Unstructured Voice to Strategic Insight: How InXiteOut Powers an Automotive Leader's Customer-First Strategy

Client Context

The client, a Fortune India 500-listed global automotive leader, is focusing on building customer-centric products and experiences. As part of this strategy, the client conducts regular feedback surveys with prospects, owners, and rejectors (those who considered but didn’t choose the brand) to gauge customer sentiment, identify gaps, and benchmark against competitors.

These surveys are conducted via phone interviews and produce large volumes of unstructured voice data. Analyzing this data has been mostly manual, labour-intensive, and hard to scale. Additionally, the absence of a standardized research design has led to inconsistent results and delays in turning feedback into action.

The client needed a reliable, scalable solution to process large volumes of consumer conversations and enable faster, 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 first step involved implementing a standardized data collection framework 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. It 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 voice intelligence

We leveraged MEGHNAD, InXiteOut’s proprietary VoC accelerator, to automate the transcription and interpretation of recorded survey calls. It handled multiple languages with high 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.

Delivering 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.

Technology stack used

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

Benefits Delivered

We executed over 25 strategic research initiatives 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: Automating the transcription and tagging process significantly accelerated the cycle from raw voice data to actionable insight.
• Eliminated manual variability: The solution ensured high accuracy and consistency, removing the errors and subjectivity of manual analysis.
• Accelerated decision-making: By delivering insights faster and more reliably, the client's product, marketing, and service teams could make quicker, data-backed decisions.

Strategic & Business Impact

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

• 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.