What exactly is the Adtriba Unified Marketing Measurement feature?
The UMM feature is an extension of the Adtriba dynamic multi touch attribution and models channel performance similar to marketing mix modeling on highly aggregated data. The goal is the holistic evaluation of advertising performance across all channels and measures in one attribution model. The UMM approach combines the tactical world of digital attribution with the strategic world of marketing mix modeling.
Finally, UMM can be used to quantify the effect of offline advertising measures on classic online brand traffic (paid search, direct, SEO).
How do I know if I am the right candidate for the UMM feature?
UMM is suitable for companies that:
- invest in offline marketing measures in addition to online marketing measures, such as print, unaddressed direct mail, posters, radio, etc.
- have a very high amount of organic / brand traffic
- are highly seasonal affected
What can I expect when I integrate UMM?
Die UMM Kanäle haben einen maßgeblichen Einfluss auf die Leistung von den organischen Kanälen. Sobald man einen UMM Kanal in das bestehende Set up integriert ist der folgende Effekt auf die organischen Kanäle zu beobachten:
- The organic channels will 'deliver' part of their conversions to the UMM channel. However, it remains to be seen which organic channel is most strongly influenced by the respective UMM channel.
Example: The XYZ radio campaign generated 500 direct conversions, 200 paid search conversions and 150 SEO conversions.
Which marketing measures and channels can I map with the UMM?
In general all offline measures that could have a branding effect such as Radio, poster advertising, direct mail, etc.
However, other effects can also be included, such as trade fairs, product launches, sponsoring appearances and much more.
How must the data be delivered and do I need a data history?
All data must be provided according to our Adtriba data template. A data history of approximately 2 years is required for one-time modeling. The further modeling will then be fully automated.
The frequency in which the data must be delivered can be decided on a case-by-case basis. Generally, this can be a one-time data delivery, but also a weekly or monthly one. A more frequent data delivery is not necessary for most offline channels due to the aggregation level.
How can I actually work with the results?
Case I - Operationalisation of seasonal effects on the baseline
The basis of UMM modeling is the so-called baseline. The baseline makes a statement about how high the share of conversions is, which would be generated anyway, even without marketing.
- In many cases, the baseline can react to seasonal effects. In some seasons the share of non-marketing influenced purchases is therefore higher than at other times.
- Depending on the seasonal effect, it can be assumed that the marketing spend must be increased or decreased to achieve the same result. So if the seasonal effect is high, you don't have to invest so much money in marketing, because the seasonality generates more conversions even without marketing.
- Currently, the baseline is only available in Adtriba's backend, but will be available in the UI soon.
Case II - Budget planning of offline channels
- Classic offline channels are often cost-intensive and follow longer budgeting cycles than online marketing. In this case, a solid basis for decision-making is therefore required, which UMM provides. Finally, on the basis of the UMM, budget planning can be carried out along the individual measures, taking into account seasonal factors and other offline measures.
What else do I need to consider when using UMM?
The model and the modeling of the effects can only be as good as the data it is based on. So here it is: garbage in, garbage out. One should always pay attention to the quality and consistent quality of the data supplied.
The UMM feature is currently in the beta phase, the model is constantly being further developed and the figures are checked regularly. Nevertheless, there may be isolated changes and fluctuations in the figures.
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