How a Financial Leader Achieved 70% Cost Savings and Deeper Economic Insights with MEGHNAD 

Client Context 

A leading USA-based financial services provider sought to turn its nationwide online surveys into a competitive advantage. Their goal was to move beyond high-level summaries and identify specific, high-potential consumer segments by understanding the citizens' true economic perceptions and financial preparedness. 

The Challenge: Drowning in Data, Starving for Insight 

The client's existing methods were failing. They were facing a threefold challenge: 

• Vast Volume: Tens of thousands of responses per survey. 
• High Diversity: A complex mix of quantitative scales and text-based questions. 
• Deep Nuance: Thousands of rich, open-ended responses that held the most valuable insights but were impossible to analyze manually. 

This meant that by the time a report was manually compiled, the insights were already stale and superficial. They were spending a significant budget to learn what people thought, but had no scalable way to understand why they felt it. The client needed a solution to get automated, granular, and actionable intelligence at speed. 

The InXiteOut Approach 

Partnering closely with the client, InXiteOut explored the survey's key objectives and how open-ended questions could reveal respondents' true economic perceptions and the state of their financial preparedness. 

We then deployed an end-to-end data analytics framework leveraging MEGHNAD, our proprietary VoC accelerator.   

Theme configuration 

Based on the client's goals and an analysis of sample data, a comprehensive list of themes was curated for MEGHNAD. From the open-ended survey questions, 20+ themes were identified across 5+ hierarchical buckets, including: 

• Financial planning  
• Financial confidence  
• Economic factors  
• Personal factors  
• Employer perception and more   

These themes served as the primary framework for MEGHNAD to categorize the specific drivers behind each respondent's sentiment.  

GenAI-powered analysis 

MEGHNAD's Generative AI engine was configured to execute a sophisticated two-step analysis: 

1. Evaluate overall financial confidence: First, it performed a deep contextual analysis of each free-text answer to evaluate the respondent's overall financial confidence. This provided a clear, high-level metric for each person, scoring their sentiment from high confidence to high fear. 
2. Assess key drivers across themes: Next, the engine assessed the key drivers for that score. It identified the specific reasons for confidence or fear (e.g., "inflation," "job security," "market volatility") and automatically categorized these drivers into 20+ thematic factors (e.g., 'Economic Factors' or 'Personal Factors'). 

This two-step process provided the client with not just the "what" (the overall confidence score) but also the critical "why" (the specific drivers, sorted by theme) 

Insight synthesis and multi-dimensional analysis 

This is where the raw data was transformed into true business intelligence. The MEGHNAD-generated insights (confidence scores and drivers) were synthesized with socio-economic attributes to facilitate multi-dimensional analysis. This is where the solution provided its deepest value. 

For example, by correlating the 'Employer Perception' theme with augmented data, the solution revealed a critical insight: respondents in the gig economy expressed 40% lower financial confidence than salaried employees, even with similar incomes. This single insight immediately identified a high-value, underserved segment. 

Finally, all insights were fed into interactive Power BI dashboards for real-time exploration by client leadership. 

Technology stack used 

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

Benefits Delivered 

The project delivered the following measurable outcomes: 

• 70% cost reduction: Achieved a 70% reduction in processing and analysis costs by automating the end-to-end workflow. 
• 75% faster processing: Decreased data processing time by nearly 75%, accelerating the timeline from data collection to strategic action 
• Identified new high-value segments: The solution directly fulfilled the client's primary goal. The granular analysis pinpointed three new, high-potential consumer segments (such as the "under-confident gig worker"), enabling the marketing team to craft targeted strategies. 
• Enhanced strategic granularity: The ability to dissect economic perceptions at micro-segment levels allowed the client to move from broad assumptions to precise, data-driven decisions. 
• Dynamic data exploration: Empowered decision-makers with on-demand, customizable dashboards for insight-driven strategy formulation. 

Ready to find the high-potential customer segments hidden in your data? Contact us today to learn how InXiteOut’s GenAI solutions can turn your complex data into actionable intelligence.