How many touchpoints does an average customer journey have?
This question cannot currently be answered in the user interface, but with the help of the CJ export. To do this, you can divide the number of all touchpoints by the number of all conversions in the export. This determines the average number of touchpoints in successful customer journeys.
How many touchpoints does an average Customer Journey have with product 'XYZ'?
By enriching the custom export with product details, or by merging our export with the CRM data, this question can be mapped. This is currently not possible in the Adtriba user interface.
How long will the average customer journey take (in days)?
This question can also be mapped by the Adtriba custom export. The keys here are the timestamps of the touchpoints and the respective position of the touchpoint.
Which customer group is particularly favourable for me to purchase in the display area, for example?
Here, too, it is worth merging the Adtriba Customer Journey Export with the CRM data of the respective company.
- Analysis of the Order IDs of the customer journeys with a display touchpoint
- filtered according to the Order Ids where a specific product was purchased
This analysis can be used to answer the above question quite simply and straightforwardly.
How was the performance of my campaign across all channels? Which channel generated the most revenue from my campaigns?
By filtering the custom export according to the campaign identifier, an aggregation across all channels can be created which answers the above question. A mandatory requirement is the stringent naming of the campaign across all channels.
In how many 'successful' and 'unsuccessful' CJ was a campaign TP included?
By default, we only provide our customers with the successful customer journeys. In many cases, the unsuccessful customer journeys do not contain any evaluable data, as they have an infinite number of touchpoints, for example.
How many cross device CJ do I have in average?
This report is a custom query and can only be answered by Adtriba Support via a database query.
Which ad had a particularly good performance on which device?
We provide this information in our Customer Journey Export.
Which cross effects are visible between the channels // Which PAID channels often work together in successful CJ's?
The Customer Journey Report is an excellent tool to answer this question. It shows the paths that generated the most conversions. Furthermore, the paths can be filtered according to the respective channels and their position in the customer journey.
What is the predictive ROI / revenue of an AD vs. actual ROI / revenue?
At Adtriba, we are working on calculating a predictive ROI as well as a predictive CLV-Value. However, due to our product roadmap, we are not yet able to make a clear statement about the release date.
How much % of my sales can I influence through (digital) marketing?
Our Unified Marketing Measurement Feature will answer this question. In the feature a baseline is modeled, which includes seasonal effects and can indicate how much % of all sales can be influenced by marketing.
Why does a channel lose compared to a rule-based (Last-Click) Model?
This can be caused by different reasons. But in most cases it is caused by the fact that the previous model overestimated the channel due to its position in successful customer journeys. A classic example is paid search. In many cases, Paid Search 'loses' conversions when comparing the rule-based and the dynamic model. This is because paid search is often at the end of a customer journey. Display, on the other hand, is often a 'middleman' who is often in the middle of the customer journey and is undervalued in classic rule-based models.
Which channel is often at the end / beginning / middle?
With our Customer Journey Report, you can easily analyze which roles and positions the respective channels take in the customer journeys.
Are view touchpoints overrated in the dynamic model? Especially when unseen banners are captured in the tracking?
Our model treats all view-touchpoints as regular touchpoints. We believe that a different evaluation of views means a departure from data-driven attribution and a return to a tendentious evaluation. Our attribution model is so well developed, that touchpoints that have no effect would not be assigned a value. We assume that ads that are never seen will also appear in many non-converting customer journeys and would therefore receive little or no credit in the dynamic attribution. If you really want to know exactly, you can fall back on so-called view-through providers, which only measure the 'true views', i.e. the views where the banner was actually seen.
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