Quickly Mine Customer Feedback for Valuable Insights

App Orchid partnered with a leading communications provider to transform their unstructured customer feedback into a powerful, actionable source of insights, demonstrating the power of a semantic layer and insights engine.

The Problem: Drowning in Unstructured Data

The communications provider faced a significant challenge: a massive influx of unstructured customer feedback. With thousands of customer comments coming in daily after calls and communications, they struggled to extract meaningful, actionable information. The initial dataset alone consisted of 100,000 records from multiple data sources (CRM, XM, publicly available sites such as Trustpilot, etc.), and the provider needed a way to perform sentiment analysis and classify responses into common themes to make sense of the data and improve customer experience.

The Solution: A Semantic Layer and LLM-Powered Engine

App Orchid leveraged its semantic layer and insights engine, built on its conversational analytics solution, to tackle the problem. The team implemented an LLM-based solution using their pipeline composer for their platform. This innovative approach went beyond basic data processing to perform sentiment analysis, identify key themes and sub-themes, and classify responses into categories like product and service.

A crucial part of the solution was the LLM's ability to preprocess the unstructured data. It transformed the raw text into a structured format that could be absorbed by App Orchid's ontology and combined with other structured data sources.

The Results: Enhanced, Contextualized Insights

The new solution delivered a 360-degree view of the customer. By incorporating additional data points such as call analytics, contract information, and product release data, the insights engine provided a much richer understanding of customer behavior. This allowed various teams to answer complex questions, such as

• Product teams
  
  • ‍What features are most complained about, or requested?
  • How customer sentiment shifts after a new product launch?‍

• Customer success
  
  • ‍How the performance of individual account managers affects feedback?
  • What competitors are being switched to by unhappy customers?

A key differentiator of this approach is the ability to contextualize unstructured data with structured data. Unlike simple enterprise search tools, App Orchid's solution combines analytical and textual data to add a new dimension to insights. This powerful combination of technologies has a broad range of applications across various industries, including:

• Utilities and Oil & Gas: Analyzing field service orders and notes to identify frequently failing parts or manufacturers.
• General Industry: Mining  notes to discover common  issues and improve operational efficiency.
• Healthcare: Analyzing patient notes, doctor's reports, and hospital records to identify trends in diagnoses, treatment effectiveness, or medication side effects.
• Finance: Processing customer service call transcripts, loan application notes, and email correspondence to assess customer sentiment, identify fraud patterns, or improve service quality.
• Hospitality: Analyzing customer reviews from various platforms to understand guest satisfaction, identify common complaints about services or facilities, and improve the overall guest experience.
• Manufacturing: Using the solution to analyze maintenance logs and quality control notes to predict equipment failures, optimize production processes, and identify recurring issues with specific parts or manufacturers.
• Retail: Gaining insights from customer reviews, social media comments, and call center interactions to understand product performance, customer preferences, and marketing campaign effectiveness.

By providing a comprehensive and contextualized view of customer data, App Orchid enabled the communications provider to move from simply collecting feedback to proactively driving business improvements and enhancing the customer experience.

Discover how App Orchid can help your operations today.