Understanding Google Analytics Multi Channel Funnels
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Last month I had the pleasure of participating in a Google+ thread started by Daniel Waisberg to discuss the usefulness of Multi Channel Funnels (MCF) in Google Analytics. While I agree with my colleagues who said that the path analysis report like the one shown in the post are indeed not actionable (by the way, that picture represents the path used by a single user before converting!), I do feel that MCFs are a great addition to Google Analytics and have found valuable insights for clients. In this post I will share a few of my favorite conversion segments and MCF analysis methods, as well as discuss what MCFs add (and don't add) to GA.
Understanding Multi-Channel Funnels
If you are new to Google Analytics, or to this feature in particular, here is a video created by Google that explains the idea and value behind it.
It is important to clarify how MCFs differ in their attribution reporting from standard Google Analytics reports. (Hat tip to Adrian Vender for pointing out this help page which does a good job explaining things). The most important difference is in the way that MCFs deal with Direct traffic.
In standard Google Analytics reports, if a user visits a site via some sort of campaign (i.e. thereby setting the utmz campaign cookie with values such as source, medium keyword, etc) and then returns to the site by typing in the URL to the address bar or a bookmark, the subsequent visits are reported as additional visits from whatever source/medium/keyword was last recorded. *Multi Channel Funnels do not take the campaign cookie into account when reporting direct traffic.* The impact will oftentimes be seen as larger 'last interaction' conversion values for direct traffic in MCF than the corresponding 'Direct' traffic source in other GA reports.
Personally, I find this departure from the standard way that Google Analytics (GA) handles last click attribution a bit challenging. My preference would be to see the Last Interaction Conversions remain consistent with other GA reports. Indeed, I tend to look at MCFs in order to understand how areas with large spend (in general PPC and SEO) are giving me a greater bang for my buck than what I'm seeing in standard GA reports. At the very least, the ability to toggle between the MCF model and the standard cookie based model would be helpful. For now, some deep dive analytics is simply what is called for as long as this fly is in the ointment.
Interestingly, for ecommerce transactions the normally not so useful Time to Purchase report (Standard Reporting => Conversions => Ecommerce => Time to Purchase) provides some new insights in light of Multi-Channel Funnels. Normally, I call the Time to Purchase report "not so useful" because it only shows count of visits or days to purchase from the moment the utmz campaign cookie was set, not taking multi-touch attribution into account. However, noting how Direct Traffic is reported in MCFs, we can see how many conversions would actually be reported in standard reports as Last Touch.
Analyzing Data from Multi Channel Funnels
The discrepancy between the way standard reports and MCFs deal with Direct Traffic notwithstanding, I have found the following techniques allow me to clearly identify areas where clients are getting a bigger return on investment on inbound marketing spend than we would have otherwise known about without MCFs. By far, I find the most useful report the Assisted Conversions report.
Branded and Non-Branded Keywords
In the same way that segmenting between branded and non-branded keywords is a must in regular GA reports, it is also a must for MCFs. The beautiful thing about MCFs is the ability to see (and quantify) what web analysts have known all along - that other touch points often proceed branded searches. One of the first things I do when I begin working with a new profile is to create a copy of the basic channel groupings....
.... and then separate out branded keywords from Paid and Organic Search.
I then create a new grouping to include just branded keywords. The Custom Channel Grouping ends up looking somethings like this:
Getting Actionable Data
Once Channel Groupings are configured to match one's particular website and tracking needs (noting that I always feel branded keywords should be separated out), we can begin to apply conversion segments to the Assisted Conversions report. One of my favorite conversion segments is the following:
By applying this segment, we're able to see in quantifiable numbers the impact of paid search spend had on conversions in other channels. Noting my comments above about how MCFs treat direct traffic, I pay more attention to the non-direct channels that receive last touch conversions. The following is looking at the basic channel groupings.
I love the fact that I can see the impact of Paid Search on other channels. The actionable takeaway in most cases has to do with bid management. For ecommerce retailers, I rely heavily on the Adwords ROI calculation to determine if a paid search campaign is maximizing profits (keeping the client's margins in mind as well, of course). With MCFs, when I can see for a fact that some Adwords clicks are indeed impacting sales down the road or not, I am able to make a much more informed decision about whether or not it makes sense to boost bids (as I'm sure that many readers out there will nod their heads in agreement that the paid search marketplace can be quite competitive). For advertisers doing retargeting, MCFs expose how users got to the site before the retargeting sealed the deal.
To see how often my paid search campaigns also include a branded search in the users path towards conversion, I apply the following filter in the Top Conversion Paths report on top of a custom channel grouping.
I don't spend time looking at the rows upon rows of path information itself as it is not actionable. That bears repeating, as I've seen too many clients bang their heads against a wall trying to extract meaning from the linear path data in the Top Conversion Paths report. Don't waste your time trying to find insights in row upon row of path data. I do, however, take a look at the sum of the transactions that had both a paid and branded interaction to see what extent my super-converting branded keywords were or were not influenced by paid search campaigns.
In a similar vein, I recommend using conversion segments to determine if particular keyword segments or display advertising campaigns etc are having an upper funnel effect or not. Conversion segments are very extensible and are useful in confirming whether or not poorly converting traffic sources are indeed a waste of time and resources.
Challenges with Multi-Channel Funnels
I've already mentioned a few challenges people face with Multi Channel Funnels (namely, wasting time trying to unpack path reports and the discrepancy between Last Interaction Conversions and the Last Touch attribution model in standard GA reports).
Another significant issue, in my humble opinion, is that the look back window is only 30 days. For sites with a longer sales cycle, 30 days just doesn't cut it. For those businesses, passing an anonymous visitor ID into a custom variable is a necessary prerequisite for an analyst to do his or her own multi touch attribution analysis via the API or through a CRM integration. Of course, Multi-Channel Funnels do not currently support attribution modeling, though it is something on the Google Analytics Premium roadmap.
Lastly, MCFs have a lot room for improvement when it comes to the options available in conversion segments. While the Adwords options are quite robust, I would love to create segments by events, custom variables, page paths, and landing pages. It would be great to see if a user took a certain action on a site during a visit (for example a video view, or email signup) and then converted a few visits later down the road.
Currently, Google Analytics is not designed for visitor segmentation (advanced segments are visit level). The addition of Multi Channel Funnels was a major upgrade in this area, though there is still a long way to go. With the almost dizzying pace that the GA team has been improving their product over the past few years, I would not be surprised to see another "game changing" product update in the next year.
The addition of Multi Channel Funnels to Google Analytics just over half a year ago was a huge improvement to the product. While MCFs do have room for improvement and can be confusing and difficult to use at times, I believe they have tremendous utility. In a web ecosystem where advertisers and marketers large and small are spending real money to try and get ahead, Multi Channel Funnels can provide meaningful and actionable insights when used properly.