Measure Marketing, Explain Results.

Marketing Mix Models built to separate baseline, incremental contribution, and external factors.

From Business Question to Model

Every project starts with a concrete decision: allocate budget, estimate incrementality, evaluate saturation, simulate scenarios, or distinguish creative effect. The model is designed around these questions, not the other way around.

Marketing Mix Modeling, Not Just Media Mix Modeling

We analyze the contribution of media within the entire business system: promotions, price, distribution, seasonality, market trends, competition, sales channels, and brand dynamics.

A Flexible Modeling Approach

We use classical regressions, inferential models, Bayesian approaches, and hierarchical frameworks based on available data, granularity, and problem complexity.

Short-Term, Long-Term, and Brand Building

We estimate the immediate effect of campaigns and the contribution that accumulates over time, connecting media, awareness, consideration, brand equity, and future baseline.

Creativity, Media, and Context

When data allows, we compare different periods, copy, and planning to distinguish creative effect, media pressure, and contextual factors.

From Analysis to Decision System

Results feed into CoreMMMix and CoreForecast, making the model an operational base for visualizing contributions, comparing scenarios, and supporting planning.

Recommended Techniques

Model Design

We design the causal structure of the model starting from baseline, media, promotions, price, distribution, seasonality, and external factors.

Why it matters: Avoids superficial attributions and clarifies the role of each lever.

Regression & Bayesian Modeling

We use classical regressions and Bayesian approaches based on available data, granularity, and problem complexity.

Why it matters: Allows managing imperfect data or correlated variables without losing model readability.

Adstock, Decay & Half-Life

We estimate how long media effects last after exposure, distinguishing immediate response, carry-over, and accumulation over time.

Why it matters: Separates the tactical impact of the campaign from its contribution to the future baseline.

Saturation & Response Curves

We derive response curves to understand when a channel continues to scale and when it enters a saturation area.

Why it matters: Helps reallocate budget where marginal value is still positive.

Creativity vs Media Effect

When data allows, we compare different copy, formats, and periods to distinguish creative effect, media pressure, and context.

Why it matters: Makes an often qualitative evaluation more objective.

Validation & Robustness

We verify economic consistency, estimate stability, collinearity, alternative specifications, and predictive capability.

Why it matters: Makes the model more defensible in internal discussions and subsequent updates.

Most Requested

Baseline vs Incremental

Show observed sales broken down into baseline, incremental media contribution, promotions, and external factors. Goal: clarify what belongs to structural demand and what is generated by marketing levers.

Saturation Curve

Represent a growing curve with diminishing returns, highlighting the efficiency area and the saturation area. Goal: show where an extra euro still produces value and where it starts to dissipate.

Short-Term vs Long-Term

Pair two adstock curves: a faster one for tactical effects and a more persistent one for brand contribution and future baseline.

Creativity vs Media

Compare two periods with different copy, planning, and context, highlighting how the method helps separate media pressure and creative effect.

Want to understand what your data truly reveals?

We start with business questions, evaluate the quality, granularity, and depth of available information, and define the most suitable methodological path to isolate marketing’s contribution.

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