Solution Finder
What is your customer trying to achieve? Pick the problem closest to what they actually said — this translates it into the right methodology, data requirements, and Lab.
You said: “Determine how to allocate future marketing budget”
Marketing Mix Modelling
Investment & StrategyWhy this fits
MMM's budget optimisation capability directly simulates reallocating spend across channels against a fixed total budget.
What 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 required
- 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.
Implementation approaches
Vendors
Risks & limitations
Risks
- Insufficient historical data or spend variation produces unstable, unreliable channel estimates
- Omitted variable bias if major non-marketing drivers of revenue aren't included
- Overconfidence in point estimates without communicating model uncertainty
Limitations
- MMM is an aggregated, statistical estimate — not a guaranteed causal measurement of any individual campaign
- Struggles to isolate the effect of channels that never had their spend varied
- Requires periodic refresh as market conditions and media behaviour change