LLMs in Qualitative Market Research

The intersection of voice-to-text technology and Large Language Models (LLMs), like Relevance.ai, is creating a seismic shift in the landscape of qualitative market research. Traditionally, qualitative research has been a labour-intensive task, fraught with the challenges of capturing, transcribing, and analyzing vast quantities of unstructured interview data. However, as businesses strive to understand the nuances of consumer behaviour and preferences, the integration of advanced voice-to-text systems and LLMs is set to revolutionize the field, unlocking efficiencies and insights that were previously unattainable. Indeed LLMs in qualitative market research could potentially drive up productivity in a sector where interview analysis is an expensive process.

Voice-to-Text Technology: Capturing the Nuances of Human Speech

The proliferation of voice-to-text technology has been a game-changer in how data from research panels is collected. With the ability to accurately transcribe human speech in real-time and identify and keep track of individual speakers, this technology ensures that every opinion, suggestion, and subtle variation in tone is captured with precision. This not only streamlines the process of data collection but also preserves the richness and authenticity of the respondents’ voices. When applied to focus groups, interviews, and other qualitative methodologies, voice-to-text systems enable researchers to gather verbal data with unprecedented ease and accuracy. Furthermore, AI-driven sentiment analysis can identify positive and negative emotions along the way

Large Language Models: From Data to Decisions

Large Language Models, such as Relevance.ai, represent the cutting edge of artificial intelligence in text analysis. These models have the capability to understand context, infer meaning, and uncover patterns within large sets of text data. By analyzing the transcriptions produced by voice-to-text systems, LLMs can quickly sift through the colloquialisms and intricacies of spoken language, transforming qualitative feedback into actionable insights.

The Synergy in Market Research

The synergy between voice-to-text systems and LLMs like Relevance.ai is particularly transformative for market research. This combination allows researchers to:

  1. Increase Efficiency: Automation of transcription and preliminary analysis cuts down on time and resources spent on data processing.
  2. Enhance Accuracy: The integration reduces human error in data transcription and ensures that the subtleties of human communication are not lost.
  3. Scale Up: Researchers can handle larger volumes of qualitative data, making it possible to conduct more extensive and robust studies.
  4. Gain Deeper Insights: With advanced analytics, LLMs can identify trends, sentiments, and themes that might elude even the most experienced human analysts.
  5. Drive Innovation: By quickly identifying consumer needs and gaps, companies can pivot and innovate with greater agility.

Case Studies and Applications

Businesses across various sectors are leveraging this technology to stay ahead of the curve. For instance, a consumer goods company might use voice-to-text and LLMs to analyze customer feedback from social media, call centers, and focus groups to guide product development. Meanwhile, a healthcare provider might utilize the technologies to interpret patient discussions and improve care services.


The integration of voice-to-text systems with Large Language Models like Relevance.ai is more than a mere enhancement to qualitative market research; it is a revolutionary step forward. LLMs in qualitative market research enable the efficient and accurate analysis of spoken data and this synergistic technology offers a deeper understanding of consumer behavior and provides a competitive edge to those who adopt it. As we continue to refine and develop these tools, the potential for new insights and innovations in market research is boundless, promising a future where businesses are more closely aligned with the needs and desires of their customers than ever before.

Frictionless App Development

If you’re a pilot, you know that you divide people into two groups: pilots and not pilots. The same could be said for coders: you either turn caffeine into code or you don’t. The world has just changed for coders. (Don’t even ask about pilots who code.) Frictionless app development, the process of developing apps without the need for techies or investment, is here and these apps can be deployed without the need for a tech stack. App developers no longer even need to know what a tech stack is. OpenAI’s recent announcements (6th November 2023) showed that app creation – and importantly, deployment – is no longer the domain of techies. Yes, AI tools have revolutionised code development too and improved developer productivity dramatically in 2023 but app development has just been democratized.

Understanding frictionless app development

Frictionless app development is the process whereby anyone with a bit of intelligence will be able to think of an app they want for their own purposes or for their business, tell an AI what it needs to do and have the AI build the app, with the AI clarifying needs as appropriate using natural language understanding. This will allow people just to talk to an AI saying something like “Build me an app that will send an email to the managers of all our outlets if cold weather is forecast in their area in the next 4 days so that they can preemptively adjust their stock displays to make jumpers and gloves more prominent and thus increase sales.” The AI will then build the app, integrating with the necessary emailing lists and so on. The cost of developing this app will roughly be your own time costs.

By making it incredibly easy to build an app, the friction involved in app development disappears: it won’t require expensive techie time and therefore won’t need at least some semblance of a cost-benefit analysis before anything happens; it won’t require detailed specification writing because the AI will usually be able to work out what you want and ask you for clarification if it can’t; deployment won’t require complex integrations because tools like GPTs can be added to a web page easily and these GPTs can be linked up to other tools via integrators like Zapier.

Development cost barriers tumble

So cost barriers come tumbling down. Techie availability will no longer be an issue, and all techies have infinite backlogs. The channel pinch between idea and implementation disappears making apps that were previously too costly now worth building. Just as importantly, as people recognise how easy it is to build apps, more minds and thus more creativity will be brought to the development fold increasing the range and depth of ideas that get turned into apps.

By prioritizing ease of use, automation and integration, frictionless app development strips away the complexities that traditionally bog down software projects or stop them from starting in the first place. Frictionless app development means a smoother, faster path from concept to deployment.

The two markets for apps

People will broadly develop apps for two reasons. There will be one category built for personal or internal reasons. The other category will be those the developer intends to market on the GPT Store or elsewhere. The former won’t have competition. The latter will struggle for air. The half life of apps will decrease and App store SEO will be the new skill set in demand.

The importance of business agility

The ability to pivot and adapt to market changes fast is a superpower and frictionless app development is a supercharger. However, friction free app development won’t help those businesses that either fail to exploit the technology or those that simply don’t know about it. It is even more true now than it was to say that AI won’t take your job, but a competitor who uses AI will. This may happen rather faster than you expect!

Experimentation and innovation with lower risks

One of the most exciting aspects of frictionless app development is the freedom it affords businesses to experiment. With a lower cost of entry and quicker turnaround times, companies can explore innovative features and services with less fear of financial failure. This environment of experimentation can lead to breakthroughs that might otherwise never have seen the light of day.

This freedom also encourages a culture of continuous improvement within organizations. Developers are empowered to refine and iterate, knowing that the deployment of their improvements will be swift and seamless. Failures, an extremely important and intrinsic part of product development and fear of which is often a brake on activity, won’t be so costly and career destroying. This will foster great innovation.