[Build Backlinks Online] How to Set Up Meaningful (Non-Arbitrary) Custom Attribution in Google Analytics

Build Backlinks Online has posted a new item, 'How to Set Up Meaningful
(Non-Arbitrary) Custom Attribution in Google Analytics'

Posted by Tom.Capper
Attribution modeling in Google Analytics (GA) is potentially very powerful in
the results it can give us, yet few people use it, and those that do often get
misleading results. The built-in models are all fairly useless, and creating
your own custom model can easily dissolve into random guesswork. If youâre
lucky enough to have access to GA Premium, you can use Data-Driven Attribution,
and thatâs greatâbut if you haven't got the budget to take that
route, this post should show you how to get started with the data you already
have.




If you've read up on attribution modelling in the past, you probably already
know whatâs wrong with the default models. If you havenât, I
recommend you read this post by Avinash, which outlines the basics of how they
all work.


In short, theyâre all based on arbitrary, oversimplified assumptions
about how people use the internet.

The time decay model

The time decay model is probably the most sensible out of the box, and assumes
that after I visit your site, the effect of this first visit on the chance of me
visiting again halves every X days. The below graph shows this relationship with
the default seven-day half-life. It plots "days since visit" against "chance
this visit will cause additional visit." If it takes seven days for the repeat
visit to come around, the first visit's credit halves to 25%. If it takes 14
days for the repeat visit to come around, the first visit's credit halves again,
to 12.5%. Note that the graph is steppedâI'm assuming it uses GA's "days
since last visit" dimension, which rounds to a whole number of days. This would
mean that, for example, if both visits were on the day of conversion, neither
would be discounted and both would get equal credit.




There might be some site and userbase out there for which this is an accurate
model, but as a starting assumption itâs incredibly bold. As an entire
model, itâs incredibly simplisticâsurely we donât really
believe that there are no factors relevant in assigning credit to previous
visits besides how long ago they occurred? We might consider it relevant if the
previous visit bounced, for example. This is why custom models are the only
sensible approach to attribution modelling in Google Analyticsâthe simple
one-size-fits-all models are never going to be appropriate for your business or
client, precisely because theyâre simple, one-size-fits-all models.


Note that in describing the time decay model, Iâm talking about the
chance of one visit generating anotherâan important and often overlooked
aspect of attribution modelling is that itâs about probabilities. When
assigning partial credit for a conversion to a previous visit, we are not saying
that the conversion happened partly because of the previous visit, and partly
because of the converting visit. We simply donât know whether that was the
case. It could be that after their first visit, the user decided that whatever
happened they were going to come back at some point and make a purchase. If we
knew this, weâd want to assign that first visit 100% credit. Or it might
be that after their first visit, the user totally forgot that our website
existed, and then by pure coincidence found it in their natural search results a
few days later and decided to make a purchase. In this case, if we knew this,
weâd want to assign the previous visit 0% credit. But actually, we
donât know what happened. So we make a claim based on probabilities. For
example, if we have a conversion that takes place with one previous visit, what
weâre saying if we assign 40% credit to that previous visit is that we
think that there is a 40% chance that the conversion would not have happened
without the first visit.




If we did think that there was a 40% chance of a conversion being caused by an
initial visit, weâd want to assign 40% credit to âPosition in
Pathâ exactly matching âFirst interactionâ (meaning visits
that were the user's first visit). If you want to use âPosition in
Pathâ as your sole predictor of the chance that a visit generated the
conversion, you can. Provided you donât pull the percentages off the top
of your head, itâs better than nothing. If you want to be more accurate,
thereâs a veritable smorgasbord of additional custom credit rules to
choose from, with any default model as your starting point. All we have to do
now is figure out what numbers to put in, and realistically, this is where it
gets hard. At all costs, do not be tempted to guessâthat renders the
entire exercise pointless.

Tested assumptions

One tempting approach is simply to create a model based to a greater or lesser
extent on assumptions and guesswork, then test the conclusions of that model
against your existing marketing strategy and incrementally improve your strategy
in this manner. This approach is probably better than nothing for improving your
market strategy, and testing improvements to your strategy is always worthwhile,
but as a way of creating a realistic attribution model this starting point is
going to set you on a long, expensive journey.


The ideal solution is to do this process in reverseârun controlled
experiments to build your model in the first place. If you can split your users
into representative segments, then test, for example,

the effect of a previous visit on the chance of a second visit
the effect of a previous non-bounce visit on the chance of a second visit
the effect of a previous organic search visit on the chance of a second visit

and so on, you can start filling in your custom credit rules this way. If your
tests are done well, you can get really excellent results. But this is
expensive, difficult, and time consuming.


The next-best alternative is asking users. If users donât remember
having encountered your brand before, that previous visit they had probably
didnât contribute to their conversion. The most sensible way to do this
would be an (optional but incentivised) post-conversion questionnaire, where a
representative sample of users are asked questions like:

How did you find this site today?
Have you visited this site before?
If yes:
How many times?
How did you find it?
Did this previous visit impact your decision to visit today?
How long ago was your most recent visit?

The results from questions like these can start filling in those custom credit
rules in a non-arbitrary way. But this is still somewhat expensive, difficult
and time-consuming. What if you just want to get going right away?

Deconstructing the Data-Driven Attribution model

In this blog post, Google offers this explanation of the Data-Driven
Attribution model in GA Premium:


âThe Data-Driven Attribution model is enabled through comparing
conversion path structures and the associated likelihood of conversion given a
certain order of events. The difference in path structure, and the associated
difference in conversion probability, are the foundation for the algorithm which
computes the channel weights. The more impact the presence of a certain
marketing channel has on the conversion probability, the higher the weight of
this channel in the attribution model.The underlying probability model has been
shown to predict conversion significantly better than a last-click methodology.
Data-Driven Attribution seeks to best represent the actual behaviour of
customers in the real world, but is an estimate that should be validated as much
as possible using controlled experimentation.â (my emphasis)


Similarly, this paper recommends a combination of a conditional probability
approach and a bagged logistic regression model. Don't worry if this doesn't
mean much to youâIâm going to recommend here using a variant of the
much simpler conditional probability method.


I'd like to look first at the kind of model that seems to be suggested by
Google's explanation above of their Data Driven Attribution feature. For
example, say we wanted to look at the most basic credit rule: How much credit
should be assigned to a single previous visit? The basic logic outlined in the
explanation from Google above would suggest an approach something like this:

Find conversion rate of new visitors (letâs say this is 4%)
Find conversion rate of returning visitors with one previous visit (letâs
say this is 7%)
Credit for previous visit = ((7-4)/7) = 43%

To me, this model is somewhat flawed (though Iâm fairly sure that this
flaw lies in my application of Googleâs explanation of their Data-Driven
Attribution rather than in the model itself). For example, say we had a large
group of repeat visitors who were only coming to the site because of a previous
visit, but that were converting poorly. Weâd want to assign credit for
these (few) conversions to the previous visits, but the model outlined above
might assign them low or negative credit; this is because even though
conversions among this group are caused by previous visits, their conversion
rate is lower than that of new visitors. This is just one example of why this
model can end up being misleading.

My best solution

Figuring out from our data whether a repeat visitor came because of a previous
visit or independently of a previous visit is hard. Iâll be honest: I
donât know how Google does it. My best solution is an approximation, but a
non-arbitrary one. The idea is using the percentage of traffic that is either
branded or direct as an indicator for brand familiarity. Going back again to how
much credit should be assigned to a single previous visit, my solution looks
like this:

Calculate the percentage of your new visitor traffic is direct, branded organic
or branded PPC (letâs say itâs 50%)
Note: Obviously most of your organic is (not provided), so I recommend
multiplying your total organic traffic by the % of your known keyword traffic
that is branded. As (not provided) approaches 100%, youâll have to use PPC
data to approximate your branded organic traffic levels.
Calculate the percentage of your 2nd-time-visitor traffic is direct, branded
organic or branded PPC (letâs say itâs 55%)
Based on the knowledge that only 50% (in this case) of people without previous
visits use branded/direct, approximate that without their first visit weâd
only have seen (100%-55%)*(100/50)=90% of these 2nd time visitors.
Given this, 10% of visitors came because of a previous visit, so we should
assign 10% credit for 2nd time visits to the first visit.

We can use similar logic applied to users with 3+ visits to calculate the
credit deserved by âmiddle interactionsâ.


This method is far from perfectâthatâs why I recommended two
others above it. But if you want to get started with your existing data in a
non-arbitrary way, I think this is a non-ridiculous way to get started. If
youâve made it this far and you have any ideas of your own, please post
them in the comments below.
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