Simulate Budget Scenarios Before Investing

Simulate the impact of different budget allocations across channels, periods, and markets before investing, to choose the most effective and sustainable scenario.

01

What if €50k Moves from TV to SEM?

Core Forecast is the self-service tool built on your Marketing Mix Modeling to simulate budget reallocations, anticipate their effects, and present quantified decision scenarios to the Board.

It answers budget reallocation questions using saturation curves consistent with historical channel performance. It compares investment scenarios and clarifies the expected consequences of every change in the mix.

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02

The Evolution of Core MMM

Core Forecast is an integrated add-on to Core MMM. It is only available to clients with an active Core MMM model, as it operates on the model’s own parameters.

Much more than a historical report, Core Forecast transforms the MMM model into an interactive tool to explore past performance and evaluate future implications. It allows you to isolate baseline and incremental media contributions, navigate data, and simulate how different investment choices can alter expected results.

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It's right for your team if:

Econometric Model

You seek decision-making autonomy between econometric model refreshes.

Ready Simulations

You frequently need to prepare for the Board and require simulations ready in minutes.

Self-Service Tool

You want to give your team a self-service tool to simulate different media allocation scenarios.

Different Views on Your Business:

Trend Analysis

Historical trend of the main KPI, decomposed by each channel's contribution. Understand where your results come from.

Weekly Attribution

Weekly attribution of sales to active channels, with drill-down by period and sub-perimeter.

Monthly Forecast & CAC

Monthly forecast of KPI and CAC based on the selected investment scenario, with detail by period and sub-perimeter.

ROI

Updated ROAS per channel, with credibility interval, always available to compare performance and scenarios.

Media Performance Simulation

Simulation of mix scenarios: what happens if I move X from channel A to channel B, keeping the total budget constant.

Total Sales Forecast

Simulation of the total KPI as the overall budget changes: what happens if I increase media spend by 10%? By 20%? Halve it?

Frequently Asked Questions

How many scenarios can I simulate and compare?

There are no predefined limits. The team can modify budgets, channels, periods, and markets and quickly compare the expected effects. For example, they can evaluate whether to shift €50,000 from TV to SEM or increase the overall budget by 10%.

Why does Core Forecast require an active Core MMM model?

Because it does not use generic benchmarks: every simulation is based on the parameters and response curves of the client's Core MMM model. For example, an increase in Paid Social budget is estimated based on the observed saturation for that brand, not on market averages.

How is a scenario built in Core Forecast?

The team enters investment hypotheses for channel, period, or market into a structured sheet. Core Forecast returns the expected effects on KPI, CAC, and ROI. For example, it allows comparing a plan with higher pressure in the peak quarter with one more distributed throughout the year.

What is the reliability of the simulations based on?

Reliability depends on the quality of the Core MMM model, the depth of historical data, and the consistency of the entered hypotheses. Simulations apply the estimated response curves for each channel: for example, if TV is close to saturation, Core Forecast evaluates whether additional budget can generate more value on another medium.

How does Core Forecast stay updated over time?

With each Core MMM refresh, we update Core Forecast's parameters. For example, if a channel changes performance or reaches saturation sooner, subsequent simulations incorporate the new behavior and keep the tool aligned with business evolution.

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Want to see how it works?

In a 30-minute demo, we’ll show you Core Forecast on a real dataset and evaluate together how to apply it to your model.