Client Context 

The client, a Fortune India 500-listed global automotive leader, focuses on building customer-centric products and experiences. To support this strategy, regular feedback surveys are conducted with leads, owners, and rejectors to gauge customer sentiment, identify gaps, and benchmark against competitors.  

These surveys are executed via phone interviews, generating large volumes of unstructured voice data. Historically, the analysis of this data was predominantly manual, labor-intensive, and difficult to scale. Furthermore, inconsistent data quality was observed due to the absence of a standardized research design across business units, which hindered the comparison of feedback and the derivation of actionable insights at scale.  

Consequently, a scalable, automated solution was required to process thousands of consumer conversations and facilitate data-driven decision-making. 

The InXiteOut Approach  

To address the challenges of scale, consistency, and timely extraction of high-quality insights from unstructured, voice-based survey data, we implemented a structured, three-pronged approach: 

Standardizing research design and insight framework 

The initial phase involved the implementation of a standardized data collection framework aligned with specific objectives. Close collaboration was undertaken with brand teams to understand the range of problem statements and feedback dimensions across different survey types (leads, owners, and rejectors). 

Based on these requirements, the following elements were defined: 

• Best practices for script design were established to ensure consistency across call objectives while retaining the flexibility to capture open-ended insights. 
• Guidelines for call execution and response capture were developed, improving the quality and completeness of recorded feedback. 
• A standardized nomenclature system was introduced to classify themes, topics, and sentiments consistently. 
• An insight hierarchy framework was created to structure responses into actionable buckets, streamlining analysis and enabling meaningful comparisons. 
• This foundational layer ensured that the significant volume of customer voice data was collected in a standardized format optimized for analysis. 

Leveraging MEGHNAD for voice intelligence 

MEGHNAD, InXiteOut’s proprietary VoC accelerator, was leveraged to automate the transcription and interpretation of recorded survey calls. The system handled multiple languages with high accuracy, producing output in English. 

• Automated Transcription: Raw audio was converted into transcripts that maintained the natural flow of conversation, enabling accurate interpretation. Contextual question-answer pairs were also generated, facilitating the tracing of specific feedback for manual verification when required. 
• Structured Data Enrichment: Each response was tagged across key themes such as product, service, and pricing. This process provided a comprehensive view of the feedback while significantly reducing manual effort and improving the speed and quality of insight extraction. 

Delivering contextualized insights and reporting  

To ensure the output directly supported decision-making, a post-processing framework was developed, tailored to specific research objectives such as feature feedback, competitive analysis, and dealership experience. 

This included: 

• Mapping insights to client-specific taxonomies and business priorities. 
• Creating executive dashboards and deep-dive templates for different stakeholder groups, including leadership, product, and marketing teams. 
• Enabling ongoing comparative analysis across cohorts and survey waves to support trend identification and performance tracking over time. 
• This end-to-end approach facilitated the conversion of previously fragmented customer feedback into a strategic input for product development and customer experience enhancement. 

Technology stack used  

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

Benefits Delivered 

Over 25 strategic research initiatives were executed using this approach, delivering consistent improvements in efficiency and insight quality. 

Operational Efficiency 

The automated, standardized framework provided immediate and measurable efficiency gains: 

• 30-40% reduction in analysis time: The cycle from raw voice data to actionable insight was significantly accelerated through the automation of the transcription and tagging process. 
• Eliminated manual variability: High accuracy and consistency were ensured, removing the errors and subjectivity associated with manual analysis. 
• Accelerated decision-making: By delivering insights faster and more reliably, quicker, data-backed decisions were enabled for product, marketing, and service teams. 

Strategic & Business Impact 

The solution transformed customer feedback into a reliable strategic asset, providing data-backed guidance for critical business decisions, such as: 

• De-Risking a critical design change: The potential sales impact of a proposed fuel tank capacity reduction was assessed by correlating refueling behaviors with operational realities. [Read the full case study: How AI-Driven Insights De-Risked a Critical Design Change for a Global Auto OEM] 
  
  
• Accelerating EV adoption: The mindset of "EV Aspirers" was decoded to understand the critical trade-offs between range anxiety, premium feature expectations, and price sensitivity. [Read the full case study: How VoC Intelligence Helped an Auto Major Define its EV Roadmap] 
  
  
• Optimizing the early ownership experience: The real-world ownership experience of a new lifestyle SUV was benchmarked against the brand's positioning to validate product-market fit. [Read the full case study: How MEGHNAD Insights Helped Optimize the Early Ownership Experience of a New Lifestyle SUV] 
  
  
• Decoding the commercial vehicle rejector: Deep competitive benchmarking was leveraged to decode the root causes of customer rejection across product utility, and the buying experience [Read the full case study: Decoding the Commercial Vehicle Rejector: How Competitive Intelligence Reshaped an OEM's Strategy]