Yep. And when looking at data you have to be very clear on whether the conversion data you are seeing reflects "date of click" vs. "date of conversion." "Date of Click" data will continue to change throughout the duration of your reporting window whereas "date of conversion" does not.

This can get sticky in things like AdWords where the main UI reports on "date of click" and keeps changing, whereas Search Funnel reports report by "date of conversion."

Fortunately, most bid management platforms like Marin let you make that distinction in reporting.

Date of click is fine as long as you are understanding your reporting over fixed timeframes. e.g. What is my Day 7 ROAS on spend from 2 weeks ago, that will never change once you get past 7 days from the window of spend that you are looking at.

Date-of-conversion has the different (and in my opinion worse) issue of being 100% accurate in terms of current cost-per-conversion but under-values all marketing channels. This is because it attributes the cost of visits only to conversions that have already happened, while more conversions may later be attributed to those visits. This means that a conversion based model is actually worse for channel analysis.

Agreed on both points. Ultimately, everyone should try to gain a better understanding of the duration and number of touch points in their sales cycle. This directly impacts the lookback window you set. It is also important to know what these are when comparing any data across platforms.

For example, right now I'm going from a 90 day window for certain goals in AdWords because I know that while the majority convert on that goal in ~30 days, there's a long tail. Unfortunately, Marin restricts their window to 30 days, so any deeper digging will need to occur within Google Analytics or data from our data warehouse.

Would be great to learn more about solutions you've seen in the wild since it seems like you have a lot of experience on this topic. If you have time for a chat my email is in my profile (tried to find you but shostack is ambiguous :)

Isn't that an argument for assessment on a campaign basis? I'm still wrapping my head around his calculations. It looked like he included the cost of the ads prior to the reporting window in his multi-touch model. That's a fair way of measuring the true cost of user acquisition, but if the ads you are paying for today won't pay dividends until you're out of the reporting window, it would skew the ROI.

I'm not sure if this is the same thing as what you cited, as my argument is about the cost association during the reporting window as opposed to the future revenue association. I can't figure out a way to back those out in real-time (i.e. in enough time to make adjustments to the campaign) though.

In long-running campaigns, I still typically want to be able to measure changes to performance over time.

With a fluency in the ramifications of the attribution models and windows, I like to look at multiple attribution models (last-click and parabolic multi-touch) to draw conclusion about performance.

My takeaway from the post was simply, "Be aware of how much attribution models influence metrics," and I think that's a critical point.

Indeed, that is the most salient point. I like your suggestion of looking at many attribution models to assess true performance. This seems especially relevant in larger organizations where there is an incentive to game the budgeting process - presenting just the best possible case is what marketing is all about but can lead to skewed perspectives of success.

This is definitely true, you can think of that as Day 1 ROAS/ROI vs Day N ROAS/ROI. So when looking at your marketing spend you can see how it pays back over time since depending on how you attribute it could change forever (e.g. if you give some value of each conversion back to the original conversion source/sources).