You Ask, I Answer: Custom Attribution Models in Google Analytics?

In today's episode, D asks with regard to your attribution models in your uh artificial intelligence talks. Do you need to create custom reports to get this data into Google Analytics? If so, how do you go about doing that or anything you can use to provide uh some help? So this is in reference to the talk that uh both I and uh my business partner Katie Robert give on uh five applications of AI for marketing, one of which is doing attribution analysis with Google Analytics data. Google Analytics has attribution models built in.

Uh it has the the five standard ones first touch, last touch, linear, time decay, and of course uh your own custom models. But the custom models are largely um position-based models. That's not bad, right? That's okay. Well, I mean, first touch, last touch are worthless.

But um time decay is a is an okay model, and then customizing it with your own weights isn't an okay model. But that is relatively they're the models that both Google Analytics and and other companies like Adobe Analytics and stuff provide are built for efficiency and speed. They are not built for accuracy because the better models for attribution analysis are very computationally costly. Uh they take minutes, sometimes even many minutes, close to an hour, depending on the model, to run. It is not something that a vendor is going to offer.

You know, click this button and come back in an hour, come back, you know, in in 30 minutes, or go get a sandwich. Um, that is a a very unsatisfying user experience, and so uh vendors do not offer that. Um, but if you want the best possible answers, uh, those are the kinds of models you need to do. So these models are things that you have to essentially build with uh your own custom code, or obviously hire a company to do it for you, um, so that you can get access to the best possible data. One of the things that is a natural evolution in your analytics journey is going from a pre-packaged product like Google Analytics being the source of all of your information to more and more those applications being data providers, and then you yourself having to do the computation, the analysis and stuff offline outside of the application because the application just doesn't support the kinds of requests that you want to be doing.

Um that's not to say that there's anything wrong with Google Analytics or Adobe Analytics or any of these uh companies. There's nothing wrong with their software, it's just that at a certain point your sophistication as a data-driven marketer rises above the feature set that's in the application, and that's a good thing. If you find that you're getting fewer and fewer answers from the built-in software, and you need more and more work outside of the software, that means you're getting probably means you're getting to tougher questions with more valuable answers, right? It's super easy to look and say, Oh, how many goal completions did we get, or uh how many how much traffic did we get, or what percentage of people uh use a smartphone to get to our device? And those are important questions, those are questions you need the answers to, but they're easy, easy questions to answer.

Harder questions are things like what are the behaviors of specific segments? Which attribution model works best? How do we do attribution modeling at the individual channel level rather than uh the groupings that are built in? And those are things that require more processing outside. And then you start getting into the really complex stuff.

Like, what about taking into account activity data that is offline? What about taking into account activity data that it doesn't have a uh direct line of sight to our goal completions? What about blending in our CRM data? All those things exist outside of the application because that's not what Google Analytics is for, and you have to use third-party software, third-party tools, your own software, your own tools, to get those answers. So when you find that you are at that point, that is a that's a moment worth celebrating because you have reached a level of sophistication as a digital marketer that indicates you are creating real value for your company.

You are asking tougher and tougher questions, demanding tougher and tougher answers. One of the things that you'll have to change then also is giving consideration to what vendors you use. Because if you use the built in software less and less, then paying for uh an expensive uh fancy uh user interface becomes less and less important, and the quality and quantity of data you can export from an application becomes more important. There are services out there, um, we are uh partners and strong friends with uh the folks over at TalkWalker. They have a nice application, you know, it's good, but I don't use it all that much.

I use it for the data export. They have some of the richest, most robust data that you can have access to on social media and listening and stuff like that, way more than I was ever able to get out of other uh competing software packages by 10x. Um that means that I can build better models, I can run more complex reports, I can do better analysis. So for you and your journey, you'll have to once you start running into things like hey, uh, what does it mean there's a 5,000 row export limit? That's no that's no good.

Uh that's again another sign that you are you are reaching uh increasing levels of sophistication with your digital marketing. Same for SEO tools. I've used Moz and RFs and SEMrush and Spyfu and all these and Bright Edge and all these tools, and I like RFs the best, not because their interface is beautiful, I mean it's okay, it gets the job done, but their data export is second to none. I mean, that millions of lines of of data every month, which you need to be able to do modern-day SEO. Modern day SEO is all powered by machine learning, so you better be building your own machine learning models for these things.

So, can you build back to D's question? Can you build this technology yourself? Absolutely. And in fact, uh, in both uh my talk and KD's talk, we explain the methodology used. It is Markov chain modeling.

So if you are familiar with Markov chains, do an implementation with the data from Google Analytics. If you're not familiar with Markov chains, that's a good starting point for your line of inquiry for your uh area of study to figure out how you can use that. Uh one area that I'm personally looking at right now is uh taking up a level to hidden Markov models because there are some things that are hidden and some data that's visible, and I'm trying to figure out how do I build software that can answer that question of what's hidden, because what's hidden for us as marketers is what's in somebody's head, right? The classical example is you know, if you don't have Windows in your office, but your coworker comes in and they're wearing a raincoat and it's all wet, you can make an inference with strong probable probability that it's raining out, right? So you have a known piece of data, but you have the unknown data about what's going on outside.

The same is true for data in Google Analytics, right? You see organic searches, you see conversions, you see some things, but you don't see why somebody made a decision, you don't see offline, you don't see uh word of mouth, but you could infer those things potentially with a technique like hidden markov models or some of the more advanced deep learning methods. And so that's my own personal line of inquiry taking the data that we do have access to and trying to infer the stuff that we do not have access to. So that's uh where my journey is going and where I hope your journey is going as well, because the technology now exists for us to be able to get answers to some of these really difficult questions in marketing, and that's that's exciting. That is exciting times.

Otherwise, uh leave any other comments below. Subscribe to the YouTube channel and to the newsletter. I'll talk to you soon. Take care.