Marketing Mix Modeling (MMM)

Marketing Mix Modeling is a complex analytical project: on aggregate historical data (sales, channel spend, prices, promo, seasonality, external factors) we build a regression/Bayesian model estimating each factor's contribution to sales, saturation and response curves, and help assess budget reallocation. MMM works on aggregates and needs no user tracking — so it sees offline and brand advertising where click attribution is powerless. Honestly about the nature, this is key: MMM is a STATISTICAL ESTIMATE with significant uncertainty and assumptions (variable choice, lags, saturation shape); results are directional/indicative (with intervals), not exact numbers, and depend heavily on data quality and variability; it is a correlational model — it shows relationships, not strictly proven causation. Honestly about data, critical: MMM requires LONG history and sufficient spend variability (if a channel always spent the same, its effect cannot be estimated); short/poor data gives an unreliable model (garbage in, garbage out). Honestly about the essence: this is an estimate for strategic budget decisions, NOT optimization and NOT growth by itself and NOT an ROI guarantee — reallocation and its execution are done by you; the model does not 'increase sales', it helps decide. Honestly about the role: MMM complements but does not replace attribution and experiments — the best conclusions come from MMM + incrementality tests (separate). Aging: the model needs periodic refresh. An important boundary: this is MMM, not end-to-end/click attribution (separate), not incrementality experiments (separate) and not running ads. For small business with a short and simple media mix MMM is overkill and unreliable — we say so honestly. Picture this: instead of 'offline and brand are not digitized, we split budget by gut' you get an estimate of channel contribution with intervals for strategic decisions. The base price starts from 60,000 ₽ per project; it depends on history length, the number of channels and factors.

• Offline and brand advertising are not assessed by click attribution.
• You do not understand channel contribution to sales at the business level.
• You split budget between channels by gut feel.
• You need a return estimate where user tracking is impossible.

• Collecting aggregate data (sales, spend, prices, promo, seasonality)
• Building a regression/Bayesian MMM model
• Estimating factor contribution with confidence intervals
• Channel saturation and response curves
• Budget reallocation scenarios (indicative)
• A report with assumptions, intervals and limitations
• Reviewing results with you
• Recommendations on combining with experiments and refresh

• An estimate of channel contribution, including offline and brand (with intervals)
• Understanding of returns where attribution fails
• Guidelines for budget allocation (execution — separately)
• A base for strategic decisions (no ROI guarantee)

• We clarify channels, factors, history availability; collect data
• We build and validate the model, compute contribution and intervals
• We compile a report with scenarios and limitations, review with you

• Will MMM show each channel's ROI exactly?

  No. MMM is a statistical estimate with assumptions and intervals, not an exact truth; it is a correlational model, it shows relationships, not strictly proven causation. Results are indicative and depend on data quality and variability.

• Will MMM raise sales?

  Not by itself. It helps decide how to allocate budget more reasonably, but reallocation and execution are done by you. The model does not 'increase sales' or guarantee ROI — it is a tool for strategic decisions.

• Do I have enough data for MMM?

  MMM requires long history and sufficient channel-spend variability. On short/poor data or with constant identical spend the model is unreliable. For small business with a simple media mix MMM is often overkill — we say so honestly; a combo with experiments is better.