Transforming manual product review analysis into an AI-powered summarization engine that delivers faster insights, sharper sentiment analysis, and data-driven decisions at scale.

Challenges

Lift Focus

Intense focus on all datasets/signals during the initial launch of a new product. Includes focus on product reviews to gauge market performance.

Traditional Method

Stakeholders manually scrutinize weekly product reviews. They write summaries of pros, cons, trends, etc. This process is time-consuming and typically limited to several weeks for individual products.

Business

Identify Product Strengths and Weaknesses
Varying Sentiment Scores Across Different Product Versions
Personalized Promotions and Rewards Planning
Customer Behavior and Preferences
Improving Product and Service Quality

Solution

Product Reviews Summarizer. OpenAI’s LLM-based summarizer was built at the Week-Product-Source-ProductName level.

OPEN AI GPT Models

GPT-4 – 8,192 tokens
GPT-4-0125-preview – 128,000 tokens
GPT-4o – 128,000 tokens

Prompt

Extractive & Abstractive summaries, Product Strengths & Weaknesses, Actionable insights, Aspect-based sentiment.

Results And Insights

A Streamlit working prototype/PoC was created that allows stakeholders to select a product, timeframe, and other attributes, generate summaries from review data, and export results.

Impact

Enhanced decision-making, reducing response time to market feedback by 60%.

Expected to increase actionable insights by 40%. Reduced summary generation time from 4 hours to 5 minutes. Anticipated to lower operational costs by 55% annually.