THE PROBLEM
The client, a Fortune India 500-listed global automotive leader, is focusing on building customer-centric products and experiences. As part of this approach, the client is running regular feedback surveys across prospects, owners, and rejectors (those who considered but didn’t choose the brand) to capture customer sentiment, identify gaps, and benchmark against competitors.
These surveys are conducted through telephonic interviews and are generating large volumes of unstructured voice data. Analyzing this data has been largely manual, effort-intensive, and difficult to scale. In addition, the lack of a standardized research design had resulted in inconsistent outputs and delays in turning feedback into action.
To address these challenges, the client was looking for a robust and scalable solution to process large volumes of consumer conversations and enable quicker, insight-led decisions.
INXITE OUT APPROACH
Standardizing research design and insight framework
The first step involved implementing a standardized data collection design aligned with the client’s specific objectives. We worked closely with the client’s marketing teams to understand the range of problem statements and feedback dimensions across the different survey types: prospects, owners, and rejectors.
Based on these discussions, we defined:
- Best practices for script design, which ensured consistency across call objectives while retaining flexibility to capture open-ended insights
- Guidelines for call execution and response capture, which helped improve the quality and completeness of recorded feedback
- A standardized nomenclature system to classify themes, topics, and sentiments
- An insight hierarchy framework that structured responses into actionable buckets, streamlining analysis and enabling meaningful comparisons
This foundational layer ensured that the huge volume of customer voice data could be collected in a standardized format with the intention of analysis.
Leveraging MEGHNAD for conversation intelligence
We leveraged MEGHNAD, Inxite Out’s proprietary conversation intelligence accelerator, to automate the transcription and interpretation of recorded survey calls. It handled multiple languages with a high degree of accuracy and produced output in English.
MEGHNAD converted raw audio into transcripts that maintained the natural flow of conversation and enabled accurate interpretation. It also generated contextual question-answer pairs, which made it easy to trace specific feedback for manual verification when needed.
In addition, MEGHNAD created enriched structured data by tagging each response across key themes such as product, service, pricing, and others. This helped provide a comprehensive view of the feedback while significantly reducing manual effort and improving the speed and quality of insight extraction.
Contextualized insights and reporting
To ensure that MEGHNAD’s output could directly support decision-making, we developed a post-processing framework tailored to the client’s 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 helped convert previously fragmented customer feedback into a strategic input for product development and customer experience enhancement.
RESULT
We executed over 25 strategic research initiatives using this approach, delivering consistent improvements in efficiency and insight quality. The solution reduced analysis time by 30 to 40 percent, ensured high accuracy and consistency, eliminated manual variability, and helped accelerate decision-making cycles across functions.
Some of the key objectives addressed through these projects included
- Guiding product decisions: Helped assess the potential impact of a proposed fuel tank size reduction on consumer demand, leading to a data-backed outcome
- Identifying EV adoption barriers: Uncovered key reasons behind hesitation to adopt electric vehicles, including concerns about charging infrastructure, cost of ownership, and range anxiety
- Understanding ownership experience: Enabled a holistic view of the ownership journey across newly launched passenger and commercial vehicles, helping prioritize the most impactful improvements
- Supporting competitive benchmarking: Enabled structured comparisons across product, dealership, financing, and marketing dimensions to identify gaps and areas of differentiation
This approach transformed unstructured customer voice into a reliable and actionable input for decision-making across product, marketing, and customer experience teams.
