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KPIs for MMM should be defined per each analysed period. It should be something that we measure weekly or monthly.
Most common KPIs are:
The best results we get modelling sales either by volume of by value. More often we model sale volume. Because then we can also include price into the model.
Other KPI that can be considered
We can model different KPIs than sales but it depends on the brand and values that we have. Awareness is first of all difficult to measure and values usually do not change much over time.
Adstocks represents how long and how strong ads have an impact over time. It means how long is affect KPI, after the ad is displayed. Two main groups of functions for adstock are:
Main pros and cons of adstock functions
| function | pros | cons |
| geometric | easier to calculate | ad effect lasts forever |
| convolution | defines how long ad effect lasts (how many days or weeks) |
more difficult to calculate |
Please check the example; it is easier to understand with on live example
Exaples calculated for ad spend week1 = 10 and week2 = 5
| parameters | conv1 | conv2 | conv3 |
| 1 | 0.5 | 0.2 |
calculation
| week | x [$] | adstock | xi*conv1 + xi-1*conv2 + xi-2*conv3 |
| w1 | 10 | 10 | = 10 * 1 + 0 * 0.5 + 0 * 0.2 |
| w2 | 5 | 10 | = 5 * 1 + 10 * 0.5 + 0 * 0.2 |
| w3 | 0 | 4.5 | = 0 * 1 + 5 * 0.5 + 10 * 0.2 |
| w4 | 0 | 1 | = 0 * 1 + 0 * 0.5 + 5 * 0.2 |
| w5 | 0 | 0 | = 0 * 1 + 0 * 0.5 + 0 * 0.2 |
MMM is not just a linear model; we have to make a few adjustments
yes to: statistics and data
no to: cookies and polls
MMM is the best way do give budget recommendation having many marketing channels, which are not connect. Because:
Panel data give some insight but they do not cover all the media. It is possible that in our case panel cover all of our activities. Still it is a sample data. If your brand is small and there is not a lot of activities it may not be captured.
Declarative survey are a great tool for example to understand brand awareness. But they have issues. People usually don’t remember what and where they saw in terms of media and ads. Or they think that they remember.
It is difficult to attribute the effect of marketing activities from online ads, radio, press or on-site. Even within online space it is difficult to measure effectiveness e.g. of ads and influencer marketing. The most objective way to create statistical model aka MMM (Marketing Mix Model)
KPIs:
ads:
other promotions:
holidays:
For a good model, we need to collect as much information about factors that impact our KPI as possible.
Most common elements are:
For MMM, we need data over time, each factor with the same frequency.
The most popular data granularity used in MMM are:
It may look like the more detailed data, the better. But for some media is impossible to give, e.g., daily data. That’s why there has to be some kind of compromise. Usually, it is weekly data.
Statistical model allow us to calculate/estimate the impact of marketing activities based purely on number, not human judgement.
MMM works on time series data. That means that in order to create a model we need past data (e.g. last 2 years weekly data about sales and marketing spendings). That also means that having a model we can forecast/estimate future level of KPIs based on planned activities.
Not everything can be quantified but for sure we can quantify spendings on adds.
How does the statistical model work, and what is the benefit?
weekly data with ads spent and KPI (sales)
| date | online ads | newsletter | sales |
|---|---|---|---|
| week1 | 15$ | 1 | 120 |
| week2 | 15$ | 0 | 150 |
| week3 | 10$ | 0 | 140 |
| … | … | … | … |
| week53 | 15$ | 0 | 130 |
sales = 100 + 2 * (online ads) + 10 * newsletter3. future plans
we know media plan for upcoming weeks
| date | online ads | newsletter | sales |
|---|---|---|---|
| week54 | 15$ | 1 | ? |
| week55 | 15$ | 0 | ? |
| week56 | 10$ | 0 | ? |
having a model we can forecast the result (sales)
| date | online ads | newsletter | sales |
|---|---|---|---|
| week54 | 15$ | 1 | 140 |
| week55 | 15$ | 0 | 130 |
| week56 | 10$ | 0 | 120 |
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