Retail | E-commerce
Review Summarization and Analysis
Objective
The client had received a large volume of reviews on their website and wanted a mechanism to better understand customer reviews and sentiments.
Challenges
- Retailers failed to capture accurate sentiments towards products and missed opportunities for improving products.
- Shoppers had to read multiple reviews to convince themselves before making a purchase, which led to a longer purchase cycle and sometimes dropouts.
Solution Proposed
- Azure OpenAI-based solution accelerator was used to summarize reviews & extract sentiments for key product attributes.
- Aided shoppers in finding relevant product information quickly & captured accurate sentiments for the retailer to improve products.
Outcome
- Increased website traffic by 5% via improved click-through rates.
- Improved capture rate optimizing pricing decisions across the product portfolio.
Design & Thinking Wins
- Improved User Experience: The solution provided shoppers a more efficient and informative way to find products.
- Data-Driven Product Improvement: Retailers gained valuable insights from customer feedback to make data-driven product decisions.
- Enhanced Customer Satisfaction: Reduced return rates and improved product quality led to increased customer satisfaction.
Disclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
