Media Optimizer helps plan marketing budgets based on the Marketing Mix Modelling results
Media Optimizer is a planning tool that enables users to
Find the optimal media mix for the given budget
Find out the required budget to meet a specific sales target.
Media Optimizer builds on the Marketing Mix Modelling results:
It uses the historical investments to find out reference values for the budgets and sales targets.
It derives the marketing response curves from the Marketing Mix Model.
Media Optimizer uses historical values as a reference by default
Media Optimizer uses the diminishing returns of the marketing response curves to find the optimal budget allocation. The optimization is limited by budget and number of week constraints.
By default, the budget and the number of active weeks per marketing activity are set to historical reference values.
User can give the Optimizer more freedom to find the optimal budget by adjusting the marketing activity specific budget constraints.
User can also specify the active weeks per marketing activity either by setting the number of weeks or by selecting specific weeks from a calendar.
The recommended approach is to make gradual adjustments instead of drastic changes.
Baseline forecasts are included in the optimization based on a set of pre-computed scenarios
Sellforte’s analytics pipelines compute a customizable set of baseline scenarios that users can select from when running the Optimizer.
Users can choose different baseline scenarios for different Optimizer scenarios to find out how budget allocations would change if e.g. business growth is slower or faster than expected.
Initially the baseline scenarios are limited to varying year-on-year growth scenarios but additional scneario types are under development.
For historical date ranges, the historical baseline values are used.
Media Optimizer infers the seasonality of marketing uplifts from the baseline seasonality
Optimizer uses the seasonality and trends in the baseline to adjust predictions: higher uplifts and better ROIs are predicted for times when baseline is larger.
The time variation of the predictions is utilized to find the optimal allocation over weeks.
Time variation in the predictions is also used to find the optimal weeks if the number of active weeks is specified.
Media Optimizer needs sometimes guidance from the user to understand real world limitations
MO does not understand media planning and media-specific limitations and human intervention/adjustments might be required
MO might sometimes suggest too high/too low budgets (eg. too high investment relative to target group size) or
MO might recommend investing in channels which are unfit to planned campaigns (eg. recommendation to invest a significant amount to search for brand-campaign)
Media Optimizer is biased towards past investments and our own MMM results optimizer will not recommend budget for media that has not been used or tested befor
Media Optimizer forecasts average performance. The predictions can differ even drastically from individual historical MMM result data points.