Are Digital Twins the Answer When You Can’t Reach the Audience?

A global consumer company faced a significant challenge: their target consumer group was extremely difficult to recruit for research due to its low prevalence in the general population. Traditional primary research approaches were proving too slow, expensive, and unreliable for generating timely insights. How could the client deepen their understanding of this group’s behaviors and perceptions without relying on high-cost, low-yield recruitment efforts?

To address this challenge, Sparq Intelligence leveraged AI-powered digital twins to simulate hard-to-reach consumer populations, enabling scalable insight generation without sacrificing analytical rigor.

The foundation for the digital twins came from a mix of client-provided survey data and external benchmark data sources. These quantitative insights focused on consumers’ perceptions of products in a specific category and their behaviors around the products. This hybrid dataset was used to build a robust training environment for modeling human-like behaviors in this specific audience and evaluating the performance of the digital twins.

We trained digital twin models to emulate the decision-making and mindset of this consumer group. These AI-powered twins could then be queried like a virtual panel, offering scalable, on-demand access to nuanced perspectives that would be otherwise hard to gather.

To ensure the accuracy and reliability of the models, we conducted a two-pronged validation process:

• Holdout Validation: Certain questions were held back during training, then used to assess the twins’ ability to match human responses.

• Cross-Data Validation: Twin outputs were benchmarked against a separate dataset for an external reference check.

We evaluated performance across both aggregate metrics (mean, standard deviation, top-2-box) and individual-level comparisons (% exact match, % match within one scale point, and mean absolute error).

The results showed, for key Likert scale questions, consistent means and standard deviations at the aggregate level. At the individual level, we achieved 90%+ accuracy within one scale point and mean absolute errors of approximately half a scale point — demonstrating strong alignment between the twins and real human data.

This engagement provided robust proof of concept that digital twins can fill critical gaps in market research for difficult-to-reach populations:

• Accuracy: AI outputs showed high fidelity, aligning closely with real human data across key metrics.

• Cost & Feasibility: Enabled rigorous insight generation without the costs or risks of low-incidence recruitment.

• Scalability: Once built, the virtual panel of digital twins could be extended to simulate other consumer groups using similar methods.

• Speed: Access to the digital twins dramatically shortened the insight cycle, providing answers in a day rather than weeks or months.

• Client Confidence: The client found the results both reliable and actionable, viewing this as a successful innovation pilot.

This case illustrates how digital twins can provide a breakthrough for brands struggling to reach specific audiences through conventional research. With the ability to simulate and scale insights from even the most elusive consumer groups, Sparq Intelligence empowers life science leaders to make smarter, faster, and more cost-effective decisions.

By combining AI-driven innovation with research best practices, Sparq Intelligence is reshaping what’s possible in consumer insights — transforming constraints into opportunities.