marketing, and consumer research teams.
The Solution
The Minerva team conducted a two-day workshop to understand the client's requirements and created an approach for the same. They set up automated data mining bots and a technology stack for creating an end-to-end solution. The team collected, sanitized, stored, and analyzed multi-region data covering qualitative and quantitative measures and KPIs. They provided periodic and ad hoc analysis based on different requirements from global stakeholders. They also created a buzzword cloud which helped in understanding attributes that really mattered to a large audience.
Operational Impact
Quantitative Benefit
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