Powers Sports Memorabilia
Judge the ads on new customers, not on everyone
Three methods, one budget decision, and no increase in spend.
Read the studyMeasurement beyond user-level attribution
Marketing Mix Modelling
Estimate what actually moved revenue, including channels that clicks and pixels cannot fully explain.
Platform attribution follows the journeys each platform can observe. It does not, by itself, tell you the incremental effect of each channel. MMM uses aggregated historical data (media spend, revenue or another KPI, seasonality, promotions, and control variables) to estimate channel contribution, diminishing returns, and budget scenarios without relying on user-level cookies or identity matching.
Contribution vs spend by channel
SearchMetaInfluencer
budget allocation
Search
Meta
Influencer
Illustrative model output.
Swishiy builds and interprets models using modern open-source frameworks such as Google Meridian and Meta Robyn, with model choice driven by the data rather than the logo. The output is not another attribution dashboard: it is a decision model for questions like “what happens if we move budget from channel A to channel B?”
Good fit
- Meaningful spend across multiple channels, especially where offline or hard-to-track channels matter.
- A reliable source-of-truth KPI and real historical variation in media activity.
- Typically at least two years of weekly history for geo-level models, often more for national-only models.
Not ready yet
- Too little historical data, or too little variation in spend to separate effects reliably.
- No trustworthy business KPI, missing media history, or major gaps in the aggregate inputs.
- A need for daily campaign optimization. MMM complements platform measurement and experiments; it does not replace them.
The deliverable
A documented model, channel contribution and ROI estimates with uncertainty, response curves and marginal returns where supportable, scenario-based budget recommendations, and a readout focused on decisions rather than statistical decoration.
Same method, opposite answers
Two brands, one model, and two budget decisions that point in different directions. In one, most revenue arrives without any ad at all. In the other, almost none of it does. That contrast is the argument for modelling rather than assuming.
Anonymised, US direct to consumer brand on Shopify
Their healthiest looking metric was the warning sign
A record January, a loss in February, and the metric that explained both.
Read the studyFind out what is actually incremental.
We will tell you honestly whether your data history can support a model worth trusting.