So when it comes to attribution modeling, it’s got to get ugly and we all know it. It’s like a typical discussion on UX where no matter who you are, you will always have something to say. I have seen some excellent attribution models and some very ugly ones and the one thing that differentiated the two was the data-driven approach in the former.
Here are some of the incredible multi-channel attribution models that come as default.
If a significant percent of your conversions have a greater than one path length, you have an attribution problem. Combine that with the excellent multi-channel conversion visualize and you have yourself a view of your marketing that will freak you out.
The simplest way to start is to look at your Assisted Conversions report in Google Analytics. Look at the last column: Assisted/Last Click or Direct Conversions.
• If you see a value less than one, that channel has a higher tendency to drive last click conversions.
• If you see a value greater than one, that channel has a high propensity to be present earlier in the conversion cycle. These channels are getting zero credit in last click attribution platforms
There is a free tool inside Google Analytics called Model Comparison Tool. It allows you compare different attribution models and see the results in real time.
Apply the right model and you will not only distribute conversions across multiple touch points, but you can also look at the impact on the CPA. You can even get great first-step guidance about how to rebalance your portfolio from that last column.
The recommendations shown in the table above by the arrows is totally dependent on the type of model you use, so it’s pretty much like a magic wand, it’s not doing anything magical but yes we think it does. So how to choose the right attribution for the actual magic to happen is the real question.
With that in mind, let’s look at the standard models available inside Google Analytics (and some of the high-end analytics or attribution analysis tools).
Just so we have a visual guide through this learning process, let’s use the above image as a reference. Look up, memorize the steps to conversion. Ready?
This is the standard attribution model in all web analytics tools. It is applied to all the standard reports you see.
The fact that all the conversion is attributed to the last interaction is silly. Thus this model is just a baseline to make you understand other models and should not be used in the default manner it is in.
Social, Organic, and Referral were also involved. We should figure out some way to identify their contribution to the conversion process because they were involved in some form.
Historically, all tools used last-click attribution because the one thing they could confidently say is what drove the converting visit. And they did not have the technical horsepower to do Visitor-centric analysis. Both these problems are solved now.
The only use for last-click attribution now is to get you fired. Avoid it.
Google Analytics is bipolar.
All standard reports in Google Analytics give 100% of conversion credit to the last “campaign” prior to the conversion. Campaign is defined as anything but Direct traffic. So, the campaign could be Social, Organic Search, Email, Display, Affiliate, Referring Site … anything really.
This deliberately understates the Direct visits that lead to a conversion. In our picture below this model would say all credit goes to Referral.
This is imprecise. Why give credit to a campaign if it took me another visit where I remembered your URL and typed it in and came to your site? Why should the visit where, say, I saw a great promo or you recommended something based on my prior visit not get some credit for the conversion?
Why undervalue Direct? Why undervalue a marketer’s efforts to create brand recognition and brand value?
I believe this is a mistake. A historical legacy, perhaps. It should be courageously fixed.
If you are handling AdWords at your office then you should be loving this model. This model attributes all the conversion juice to the last click coming from AdWords. Personally, I hate this model for its bias and you should also do the same.
The reverse of the last click. Rather than giving all the credit to the last click, give all the credit to the first click.
With last-click attribution, there is at least some certainty that something about that campaign, something about that visit to the site, resulted in a conversion. With the first click you just have faith.
As the name goes this model attributes the conversion juice linearly to all the channels which played a part in getting conversions. Now the problem is evident, and the model seems a little too fishy.
This model is wrong but a little less wrong. If you are an unbiased marketer, you are then not a good marketer.
If someone threatens your life, use this model. Give everyone who contributed a participation certificate. But if you are not in a life-threatening situation, other models might help you actually understand which channels are contributing more value and which are not. And two of those models are just one click away.
So far we have considered all the models which can’t be used directly without manipulating. The time decay attribution model attributes conversion juice to media touchpoint according to their place in the conversion path. So a media touchpoint that comes later in the conversion path is given more importance as compared to a media touchpoint that comes first.
We could argue about how much credit the last few should get and how much the rest and how much the first. (Or we could not.) But overall it does seem to make sense that the further back a media touch point is (Organic Search and Social in our example) the less credit it should get
One of the cool things about this model is that you can customize the half-life of decay and insert your own data-backed feelings into the attribution process.
If you are going to start doing attribution modeling, the time decay model is great, passes the common sense test, way to dip your toes. Go to the Model Comparison Tool, click on Select Model, choose Time Decay, and let thoughts be provoked!
By default, the Position Based model attributes 40% of the credit to the first and the last interaction, and the remaining 20% is distributed evenly to all the interactions in the middle.
1. See my perspective on the first click attribution model above. 2. Understand why I believe that as designed the default position-based model is sub-optimal. 3. Promise me you won’t ever use the default one. 4. Feel really great you dodged a bullet.
Of the six attribution models available, there is one that you can use with little thought and still get value (Time Decay). One is not great, but won’t completely kill you (Position). Three are so weak that you should not acknowledge them if they pass you in the street (and actively warn your friends to avoid them!).
Why are there so many models? The known world is smaller than the unknown world. There are always corner cases, there are always weird scenarios, there is always someone who wants to do something odd. All these reasons are good reasons for all these models to exist. But do go into using any model with open eyes.
There is one more thing you can do after you are done with the first step, playing with and experimenting with the results of the Time Decay model. You can create a customized attribution model.
This is where the real fun begins. The personalized Attribution Model allows you to properly take care of the bugs in the previous models and make the attribution model that suits your business the best.
With the custom modeling tool you can use the Linear, First, Last, Time Decay, and Position-Based models as your starting point, and then layer in other factors you consider to be important for your business to create your own attribution model.
Here are the steps to create your own attribution model.
Step 1: Select the baseline model.
I start with the Position-Based. Then specify the amount of conversion credit based on the position. Here’s what I use…
If you’ve read this post carefully to this point, this distribution of credit should not come as a surprise to you. From all my experimentation I’ve found that taking out the last channel (whichever one it is) causes a material impact on the conversion process, so it gets a “good amount of credit.” The middle channels have an important role in driving people to the last interaction, they are recognized for that. The first interaction deserves some credit for the conversion, but not as much as the middle or last — for obvious reasons.
My distribution above is a good starting point. It is also really easy for you, as I often do myself, to experiment with different distributions, note the impact and optimize.
Step 2: Select the lookback window.
My process for picking the optimal time period to look for campaigns/interactions/media touchpoints to distribute credit over is to use the Time Lag report in the Multi-channel Funnels folder. It gives you the distribution of typical behavior.
Step 3: Select the engagement-based credit option.
We now go in and apply a rather clever rule to adjust credit for our campaign based on the behavior of the user that came to our site. This is particularly important for the touchpoints prior to the last click.
Time on Site is always a tricky computation. In all Web Analytics tools, unless you apply custom code, time on site is not computed for bounce visits or for the last page viewed in a visit.
Hence, I prefer to use Page Depth as a proxy for site engagement.
In this step we are telling GA to give more credit to campaigns that deliver users that have a higher engagement with the site. So if a user from campaign X see five pages during the visit on my automotive website and campaign Y sends a user that bounces, campaign X will get more credit.
Only seems fair. And now you can see how some of your credit distributions in step one will be auto-corrected based on the type of engagement campaigns deliver.
Step 4: Apply custom credit rules.
Here you can literally apply any custom rule you want. You can go in and say “for all bounced visits from rich media display campaigns give the campaign 2x the credit.” You would not do that, but you can. You can do the reverse, “give every campaign with Bounced Visits zero times the credit of other interactions in the conversion path.”
I take a simpler first step. I want to value my campaigns based on the interaction they deliver. If there is only an impression (people only see the ad), I value that a lot less than ads that get people to click on them.
To do that first I choose Interaction Type. Then I choose Click from the Exactly Matching dropdown.
Finally, I would like to have ads that get clicks to be extra rewarded and, in this case, get 1.4 times the credit of other campaigns in the conversion paths (in comparison to ads that just get impressions).
Why 1.4? After some experimentation, that was determined to be the optimal amount of value for this business (remember the custom model questions above?). There is no way out, you have to experiment.
That’s our last step.
Other ideas for this last step include the ability to give generic or brand keywords more or less credit. Or giving Direct or Social more or less credit. Or giving all Social visits that are the last click prior to conversion only half the credit compared to other interactions in the path (Include Position in Path Exactly Matching Last and Include Source Exactly Matching Social, where Social is your campaign tracking parameter).
Totally your call. Just remember to drag your common sense along when you sit down to do this.
[sidebar] Once again in step four you see how clever use of custom filters can auto-correct some of your earlier assumptions related to distributions of credit in step one. If campaigns in the middle, or the first one, don’t have the optimal interaction they will automatically be penalized.
That is all it takes, four simple steps, a pinch of understanding your business, and a sprinkling of common sense.
It should be completely obvious to you that this model is based on a specific client’s business environment, my experience, and business priorities.
While I believe it will serve as a good starting point for your very own custom attribution model, it might not be optimal for you.
Hence, more than anything else, I would love for you to follow the thought process and the reasons for making choice x or choice y. Then apply that level of critical thinking as you go about creating a model for your digital business.
The custom variables in GA are defined as a key-value pair to add additional custom segments to your data. The custom variables can help in segmenting data according to some key page, session, and visitor-level attributes. I know all this might be going above your head so hang on and I promise by the end […]