John asks, "Anybody else have a go to tutorial for marketing analytics that you are a big fan of?"
Marketing analytics is a surprisingly underdeveloped field, owing to the fact that a lot of marketers... well, don't want to do analytics work. There are, according to the Bureau of Labor Statistics, over a million people in America employed in marketing. Yet based on estimates from the Google Community, there are roughly 15,000 people in the world who have earned GAIQ certifications, and of that, maybe 10,000 in America. That's 1% of marketers who have earned a certification for software that runs on 63% of the Fortune 500 websites.
Where we get our tutorials from now is in porting concepts from one domain to another, since there's relatively little developed for advanced analytics practitioners. Watch the video for full details.
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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 video, john asks, anybody else have a go to tutorial for marketing analytics that you're a big fan of? Oh, no, not beyond the basics.
And the reason for that is that marketing analytics is a surprisingly underdeveloped field.
And that's really open to the fact that a lot of marketers don't want to do analytics work, right? They, they got into marketing, to be creative, to be expressive to do stuff, other than crunch numbers.
And there are still not a, there still isn't a large flux influx of people coming into the field who are like, I am a quantitative specialist, and I want to do marketing work for a couple of reasons.
One, if you have those skills, there are many more lucrative pathways to take the marketing, you know, if you have truly, you know, top notch quantitative skills, a pharma tech company, all these different firms have much higher paychecks for people with those skills than marketing typically offers.
So that's one reason.
The other reason is that, you know, as a result, there isn't a lot developed.
So yes, it's a Greenfield in terms of a market opportunity.
But it is also very difficult for somebody, once they've done the very basic basics, to take it to the next level, because there is no next level, you have to write the next level yourself.
Consider this, in the Bureau of Labor Statistics, at least according to the most recent career vintage, which was like may 2018.
About a year ago.
There were are there are over a million people in the United States of America alone, who are in the profession of marketing, they are marketers, and they have identified through their job data that they are in marketing million people, that's a lot of people.
Yet, based on estimates from the Google community, you can see this in the in the actual in the AdWords community, there are roughly 15,000 people on the planet, planet who have earned Google Analytics, individual qualification certifications, and all of that 15,000 maybe 10,000 are in America.
So million people 10,000 have done the certification.
Now, you might say, well, that's, you know, finance 1%, that's okay.
But then when you think about it, 63% of the Fortune 500 use Google Analytics, large, large companies.
And so there is just this disproportionate mismatch between companies that are using the technology that have deployed it in some capacity.
And people who are qualified service, I should say certified operate, because you can be qualified to use Google Analytics, even if you've never taken a course in it.
But I've taken the certification, and Google has given them the stamp of approval saying, you know what you're doing 1%.
So to the question of where do you go to, for tutorials for marketing analytics, take the basic tutorials, and the and the Google Analytics certification, I think that's an important one, take a statistics course, that's a super important one.
And then pick up a, a statistical or data science tool.
And what you're going to end up doing is what I end up doing a lot in my own work, which is you couldn't be looking for models and algorithms and techniques that you can port from one domain to another.
It's called transfer learning on the machine side, it's it's called Learning for humans.
Because there's relatively little, it's been developed for advanced analytics practitioners.
But you can take concepts that are proven mathematically proven to be reliable.
and move them from point A to point B, move them from healthcare, or finance, or insurance or law, and move those concepts into marketing and apply their techniques in marketing.
One, I can't remember, I think I've done a video on this somewhere, takes from the stock market, there's a huge array of tools and techniques you can use from the stock market from financial analysis that apply really well to marketing data.
And in fact, it works better on marketing data than it does on the stock market.
Because the stock market has so many inputs, many of which are hidden, that it's very difficult to use these tools to forecast well.
But if you're doing it on, for example, your web analytics, there is no shadow brokerage of your people conspiring to manipulate your website traffic numbers, you can barely get people to go to our website, period, we don't have to worry about people who are like, you know, secretly manipulating to inflate our website numbers, that that doesn't happen.
That you can use stock market techniques on marketing data with great effect, things like moving average convergence divergence indicators, candlestick charts, bollinger bands, all the different techniques that the stock market uses to detect anomalies in an analytical stream, we can use to detect anomalies in our marketing data and say, Hey, something's going on here, we should pay attention to this.
And these techniques, again, they're all proven, they're decades old, in many cases, then they are not used in marketing at all.
So pick the statistical platform of your choice.
You know, our and our studio Python, and you know, why hats, rodeo tool, SPSS from IBM, SAS, whatever tool you want, find a discipline that you have some domain knowledge in, or you can build quickly in like, finance, or health care, or whatever.
It is another good example, it has been dozens of algorithms for anomaly detection for monitoring servers and things that we can bring into marketing.
And that's where you're going to get your go to tutorials, you're gonna have to write them on how do you apply this technique to this type of data, so that you can achieve some kind of result.
So the bad news is, there really aren't many after the basic level, the good news is there really aren't many after the basic level, which means that you can develop something that gives you in your company a substantial competitive advantage.
You can build in, you can create a piece of software that could be market leading because it doesn't exist yet.
Right? It's just not out there.
Another example, from physics, there are many, many, many well proven radioactive decay models.
Guess what radioactive decay looks like? It looks like the fall off for the effectiveness of certain marketing campaigns, right you can you train a decay curve on things like email, open response rates, how long does it take before email becomes ineffective? How long does it take for a social media posts to become ineffective, you can take a model from physics and apply it to marketing.
But you just have to be able to think in more than one discipline.
So I'd urge you to look at your own background.
What do you have domain expertise in outside of marketing? And are there things from that profession that you can bring into marketing and create those go to tutorials, for technical sites, I like Stack Overflow.
I like the Stack Exchange family.
I Like GitHub, tons of great stuff on GitHub.
I like Reddit.
There's great great forums on Reddit for stats and machine learning.
I like the classes over at IBM cognitive class cognitive class.ai ai.
So there's tons out there.
You just have to bring it into marketing.
I look forward to seeing what you do with it.
Because anything that comes into the community is a net benefit fit to all of us.
So great question, john.
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