Beyond Surface-Level Segmentation

By Quillit

• article

• AI
• Artificial Intelligence
• Reporting
• Depth Interviews
• Online Focus Group Hosting
• Qualitative Research
• Audience/Consumer Segmentation
• Behavioural Analytics

Market segmentation has always been central to qualitative research, but the process of identifying meaningful behavioural differences across participant groups remains one of the most time-intensive aspects of analysis. We have seen countless researchers spend days manually coding transcripts, building comparison matrices, and extracting segment-specific quotes to build a complete picture of how different audiences think and behave.

Recently, we worked with a biotech organisation that needed to understand how three distinct market segments approached purchasing decisions for laboratory instruments. Their challenge was not just understanding the buyer journey, but mapping the specific nuances in priorities, pain points, and decision criteria across academic researchers, healthcare institutions, and commercial R&D operations. With 50 in-depth interviews to analyse, the traditional approach would have required weeks of careful manual coding and comparison.

This case provides useful insights into how AI-assisted analysis can accelerate segment research while maintaining the rigour that professional researchers require. It was shared live on the webinar “From Data Overload to Business Strategy: A Biotech Case Study on Actionable Insights”, presented by Quillit, at the Insights to Action Summit in October 2025. The full video replay is free to watch here:

The Segmentation Challenge

The organisation faced a common problem. They knew their three segments existed, and they had recruited accordingly. But understanding the behavioural differences required a systematic analysis of how each segment discussed their needs, evaluated options, and made decisions. They needed to identify patterns that would inform distinct marketing messages, sales approaches, and product priorities for each group.

Traditional segmentation analysis involves reading through every transcript, coding responses by segment, and building comparison frameworks to spot differences. Even with a small study of 50 interviews, this process can take a skilled researcher two to three weeks of focused work. The risk is not just the time investment, but the cognitive load of holding multiple segment perspectives in mind while trying to identify subtle but meaningful differences.

Speaker Segmentation as Foundation

The key to effective AI-assisted segment analysis is proper data structure before analysis begins. Generic AI tools struggle with segmentation because they lack the ability to reliably track which respondent belongs to which segment throughout their analysis. We have seen researchers try to use general-purpose AI by including segment labels in their prompts, but the results are inconsistent because the AI cannot systematically maintain segment boundaries across multiple queries.

We designed Quillit with a speaker segmentation feature that requires researchers to assign segment tags to individual speakers before analysis. This creates a metadata layer that the AI can reference consistently. For the biotech study, the research team uploaded their 50 IDI recordings and assigned each respondent to their appropriate segment: academic, healthcare, or commercial.

This upfront tagging takes approximately 10 to 15 minutes for a study of this size. The investment pays off immediately because every subsequent analysis can reliably filter and compare by segment without risk of the AI confusing which respondent said what.

The Response Grid Advantage

Before diving into segment-specific analysis, the team used Quillit to generate a response grid. This feature imports discussion guide questions and extracts all responses across interviews, organising them in a tabular format with segment filters and keyword search capability.

The response grid serves two purposes. First, it provides a bird’s-eye view of the data that helps researchers spot patterns quickly. Second, it creates a validation layer for later AI-generated insights. When the AI identifies a segment-specific behaviour, researchers can use the response grid to verify that the pattern holds across multiple respondents in that segment.

For researchers accustomed to Excel-based transcript analysis, the response grid provides familiar functionality with the added benefit of automatic extraction and organisation. The biotech team used this view to get oriented with their data before asking more complex analytical questions.

Asking Segment-Specific Questions

With speaker segmentation in place, the team could ask targeted questions about each segment. They wanted to understand what drove purchasing decisions for each group, so they queried Quillit with segment-specific prompts.

For commercial respondents, the AI identified that competitive advantage and scaling R&D operations were the primary drivers, with a focus on automation and throughput for drug development. The analysis included verbatim quotes that validated these findings, allowing the team to verify that the AI had correctly interpreted the data.

For healthcare respondents, the priorities shifted dramatically. Accuracy, compliance, and patient care quality emerged as drivers, with focus on hospital system integration and vendor support requirements. These were not just different emphasis areas but fundamentally different evaluation criteria that would require distinct sales approaches.

Academic respondents showed yet another pattern. Flexibility and analytics capabilities were central, driven by the need to publish novel findings and secure future funding. Their focus was on versatility for diverse experimental designs and AI-assisted analysis capabilities.

The AI analysis surfaced these differences in approximately 15 minutes of query time. More importantly, each insight included citations back to the original interview recordings, allowing the research team to verify accuracy and extract supporting clips for presentations.

Comparative Analysis at Scale

Beyond understanding each segment individually, the team needed to identify similarities and differences across segments. This type of comparative analysis is particularly challenging manually because it requires holding three different segment profiles in mind simultaneously while identifying both common ground and points of divergence.

Quillit generated a comprehensive comparison that showed shared pain points across all segments: budget constraints, integration complexity, staff training requirements, timeline pressures, and the challenge of balancing thorough evaluation with operational needs. These commonalities suggested universal messaging themes that could work across segments.

The analysis also revealed shared technology interests. All three segments wanted real-time multi-parameter analysis, AI-assisted experimental design, improved system integrations, faster processing speeds, and additional measurement capabilities. For the product team, this finding was valuable because it showed where R&D investment would have cross-segment appeal.

However, the priority ranking differed significantly by segment. Academic buyers prioritised experimental flexibility first, advanced analytics second, and multi-parameter capability third. Commercial buyers led with throughput and scalability. Healthcare emphasised accuracy and compliance above all else.

These priority differences have immediate practical implications. A salesperson approaching an academic institution should lead with versatility and analytical power. The same salesperson talking to a commercial buyer should emphasise speed and scalability. For healthcare, the conversation must start with accuracy and regulatory compliance.

The Validation Question

We are often asked about accuracy when researchers first evaluate AI analysis tools. The concern is understandable. If the AI is wrong about segment differences, the downstream business decisions based on those insights could be costly.

Quillit addresses this through systematic citation. Every insight generated includes references back to specific moments in the original interviews. The biotech team used these citations to spot-check the AI’s interpretation. They could click through to hear the actual respondent quote in context and verify that the AI had correctly understood the meaning.

In practice, we see researchers validate approximately 10% to 15% of AI-generated insights through direct citation review. This sampling approach provides confidence without requiring full manual review of every finding. Among our clients, 80% report accuracy rates of 98% or higher.

Business Impact

The segment analysis had a measurable business impact. The marketing team developed distinct messaging frameworks for each segment and optimised channel strategies based on where each persona congregates. The sales team adjusted their approach to lead with segment-appropriate priorities and set more accurate timeline expectations based on the complex stakeholder dynamics the research revealed.

The product team used the priority rankings to sequence their development roadmap, focusing first on features with cross-segment appeal and then planning segment-specific enhancements.

Perhaps most importantly, the research team completed this analysis in days rather than weeks, allowing the business to act on the insights while they were still timely.

Applicable Beyond Biotech

While this case focuses on biotech, the approach applies to any research where segment analysis matters. We have seen similar applications in technology product research, healthcare services, financial services, and B2B software evaluation studies.

The key requirement is that segments are defined at the recruitment stage and that researchers can assign segment tags to individual participants. From there, the AI can handle the systematic extraction and comparison that makes segment differences visible and actionable.

For researchers managing multiple concurrent studies or facing pressure to deliver insights faster, AI-assisted segment analysis offers a way to maintain analytical rigour while dramatically reducing the time from data collection to insight delivery.

This article is based on a case study shared live on the webinar “From Data Overload to Business Strategy: A Biotech Case Study on Actionable Insights”, presented by Quillit, at the Insights to Action Summit in October 2025. The full video replay is free to watch here:

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