The Importance of Crediting Logic
After living, eating, and breathing data in the affiliate marketing industry for over 16 years, I am often asked about innovative ways customers have used “Big Data.”
A recent example involved an advertiser who was trying to figure out how to optimize their online media buys. Prior to “Big Data” they had very little insight into how customers interacted with the media they were buying. All sales were credited back to the media who had the last click with no insight into what other media helped influence the sale. The advertiser realized that last click crediting was not an effective way to assess media performance, so they looked into attribution modeling, which involved “attributing” the revenue of each sale across all of the touch points in the conversion path based on some predefined set of rules or algorithm. This is where most advertisers stop – they just look at the ROAS of each media based on the attributed revenue and then adjust spend accordingly. The problem is that this is STILL shortsighted, since it does not address how affiliates get credited or how to optimize cross-channel marketing initiatives.
What this advertiser did was brilliant! They looked past the attributed revenue reporting and instead focused on the more granular conversion path data. The analysis of the data revealed that they had two type of partners. One type that was good at getting customers in the door (i.e. “introducers”) and another type good at getting customers to complete a purchase (i.e. “closers”). The problem is that the introducers were never getting credit for a sale and hence not incentivized to promote the advertiser anymore, while at the same time the closers were being overpaid on certain sales. The net result is the advertiser lost this key introductory part of the conversion path and ended up getting less sales. So, what did the advertiser do?
They decided to create a new payable conversion event for the introducers. Each time an introducer got a customer to do an email sign-up they would earn $2.00 per lead. However, they did not want to pay multiple commissions on the same customer completing a lead AND a sale. Given this crediting scenario the advertiser came up with the following contractual terms.
Introducers: Get $2.00 per lead and 0% per sale based on being the first click.
Closers: Only get 5% per sale based on being the last click.
If a sale has an introducer who got the first click and a closer who got the last click, then they credited the sale to first click.
As an example, if a customer clicked on an ad from an introducer and generated an email lead, then later clicks on an ad from a closer and makes a $100.00 purchase, the payout would be $2.00 to the introducer for the email lead. The introducer would also get credit for the sale for being the first click, but be paid $0 for it since the terms are 0% of the $100.00 sale.
Thanks to “Big Data,” some clever analysis, and utilizing Impact Radius’ advanced crediting rules, the advertiser was able to grow the introducer partner channel while at the same time lower the cost basis on the closers. The net result was more sales revenue and a higher ROAS across the board. Attribution reporting may have highlighted the need to value certain partners more and certain partners less, but without digging deeper into the data and having the ability to use more sophisticated crediting logic, the optimal course of action could not be taken.
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