Telecom Giant Cuts Churn by 20% by Unlocking Survey Insights with MEGHNAD 

Client Context 

A top-3 telecom provider in the region, serving over 10 million active users, was facing significant customer churn. To pinpoint why customers were leaving, the company proactively conducted thousands of telephonic exit surveys with departing subscribers. 

The Challenge: Insights Trapped in Audio Data 

The client's proactive strategy hit a critical bottleneck. While they successfully gathered thousands of survey calls, the valuable "why" behind their churn was trapped in a massive, growing archive of unstructured audio recordings. 

Their analysis approach created three critical business problems: 

• Highly time-intensive: The manual review process took weeks, or even months, to analyze a single batch of surveys and produce a report. 
• Outdated insights: By the time decision-makers received the analysis, the insights were outdated, and the at-risk customers had churned.  
• Unscalable: The process was inconsistent, subjective, and impossible to scale, hindering the ability to spot emerging trends in real time. 

The company needed to move from delayed, manual review to automated, real-time intelligence. They sought an advanced solution that could understand and automatically extract actionable churn drivers directly from the raw audio files at scale. 

The InXiteOut Approach 

We deployed MEGHNAD, our proprietary VoC Accelerator, to create a seamless, automated pipeline. This pipeline transformed raw audio files into actionable churn intelligence in two core phases. 

AI-powered insight extraction 

MEGHNAD's unified engine was configured to handle the entire insight extraction process in one seamless flow. 

First, the engine ingested the client's massive archive of high-volume, Arabic audio files. It simultaneously translated and transcribed this complex audio into standardized, analysis-ready English text. 

Then, the Generative AI-powered Comprehension Engine analyzed these transcripts to automatically extract three distinct layers of information from every single call: 

• Critical Churn Drivers: The engine identified and categorized the reasons for customer dissatisfaction, tagging key themes like 'Network Quality', 'Pricing & Billing', 'Competitor Offers', and 'Customer Service Issues'. 
• Sentiment and Intent: It analyzed the transcript to assign an overall sentiment score, identifying which churn drivers were causing the most intense dissatisfaction. 
• Key Demographic Data: To add context, the engine also extracted structured data from the conversations, such as service type (prepaid/postpaid), monthly spend, and other relevant usage information. 

Synthesis and analysis  

In the final step, the rich, multi-layered insights extracted by MEGHNAD were merged with the client's internal customer profile data (e.g., plan type, tenure). This synthesis created a comprehensive, 360-degree intelligence repository. 

Key patterns were then identified by analyzing this enriched data across specific customer micro-clusters. This allowed the client to see, for example, that 'Network Quality' issues were most impacting 'high-spend postpaid users,' enabling a precise retention response. 

All insights were delivered via an interactive Power BI dashboard. 

Technology stack used 

• MEGHNAD, IXO’s VoC Accelerator 
• Azure ETL Platform 
• Power BI 

Benefits Delivered  

The following key outcomes were achieved:  

• 20% Churn Reduction: This was the primary business win. By pinpointing which churn drivers affected which customer micro-clusters, the client launched highly targeted retention campaigns. This approach slashed the churn rate by ~20% for high-priority product lines within just three months.  
• 80% Faster Time-to-Insight: The automated intelligence pipeline reduced the end-to-end survey analysis time by nearly 80%. Insights that previously took months to compile were now available in hours, enabling the client to act before at-risk customers were lost. 

With MEGHNAD, the telecom provider could leverage its customer feedback as a strategic asset, enabling faster and smarter decisions that directly protected its customer base and revenue.