Dangers of Intra-Affiliate Data
Affiliate marketing has been a successful marketing channel for well over a decade. As the industry has evolved, so has the data that is analyzed as well as the management strategies. One of the biggest and most recent evolutions has been around attribution. By understanding the click path data, affiliate managers can see where and when an affiliate was involved in a conversion and whether any other affiliates were also involved. This data is very helpful when trying to determine the value of specific affiliates.
Intra-affiliate is defined as click path or attribution analysis that only considers the affiliate channel and ignores other paid marketing channels. Although not all affiliate networks have this more comprehensive data, the ones that do should be able to provide reports that show how many and which affiliates were involved in conversions. On average, most affiliate programs generate 15% of total conversions for an advertiser (+/-10%). Herein lies the problem with attribution data generated by affiliate networks: they are only able to look at a small percentage of the overall marketing efforts. Affiliate networks do not have 85% of the data!
If two or more affiliates participate in a conversion, the advertiser only pays one affiliate (typically based on last click). But if two or more other marketing channels are involved like display and paid search, then there are multiple costs for that one conversion (CPM+CPC+CPA). If you want to understand the value and costs of each affiliate, you need to consider all marketing channels and the corresponding costs.
Furthermore, affiliate networks cannot tell you when an affiliate participated in a conversion that was credited to another marketing channel. This is valuable data that helps you understand how and when affiliates are supporting other marketing channels (at no incremental cost to you).
Let’s take a look at how this works. We’ll use two affiliates and three marketing channels (paid search, email, and display).
In intra-affiliate data, our view shows that Affiliate A drove $1M in sales and Affiliate B drove $2M in sales for the month of December.
- When Affiliate B was credited with the sale, 20% of the time Affiliate A was also involved but wasn’t credited.
- When Affiliate A was credited with the sale, 25% of the time Affiliate B was also involved but wasn’t credited.
- So 80% of Affiliate B’s sales and 75% of Affiliate A’s sales only involved one affiliate.
Using multi-channel attribution to analyze the same affiliate’s data, we see a different picture. Now that we are including three other marketing channels, we get a completely different view:
- When Affiliate B was credited with the sale, 20% of the time Affiliate A was also involved but wasn’t credited and 40% of the time paid search was involved, 15% of the time email was involved, and 5% of the time display was involved.
- When Affiliate A was credited with the sale, 25% of the time Affiliate B was also involved but wasn’t credited and 60% of the time paid search was involved, 18% of the time email was involved, and 10% of the time display was involved.
- Affiliate A costs this advertiser more money vs. Affiliate B due to the increased overlap in other marketing channel costs.
- We can also see that Affiliate A (16%) and Affiliate B (9%) contributed to conversions credited to other paid marketing channels with no incremental marketing costs (since affiliates are only paid when they win the conversion).
As you can see, there is dramatically more information available to an advertiser when analyzing multi-channel data. Being armed with these insights, marketers can dramatically improve their ROAS by taking other marketing costs into the overall calculations and then optimizing spend. Without the whole marketing picture, you are essentially flying blind and unable to get the most out of your affiliate channel.
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