You Ask, I Answer: Tools or Concepts in Marketing Data Science?

In today's episode, Jessica asks, which should we focus on learning most in marketing data science? Concepts or tools? Without a doubt, concepts. Think about it. When you learn to cook, you learn frying, right?

And baking, broiling. You don't learn a specific model of oven. You don't learn a particular brand of frying pan. You learn how to do the thing. And ideally, that knowledge is one that you can transfer broadly within that category.

So if I have a an eight-inch frying pan, a six-inch frying pan, or a wok, uh I should be able to apply the same principles of frying. You learn painting, right? Not a specific brush, although you may have techniques that are well suited for a certain type of brush, but you learn painting. You learn you learn to play piano, right? And in theory, you should be able to play any piano, whether it's a little, you know, 32-key miniature USB device or like an 88-key grand piano in in a Carnegie Hall.

You learn to play the piano, not just one kind of piano. Marketing data science is exactly the same. You learn how to apply different concepts, different ideas, different techniques to data, and not necessarily a specific tool. Now, do you need to use some tools? Yes, absolutely.

You cannot fry without a frying pan. And if you don't know how to handle a frying pan safely, you're gonna have a bad time. The same is true in data science. You need to be able to use tools like Python or R or IBM Watson Studio. Uh but you use it in the service of the concept.

So learning things like regression and multiple regression, classification, clustering, association, dimension reduction, principal component analysis. Any of these techniques are things that you absolutely need to learn, and what tool you use to apply those techniques is largely up to you. You have any number of these tools. I would say start with the open source ones because A, they're free, and B, when you're writing the code, you have the most control over the techniques and over the tools. You don't really see what's going on.

You don't understand caramelization of the MyArd reaction in one of those fancy machines. You absolutely see that in a good old-fashioned frying pan, and you understand what's happening to your food. Maybe change some ingredients. Same is true in data science. If you stick all your data into a really fancy auto ML system, you might see some of what happens on the inside, but not really, right?

As opposed to uh writing a regression algorithm yourself or using XGBoost uh or using uh you know lasso or ridge regression. When you do those things, you see the outcome pretty clearly. Does it take longer to learn that that way? Yes. Do you take longer to get to uh usable production results that way?

Yes, absolutely. But in doing so, you learn how the techniques work and more importantly when they don't work. When you're doing marketing data science, that's really important. The ability to say, I know when ridge regression or lasso regression or logistic regression or linear regression are the right choices to make based on the data that I'm working with and the outcome I'm trying to achieve. If you leave it all up to a machine, it may or may not make the best choice for your data.

I have run into that personally, gosh, so many times, where an auto ML algorithm will try to do a bunch of stuff on a data set and it doesn't understand some of what's going on. Uh here's a a very straightforward example. A lot of these automated data science tools operate on the data pretty naively. They won't look, for example, for near zero variables, which are variables where most of the time they're zero. Uh they don't look for or knock out um correlates.

So let's say here's an easy one. Uh let's say you're doing an analysis of tweets, right? And you have all these tweets and you've done character counts, uh, you know, 140 characters, 170 characters, 180 characters, and then you do uh an analysis of how many words, you know, 10, 15, 20, 30 words in a tweet. The two numbers, character count and word count, are going to be perfectly correlated, right? Because they're they essentially are are derivatives of each other in some ways.

If you put that into a machine learning algorithm that is trying to predict or understand what feature, what column in your data set has the highest relationship to an outcome you care about, like you know, retweets, those two columns can screw up the analysis because they are so highly correlated. What you would have to do as a subject matter expert is look at that and go, okay, which one do I care about more? You know, Twitter makes character count a lot more important than word count. So they'd say, let's knock out uh word count. We don't necessarily need that, but we do want to have that character count in there.

This is something that again, a lot of automated data science tools will not know to do. They will not know to do that, or they won't be able to do that because they can't tell which is more important. You have to understand the concept of correlates, and you, as a subject matter expert in your data, have to say, you know what, let's get rid of word count. They're highly correlated, they're probably not uh going to yield tremendous insight together, so knock out one of them and let's and use that for for the same regression analysis. You can't do that without an understanding of the concepts.

If you only focus on the tools, you'll follow the instructions on the tools, push the buttons, and you may not get the best analysis. Now, is that a shortcoming in the tool? Yes. Yes, it is. Um, is that something that the vendor of that tool could fix?

Maybe, but probably not, because again, that decision about which column to knock out is a subject matter expertise decision, and that's something that machines simply do not have. So, learn the concepts. Focus on the concepts, build expertise in the concepts. In doing so, in applying that, you will get to a point where you will learn the tools, right? As a natural outgrowth of trying to learn the concept and trying to make the concept work.

Now that doesn't mean you shouldn't pick up the manual and learn the tool, too. But focus on the concepts. That's where you're going to get the most value out of marketing data science, the most value out of the education and professional development you're trying to do. It will give you the best understanding of how things go wrong and ways to fix it when something goes wrong, when you get an analysis that makes no sense. Only knowing the concepts will get you past that hurdle.

Otherwise, you'll spit out a spurious analysis and maybe make some bad decisions from it. It's a really good question. Could spend a lot of time on this. But focus on those concepts. If you have follow up questions, leave them in the comments box below.

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