Feb 20, 2025
By Dan Wasserman

Where Digital Twins Go Wrong (And 3 Ways You Can Avoid These Pitfalls)

Developing Digital Twins from market research allows brand teams to explore and emulate how an audience’s preferences, attitudes, and reactions will play out in the real world. Based on real respondent data, digital twins are enriched with behavioral insights that allow us to develop and test innovative ideas, messages, concepts, and processes in ways traditional research methods simply cannot match.

Why use Digital Twins?

Digital twins make it possible to explore countless consumer, patient, HCP, or decision-maker scenarios quickly and confidently – all without the cost, time, or risks of traditional methods. These AI-powered respondents let you explore possibilities that would be costly to research directly while still delivering the precision and depth you need. Plus, they’re fast. You can simulate potential outcomes, evaluate new ideas, and fine-tune processes in days, not months.

The Limitations of Digital Twins

Before we dive in, we have to acknowledge the limitations of digital twins. There are a few cases in which digital twins are not the right fit for your project, for example:

Unrelated Queries: the questions you ask digital twins must also relate to the data set they were trained on. For example, if the training data set involves asking physicians about treating patients with obesity, you may ask about obesity, diabetes, and heart conditions. However, if you start to ask about cancer, although you’re reaching out to physicians, you won’t have data in the training set to inform digital twins to process, think, and respond appropriately.
Brand Recall: humans play a vital role in the nuanced interpretation and processing of information, while models process information distinctively from humans, leading to inaccurate recall-based study results.
Some Cultural Groups: Generative AI has been shown to be largely reflective of white, western culture. As a result, extra care needs to be taken to ensure that creating digital twins of minority groups or cultures outside the West are created appropriately. Some topic areas, such as exploring social determinants of health in minority groups, are not a great fit based on current limitations.

Considerations for Ensuring Digital Twins Will Deliver Reliable Results

Digital twins have the potential to deliver faster and more scalable insights. If you are vigilant of the potential limitations before you start employing this approach, you can maintain the rigor and reliability of human-driven research.

1. Consider the Data Used to Create the Model

A digital twin’s efficacy is intrinsically linked to the quality of the data on which it is based, a concept reiterated by experts at McKinsey. These intricate models demand high-quality, robust, and complete data to effectively reflect and address real-world complexities. When data is flawed or incomplete, the fidelity of the digital twin is compromised, leading to inaccuracies and ineffective modeling outcomes.

At Sparq, we understand the critical importance of data integrity in crafting effective digital twins. We prioritize the use of training our models using individual-level data over aggregated data (from research reports) or foregoing any training by generating purely synthetic data (generated by LLMs). The advantage of utilizing individual-level data lies in its ability to retain the nuanced insights of raw data, which are often lost when averaged in aggregation processes like segmentation studies. By focusing on this granular level of detail, we can ensure that the insights our digital twins provide are rich, detailed, and closer to reality.

2. Consider The Modeling Process

The digital twin concept, though relatively new to market research, has long found its foundation in the life sciences arena. At Sparq, we believe in embracing a fluid and flexible modeling approach that adapts to the rapidly evolving landscape of digital technology. This flexibility facilitates the integration of the latest advancements in modeling processes, ensuring our models remain on the cutting edge.

Validation is a cornerstone of our approach, underscoring the need to affirm the usefulness and reliability of our digital twins. Validation can be integrated into the modeling phase or conducted separately, where models are meticulously compared with real-world data. Through a consistent cycle of validation, you can preserve the accuracy of your insights. We’re also committed to staying in alignment with academic literature and engaging in continuous education and training in this field. This proactive learning approach helps avoid potential modeling mistakes and strengthens the expertise required to navigate this evolving domain effectively.

3. Consider The Use Case of the Model

When applied appropriately, digital twins are formidable tools, excellently suited for a variety of strategic operations. At Sparq, we use digital twins in a few ways:

Modeling Niche Audiences – A cost-effective and time savings way to continuously generate insights from difficult-to-reach audiences
Conducting Innovation Assessments – Rapidly test hypotheses
Scenario Testing – Rapidly explore the impact of various market scenarios
Sensitive Topics – Understand hypothetical reactions to questions that may be too sensitive or impossible to directly ask your audience
Small Scope – Pulse your audience when a full traditional study is not warranted

Digital twins offer flexibility in scenarios where legal or ethical restrictions might limit human research. For example, they can be used to explore questions about a competitive offering or even topics that might raise adverse events in healthcare without the same level of scrutiny or risk as human participants.

Just like you can train digital twins with specific data, you also have the ability to remove information from them. For instance, if you’ve already primed the model with details about a new brand entering the market but later wish to ask questions without that context influencing its responses, you can change the model to disregard any previously learned information. This feature is useful when you want to discuss a product or feature without preexisting associations impacting the outcome.

At the heart of digital twin technology is its ability to simulate real-world interactions and forecast potential outcomes. While digital twins excel at modeling consumer behavior and testing scenarios, their real value comes from helping teams make informed decisions faster and with greater confidence.

This is where Sparq Intelligence comes in. Sparq ensures that digital twin models reflect real-world complexities as accurately as possible. Whether it’s evaluating audience reactions, testing market scenarios, or exploring sensitive topics, Sparq provides a structured, scalable approach that enhances decision-making.

Learn More

Dan Wasserman

Head of AI Solutions

Dan is the Head of AI Solutions for Sparq Intelligence. He is a leader in generative AI and market research, with 15+ years of experience bridging technology and business strategy. He drives AI innovation to deliver fast, precise, and impactful insights. A recognized thought leader and speaker, he helps businesses turn data into confident decisions.

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