Comparison
Imagine All The People vs Synthetic Panel
Reweighting an audience is not constructing a population.
Synthetic panels make it possible to question artificial audiences quickly. The difference lies in how that audience is built: available segments, classical calibration, or a global distribution under constraints.
Four differences, stated plainly.
Thesis
What distinguishes the approaches is not speed, but how the population is constructed.
- Synthetic panel
Audience or segments available within the provider's offer.
Imagine All The PeoplePopulation constructed for the decision.
- Synthetic panel
Calibration methods may include raking, IPF, weighting or other approaches depending on the provider.
Imagine All The PeopleCan globally optimise a large set of crossed statistical constraints.
- Synthetic panel
Very fast to activate on standard targets.
Imagine All The PeopleBusiness variables, specific constraints and bespoke populations.
- Synthetic panel
Questioning a synthetic audience.
Imagine All The PeopleSimulating decisions, their trajectories and their collective dynamics.
What changes, dimension by dimension.
| Dimension | Synthetic panel | Imagine All The People |
|---|---|---|
| Starting point | Synthetic panelAvailable audience / segment / brief | Imagine All The PeopleDecision + population to be built |
| Population | Synthetic panelDepends on the provider's offer | Imagine All The PeopleBuilt from reference distributions |
| Calibration | Synthetic panelRaking, IPF, weighting or other methods depending on the provider | Imagine All The PeopleProbabilistic methods + constraints + Maximum Entropy depending on configuration |
| Many constraints | Synthetic panelStrongly dependent on the architecture used | Imagine All The PeopleDesigned to handle many cross-tabulations and layered constraints |
| Business variables | Synthetic panelDepending on available segments | Imagine All The PeopleCan be added to the constructed population |
| Replay | Synthetic panelDepending on the tool | Imagine All The PeopleSame population, same individuals |
| Collective dynamics | Synthetic panelDepending on the tool | Imagine All The PeopleGroups, influence, cascades and interactions |
| Recommendation | Synthetic panelInsights and results depending on the offer | Imagine All The PeopleStrategic and operational recommendations derived from simulations |
| Main limit | Synthetic panelDependence on the population model offered by the provider | Imagine All The PeopleGreater methodological complexity to build the population |
When constraints multiply, the problem changes in nature.
Reproducing a few marginal distributions is relatively simple. The problem becomes more complex when the population must simultaneously satisfy many cross-tabulations between attributes.
Imagine All The People combines structured probabilistic models and maximum entropy methods to build or adjust distributions under constraints.
No method is superior in every circumstance: raking handles many situations, and calibration approaches vary from one provider to another. The benchmark describes a specific protocol, not a general hierarchy.
- 40attributes
- 19,409constraints
- −73%error vs raking
Benchmark of statistical construction. This result does not measure the quality of behavioural answers.
An illustrative example.
Decision
Test three price architectures across five market segments.
Synthetic panel
Select the available segments and question them.
Imagine All The People
Build the population matching the market, integrate business constraints, then replay each price architecture on the same individuals.
Each approach has its own domain of relevance.
Choose a synthetic panel if…
- standard target;
- fast turnaround required;
- the available population fits the problem.
Choose Imagine All The People if…
- specific population;
- many attributes;
- cross constraints;
- decision sensitive to population structure;
- successive scenarios.
What these pages do not claim to prove.
The statistical robustness of the population does not automatically transfer to behavioural answers. Building the population and validating the answers are two distinct layers.
