Continuous improvement: How big players achieve enormous effectivity and growth
Resources: Articles, Case Studies, Power BI, Wiki, Client Success Stories, Whitepapers and Marketing Insights from the Roivenue team.
Resources: Articles, Case Studies, Client Success Stories, Whitepapers and Marketing Insights from the Roivenue team.
Over the years, many of you marketers and data scientists have built numerous reports, dashboards and performed countless analyses on top of ROIVENUE™ data.
“If you think it’s expensive hiring a professional to do a job, try hiring an amateur”
AXA Assistance identified the good affiliate publishers from the bad 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 themselves but also to other performance channels, and take the whole customer journey into account."
Everybody who uses the services of affiliate partners ponders their worth. 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.
An ideal affiliate partner will bring new customers to your website without any help from the other channels. 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. The client had preformed a previous analysis that revealed a deeper look into the issue would be needed in order to produce a publisher evaluation. At this point, it became clear: a data-driven attribution based solution would be helpful, as it could account for the entire customer conversion path.
This is a scenario perfect 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.
As a 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.
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 has been contact with other paid channels signals that the affiliate was not strong 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 a conversion path after paid channels. In this case, the affiliate’s value to the whole marketing mix is highly questionable and it is correct to assume that this affiliate publisher cannibalizes on other paid channels in the mix.
In order to be able to divide customer paths into the 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:
All of these other channels – 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 is 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 path 3. This index represents a reversed value of the division of count of path 3 to the sum of counts of paths type 1 & 2. 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 type 3 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 considers both conversion and non-conversion paths but using a slicer, we can specific one or the other.
It is finally possible to compare publishers to one another and decide if cooperation should be retained or stopped.
Now, without 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 path 3. It is also important to view value of the index for each publisher with accounting for 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 ROI of its affiliate program as well as it's entire marketing mix.