Research that keeps moving, even when reality gets in the way
Panelists miss sessions. Studies vary in structure. Data gaps appear. And when they do, the cost is real. Delayed reporting, weakened analysis, expensive repeats. Digital twins are our answer: AI-powered synthetic models that fill missing data reliably, without compromising the integrity of your results.
About the innovation
What are Digital Twins?
A digital twin is a synthetic model of a research participant, built from historical data and trained to reproduce that individual's response patterns across products, attributes, and contexts. When data is missing, a digital twin does not replace it with a generic average. It makes an informed, individual estimate based on how that specific respondent has behaved before, in similar situations.
The result is a dataset that is more complete, more consistent, and more analytically robust, without additional fieldwork, without delays, and without compromising the integrity of your decisions.
Beyond averages
Filling gaps with mean imputation seems harmless, but it quietly flattens variance and weakens the relationships between variables. That underlying structure is exactly what multivariate analyses rely on. Digital twins preserve it, because they account for each individual's response style, the product context, and the relationships within the data.
Validated before deployment
Every digital twin is built on the client's own data and validated rigorously before it is applied. We test performance by deliberately removing data and checking how accurately the model recovers it, across missing attributes, missing products, and missing panellists. Nothing is deployed until it has proven itself on your specific dataset.
Why Digital Twins matter
Real-world research never goes fully perfect. Participants are absent. Studies run under time pressure. Data arrives with gaps. Digital twins are built for exactly those conditions, helping research teams protect decision quality when circumstances are not ideal.
Understand what your data is really telling you, even when it is incomplete. Digital twins reconstruct missing responses at the individual level, preserving the analytical structure that pattern recognition and benchmarking depend on.
Keep research programs on track. When a panelist is absent or a study runs short, you no longer face a choice between delay and compromise. Digital twins give you a third option: continuity with confidence.
Model outcomes before committing to costly fieldwork repeats. Digital twins let you assess how robust your dataset is, explore the impact of data gaps, and make smarter decisions about when additional research is truly necessary.
Start protecting the quality of your research data
At Haystack Consulting, digital twins are built for rigour, not convenience. Every model is validated on your data, in your category, before it is ever applied.
Ideal Partner
Why partner with Haystack Consulting for Digital Twins
The idea for Digital Twins came from a practical problem our sensory research teams kept running into: expert panels generate irreplaceable data, and when gaps appear, the consequences ripple through timelines, budgets, and decisions.
Our approach is grounded in validated machine learning methodology. Using a tree-based modelling framework, Random Forest CART, we train models that map the non-linear relationships in research data far more accurately than average-based alternatives. But the underlying logic applies wherever research data is collected over time and completeness matters, and we are actively exploring applications in quantitative tracking, longitudinal studies, and high-volume product testing pipelines.
Unique process
Built on your data, validated on your terms
Digital twins are not a black-box solution. Every model is purpose-built for the client's specific category, panel, and dataset, and tested before it touches a single real study.
Individual, not average
The core difference between a digital twin and conventional imputation is specificity. Our models learn how each individual participant scores, their personal response shape, their tendencies relative to the group, and the way attributes interact in their data. When a gap appears, the model draws on all of that context to make an estimate that reflects how that person would actually have scored, not how the average panellist might have.
Rigorous validation at every step
Before any digital twin is deployed, it goes through a structured validation programme: holdout testing to confirm predictive accuracy on unseen data, train-on-synthetic and test-on-real procedures to confirm the model has captured the essence of the original data, and systematic degradation testing where attributes, products, and entire panellists are removed to stress-test performance. Only models that pass all three stages are applied to real studies.
Brand aligned
A partner who keeps analytical integrity at the core
We see synthetic data as a tool for precision, not a shortcut. Every digital twin is designed to protect the quality of your research, not to hide its gaps.
Hybrid precision
Digital twins fill what is missing. Real panellists and respondents generate the data they are trained on. The two work together, so your research stays complete without losing its human foundation.
Scientific foundation
Tree-based machine learning, rigorous stress-testing, and performance benchmarking against real data, at every stage, before deployment.
Tailored to your data
No generic models. Every digital twin is trained on your historical data, in your specific context, and validated on your dataset before it is applied.
Ethically designed
We never apply a model we cannot explain or validate. Clients understand what the twin does, how it was built, and where its limits are.
How we build and deploy your Digital Twin
From data assessment to live deployment, every step is designed to protect quality and build confidence before the model ever touches a real study.
Assess
We review your historical dataset, volume, structure, category context, and the nature of the gaps you are facing. We do this to determine whether digital twins are the right solution and what performance to expect.
Validate
The model is stress-tested through holdout validation, train-on-synthetic/test-on-real procedures, and systematic degradation testing. Performance is benchmarked against mean imputation at every stage.
Activate
We deliver complete, robust datasets alongside full transparency on where synthetic data was applied, so your team moves from data to decision with confidence.
Build
We train a Random Forest CART model on your historical data, capturing individual response patterns, attribute relationships, and participant-specific behaviour within your category.
Deploy
Once validated, the digital twin is integrated into your research workflow, filling missing data reliably, on demand, without disrupting existing processes or timelines.
Scientific foundation
We train a Random Forest CART model on your historical data, capturing individual response patterns, attribute relationships, and participant-specific behaviour within your category.
Scientific foundation
Tree-based machine learning, rigorous stress-testing, and performance benchmarking against real data, at every stage, before deployment.
Validate
The model is stress-tested through holdout validation, train-on-synthetic/test-on-real procedures, and systematic degradation testing. Performance is benchmarked against mean imputation at every stage.
Deploy
Once validated, the digital twin is integrated into your research workflow, filling missing data reliably, on demand, without disrupting existing processes or timelines.
Activate
We deliver complete, robust datasets alongside full transparency on where synthetic data was applied, so your team moves from data to decision with confidence.
Don’t just take our word for it
"Great team to work with. Always innovating research agency for sensorial research!"
"Haystack brainstorms with us as a partner to create challenging, innovative, and custom-made projects."
"We have great professional experience with Haystack! They are reliable, dynamic, pleasant and highly flexible. They are specialised in sensory research among others."
"Haystack has a critical approach embedded in their company culture and methodologies. That is why we prefer to work with Haystack."
Ready to shape your digital twin story?
Missing data will always be part of real-world research. If you are ready to explore how digital twins can help you protect analytical integrity, reduce operational friction, and keep your research moving, we would love to talk.
Frequently asked questions
Digital twins are a new capability in research. Here are the questions we hear most from clients exploring what is possible.
Can digital twins be used outside of sensory research?
Sensory panels are our primary validated use case. The methodology is well-suited to any research context where data is collected repeatedly over time, individual response patterns are meaningful, and completeness matters — such as longitudinal tracking studies, high-volume product testing, or quantitative panels. We approach each new application with the same validation rigour: build, test, and deploy only when performance is proven.
What exactly does a digital twin replace?
A digital twin fills missing data points — not real participants. It does not replace a panelist or respondent in a study. It reconstructs the scores or responses they would likely have given, based on their historical behaviour, when their data is absent or incomplete.
How long does it take to build and validate a digital twin?
It depends on the size and structure of your historical dataset. We provide a realistic timeline estimate during the initial assessment phase, before any model development begins.
Will clients know when synthetic data has been used fro digital twins?
Always. Full transparency is non-negotiable. Every dataset that includes digital twin outputs is clearly documented — specifying which data points were reconstructed, by which model, and with what level of validated accuracy. Clients make decisions with complete visibility into what is real and what is synthetic.
Is digital twins not just a more sophisticated version of mean imputation?
It is significantly more sophisticated — and the difference matters analytically. Mean imputation replaces missing values with a group average, which flattens variance and weakens the relationships between variables. A digital twin makes an individual estimate based on that specific participant's response patterns, the product context, and the relationships within the data. In our validation work, digital twins consistently outperform mean imputation — particularly when multiple participants are missing simultaneously.
How much historical data do you need to build a reliable model for digital twins?
It depends on the complexity of the study design and the number of attributes and products involved. We assess data suitability before committing to model development and will always advise honestly if the dataset is not yet sufficient to support a reliable twin.
Start your digital twin journey today
The gap between incomplete data and confident decisions does not have to be a delay.
If you are ready to explore how AI-powered synthetic data can strengthen your research process, our team is here to help.
