Customer question
“Which marketing channels are actually driving business growth?”
What this solution does
It looks at years of weekly data — what was spent where, and what else was happening (price changes, promotions, weather) — to work out how much of your revenue each marketing channel is really responsible for.
Specifically, it answers: Across all marketing channels and non-marketing factors (price, promotions, seasonality, macro conditions), what is each channel's actual contribution to revenue, and how should budget be allocated to maximise return?
How it works
Historical time-series data on marketing spend by channel, plus control variables like pricing and seasonality, is used to statistically estimate each channel's contribution to a business outcome (typically revenue or sales), producing channel-level ROI and enabling budget scenario simulation.
Data requirements
- At least 2–3 years of weekly (or similar granularity) historical data
- Marketing spend by channel over that period
- Revenue or sales outcome data at matching granularity
- Control variables: pricing, promotions, seasonality, weather, economic conditions
- Promotional and campaign calendar
Discovery questions
How much historical sales and spend data is available?
MMM requires enough historical observations and variation in spend to reliably estimate the relationship between marketing activity and business outcomes.
Has media spend varied meaningfully across channels and time, or has it been fairly constant?
A model can't separate the effect of a channel from other factors if that channel's spend barely changed — there's no variation to learn from.
What control variables (pricing, promotions, seasonality) are tracked and available?
Omitting major drivers of the outcome variable risks the model wrongly attributing their effect to marketing channels.
What decision is this model meant to inform — budget reallocation, channel mix, new channel testing?
Shapes whether the priority is contribution accuracy, response curve precision, or optimisation scenario flexibility.
Build an experiment
Generate synthetic data
Data readiness
Run model / simulation
Results
Business ⟷ Technical interpretation
topChannel is estimated to be the strongest contributor to revenue, delivering an estimated ROI of topChannelROIx. Reallocating budget per the optimiser's suggestion could increase estimated revenue by optimisedUplift.
Shown as a template — the highlighted tokens are filled in once the model has been run.
What would you do next?
- Use the response curves to identify which channels are near saturation versus still scaling efficiently
- Treat the budget optimiser output as a starting hypothesis to test, not a final allocation decision
- Refresh the model periodically as market conditions and media mix evolve
Solution & vendor landscape
Implementation approaches
Mutinex
Marketing Mix Modelling
Analytic Partners
Marketing Mix Modelling
Google Meridian
Marketing Mix Modelling · Incrementality
Lifesight
Marketing Mix Modelling · Incrementality