Efficient collection and utilization of qualitative feedback from Generation Z, mapped onto digital twins for in-depth analysis.

The Challenge

A large German insurance provider had recently developed a new TV commercial and needed to understand how Generation Z would respond to the ad. Their primary goal was to gather qualitative feedback that could be leveraged not only to gain insights for future commercials but also to inform upcoming social media campaigns and other communication efforts.

Qualitative research was the method of choice, but it comes with a number of limitations:

Cost: Traditional qualitative research, especially involving in-depth interviews with target groups, is expensive. It requires experts to design and conduct the studies, as well as compensating participants for their time.
Time: Gathering and analyzing qualitative data can be incredibly time-consuming. The process of setting up interviews, conducting them, and then transcribing and analysing the data often takes weeks, if not months.
Flexibility and Sustainability: Once the data is collected and a results report is generated, there is little room for flexibility. Further inquiries often require starting the research process from scratch, making it difficult to sustain the value of the insights over time.

The Solution

The insurance provider turned to experial, leveraging its AI-powered approach to market research. The goal was to obtain quick and cost-effective feedback from Generation Z on their new TV commercial. Here's how experial addressed the challenge:

Targeted Audience Acquisition: experial collaborated with a panel provider to recruit 250 participants aged between 18 and 29.
Efficient Data Collection: Participants received a link that they could open on their smartphones. They were then presented with TV commercial and 14 questions related to the ad, which they could answer via voice messages, similar to WhatsApp voice notes.
Rapid Feedback Gathering: Within just two hours, qualitative feedback from all 250 participants was collected.
Advanced Data Analysis: The qualitative responses were analyzed using large language models (LLMs), producing a traditional results report. Additionally, the feedback was stored within the experial software solution in the client's account.
Interactive Insight Search: The insurance provider could now interact with the dataset using a search bar, allowing them to ask specific follow-up questions. The feedback from the 250 participants was mapped onto digital twins, enabling further exploration, such as querying responses by gender or specific ad elements.
Synthetic Extrapolation: experial's platform even allowed for synthetic extrapolation of responses. If a specific answer wasn’t available in the initial dataset, the LLM could generate probable responses based on the existing data of the respective participants, providing additional insights. The ability to ask follow-up questions and explore the data in-depth ensured that insights were not only quickly obtained but also continually relevant and actionable, leading to a more sustainable and broad use of the research findings.

The Results

The results of this pilot was a great success, the numbers speak for themselves:
Speed: The entire process, from gathering feedback to providing the data in the interactive insight search, was completed in just four days—a 90% reduction from the usual eight weeks required for traditional qualitative research.
Cost Efficiency: The project was completed at approximately 20% of the usual cost, representing over 80% savings in money.
Enhanced Insight Utilization and Sustained Engagement: The insights were actively utilized by 7 teams (the advertising agency involved in creating the TV spot), compared to only one team in previous projects.

Conclusion

By leveraging experial’s Digital Twin technology, the insurance provider experienced not only speed and cost-effectiveness but also unprecedented flexibility and depth in data analysis, leading to a more profound and sustained impact across the organization.