Understand MMM
- A statistical method to study the impact of marketing campaigns on different metrics, e.g., traffic, clicks, conversions, sales.
- It is not a new approach.
- Every MMM model is more or less a regression.
- Xs: campaign spends+activities, an other control variables:
- Macro-economic factors (GPD, unemployment rate)
- Seasonal factors: holidays, events.
- Competing variables: competitor’s spending.
- Price changes or promotions.
- External factor: Weather.
- Group indication: region, city
- Y: ROI, KPIs.
- revenue, conversion, customer acquisition.
- Usually: based on MLE.
- Xs: campaign spends+activities, an other control variables:
Why bother?
- Understands your past campaigns to make decisions in the future: budget allocation.
- What was my ROI? How did a certain channel (e.g., Facebook) drive my revenue or other KPI of interest?
Meridian (Google)
Why Meridian?
- Traditional MMMs: use standard regression –> MLE: need a lot of data to be stable. No info about uncertainty.
- Provides only point estimates of each coefficient.
- Bayesian MMMs: Bayes Theorem + MCMC sampling to estimate coeff.
- Provides est. of distribution of coeff.
- Bayesian models work better when having less data or with missing values, or being too sparse.
- Thanks to assumption about Prior.
Concepts
- Adstock: models the effect of spend on sales being not instantaneous but accumulating over time.
- Saturation: models the effect of spend onf sales being not linear but saturates at some point (i.e., diminishing returns).
Other features
- Geo-level modeling: Meridian can perform hierarchical modeling.
- Meaning you can model multiple regions together.
- This is a good tradeoff between having separate models (unpooled) vs one single model that averages all regions (pooled).
Components
- Y: signal
- Xs:
- Adstock (the main x). It’s called Media variables.
- with slope, Hill function, geometric decay rate.
- External control covariates (z).
- Intercept, Stochastic Intercept.
- time-varying, to account for trend, seasonality.
- Adstock (the main x). It’s called Media variables.
Example use cases
- Effect of different ad channel spending on weekly sales.
pip install google-meridian- Control variables: holidays.
- Can viz the contribution by baseline and marketing channels on top of baseline.
- Results can be sensitive to different model’s parameters.
- Can have ROI for each channel.
- Can use the result to optimize budget: it propose an optimal allocation of spends to maximize revenue.
- Use response curves: they describe the relationship between spend and the resulting incremental revenue.
- Use that to viz diminishing points. But Meridian offers optimizer for you!