This blog helps businesses understand what Generative AI is, how it works, and why they should care to learn more about it.
Generative AI has become one of the biggest buzzwords in business, and for good reason; it’s reshaping and streamlining how companies operate – with market research as no exception. The rise in Generative AI marks a transition from tedious, operational research tasks to strategic, high-value focus. quantilope is just one of the many businesses leveraging AI technology to deliver faster, deeper, and more actionable insights for effective, data-driven decisions.
Table of Contents:
- Generative AI in market research
- How Generative AI works with consumer data
- Generative AI’s benefits for insights teams and brands
- Applications across the research workflow
- Risks and ethical considerations
- Future outlook for the Generative AI market
- Steps to start using Generative AI market research
- Turn AI marketing insights into action with quantilope
Generative AI in market research
At its core, Generative AI (or, Gen AI) is a type of advanced artificial intelligence that doesn't just analyze existing data—it creates entirely new, original outputs based on patterns from existing datasets (e.g. online survey data, qualitative insights, social media content, transactional data, or market reports).
The most well-known examples of Generative AI today are Large Language Models (LLMs); think: Google’s Gemini, OpenAI’s ChatGPT, and Microsoft’s Copilot. These complex systems, powered by advanced algorithms and machine learning, can simulate human-like thought processes. Individuals can use these models for anything from everyday search topics (e.g. “How do I make a roasted chicken?”) to interpreting complex information (e.g. “What does this financial model tell me about the current market?”) to generating unique images or graphics (e.g. “Create an image of a dog wearing a snowman costume and sledding down a hill”).
In the context of market research, we can use Gen AI to help streamline the end-to-end insights process. quantilope realized Gen AI’s potential early on, building it into our AI co-pilot, quinn, to instantly draft survey questions, generate chart headlines/takeaways, and automate entire dashboard summaries. As quinn takes care of these more tedious tasks, researchers are freed up to focus on the strategic application of their findings.
Back to Table of Contents
How Generative AI works with consumer data
So, how does Gen AI’s sophisticated technology interact with consumer data to arm researchers with actionable insights?
Gen AI is trained on vast datasets of consumer information. It essentially "reads" all this data to understand underlying patterns. By processing this data, Gen AI systems can effectively understand and model consumer behavior and sentiments. quantilope’s quinn can even process, analyze, and interpret unstructured data from open-ended responses using Natural Language Processing (NLP). As a result, quinn can generate real-time follow-up questions while consumers are still active in a survey, diving deeper into a topic for richer results.
With Gen AI’s ability to use training data to generate new unique outputs, brands can effectively forecast consumer preferences, simulate market scenarios, and predict customer preferences without having to wait weeks for fieldwork or reporting.
Back to Table of Contents
Generative AI’s benefits for insights teams and brands
Incorporating Generative AI tools into your research process can drastically improve the speed, cost, and depth of your insights.
More specifically, teams that leverage Generative AI benefit in these key areas:
Speed and efficiency
- Real-time data analysis
- Instant report generation
- Task automation
Cost reduction
- Reduced manual efforts
- Realistic simulations before expensive fieldwork/testing
- Automated data processing/aggregation
Deeper marketing insights
- Uncover hidden patterns, trends, and correlations
- Generate powerful predictive models
- Personalized insights for content creation
Scalability across markets
- Easily digest and interpret global datasets
- Generate market-specific research
- Generate actionable segments based on demographics and psychographics
Back to Table of Contents
Applications across the research workflow
Gen AI is revolutionizing the market research workflow by automating and enhancing every stage, from initial concept to final report delivery. It can help researchers quickly analyze vast, complex datasets, generate new content (like summaries, personas, or new survey questions), and uncover insights at unprecedented speed and scale.
Researchers can use Gen AI for:
- Survey and discussion guide creation: Use AI tools to draft survey questions and moderator guides, optimize question wording, and reduce bias.
- Synthetic respondent simulation: Generate realistic, diverse customer responses to test initial concept ideas before proceeding to traditional focus groups which often require expensive fieldwork.
- Automated coding and theming: Instantly extract core themes and sentiment patterns from thousands of open-ended responses, and code qualitative research data using NLP without manual effort.
- Instant executive reporting: Automatically generate summaries, visualizations, and tailored reports for various stakeholders, ensuring faster decision-making.
quantilope is one example of a platform strategically using Gen AI. Leaning on the platform’s AI co-pilot, quinn, quantilope users can accomplish more in less time.
Key quantilope Gen AI features:
- quinn chat: quinn’s chat interface where researchers can communicate directly to ask questions and get inspiration. It provides suggestions for survey inputs, assists with creating advanced method inputs, and helps streamline the project’s end-to-end timeline.
- CEP Generator: This proprietary, AI-driven tool curates an initial list of Category Entry Points (CEPs). CEPs are the specific needs, occasions, or motivations that prompt a consumer to think of a product category - a framework developed by Professor Jenni Romaniuk at the Ehrenberg-Bass Institute for Marketing Science. By automating the generation of this foundational list, quantilope helps brands quickly move toward strategic brand growth focused on Mental Availability.
- AI Probing: This feature automatically generates follow-up questions for open-ended survey responses. It allows the research platform to "talk back" to the respondent, diving deeper into their initial feedback without requiring manual setup for every possible answer.
- Automated Reporting & Synthesis: quinn automatically generates descriptive chart titles and dashboard summaries based on the analyzed data. This significantly cuts down on the time researchers spend writing up findings, allowing them to focus on strategic action instead.
By integrating Gen AI with a suite of automated, advanced research methods (like Conjoint, MaxDiff, etc.), quantilope's platform is designed to deliver high-quality, complex insights much faster and more cost-effectively than traditional research approaches.
Back to Table of Contents
Risks and ethical considerations
As with any powerful technology, incorporating Generative AI requires careful consideration of potential risks. Below are a few areas to keep in mind when considering or leveraging a Gen AI tool.
- Data privacy and security: When leveraging Gen AI, it’s important to address concerns about the creation of synthetic data and the protection of consumer information. Clearly communicate the underlying methodology and that explicit consent is required for maintaining data privacy (e.g. GDPR/CCPA compliance).
- Bias and hallucinations: AI is only as good as its training data. Poor data inputs lead to biased outputs. We can actively guard against "hallucinations" (AI tools generating plausible, but entirely false, data) through rigorous data quality checks.
- Human oversight requirements: Remember that AI-driven capabilities augment rather than replace human researchers. Human researchers will always remain essential for insights interpretation and strategic decision-making.
Back to Table of Contents
Future outlook for the Generative AI market
The future of Generative AI in market research is centered around rapid growth and standardization. Below are some of our predictions for the future of Gen AI in the industry.
- A rapidly-expanding artificial intelligence market: Adoption of AI tools is expanding quickly, with an increasing investment in robust, AI-powered research platforms and providers. As demand scales, so will available tools.
- Evolving regulations: As AI technology matures, expect to see emerging industry guidelines for ethical and compliant AI use in research, particularly concerning data collection.
- Talent and skill shifts: Researcher roles are evolving. Future insights professionals will focus less on manual data handling and more on the strategic application of their insights to make better business decisions.
Back to Table of Contents
Steps to start using Generative AI market research
Knowing the impact Gen AI can have on a market research organization, below are a few steps to start taking advantage of all it has to offer.
- Audit current research processes: Identify the most repetitive, time-consuming tasks that would make good contenders for automation.
- Identify quick-win use cases: Start small with low-risk use cases, like survey drafting or basic coding, to build confidence and experience with AI tools.
- Pilot with a trusted vendor: Select established, reputable providers with proven generative AI tools. Test the tools on small projects before full implementation.
- Measure and scale: Track the actual efficiency gains and quality improvements using key metrics. Once validated, gradually expand AI use across the organization for more research activities.
Back to Table of Contents
Turn AI marketing insights into action with quantilope
Once you decide to leverage an AI tool in your insights process, you'll have to choose between a variety of available tools on the market. The tool you choose should be a seamless extension of your research team, rather than a replacement of valuable human expertise. Look for the following elements when choosing an AI tool for your organization:
- Integration with existing tools: Ensure seamless connection with your current research stack, looking specifically for APIs and data export capabilities.
- Model transparency and compliance: Evaluate the vendor's approach to data privacy, security, and model explainability.
- Service and support levels: Assess training resources and ongoing technical support for your chosen AI tools.
quantilope is a recognized leader in AI for market research, seamlessly combining powerful Generative AI with comprehensive, advanced research methodologies. quantilope’s AI co-pilot, quinn, offers everything from AI-powered survey drafting, automated coding, and instant report generation. With quinn, you not only get to your insights faster, but you can be confident the insights are validated, contextualized, and ready for strategic decision-making.
Get in touch below to learn more about Generative AI capabilities with quantilope!
Frequently Asked Questions:
.png)
