Maggie asks, "Is there a way to build a custom data-driven attribution model to include social impressions (Facebook / Instagram) data?"
There are a couple of different ways of handling this. Both require machine learning, but both are very possible and something that I've done for Trust Insights customers very recently. You're either going to be looking at Markov chain modeling if you have the data flowing into GA, or something like gradient boosting machines if you have very high resolution data. Watch the video for more and an example.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today's episode, Maggie asks, Is there a way to build a custom data driven attribution model to include social impressions such as Facebook and Instagram data? Yes, there's always a way.
There are a couple of different ways of handling this particular situation where you want to deal with traffic from social, both require machine learning, and both very possible and something that actually I've done very, very recently for a couple of Trusted Sites customers.
Let's look first at the traffic based one.
Because that one is, I think, probably the best overall blend of social and Google Analytics data.
Google Analytics gives you path information in their multi channel funnels API, which is how people interact with your company, on outside sources before they come to your website.
So for example, if they view a Facebook post, and then they click on it, and then they view an Instagram post, they click on that and they view an Email Click on that you would see in the API and the data, you know, Facebook, Instagram, email, right as the chain of evidence that leads to a conversion or doesn't.
Now, there's a machine learning technique called Markov chain modeling that you can use to pull apart those paths and re sequence them.
Understand which paths lead to conversion, which ones don't, and then what the differences are.
So in that same example, Facebook, Instagram email, let's say that leads to a conversion.
And then you have Instagram email, as a different one, no conversion.
In that very, very simple example, you could see by taking Facebook out the conversion fell apart.
So if you do this, while you have a machine to this, thousands or millions of times with all the conversion data in your analytics instance, you can then apply waiting to say, guess what facebook, facebook matters, because you know, in 87% of conversions that didn't have Facebook, they fell apart.
So that's the traffic based one.
And what I do is, I take that data, and then I condense it down something a little orange tablets actually bring this up here.
So this is an example of a digital customer journey analysis, which is what a Markov chain model is, by source and medium, from top to bottom, and from left to right is the order in which people do things because again, same back to the early example, Facebook, Instagram, email, Facebook's at the beginning of the journey, Instagram in the middle email at the end for that conversion.
So if you just essentially tally up those conversion pathways, you get this chart where you see here, impact BND is is 8.6 of our conversions, percent of our conversions are begin there.
Same with LinkedIn.
Same with Facebook, then, right at the end of the Western US Google organic search, that's a big chunk 27% is actually relatively healthy, because anything over like 15 years, you're in danger of being too vulnerable on one channel.
The all the engagement channels in the last half of the last of the journey, you see email, social, Twitter, things like that.
So this is one way of sequencing that data in order to get at how different social channels play into attribution.
This is if you have everything wired up, and you're passing as much data into Google Analytics as possible.
If you just have the impression data itself, you're doing type of modeling that essentially is called view through modeling.
And Facebook has like for example, view through conversions.
And what you're trying to do is show a mathematical relationship between the number of impressions that your social content is made, and any sort of impact in the rest of your analytics.
So what you need to do in this example, if you're just trying to work with the impression data itself, is you need to build a very high resolution table of essentially every interaction ideally broken down like the our level instead of the day level of impressions, clicks.
whatever other metrics you can get out of Facebook, whatever metrics you can get on Instagram, you put them all in one giant table.
And then each each networks, dimensions are their own columns of Facebook question Instagram impressions, Twitter impressions by hour of day, by day, day, by day by week by month, you should have you know, for example, one day should be what 1440 rows per network.
And then you have to call the Google Analytics API, and bring in things like branded organic searches, organic searches, direct traffic, where there is a lack of attribution, your total number of goal completions and things like that.
And you will use a technique probably like gradient boosting machines.
Extreme gradient boosting is the preferred method use a lot of the time for for analysis like this to say here are the here are the factors are the dimensions that drive the outcome we care about, like conversions, or branded organic searches, whatever KPI you're trying to benchmark against.
And then the software will go through and mix and match every possible combination.
The thing that's important is that you need a high resolution table, if you were to do this at like month level, you would get useless data because the sample is just too small, you really need as granular data as possible.
Google Analytics can spit out I believe you can go down to the second level, and Google Analytics data and for Facebook, Instagram, I'm I think you can get our minute out of out of those API's.
Again, you put all together, run your Gradient boosting machine, extract an answer.
And then in this instance, here's the catch, because you're dealing with essentially, at the end of the day was very, very, very fancy correlations.
You need to then build a testing plan to test the correlations if Facebook map matters the most, right? And maybe it's Facebook videos, if you have like, factors, the different types of content, you definitely want that another Facebook videos matter the most, then you build a testing plan, you run it, and you do lots of Facebook videos and see if, as the number of Facebook videos goes up, do you see a commensurate increase in the target metrics that you built with the gradient boosting machine like random organic searches, do more Facebook videos, absent? Anything else? Do you see a corresponding proportional increase in random organic searches? And you test that you test that you make sure it's not statistical noise? You test for statistical significance.
And then at the end of the testing period, you say yes, we can prove because we've done the testing properly, that Facebook videos increases your whatever KPI you care about.
So those are the two primary ways to build this kind of data driven attribution model.
I do ideally as much of your social data should come in as quick traffic as possible, but I know that always possible use gradient boosting machines using machine learning to assemble that and shameless plug if you want to help doing that because done a lot.
Feel free to reach out.
Happy to have that conversation.
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