In this video, the CIRCmodel.com CLO & founder – Beata Mosór shares a compelling case study on how to better understand your target group using AI-driven customer research.

She dives into her process of using Google AI Studio and Gemini Advanced to accelerate research, especially beneficial for consulting and research agencies, as well as companies that rely on data-driven insights.

AI-powered research process

Beata Mosór introduces an AI-powered research process designed to help consulting, research, and other agencies better understand customers and develop superior products and strategies more rapidly. She showcases a case study from the auto parts industry, demonstrating how Google AI Studio and Google Gemini Advanced were used to accelerate market analysis and product strategy development within a tight two-week deadline.

Mosór highlights her use of specific AI tools for data synthesis from various sources, including interviews, workshops, and observation data, to generate insights for new business models and competitive analysis. The presentation also offers a free consultation to demonstrate these methods, emphasizing AI’s role in transforming organizations and improving efficiency across sales, marketing, and product development.

2025 – AIBA Conference – Case study 1 – Research process Download

Key takeaways from the ‘How to better understand your customers based on research using AI‘ video include:

Leveraging AI for faster research: Mosor demonstrates how AI tools can speed up the entire research process, saving valuable time and resources.
AI-driven analysis for customer understanding: Learn how AI can help you gain a deeper understanding of your customers’ needs, pains, and desires based on real research data.
Building better products & strategies: Discover how AI can inform product development and strategy, leading to more effective solutions that resonate with customers.
Toolbox for AI-powered research: Mosor reveals her toolbox, featuring key platforms like Google AI Studio and Gemini Advanced, and demonstrates how to utilize them effectively.
The importance of live field observations: She recommends use of observation for a better understanding and more complex analysis
AI for Content Creation: If you have your knowledge in different files or you would like to use the book creation process she helps to figure out the step-by-step structure, how to create it very fast using AI.

Don’t have 30 minutes to watch the entire video? Listen the podcast summary:

UVP Canvas by CIRCmodel.com

During the video, our CLO, Beata Mosór, showcases the UVP Canvas, designed to be more accurate during the observation process while gathering data from B2C & B2B service points. Here you can grab the canvas.

Structured data gathering helps you with AI model. How? You can use the structured input/structured output formula to work with AI to deliver precise results adjusted to your needs.

2025 – UVP Canvas by CIRCModel Download

The UVP model by CIRCmodel.com

The UVP Canvas by CIRCmodel.com is based on the UVP model designed by our CLO, Beata Mosór. Drawing on her experience, she has reinvented the UVP Canvas into a more circular model. How is it different from the UVP canvas invented by Alex Osterwalder (Strategyzer.com) and the Jobs to be Done (JTBD) framework developed by Tony Ulwick (Strategyn)?

The model designed by Beata Mosór connects these two perspectives, adding the business environment (e.g., competition, market, sustainability) and transforming the ‘Gains’ concept into ‘Values’. This reframes the focus for businesses from benefits for themselves to the value they bring to society, their customers, and the value chain in general. The model is designed in a circular mode, which makes it more suitable for working with AI models.

Definitions:

Values – The values that the business would like to bring to reality, society, and the world.

Environment – The business environment (e.g., market, competition), as well as environmental changes that impact the business’s place in the world.

Functional problems – Problems related to value chain delivery, work, and the tasks that need to be done.

Emotional problems – Human-related problems that reflect the conflict between the designed/invented values the business would like to deliver to the world and society, and the reality of that society and its people.

Social problems – Problems related to value delivery to customers, the place of the business in society/community, and its reflection on them.

Tasks – All the tasks that need to be done by the business to deliver its invented/designed values to the world and society—to make it happen.

2025 – UVP Model by CIRCModel Download

Would you like to work with us on your Business or Product Strategy using CIRCmodel.com?

Check our offer for AI Product Development as a service!

AIBA Conference! Invitation

The video also serves as an invitation to the AIBA Conference in Katowice, Poland (October 8-10, 2025), where CIRCmodel.com is a media partner and co-author on the scientific-based publication. Attendees can understand better AI technology and business applications at the conference and even book a free consultation with our CLO – Beata Mosór.

Special Offer

Beata Mosór offers viewers a 20% discount on conference tickets using the code AIBA2025_MOSOR20. For more information and booking a free consultation, check out our dedicated site: circmodel.com.

Understanding Customer Research with AI: A Comprehensive Study Guide

This study guide reviews key concepts, methodologies, and tools discussed in the provided excerpts from “How to better understand your customers based on research using AI” by Beata Mosór.

I. Core Concepts & Objectives

This section outlines the central themes and goals of the presentation, focusing on the application of AI in customer research.

The Problem: Traditional market research is often slow and resource-intensive, particularly in rapidly changing markets. Manual analysis of vast amounts of data can be inefficient.
The Solution: Leveraging AI and advanced technological tools to accelerate and enhance the research process, leading to a deeper understanding of customer needs and faster development of product strategies.
Benefits of AI in Research:
- Speed & Efficiency: Significantly reduces the time required for data collection and analysis.
- Deeper Insights: AI can process and identify patterns in data that might be missed by human analysis, leading to a more nuanced understanding of customer pains, gains, and jobs to be done.
- Better Product Development: Informed by comprehensive AI-driven research, companies can build more relevant products and strategies.
- Competitive Advantage: Staying ahead in a digitalizing market by efficiently adapting to changes and understanding evolving customer behaviors.
- Target Audience: Consulting agencies, research agencies, and companies that regularly conduct research, as well as individuals looking to apply AI in various organizational functions (sales, marketing, strategy, product building).

II. Beata Mosór’s Background & Expertise

This section highlights the speaker’s qualifications and experience, establishing credibility.

Experience: 18+ years in technology, 10 years as a mentor and lecturer.
Consulting Portfolio: Consulted over 1,000 tech products and services.
Clients: Worked with corporations (Google, Sabre, T-Mobile) and startups/scale ups.
Technological Journey: Started with cloud technology, progressed to low-code/no-code, advanced enterprise software (FinTech, HealthTech, MedTech), and re-engaged with AI/Machine Learning since 2015 (voice user interfaces, health tech/med solutions).
Entrepreneurial Background: Co-founded a lean strategy agency, Project: People.

III. Case Study: Auto Parts Industry

This section details a specific application of AI-driven research in a real-world scenario.

Client & Industry: A client from the auto parts industry, operating a complex marketplace connecting casual users, service providers (mechanics), and large players (franchisers, car/auto parts sellers).
Market Challenges: The automotive and auto parts market is undergoing significant transformation (EV market growth, changing international trade deals).
Key Problem: Only 7% of the auto parts market is digitalized, creating a significant challenge for the client and a threat to non-digitalized service providers.
Project Timeline: Very tight – 4 weeks for the entire product strategy, with only 2 weeks allocated for analysis and research. This time constraint necessitated the use of AI.
Research Process (Two Sprints):
Sprint 1 (Analysis & Data Gathering):
- Kickoff workshop with the team.
- Review of client materials (marketing, sales, product).
- Initial research (observations, ideas, surveys) with team and customers.
- Mapping KPIs, OKRs, and existing data/evidence.
Sprint 2 (Data Analysis & Strategy Development):
- Three workshops: Strategic, Functional Product Analysis, Financial Analysis, Competition Landscape Analysis.
- Outcome: Business model scenarios for product strategy.

The AI-Powered Research Workflow:Kickoff Workshop Recording: Used Fireflies.ai to record the kickoff workshop (voice and video), generating summaries.
Model Training (Initial): Trained a Gemini 2.5 model (in Gemini Advanced) with the recorded workshop materials and project scope.
Scenario Generation: AI generated research scenarios based on the provided materials, including market digitalization, market trends, and competition.
Value Proposition Canvas & Ideation Scenarios: AI helped create a unique value proposition canvas for live observations and multiple ideation scenarios.

Live Interviews & Data Collection:Team conducted live interviews, recorded with Fireflies.ai.
Team documented insights from live observations on canvases.
Documents (Google Docs) were converted to PDFs for AI model input.
Model Retraining & Analysis (Iterative): Used Google AI Studio and Gemini Advanced.
Uptrained models with all collected materials: PDFs (documents, canvases), Fireflies.ai recordings (via links).
Prompts Used: Market analysis (customer pains, gains, jobs to be done), UVP analysis, product functionality mapping (Kano matrix), competition landscape analysis, financial analysis.

Circular Mode: An iterative process where AI conducted research, new materials/prompts were added, and the model was retrained, enabling fluent and efficient analysis.

Outcome: Within two weeks of intensive AI-supported research, three different, efficient new business models were created, and the company’s strategy was analyzed for a complex market.
Key to Success: Close cooperation with the client team (Lean UX process), daily discussions, on-site customer engagement, and consistent input of observed/recorded data into the AI models.

IV. AI Tools & Their Specific Applications

This section breaks down the specific AI tools mentioned and their distinct functionalities.

Fireflies.ai:
Function: Meeting transcription and summarization (voice and video recordings).
Usage in Case Study: Recorded kickoff workshops and live interviews, providing structured input (summaries, voice recordings) for AI models.
Google Gemini Advanced:
Function: Deep research and analysis, text/music/speech/code generation, creation of various outputs (podcasts, landing pages, dashboards). Primarily for research and analytical tasks.
Model Used: Gemini 2.5.
Usage in Case Study: Initial training with kickoff workshop materials, generating research scenarios, and in-depth analysis of collected data (UVP, pains, gains, jobs to be done, product functionality, financial, competition). Utilized for its robust research and analytical capabilities.
Google AI Studio:
Function: More creative and sophisticated work; generating scenarios, code, structured inputs/outputs, developing ideas, image generation (Imagen 3), video generation (Va 2/Va 3), and working with Gemma models. Easier creation of “Gems” or apps.
Model Used: Gemini LLM experimental (thinking model).
Usage in Case Study: Used for generating ideation scenarios and for more creative problem-solving aspects of the research. Offers a broader choice of models for different creative tasks.
Technological Stack: The basic AI tech stack for research highlighted by Mosór includes Fireflies.ai, Miro AI (implied as a board for collaborative input alongside canvases), Google AI Studio, and Google Gemini Advanced.

V. Additional Information & Offers

This section covers other details presented by Moser, including upcoming content and consultation opportunities.

AIBA Conference (Katowice, Silesia, Poland):
Dates: October 8-10.
Mosór’s Role: Media Partner (past and present).
Contribution: Publishing a paper on leadership and AI-based organizational transformation as part of a joint book.
Discount Code: AIBA2025_MOSOR20 (20% off tickets).
Free Consultation Offer: One-hour free consultation for ticket purchasers. Attendees can bring marketing/sales materials and data for live training and advice on technology application (AI, low-code/no-code, cloud) for sales, marketing, research, strategy, and product building.
Upcoming Videos:
August: Using AI to create videos for business promotion.
September: Advanced content creation, specifically writing a book/ebook/lead magnet in 20-40 hours.

Key Terms

Voice User Interface (VUI): A system that allows users to interact with devices or applications using voice commands. Moser’s early AI experience included VUIs.

AIBA Conference: An event where Beata Mosór serves as a media partner, focused on topics related to AI and technology.

AI (Artificial Intelligence): The simulation of human intelligence processes by machines, especially computer systems, including learning, reasoning, and self-correction. In this context, used to automate and enhance research processes.

AI Studio (Google AI Studio): A Google platform designed for more creative and sophisticated AI work, including generating scenarios, code, structured inputs/outputs, and utilizing various models for image and video generation.

Auto Parts Industry: The specific industry of the case study client, dealing with components and accessories for vehicles, currently undergoing significant digital and market transformations.

Canvases (UVP Canvas, Business Model Canvas): Visual charts or templates used for strategic management and lean startup methodologies to map out a company’s or product’s value proposition, customer segments, revenue streams, and other key components.

Circular Mode (of AI Training): An iterative process where an AI model is repeatedly trained with new data and prompts based on its previous outputs, creating a continuous feedback loop that refines and deepens the analysis.

Cloud Technology: On-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Beata Mosór’s background includes this area.

Competition Landscape Analysis: The process of identifying and evaluating competitors, their strategies, strengths, and weaknesses to understand the market environment.

Content Creation: The process of generating and publishing various forms of media, such as text, images, videos, or audio. Mosór plans to cover AI for advanced content creation like book writing.

Customer Pains, Gains, Jobs to be Done (JTBD): Concepts from the Value Proposition Canvas: Pains are negative experiences or obstacles customers face; Gains are positive outcomes or benefits customers seek; Jobs to be Done are fundamental problems or tasks customers are trying to accomplish.

Digitalization: The process of converting information into a digital format or transforming business processes and operations using digital technologies. A key challenge in the auto parts case study.

EV Market (Electric Vehicle Market): The growing sector of the automotive industry focused on electric cars, contributing to the transformation of the auto parts market.

FinTech, HealthTech, MedTech: Abbreviations for Financial Technology, Healthcare Technology, and Medical Technology, indicating specialized areas of technology application where Mosór has experience.

Fireflies.ai: An AI-powered meeting assistant that records, transcribes, and summarizes meetings (voice and video), used to capture raw data for AI model input.

Functional Analysis: Examination of a product or system’s functions to understand what it does, how it works, and its capabilities.

Gemini Advanced (Google Gemini Advanced): A Google AI model specifically highlighted for deep research, analysis, and generation of various outputs, suitable for comprehensive analytical tasks.

Gemma Models: A family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models, usable in Google AI Studio.

Gems (Google Gemini Apps): Simple applications or tools that can be created using Google AI Studio.

Ideation Scenarios: Structured prompts or frameworks designed to stimulate and guide the generation of new ideas, often created with AI assistance.

Imagen 3: Google’s advanced text-to-image diffusion model, mentioned as a tool for image generation in Google AI Studio.

In-depth Interviews (IDIs): Qualitative research technique involving detailed, one-on-one conversations with individuals to explore their perspectives, experiences, and behaviors.

Jobs to be Done (JTBD): See “Customer Pains, Gains, Jobs to be Done.”

Kano Matrix: A tool used to categorize customer preferences for product features, distinguishing between basic, performance, excitement, indifferent, and reverse features.

KPIs (Key Performance Indicators): Quantifiable measures used to evaluate the success of an organization, employee, or activity in meeting objectives.

Lead Magnet: A marketing term for a free item or service given away for the purpose of gathering contact details, such as an ebook or white paper. Moser plans to cover AI for creating these.

Lean UX Process (for Cooperation): A reference to a highly collaborative and iterative work process, often implying open communication and frequent feedback loops, similar to agile methodologies.

LLM (Large Language Model): A type of artificial intelligence algorithm that uses deep learning techniques and a massive amount of text data to understand, summarize, generate, and predict new content. Gemini is an example of an LLM.

Low-code/No-code Technologies: Platforms that allow users to create applications with little to no coding, typically through visual interfaces and pre-built components. Part of Mosór’s technological expertise.

Marketplace (Digital Marketplace): An online platform that connects buyers and sellers, facilitating transactions. The client in the case study operated such a platform.

Miro Board (Miro AI): A digital collaborative whiteboard platform often used for brainstorming, workshops, and visual planning. Implied as a tool alongside canvases for capturing collaborative insights.

OKRs (Objectives and Key Results): A goal-setting framework used by organizations to define and track objectives and their measurable outcomes.

Product-Market Fit: The degree to which a product satisfies a strong market demand.

Product Strategy: A plan for a product that outlines its goals, target audience, competitive advantages, and how it will achieve market success.

Prompt (for AI): An input or instruction given to an AI model to guide its response or generation of content.

Research Agencies: Companies specializing in conducting market research, customer insights, and data analysis for clients.

Scenarios (Research Scenarios, Ideation Scenarios): Detailed outlines or imagined situations used to guide research, problem-solving, or idea generation.

Sprints: In agile methodologies, short, fixed-length periods (typically 1-4 weeks) during which a team works to complete a set amount of work. The case study used two 2-week sprints for analysis and research.

UVP (Unique Value Proposition): A clear statement that describes the distinct benefits a product or service offers to its target customers, setting it apart from competitors.

AI-Powered Customer Research | Case Study | Automotive Industry