The last-click attribution model gives 100 percent of the credit for the sale to the last channel a customer clicked on. That model of reality would be fine if there was only one channel and one touchpoint, but with more than 50 percent of customer interactions involving multiple channels, the last-click attribution model does not do justice to key channels that drive conversions. Besides that, does it actually matter to you if your customers see a search or display ad first or last before they buy? Because you care about revenue, less about the order in which ads are served. The Internet is an open and chaotic place, so even if you could know the ideal order in which ads are served, you can do very little to force-serve ads in that way. So, let’s spend your time in a more actionable way; another way to approach attribution is to voice your hypothesis of what a new device or medium would do to your revenue.
So, what do hypothesis-driven marketers do? They run what scientists call a controlled experiment. You add a medium or device to the mix and do your best to measure lift in revenue from adding that over your baseline marketing mix. You create an incremental revenue model, which looks at customer conversion based on the treatment they received. You can randomly select people to be put into two different groups. A percent of people will be put into an A group, and a different percent of people will be put into a B group. So this might be 90% of your users, and then a randomly selected 10% will be put into a B group, which is what we’ll call the control group. Sounds a lot like the biology lab experiments back in school, right?
Incrementality testing is done by separating a small subsection of customers who don’t receive a certain marketing message and then measuring whether there is a difference in the customers who received the messages (the exposed group) and those who didn’t (the control, or holdback, group). You compare the two conversion rates between the groups that was retargeted and the group that wasn’t and see the actual lift. Imagine that your control group had a 6% conversion rate, and your exposed group 8%, which is a 33% conversion lift.The way that you set up and optimize a campaign to deliver optimal incrementality is different than if you were optimizing a campaign just for the lowest CPC or the lowest CPA. No matter what the CPC is, CPA, or anything else, this is the net impact of your medium or device new to your mix.
Here, AdRoll explains the method:
You you have experience with incremental attribution and running controlled experiments? We look forward to your comments below!
Further reading: uplift modeling article on Wikipedia
Posted in Attribution in Online Advertising, Attribution Model, Attribution Modeling, Controlled Experiments, Conversion Tracking, Last Click Attribution, Marketing Attribution Tagged with: click order attribution, controlled experiment, conversion lift, incremental attribution, incrementality testing