The use of reach and frequency is a crucial factor in effective ad campaigns, but it is not often considered in current marketing mixed models (MMMs) due to the lack of accurate reach and frequency metrics for some traditional media channels. Typically, MMMs rely on impressions as input, neglecting the fact that individuals can be exposed to ads multiple times, and the impact can vary with exposure frequency. To overcome this limitation, Meridian offers the option to model any media channel's effect based on reach and frequency data, instead of a single execution metric. This approach can potentially yield more precise estimates of marketing impact on business outcomes and aid in optimizing campaign execution through frequency recommendations.
For modeling purposes, the reach and frequency data must be at the same level of geo and time granularity as the KPI and controls data.
Additionally:
The reach data should be the number of unique individuals exposed to the channels' ad within each time period instead of the cumulative number of individuals reached over consecutive time periods.
The frequency data should be the total number of impressions divided by the reach for each time period.
The media effect is the additive contribution to expected outcome. For channels with reach and frequency data, the media effect of the \(i^{th}\) channel within geo \(g\) and time period \(t\) is modeled as follows:
Where:
- \(f_{g,t,i}^{[RF]}\) is the average frequency
- \(r_{g,t,i}^{[RF]}=L_{g,i}^{[RF]}(\overset {\cdot \cdot} r_{g,t,i})^{[RF]}\) is the transformed reach. This is scaled by population and the median value for the channel. For more information, see Input data.
This effect is calculated by first applying the Hill function to the average frequency \(f_{g,t,i}^{[RF]}\) to adjust for saturation effects. The Hill-transformed frequency for each geo and week is multiplied by transformed reach. These values are then weighted by the Adstock function to capture lagged effects of media exposure over time.
The Hill function allows for the media effect to be S shaped as a function of frequency, which means that the optimal average reach for cost effectiveness may be greater than one. The S shaped curve reflects the intuition that there might be an optimal frequency for incremental outcome value per impression. A certain minimum frequency might be necessary to reinforce brand recall, while excessive frequency can result in ad fatigue and diminishing returns.
Reach is assumed to have a linear relationship with the KPI, while holding frequency fixed. This means that each additional individual reached has the same effect on the KPI as those reached previously. This assumption is a reasonable approximation as long as the media channel does not reach individuals well beyond its intended target audience, who may be less affected by the channel. The linear reach assumption also helps avoid model overparameterization, parameter non-identifiability, and Markov Chain Monte Carlo (MCMC) convergence issues. Be careful about extrapolating this linear effect far outside the range of reach values observed in the data.
For more information about reach and frequency, see Bayesian Hierarchical Media Mix Model Incorporating Reach and Frequency Data.
Differences between assumed frequency for ROI and for optimization
There are differences between the assumed frequency for ROI and for optimization. You can adjust the assumed frequency for optimization if needed.
As discussed in ROI, mROI, and response curves, ROI measures the return of investment of a channel as it was executed during the time window for which the MMM has data. How a channel was executed includes how impressions are allocated across geos and time, and also includes the historical frequency of that channel.
Optimization assumes that future campaigns will be executed at the optimal frequency, since frequency is something often in an advertiser's control, especially for digital channels. If the optimal frequency is different from the historical frequency, a channel's performance in optimized budget allocation might not match the channel's historical performance according to ROI. This can be exacerbated if the current frequency is far from the optimal frequency.
If future campaigns won't be executed at the optimal frequency, you can use the optimization option to change the assumed frequency. This can be helpful for channels that cannot be executed at a specific average frequency.