KPIs that we want to increase/manage thanks to MMM?

**sales volume**/value- awareness
- traffic

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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:

**sales volume**- sales value

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

**awareness**- store traffic
- webpage traffic

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:

- geometric: adstock
_{i}= ad_{i}+ 𝞭 * adstock_{i-1} - convolution: adstock = 𝜮conv ⨂ ads

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 | conv_{1} |
conv_{2} |
conv_{3} |

1 | 0.5 | 0.2 |

calculation

week |
x [$] |
adstock |
x_{i}*conv_{1} + x_{i-1}*conv_{2} + x_{i-2}*conv_{3} |

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

**Adstock**– we assume that sometime the effect lasts after the contact with the ad:- adstock depends on medium type
- in the case of TV ads, it may be a few weeks; for an online ad about a week

**Saturation**– there is a limit; by investing in media, we cannot increase a demand infinitely:- in case of media investment 2 ∗ 2 ≠ 4

**Possible lag**– some actions may take some time to affect the KPI:- it may have an effect a week or a day after contact with an ad

**Predefined constraints**:- own action – affect our KPI positively
- competitors – affect our KPI negatively; of course, there are exceptions,
- some competitors’ activity may influence the whole category
- sometimes, people don’t know which brand is promoted in the ad

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:

**no cookies**data required- do
**not require unique user**data **not based on declarative survey****based on actual numbers**and statistical models

**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:

**KPI**(sales volume, awareness, e.t.c.) – one KPI that we want to estimate- factors influencing KPIs
**own actions**: media spending per channel, price, special promotions, sale, newsletters**competitors activity****holidays**(summer break, XMAS)

For MMM, we need data over time, each factor with the same frequency.

The most popular data granularity used in MMM are:

**2-year historical, weekly data**– gives the best result- 3-year historical, monthly data

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.

- What is the
**ROI of my marketing activities**? How much value each $ spent bring? - How to
**optimize the marketing budget**?**across media**– how much should we spend on each channel, what is the optimal level of spend**over time**– how to distribute ads during the year considering all the activities, holidays, and competitors’ activities.

- What are the base and incremental sales? How much
**sales are generated by marketing**activities, and what are the organic (not affected by marketing) sales? - Which ad performs better?
**How long does the effect last**? Comparison not only media channels but also specific campaigns. - What is the
**competitors’ impact**? How much and when do we lose sales because of competitors’ activity?

- Marketing Mix Modelling
**statistical analysis**to estimate the impact of various marketing activities- used for marketing
**mix optimization**:- calculation of
**ROI**for each channel/campaign **forecast**/plan future activities

- calculation of

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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