The panel of guests, including Mike Stevens (Founder of Insights Platform), Kathy Cheng (Founder of Nexxt Intelligence), Joel Anderson (EVP of Advanced Analytics at Dig Insights), and Julien Naggar (VP of Dig Insights), discuss the impact of AI on market research, areas where AI needs improvement, and tips for reliable results. They explore the comparison between human and AI-generated ideas, workplace adaptation to AI, and how to leverage tools effectively. Topics include AI in text and image processing, specificity and iteration in AI-enabled research, improving language skills with AI, and the limitations and potential of AI in answering questions.
AI-generated ideas outperformed human-generated ideas in innovation concept testing.
Conversational AI in market research surveys promotes engagement and provides valuable insights from large amounts of conversational data.
Deep dives
AI Applications in Market Research
AI is being applied to various aspects of market research, including text and image analytics, predictive models, and qualitative data summarization. The emergence of generative AI tools allows for the creation of new content based on input prompts. These tools are being used to summarize qualitative data, automate conversational surveys, and even generate reports. AI has shown promising results in innovation concept testing, where AI-generated ideas outperformed human-generated ideas. In the market research industry, AI is being leveraged to enhance the research process by providing simple fixes and augmentations in the here and now.
The Role of Conversational AI in Surveys
Conversational AI has become a key focus in market research surveys. Promoting engagement and genuine conversations with participants has been a goal of many researchers. Conversational AI allows for more creative and contextually relevant question asking, which is crucial for qualitative research. By leveraging conversational AI, surveys can feel less gimmicky and more authentic, leading to better participant experiences. Additionally, conversational AI enables the analysis of large amounts of conversational data, providing valuable insights for researchers.
The Importance of User Experience and Literacy
When using AI tools, user experience and literacy play a crucial role in maximizing their effectiveness. Users must be specific in their prompts and iterate to refine the results they receive. It is important to recognize that AI tools are not substitutes for critical thinking, but rather tools to enhance human capabilities. Users should approach AI outputs with a healthy dose of skepticism and engage in critical analysis to ensure the accuracy and relevance of the generated content. Developing literacy in AI applications allows researchers to make the best use of these tools in their workflows.
Limitations and Cautionary Considerations
While AI has made significant progress, there are still areas where it is not yet mature enough. For example, image generation for specific product-based tasks is still a challenge. It is crucial to exercise caution when using AI to avoid biases and to critically assess the quality and reliability of AI-generated outputs. AI is not a replacement for human researchers but rather a complement to their skills. It is not a substitute for critical analysis or the ability to ask the right questions in the right context. It's essential to understand the capabilities and limitations of AI and approach it as a tool rather than a magic solution.
“Do you feel like we have gotten to a place where the Chatbot can ask questions in the right way in the right context?” The answer is yes.
In this week’s episode, Meagan hosts a panel with Mike Stevens (Founder of Insights Platform), Kathy Cheng (Founder of Nexxt Intelligence), Joel Anderson (EVP of Advanced Analytics at Dig Insights), and Julien Naggar (VP of Dig Insights). The panel discusses the impacts AI has on the market research industry, areas where AI is needs improvement, and tips that produce reliable and efficient results.
Tune in to learn:
The comparison between human generated ideas and AI generated ideas
Areas where AI needs to improve to become more mature
How workplaces are adapting to the adoption of AI in their workflow
Tips to leveraging tools and making it understandable
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