You Ask, I Answer: Data Science Tools for Marketers?

In today's episode, Monina asks what tools are useful to help marketers dig deep into their organization's data. Ah, so tools are part and parcel of the uh the data science world. And if you remember, if you recall, data science is four major skill sets: business skills, uh, scientific skills, technical skills, and mathematical skills. The tools that you use at each in each of those domains uh are dependent on your skill level, especially the technical and statistical tools. Uh the answer to this question really depends on your level of skill as a marketer.

What do you bring to the table? What are you comfortable with that uh you can use to gain value? Remember, a tool is nothing more than well, I mean, a tool is literally a tool by itself, it does nothing. If you have it laying around, you know, it won't do the work itself, no matter what the vendor promises you, it's it never happens. So, like a frying pan.

If you can't cook, a frying pan, no matter how nice it is or how expensive it is, is not going to help you. You've got to have the skills to back it up. So I would put the tools question uh in three categories of beginner intermediate and advanced. And beginners tools for marketing data science are specifically about helping marketers uh report on the data they already have and extract data and maybe do a little bit of visualization. So let's um let's actually look at these.

So we'll bring this up here. So beginner tools, again, this is all about extraction of data, right? And and basic manipulation of data. So you have things like Google Data Studio, fantastic tool for getting uh data from a couple different data sources and just looking at it, right? Just being able to put together very simple dashboards.

Microsoft Excel. The Venerable Spreadsheet is one of the most popular applications of business for a reason. It's enough that to for people to understand the basics of data analysis. And you can actually do quite a lot with Excel. Um or the you know spreadsheet application of your s of your choice if you use Google Sheets, for example.

And of course, the platforms themselves, Google Analytics, Facebook Analytics, Twitter analytics, uh, your marketing automation analytics, all the the platforms have their own basic analytics built in, and those are the data sources that in many cases you are you're gonna be pulling data from. So those would be the sort of the beginner level tools. If we move up one level, intermediate tools help marketers start to understand patterns and relationships of the data. You start manipulating the data, you start um putting multiple data sets together or multiple variables together to see the relationships between uh things to try and dig in and uh and gain some insights like why did something happen. And for this, you have uh intermediate tools uh like IBM Watson Studio uh and IBM Cognos, both of those are fantastic tools.

I prefer Watson Studio because it has the ability to also scale up to an advanced tool, but certainly for with things like the SPSS visual model are built in, it's a really powerful tool for helping you manipulate and transform your data and uh and and do some advanced statistical operations, some more sophisticated statistical operations. And of course, Tableau software. Uh Tableau is uh uh the market leader in visualization uh for a reason it is fantastic software, not too hard to get started with, but you can do some really amazing advanced stuff with it. So I would classify those as the intermediate tools uh for marketing data science. And then for advanced tools, advanced tools really are about helping you manipulate your data, transform it, distill it down, run advanced algorithms against it.

If you've seen me talk about machine learning and artificial intelligence, these are uh some of the the tools that you'll use to get into that level of analysis where you want to understand what caused something. You want to see hidden relationships in your data. You want to use AI to distill down the data into just the parts that matter. Uh the two big ones here, of course, are R and Python, two programming languages, and then the ancillary tools that you need to be able to extract data at advanced levels, uh things like you know, uh command shells and being able to work with APIs natively at the sort of the bare metal version of your computer. Uh SQL, the the structured query language for working with uh structured databases, uh JSON, uh the JavaScript object notation language for working with um NoSQL databases, and so these tools really are sort of the the highest level of of data science in terms of a tool perspective that you're going to be doing the technical and statistical stuff with.

These are the ones that I've used and can speak to, uh, and I've used them recently and can speak to their effectiveness at what it is that they do. Um there are tools and vendors out there for every level of skill and every budget. So keep that in mind. These are not just the right answers. These are the ones that again I've used and I've seen other people use very, very recently uh and other business partners use, particularly on the on the beginner side.

You know, people should be using data studio. And it's okay to be using spreadsheets in the beginning. As a beginner, that's a great place to get comfortable. So if you are uncomfortable with data science, nothing wrong with firing up your spreadsheet and just trying techniques out. You can do again a lot of things, moving averages, interquartile ranges, all these uh statistical techniques you can do within a spreadsheet as well.

So it's a good way to get your feet wet. As you progress in your skills, as you progress in your growth as a marketer and as a data scientist, you will naturally run into situations where you're like, the tool I'm using right now just doesn't cut it. I can't do what I want to do in this, and that's when you know it's time to move up to that next level. And you when you start to get really frustrated and you're like, ah, if only this thing did this, then it's time to start looking at okay, what are some more solutions that are available? And these are not hard and fast rules.

You may become so fluent in the statistical stuff or in the programming stuff, even in something like you know, Visual Basic and Excel, that you might just leap straight to Python, or you might just leap straight to R and bypass that intermediate stage. That's okay. Again, there's no right or wrong answer, except that if a set of tools is starting to hinder your growth, then it's time to move up. That's really the only uh guideline I can give there. So these tools are useful, they need training.

So make sure that as you buy tools, you also buy training and buy time for training because you need to skill up on these things. Um and the training part is more important than the tool part. You can learn statistics and never really use more than the spreadsheet. Or you can buy, you know, uh tableau software and never pick it up and never use it. And that's a waste of money.

You will always do better training yourself first and then buying the tools later. So keep that in mind. But good question. It's an important question because a lot of people have questions about what tools, and it's really about what's in in the the big computer up here first. So uh if you have follow-up comments, please leave them in the comments box below.

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