Respectfully, this is a submarine PR piece. From the last paragraph:

>"It’s a lot of work to build out accurate multi-touch attribution, but there are third-party solutions out there that can simplify the process. My company Interstate makes a pretty good one in my opinion!"

That said, the importance of the topic, cross-channel attribution, cannot be emphasized enough for any company doing any sort of serious digital marketing these days.

A fantastic piece that goes much deeper would be Avinash Kaushik's article[1] on various modeling approaches. It is a great primer for both people new to the concept and approaches, as well as people with familiarity who are looking for new ideas.

The broader challenges these days are shifting towards the cross-device dilemma as well as reliance on 3rd party cookies and browsers' and users' increasingly aggressive stance against those.

-Cross-Device Dilemma-

I visit initially on mobile and then convert on desktop. Even Google has challenges with cross-device accuracy here, and depending on your mobile usage, it could dramatically impact how you view your performance. There are a variety of technology solutions out there that attempt to solve this, but ultimately there is no great solution that doesn't make me truly question the privacy aspects of what it takes to get to accurate data here.

If someone signs up for a trial and gives you a unique user ID, great, you can join that to the data from usage on other devices and understand that user's path to conversion. If they don't take an action that requires logging in, it is much murkier (not counting cookie onboarding services like Liveramp etc.).

-3rd Party Cookies-

Google and Facebook are excellently positioned because most browsers recognize their tags as 1st party. Many other networks and adtech platforms are boned here unless they can have their tags served as 1st party. I've seen variances of around 15-18% or higher in some cases between 1st and 3rd party tracking setups. While it is REALLY hard to control for all of the unknowns (no two web analytics platforms will ever give you the exact same data), my hunch is that 3rd party vs. 1st party cookies are playing a significant role here along with cookie deletion. Would love any other data folks can share on this.

Beyond that, the author talks about static models, but does not address their real limitations. At the end of the day, these look at aggregate data, when the reality is attribution occurs at the user level. Every touch point has some value, and that value is different for each user. For example, seeing a display ad on one site does not necessarily have the same value as seeing it on a different placement within that same site. Static models are very helpful directionally, but dangerous when taken as religion.

The future is in dynamic attribution models that constantly shift based on the data and whatever contributing factors you can feed in as supplementary data to the model. I'm super excited to see what Google starts doing with Adometry (check out the TV release they just had), and the other big players like VisualIQ and Convertro will likely have their technology trickle out through other platforms over time. FB's Atlas acquisition is also a big statement that they take this seriously.

Net net, attribution is the hardest challenge in the marketing/advertising world today. Period. End of discussion. It is plagued with data that is messy as hell and incredibly nuanced based on the infinite variables of everyone's unique setups. Look through a variety of attribution models as different lenses to help inform directional decisions, but be cautious on saying "this is the model we will use across everything." It might help initially, but without constantly checking it against other models, you can miss shifts in your efforts.

[1]http://www.kaushik.net/avinash/multi-channel-attribution-mod...