I’ve been documenting my attribution modelling journey….
3 Questions To Ask Yourself About Multi-Channel Attribution
When Do You Use A Last Non-Direct Click Attribution Model?
The Journey To Multi-Channel Attribution
Multi-Channel Attribution That Goes Beyond Last Click
…as I try to figure out a model that works for startups. It’s not perfect, though it’s a starting point and good is certainly better than perfect so we can launch something and iterate as we go.
If you’re a startup and resources are scarce and you can’t get money to use an attribution vendor (yet), than we’re in the same boat and this post is for you. So today I want to share the framework I’ve started to work on and what shape it has taken. To build this framework I started by answering the 3 attribution questions in my last post.
Defining Success and our Attribution Window
Customer signing up for the service is our main KPI. However, I also look at people subscribing to the blog, downloading an ebook or whitepaper. The last two are tracked because maybe a campaign you thought would be for driving customer acquisition, is actually more suited for blog subscribers or getting people to download an ebook. If your company offered webinars or product demos and you’ve those as goals (or events) in Google Analytics, than you can add that to the spreadsheet below.
I picked a 30 day attribution window for the cookie that sits on a customer’s computer. However, when I think about the effect of a campaign and how long to look at it after it launches. I picked 90 days because we don’t generally have long sales cycles and if someone takes longer than 90 days to convert, it becomes a mess to track. If I had more time I’d look at 180 days after a campaign launches to see its effect on the business. Maybe that will be round two for next year.
The spreadsheet below is what I created when I put everything together. I added in a threshold of $500 for campaigns we track in this birds eye view because anything under that limit doesn’t tend to have enough data to make a proper informed decision that doesn’t risk getting a false positive for you campaign and data. All data is fake below and just to give you ideas of what your could look like.
You may be asking yourself what each of the columns mean and how that might apply to your startup. Lets do a quick runthrough of each column in the spreadsheet.
This is the name of your campaign. Try to name it something anyone new working on your marketing team will understand when they look at the campaign.
What is the campaign about? Launching an ebook or new service, I put down what’s here. This way I can compare similar campaigns and see if I can spot any trends down the road.
If you’re launching something in a non-English market, I put it here. Similar to type above, this can help you start to spot trends in different markets.
What you spend across many platforms/networks (Facebook, Linked In ..ect). In the “Notes” column below I’ll say if one network drove more of the business. e.g. Maybe Facebook drove 90% of your ebook downloads for November.
Launching an ebook or whitepaper tomorrow? This column tracks those downloads.
People who subscribe to your blog or this could even be subscribers to your company newsletter if you don’t have a blog. If you’ve both, just add another column in your spreadsheet for tracking.
This is your main KPI and usually is a customer signing up for your SaaS business. Could also be sales of a product if you’re in ecommerces.
Last Non-Direct Click (LNDC)
I talked about this newer attribution moder last month. If you removed direct conversions in Google Analytics and looked at the second last click before someone converted and count that channel instead…that is LNDC in a nutshell. To grab this data, see the note at the end.
This is about assisted conversions which is an excel SUM formula from the two purchase path columns. See the section below on assisted conversions and how to find the data in Google Analytics.
These two column tell me how many people took less than 5 clicks to convert VS over 5 click to convert on your main KPI.
Assorted Campaign Details
When did you launch the campaign. Make a note if it spans multiple months as seasonality could come into play
Some campaigns cross over into multiple months. I put the month that the budget is getting assigned too. I try not to split campaigns budgets into multiple months as much as I can.
Where in the world are you lunching this campaign. This goes nicely with the language column above.
This is for campaign FYIs or notes that may not fit anywhere else on here.
Assisted Conversions & Purchase Path Length
You want to pick the number of clicks you’ll measure assisted conversions by looking at how many clicks the majority (85-90%) of your customers take to convert. There are three very logical and very good reasons for this:
1) This helps set a benchmark to measure success against. Benchmarks are your friend!
2) I’d argue that if most customers take 5 clicks to convert and you’ve ran a campaign that has the majority of people converting under 5 clicks. Even if that campaign isn’t the last click before someone converts, the fact that people are converting means it’s helping drive a lot of business. Might be a campaign you want to put more money and resources into.
3) Any things over 5 clicks and especially when you see people taking 15 or 20+ clicks on their path to convert and your campaign above is just one them. I’d argue these customers would have converted with just about any campaign they saw from your brand.
So if you look at the above example, not real data from my company, you see that this business gets 90% of their business in 3 clicks. I’d make our model with 4 clicks to be safe and erring on the side of cause is always good. To find this login to Google Analytics and head over to Conversions –> Multi-Channel Funnels –> Path Length and pick a 30 day date range. You’ll only see data if you’ve ecommerces setup in Google Analytics. If you don’t have ecommerces setup than you can grab similar data from AdWords and use that as an anchoring number for this report.
Steps To The Number Of Assisted Conversions Column
Login to Google Analytics and head over to Conversions –> Multi-Channel Funnels –> Top Conversion Paths. At the end of “Primary Dimension” you’ll see “Channel Groupings” with a little drop down arrow. Click that arrow and copy the MCF Channel Grouping template.
Now the new copy of the MCF Channel Grouping template should be available for you to edit, copy or share it. We only want to edit it right now. Click on the little pencil to edit your new channels grouping.
Editing this new MCF Channel Grouping will allow you to figure out, on a campaign by campaign basis, which campaigns are helping drive a conversion under or over your purchase path length of 4 clicks. First I renamed mines Paid Campaign – Multi-View but you can call it something that makes more sense in your company. Click the pencil to edit “Paid Campaigns” and change the campaign name field to name of your campaign. Google should auto-fill this as you type. Click done and then save.
The last two steps are easy as you pick a date range for your report, I do mines week by week, and search for paid in the search box. This will bring up every campaign that helped drive a conversion for your company. Add up all the conversion under and over 4 clicks and fill out the spreadsheet.
Steps To Last Non-Direct Click (LNDC) Column
Head over to Conversions –> Attribution –> Model Comparison and pick Last Non-Direct Click from the drop down menu in the middle of your screen. Take the same steps above when you edited your MCF Channel Grouping template to look at each campaign on its own. You don’t have to worry about remaking the MCF Channel Grouping template because it’s available in Google Analytics for any report that has channel grouping as an option. Now take the conversion number you get back on the paid channel under “MCF Channel Grouping” and that is what I’d put on your spreadsheet. Again look at what happens 90 days after each campaign launched. Again I do this on a week by week basis.
So you’re done right? Not even close because you’ve the data and now you need to do the hard work and analyze. You need to figure out where you’re going to spend more money based on the spreadsheet below.
This post got out of hand and is longer than I anticipated it’d be. So I’m going to do one more post about analyzing your results at the end of the months and hopefully that gets us ready for 2016.