Paul asks, “What excites you most about AI?”
In the context of marketing and data science, AI allows us to scale our inquiries to our data. We have overwhelming amounts of data, and most of it goes unused. Think about all the data just in Google Analytics. How much of it do you actually use? How much could you use, if you could take every data point into account? AI enables that.
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
- Got a question for You Ask, I’ll Answer? Submit it here!
- Subscribe to my weekly newsletter for more useful marketing tips.
- Find older episodes of You Ask, I Answer on my YouTube channel.
- Need help with your company’s data and analytics? Let me know!
- Join my free Slack group for marketers interested in analytics!
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, Paul asks what excites you most about AI? In the context of marketing, and data science, 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.
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 explore online, you will see literally, dozens if not hundreds of dimensions and metrics.
Page time, bounce rate, exit rate, page title 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 your channel groupings and or your assisted conversions.
But there’s so much and 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 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 was true for analytics software.
There’s so many data points and so many features in these things that just go on used and And we don’t know what we’re missing.
Right? 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 in a spreadsheet 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 one of my new talks, the data science one on one for marketers Talk, talk about taking all the data that you have, putting in that giant spreadsheet using data science tools, like Python, or R, and then running algorithms, machine learning algorithms against that data, mostly 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 whether it’s ecommerce 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 then you have everything else number press releases sent that day number of tweets you sent out that day, the sentiment of responses you got a pic 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 I know, may or may not find something and if it does, It may it may seem like number organic searches to these pages, or number of tweets with a poop emoji or something like that, or number of emails, you sent her an email, click through rate that day, whatever 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 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.
But 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 remarketing.
One of the things that I, I find most objectionable about marketing analytics today is that we do all this analysis, we’re really hard, really hard.
And then we don’t do anything with it.
We make this discovery these, these flashes of insight, and then we put them back on the shelf in a binder.
And we do nothing with it.
We don’t change our marketing, and therefore our results don’t change.
And eventually, either we get fed up and move on or the company implodes.
And 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 a customer number 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.
He didn’t use they put it on the shelf.
And then six months later, they Like, yeah, you know, we’re we’re going to have to terminate the relationship and we’re just not seeing the results like, Well, of course, you’re not seeing the results, you didn’t do anything on it, 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 will 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 work smarter, not harder, right? Work smarter means use AI.
You means use machine learning, it means use the data that you have, get the machines to get answers to you faster, as fast as possible.
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 a high end marketing is we might stand a chance of improving our marketing in ways that we otherwise could not.
So really good question, fun question, challenging question.
If you have follow up questions, please leave them in the comments box below.
Subscribe to the YouTube channel on the newsletter, and I’ll talk to you soon take care what helps solving your company’s data analytics and digital marketing problems.
This is Trust insights.ai today and let us know how we can help you
You might also enjoy:
- Best Practices for Public Speaking Pages
- Why I Stopped Curating Content on LinkedIn
- AI for Marketers, Third Edition, Available Now!
- B2B Email Marketers: Stop Blocking Personal Emails
- How to Think About High Bounce Rates in Google Analytics
Want to read more like this from Christopher Penn? Get updates here:
Get your copy of AI For Marketers