Elevating Customer Experience through Feedback Analysis

Solution Customer Feedback Analysis
Solution Customer Feedback Analysis
Industry CPG
Region EU
Technology Microsoft Azure
Context
For any CPG enterprise to thrive and grow, its customers should remain happy and choose it as their trusted brand. While contextualized strategies go a long way to build this relationship, listening to what the users say as feedback or review is more important. These assets, collected in various formats and through channels such as social media, are critical sources for business users to gain insights into their target audience and take the right step forward. Feedback, collected in large volumes, can often be overwhelming when extracting insights from it. Manual analysis falls short as it is time-consuming, leading to delayed actions. In this process, feedback from social media platforms is also captured to provide a more holistic view of customer sentiment. Contextualized LLMs can fast-track the process to categorize feedback and identify new trends to generate new taxonomy, detect patterns, and uncover customer sentiment. This approach enables enterprises to prioritize issues, customize services, and make data-backed decisions to ensure the customer feels heard and valued, giving businesses a critical edge in customer satisfaction and loyalty.
Problem statement

Our client, a CPG giant in the fashion and beauty products sector, received feedback and grievances from their customers through multiple channels and in different formats. To analyze these, the client had a solution with rudimentary features that provided them with preliminary insights. These were not advanced enough to take necessary actions that would address and improve customer satisfaction.

To tackle this and enhance customer excellence, the client sought a solution that would enable fast and accurate management of feedback and extract latent insights.  

Impact

  • ~150,000+ multi-lingual feedback analyzed within 6 – 12 months of GenAI-powered solution implementation  
  • Reduced time-to-insights from feedback by 90%  
  • 70% reduction in operational costs annually  
  • Increased response to negative feedback within 48 hours from 40% to 80% 

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