Affiliate managers are always looking for data to better optimize their affiliate program. There is exponentially more data available today than just 5 years ago. This can cause us to become overwhelmed or paralyzed because we don’t know how to make sense of it all. So the question I am asked most frequently is “Exactly what data should I be looking at to best optimize my affiliate program?”
The simplest way to understand data is to compare two data points. Our brains can easily recognize what is different between two numbers. So the easiest way to optimize an affiliate channel is to find two data points that can be used to understand and optimize the value of each partnership. I call these two data points the Holy Grail of affiliate marketing metrics.
When we look at typical affiliate data, we see the number of clicks, conversions, commissions, revenues, conversion rates, average order value, etc. All of these data points help paint a picture of how each affiliate is performing. But how do we determine the actual value of each partnership? Most affiliate managers recognize that more revenues are better, higher conversion rates and AOVs (average-order-value) are also good but how can we quantify the real value of each partner?
In other words, how can we determine if we are undervaluing certain partners and overvaluing others? Ideally we would want to ensure that each partnership is properly rewarded based on the value they are driving irrespective of revenues, commission, conversion rates, and AOV.
The two most important affiliate marketing metrics
How do we find these two important Holy Grail numbers? Well the first metric you need is readily available to every affiliate manager. It’s called credited revenues.
Credited revenues are the revenues each affiliate was credited with (and consequently awarded
a commission) as tracked and reported by your affiliate solution. Credited revenues are usually awarded based on last click referral logic.
So if a customer clicks on a paid search ad and then later clicks on an affiliate link, the affiliate is credited with the revenues and earns a commission. If the customer instead clicks on an affiliate link and then later clicks on a paid search ad, the affiliate isn’t credited with the revenues and doesn’t earn a commission even though they participated in the customer journey (as the second to the last click).
The next metric is the really important one and for some affiliate managers it can be harder to come by depending on the marketing solutions your company utilizes. It’s called attributed revenues. Attributed revenues are modeled revenues that look at all conversions each affiliate participated in – regardless of whether they earned a commission.
So in my example above when the affiliate was second to the last, attributed revenues would credit some of the revenues to both paid search and the affiliate. Over the course of a month or quarter each affiliate could be involved in thousands of conversions based on where they were in the customer journey.
In order to get attributed revenues, your company needs some type of attribution solution that models all of the customer touch points across all paid (SEM, display, social, retargeting, etc.), earned (social, word of mouth, etc.), and owned media (email). Most attribution solutions utilize rules based or algorithmic based models. The most objective model will be algorithmic and is the ideal metric to use as the second piece of your Holy Grail metrics. You will need to get these attributed revenues at the individual affiliate level vs. the whole channel so you can now compare the credited revenues to the attributed revenues.
Credited and Attributed Revenues Combined Equal the Holy Grail
OK – so now let’s get to the answers we have all been looking for! Let’s take our credited revenues and put them right beside our attributed revenues for each of our affiliates. As you’ll see in the table below, by comparing the credited to attributed revenues we can discern the delta between these numbers.
If credited revenues are greater than attributed, we are overvaluing that affiliate. If the attributed revenues are greater than the credited, we are undervaluing that affiliate.
So what do we do with this data?
Great question! We use it to adjust what we are paying each affiliate based on their value. For example, if we are targeting 8% as our cost of sale average for the affiliate program, we will adjust that number per affiliate based on the percentage delta – see Table 2 below.
So for Affiliate #1, we need to lower their commission from 8% to 4%. The adjusted rate compensates for the other marketing channels (that have costs associated with them even if they aren’t credited with the revenues) involved along with the affiliate.
For Affiliate #2, we can keep their rate at 8% or adjust it down to 7%.
For Affiliate #3 (most likely a content site that is more top of funnel – i.e. introducer/influencer) we should adjust their rate to 12% in order to compensate them for their contributions.
In the example above, the results of adjusting the commissions based on the value they are driving results in overall lower media costs (i.e. commissions) of $3,360 (Table 4) while also improving ROAS by 28% (Table 3).
Note: ROAS = revenue from ad campaign / cost of ad campaign
By comparing credited revenues to attributed revenues affiliate managers can adjust the commission rates based on the overall contributions each affiliate drives across all channels. Now affiliates are credited based on their value and the commission rates ensure that each is fairly compensated.
If you have questions about how to optimize your affiliate channel leveraging attributed data, please contact us at Impact Radius.