AXA Assistance differentiated the worthwhile affiliate publishers, from the extraneous ones, with a custom attribution solution:
“Separating affiliate partners who bring real value, from those who are only cannibalizing your marketing budget, can be a nightmare. Especially, if you want to compare their real ROI, not only among each other but to other performance channels, and take the whole customer journey into account.”
It’s hard to always know what affiliate partners are bringing to the table. Often, they can leach on the performance of other channels, by showing ads to customers who are very likely to make a conversion regardless of the affiliate ad’s contribution.
An ideal affiliate partner would bring new customers to your website, without any help from the other channels that you’re investing in. Such publishers deserve their commission fee. However, if the publisher steps into an already existing customer conversion paths, it deserves less credit. But how much exactly?
To make matters worse: data from the affiliates are often served in bulk, hard to work with formats. AXA had previously preformed an analysis, but it only revealed that a deeper look into the issue would be needed in order to obtain the absolute truth. What was realized, was that a data-driven attribution based solution would be helpful, as it could account for the entire customer conversion path.
This is the perfect scenario for ROIVENUE’s attribution analysis, a feature which is built in by default. Unfortunately, there was a complication. There wasn’t enough conversion paths related to the affiliates, which means that the built-in algorithm would not have enough data to make its calculation reliably.
This did not stop us. To solve this problem for our client, we designed a custom solution, which calculates and presents the data in a different fashion.
Roivenue’s attribution solution was used to capture customer paths across the whole marketing mix. All touchpoints and their costs, were automatically: loaded into the system, transformed, and exported for further analysis.
For the next step, we analyzed the mapped journeys, and divided all paths that included an affiliate touch point into three categories:
1. AFFIL ONLY
All touch points in the path are related to an affiliate – this is optimal. It shows that the affiliate’s message is strong enough to create a conversion on it’s on.
2. AFFIL THEN PAID
An affiliate has attracted the customer, but the customer later came into contact with other paid campaigns. This situation is sub-optimal.
It is valuable that the affiliate caught the customer for the first time, but the fact that there had to be contact with other paid channels. signals that the affiliate was not effective enough to close the customer by itself. This increases the cost of the path as other non-affiliate channels needed to take action.
3. PAID THEN AFFIL
An affiliate has entered the conversion path after other paid channels. In this case, the affiliate’s value to the whole marketing mix is highly questionable, and it is a safe bet to assume that this affiliate publisher cannibalizes other paid channels in the mix.
In order to be able to divide customer paths into these three described categories, we first had to map these paths.
This is where ROIVENUE’s attribution measurement comes into play, as it was designed for the reconstruction of paths and data driven attribution modelling.
All traffic is divided into channels of two types:
For the purposes of simplification, all traffic not from affiliates – both paid and unpaid – fall into the paid channels group in this article.
Our next step was to gather data for one month, to collect enough customer paths for the algorithms to produce reliable results – the more paths we collect here, the higher the precision of the evaluation that follows.
After the data gathering period, the collected material was exported to Power BI for final analysis.
Above, you can see an overview of active affiliate publishers and division of paths, related to them based on the description above.
Once we have the paths for each affiliate counted we can preform a simple calculation to see the tendency of the third path. This index represents a reversed value. The quantity of third path, is divided by of the sum of the first and second paths. The Publisher Quality Index (PQI) for publisher X is defined as follows:
The higher the value of this index, the lower a publisher’s tendency towards paths of third variety, and thus the higher is it’s perceived quality.
Publishers are evaluated, and ordered by total paths related to them in the table below. By default, this dashboard shows both conversion and non-conversion paths but, by using a slicer, we can specific one or the other.
Now, without the criteria through which to judge, the above numbers are meaningless. Our suggestion is that, like in school, a 0.6 is the cutoff point, as below this value signals a strong tendency towards the third path. It is also important to view the value of the index for each partner, which indicates the number of paths related to the publisher – the more paths, the higher reliability of the index.
After the initial setup of data collection in ROIVENUE, this analysis can take place periodically, so the behavior of the retained and new publishers can be continuously monitored, and the quality of the affiliate portfolio can be maintained.
AXA had been struggling with evaluating the quality of different affiliate publishers for years.
ROIVENUE offered a customized analysis utilizing it’s expertise in attribution.
As a result, AXA saw an increase in the ROI of its affiliate program as well as, it’s entire marketing mix.
Just book a personalized demo with ROIVENUE. We’re eager to show you how you can utilize path analysis to evaluate who is worth your ad budget.