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Synthetic Population Simulation
From official statistics and your own customer data we generate a synthetic population — a statistical twin of the real one. Thousands of virtual customers act on their own demographics and habits, and price, campaign and location scenarios are run across that population thousands of times. You see which option performs better without paying for field research first.
Details
- Synthetic population generated from official statistics and your customer data
- Thousands of agents: each a virtual customer with their own income, preferences and habits
- Scenario comparison: price, campaign, location, product mix
- Every scenario run thousands of times — the result is a distribution, not a number
- Uncertainty ranges reported openly
- Model validated by backtesting on held-out data
- The winning scenario tried for real on a small budget
- Findings fed straight into the advertising and SEO plan
A simulation does not predict the future. Its value is comparing scenarios under identical assumptions and ruling out expensive mistakes cheaply.
Contact
Aydın Uzunboy
Emailinfo@adgoritma.com
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Calls are handled over WhatsApp.
How the flow runs
These are example flows; the real one is built around your process. Press “Play” and watch where the message goes, step by step — or click any step.
Generating a synthetic population from real data, and validating it.
Message wording is illustrative; it gets written in your voice, for your product. Waiting times, conditions and channel order are decided together during setup.
What is a synthetic population?
It is a computer-generated group of people that is a statistical twin of the real one. Every individual exists separately, with their own age, income, household, city and buying habits. So you are not working with an assumption called “the average customer” but with thousands of different ones — which is how the extreme behaviour that an average hides becomes visible.
- Demographic distributions fitted to official statistics
- Behaviour rules derived from your own sales history
- Each agent decides individually; the total emerges from those decisions
- No real person’s data is copied — the generated population is virtual
Why thousands of runs?
A single run is chance; run a scenario once and the result may be luck. So every scenario is replayed thousands of times and read as a distribution rather than one figure. “Revenue rises 18%” is easy to say and misleading; the honest sentence is “in 80% of runs the increase lands between 9% and 24%”. How wide that range is tells you how risky the decision is.
- A Monte Carlo approach: the same scenario thousands of times
- Result distribution and uncertainty range
- A check on whether the gap between scenarios is meaningful
- No claim of “this one is better” when the ranges overlap
Which questions does it answer?
A simulation answers questions that can be asked. “How do we improve marketing?” is not one; “if we raise the price by 10%, how many customers do we lose and in which segment?” is. The sharper the question, the more usable the answer.
- Price: which segment can absorb how much of an increase?
- Location: where would a new dealer or store capture the most demand?
- Campaign: discount, instalments or a gift — which works on whom?
- Product mix: which variants reduce the risk of dead stock?
- Diffusion: how long word of mouth takes to saturate
Does it replace field research?
Not entirely — and we would rather say so at the start. What a simulation does is eliminate options cheaply. Testing ten price scenarios in the field is expensive; in simulation you narrow them to two, then try those two for real on a small budget. Total cost falls because the expensive mistakes were ruled out somewhere cheaper.
- Option elimination: ten scenarios down to two
- Survivors tested for real with a limited budget
- Real results fed back into the model
- The model gets more accurate after every real experiment
How is the model validated?
A model can only say something about the future if it can reproduce the past. Part of the data is never shown to it; the model predicts that unseen period and is compared against what happened. The error is written in the report rather than hidden. If the model cannot reproduce the past, we go back to calibration and publish no scenario results.
- Backtesting on held-out data, with the error reported
- Demographic distributions compared against official statistics
- Assumptions listed — which rule came from where
- Recalibration as the data changes
Its limits — what it cannot do
A simulation does not know the future. It cannot foresee behaviour never seen before, a new competitor or a crisis, because there is no data for any of them. It is also weak at absolute figures — its strength is comparing scenarios under identical assumptions. And if you hold no customer data at all, the model is assumptions dressed up in decimal places. We say this up front; surprises later are expensive.
- Cannot foresee unseen behaviour or unexpected events
- Stronger at relative comparison than at absolute numbers
- Without data, the output is only as good as the assumptions
- Does not replace a real test; it makes one cheaper
Frequently asked
We have no customer data. Can it still be done?
Partly. A demographically correct population can be generated from official statistics, but if the behaviour rules do not come from your data, the result rests on assumptions. In that case we state it plainly in the report, build on sector averages, and do not treat the numbers as certainties.
Is our real customers’ data being copied?
No. What is taken from your data is patterns, not people: how a given income group responds to price, for example. The generated population is entirely virtual, and no individual in it corresponds to a real person.
How reliable are the results?
Two things decide it: the quality of the data and the sharpness of the question. The report carries an uncertainty range for every scenario; a narrow range makes the decision comfortable, a wide one means we will not say “this option is better”. The backtest error is in the report too, so you can see for yourself how well the model tracked reality.
How long does it take, and what is delivered?
Building and validating the population depends on data preparation; the scenario runs are fast after that. What you receive is a report and a decision: the comparison of scenarios, the uncertainty ranges, the list of assumptions and a recommended next step.
What do we do with the result?
We try the winning scenario for real, on a small budget. The simulation does not make the decision; it lowers the cost of making it, because you go into the field knowing what to test. The real result then feeds back, and the next analysis is sharper.