You Ask, I Answer: What Excites You About Marketing AI?

In today's episode, Paul asks, what excites you most about AI? In the context of marketing and data science, uh, artificial intelligence allows us to scale our inquiries to our data. And I think that's a really important way to approach this. We have, as marketers, as people, we have so much data. We have overwhelming amounts of data, and we're not using it, right?

Take a real simple example. Look at Google Analytics. How many dimensions and metrics are in Google Analytics? Do you even know? If you actually go to the Google Analytics Dimensions and Metrics Explorer online, you will see literally dozens, if not hundreds, of dimensions and metrics.

Page time, bounce rate, exit rate, uh, page title, uh, source, medium, campaign, content, keyword, you name it, there's so many data points in Google Analytics. When you think about how you use that application, what do you look at? You look at maybe one or two metrics that you care about, like conversions or traffic, and you might on a good day dig one layer down and look at like your source medium or your channel groupings and or your assisted conversions. But there's so much in there. There's so much in there, and we don't use it.

And we don't know if that information is valuable. We don't know if it's not valuable because we never use it. It's like having a uh a frying pan or another kitchen utensil that you never use. You don't really know if you're missing anything, because it just sits in the drawer all the time. But what if you are?

What if there's an opportunity to use that to cook something really cool, right? Same is true with analytics software. There's so many data points and so many features in these things that just go unused. And we don't know what we're missing. Again, it could be valuable, could not be.

So the question is, how do we use more of that data? If you were to extract every single piece of data out of Google Analytics and put into spreadsheet, it'd be a really, really, really large spreadsheet with hundreds of columns. And the question is, would that be valuable? We don't know. You don't know, I don't know.

But using data science and machine learning and artificial intelligence could help us start to know. One of the ways that I recommend, in fact, in in one of my new talks, the data science 101 for marketers talk, uh, talk about taking all the data that you have, putting in that giant spreadsheet, um, using uh data science tools like Python or R, and then running algorithms, uh machine learning algorithms against that data, mostly a regression analysis, to figure out does any of this data matter? And are there hidden interactions in it that we're not seeing that could have meaning? So the way you would do that is in that massive spreadsheet, one of the columns in that spreadsheet would be conversions, right? Something we all care a whole lot about, uh, whether it's e commerce and we made the sale or whether it's B2B and they filled out the form for a demo request or whatever.

That's your target, your response variable. And then you have everything else. Number of press releases sent that day, number of tweets you sent out that day, uh, the sentiment of responses you got, uh pick any marketing metric, page views, time on site, put it all in that sheet, and then you run your regression analysis against your target response variable and say, hey, machine, tell me what combination of variables has a mathematical relationship, a correlation to the response variable, and it'll may or may not find something, and if it does, it may say like number of organic searches to these pages, uh, or uh number of tweets with a poop emoji, you know, something like that, or number of emails you sent or email click through rate that day. Whatever the the combination of variables is, that's what you get you can then go test. You can try to prove causality.

You've got correlation, now you prove or disprove causality. And that's the kind of thing that AI can help us do. It can scale up to deal with the size of the data because we can't do it. I mean, you could do it if you want to spend the next month of your life just doing the math behind this. Um AI and machine learning allows us to tap into that and get to that data much, much sooner and with a lot less pain.

And so that to me is exciting about AI within the context of marketing, our ability to classify data sets, to predict data sets, to turn data into usable information that we can then deploy for our marketing. One of the things that I I find most objectionable about marketing analytics today is that we do all this analysis work really hard, really hard, and then we don't do anything with it. We make this discovery, these flashes of insight, and then we put them back on the shelf at a binder, and we do nothing with it. We don't change our marketing, and therefore our results don't change, and eventually either uh we get fed up and move on, or the company implodes and and uh we go out of business, right? If you took the insight and you used it, you deployed it, what would that do for your business?

I remember I had a customer a number of years ago. We put together a predictive calendar about when these when certain topics in their industry were going to peak based on search volume, reliable data, and they didn't use it. They didn't use they put it on a shelf, and then six months later they're like, yeah, you know, we're we're we're gonna have to uh terminate the relationship, we're just not seeing the results. Like, well, of course you're not seeing the results. You didn't do anything with them.

Right? You literally had us run a forecast of the future, and you did nothing with it except let it gather dust. And so that's our biggest problem with marketing analytics. It is just not using the data. So if AI can help us get to those insights faster to compress the time it takes to get to them, we might stand a chance of using them more, and if we use them more, we'll might get better results.

Certainly, as we approach uncertain economic times, and people are asking, you know, how can we get more for less? How can we uh work smarter, not harder, right? Work smarter means use AI. It means use machine learning, it means use the data that you have, get the machines to get it answers to you faster, as fast as possible, that they can do way better than we can, and then use those answers. Use more of those answers than we have been doing.

So that's what excites me about AI and marketing is we might stand a chance of improving our marketing in ways that we otherwise could not. It's a really good question, fun question, challenging question. Uh if you have follow up questions, please leave them in the comments box below. Subscribe to the YouTube channel and the newsletter, and I'll talk to you soon. Take care.

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