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Individual Conversion Attribution Powered by QUISMA

In our previous articles about conversion attribution we’ve explained that it is crucial to use the right attribution model for your online marketing measures as identifying each channel’s true contribution is the foundation for every budget allocation. Common attribution models, such as the last cookie wins, first cookie wins, and many others, are of a static nature and cannot live up to the complex user behaviour in the customer journey. The solution is to use models adjusted to the advertiser and their needs.

 

Invidual conversion attribution that really fits

Invidual conversion attribution that really fits

QUISMA’s individual attribution model is an evaluation pattern, which determines the contribution of online marketing channels to each successful journey (conversion). Based on these values it distributes the conversions proportionally to the touch points. To determine the attribution we take into account several factors:
A) Main factor:
• Channel efficiency
To determine the channels’ efficiency we use modeling that takes the entire user journey into account, including journeys that didn’t lead to a conversion. Therefore path-to-conversions as well as path-to-non-conversions, aggregated on a daily basis, are incorporated into the channel efficiency. The evaluation of the individual advertising activities can then be refined by adding other factors.

B) Other factors:
• Type of interaction: different emphasis on the type of interaction whether it is a view or a click
• Touch points: different emphasis on the touch points whether it is an introducer, influencer, or closer.
• Advertising medium quality: different emphasis on the advertising quality. In display advertising, for example, you have a different emphasis depending on if it is a standalone banner, moving image, wallpaper, or others. In the search engine advertising you will have a different emphasis depending on the keyword quality, the display position, and many more.
• Time lag between the touch points: if, for instance, the same interaction happens several times within a given period of time, it counts as one interaction.
• Journey type: different emphasis on the journey whether it is a sales or a lead journey, if both are present.

If you have all this information available, you can establish the evaluation pattern, which will allow you to attribute the effect of the journeys. The result is an individual attribution model, which measures the efficiency and effectiveness of the individual channels. The calculated values will be transferred onto the existing journeys, and you will then be able to see the shares of each advertising channel to the conversion.
The result is a model which displays the course of the conversions, and evaluates the touch points. It allows a precise representation of the attribution along the touch points.

 

Attribution along the touch points

Attribution along the touch points

The attribution model is not finished with this step and can be further refined with other business factors:
C) Business factors:
• Sales target: Further fine-tuning of the model depending on the sales the journeys generated. Optimise the positions that generated the highest turnover.
• Customer scoring: Further fine-tuning of the model depending on the advertisers’ customer evaluation. Optimising the journeys at the points where the customer lifetime value and the customer equity are the highest.

The business factors enable advertisers to evaluate the channels not only in terms of conversions but also in terms of their commercial benefits. They include sales targets and the advertisers’ customer scoring. It allows you to focus your investments on journeys with a high customer value. This way, you can increasingly use the advertising channels where you have affluent and lucrative customers. Therefore, individual conversion attribution is ideally suited to quantify the contribution of online marketing channels, which allows budgets to be better distributed and adapted to different goals, such as turnover and customer scoring.



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