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
Looking at the blogs, articles, and conferences, attribution is a hot topic for today’s marketers. A quick search for the topic on your favorite search engine delivers lots of content and technologies devoted to the subject. But is attribution actually a problem for you? Is it something that currently keeps you from your goals and should you be investing your analytics dollars in understanding?
The key to understanding if you might have an attribution problem is understanding the way that customers buy your products and services. Are the customer journeys simple, short and does your conversion event happen in that single visit? Or are they typically quite complex? If you look at the paths to purchase and you see that the journeys are quite short en “one touch” paths to purchase, i.e. customers either bought on their first visit or they didn’t, then you might not have an attribution problem at all. When the majority of conversions happen on a single visit then it doesn’t matter which model you use, their output will be the same!
However, be careful in looking at your current data set only, because it might be that you have data problems because you’re not tracking journeys across different devices and touch points, meaning that you are not getting a complete picture. In case you are currently using paid search and looking to deploy video campaigns, then this medium will probably not deliver the same results on the basis of the attribution model that worked well in the past for your paid search campaigns. From the data you might quickly conclude that ‘video does not work’, where in face your attribution model ‘is not longer appropriate’. In other words, you might develop an attribution model.
This is not to say that you need to cherry-pick attribution models based on the medium – it means that you select the attribution model that best fits the path to purchase and maximizes business results. Your attribution model is not ‘fixed’, and it should not be, as the path to purchase and the channel mix changes over time to drive more business.
When was the last time when you optimized your attribution model? We look forward to your comment!
Posted in Attribution in Online Advertising, Attribution Model, Conversion Tracking, Last Click Attribution, Marketing Attribution Tagged with: attribution model, attribution modeling, attribution problem, last click attribution
Are you using many channels to drive sales, but do you allow one channel to claim all credit to the conversions? You might just be making marketing’s fundamental attribution error.
In social psychology, the fundamental attribution error, also known as the correspondence bias or attribution effect, is the tendency for people to place an undue emphasis on internal characteristics – personality – to explain someone else’s behavior in a given situation rather than considering the situation’s external factors.
In marketing, the fundamental attribution error, also known as the desktop computer bias, is the tendency for online marketeers to place an undue emphasis on the last-click to explain a given sale conversion being recorded, rather than considering the conversion’s external factors, such as banner, social and mobile campaigns.
This suggests smartphones are more a browse or research platform rather than a buy platform as most sites in this research are by now mobile optimized sites.
90% of people use multiple screens sequentially to accomplish a task over time, and 90% of marketeers with a desktop computer bias kill their mobile reach by bidding down ruthlesslessly, as they just layer the old-world model of desktop performance on mobile. Do you bid 50% lower for mobile because the mobile conversion rate is 50% lower? Are your ads shining bright on the bottom of mobile search pages?
You would agree that smartphone experiences should be personalised to show this different form of usage, but how to measure all this? See what you can do with the experience of retailer ModCloth (PDF):
“Instead of driving sales, we’re focused on generating registrations via mobile ads. Once a visitor is registered, we can leverage channels like email to convert her to a paying customer,” says Post.
A lot of other interactions are also happening on ModCloth’s mobile-friendly site: adding items to the shopping cart, social sharing, browsing numerous pages… All of these are valuable Mobile Conversions that ModCloth is tracking to better understand the real effectiveness of mobile.”
Are you making marketing’s fundamental attribution error? What do you do and measure with mobile? Leave a note in the comments!
Posted in Attribution in Online Advertising, Attribution Model, Conversion Tracking, Last Click Attribution, Marketing Attribution Tagged with: attribution model, attribution online advertising, fundamental attribution error, marketing attribution