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Step-by-step approach: Attribution models and affiliate marketing

Attribution models, especially in affiliate marketing, are still being widely discussed which suggests that publishers are unsure about their suitability.

Step-by-step approach: The effective use of budgets and an unbiased evaluation go hand in hand.

Step-by-step approach: The effective use of budgets and an unbiased evaluation go hand in hand.

Publishers frequently ask the following question:

How does the knowledge of touch point sequences help with the monetary evaluation of individual channels?

To answer this, two separate objectives are often combined:
1.The use of attribution models for the effective evaluation of budget efficiencies regarding individual channels
2.The use of customer journey insights for the unbiased evaluation of affiliates

A hypothetical sequence of clicks can help to illustrate:

step-by-step-approach-2

When looking at the chain using a simple attribution model eg equal distribution, each channel would be responsible for 25% of the conversion. But what does this mean for each individual channel when considering their costs? The SEA click as well as the SEO support needs to be paid for which leads to potentially only 50% being left for the affiliate partner which needs to be split between two.

For the above defined first objective this model is definitely just an intermediate stop in a long chain of additional steps.

We need to develop an attribution model that shows the value of each individual channel. In order to apply an attribution model to our second objective the overall view of the channels is not sufficient. The affiliate channel needs to be segmented into the network, the business model, the publishers and their inventory. This can take a huge amount of time and effort and often isn’t necessarily needed.

The reason: Most partner programmes use the last-cookie-wins model. Channel based attribution models and customer journey analysis help to adapt and enable an unbiased evaluation.

So, what does this look like? It can be illustrated with the help of hypothetical examples:

1)
Measure: Compensating last cookies from other channels with less advertising performance.
Explanation: Last cookie journeys from SEA or SEO Brand are opened for the previous affiliate contacts and their commission is reduced (either fully or by a fixed value).

2)
Measure: Define entering the shopping cart as an evaluation point
Explanation: The time when the contact was made is used to assess the advertising performance of individual touch points, either before the user reaches the shopping cart or after.

3)
Measure: The last contact before entering the shopping cart is rewarded
Explanation: If the last click happens after reaching the shopping cart, the preceding contact (if it exists) is partially remunerated.

4)
Measure: Analysing voucher partners in regards to their value
Explanation: By examining the shopping cart information in regards to partners enables conclusions about the actual advertising performance.

5)
Measure: Giving away individual vouchers
Explanation: Voucher partners are awarded with vouchers adjusted to their performance. Partners with high conversion rates after the user enters the shopping cart would for example receive vouchers with a higher minimum spend value.

6)
Measure: Bonus and cashback partners are rewarded for their advertising performance
Explanation: Cashback partners need to show the last contact in the sequence in order to receive the full bonus, which means they can also be evaluated according to when the shopping cart is entered. If the contact concludes after the shopping cart entry, the commission for the partner can for example be limited to the cashback value or the bonus point costs. In this case the rest of the commission is split over other partners.

These examples show that the complex requirements of attribution models don’t necessarily prevent affiliate partners’ unbiased evaluation in regards to their advertising performance. However, those models can’t be utilised without prior analysis and continuous adjustment.

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