Data-Driven Marketing with Kaggle – A New Perspective

07 Jun
2019

The magic word

Data.

One word, many perspectives.

For some, it’s just what gives Google Analytics something to analyze. No doubt it’s valuable, it helps plan the next winning campaign or conversely which intern to fire. We know data is not something worthless but it as technology increases it is something that is growing more and more untapped.

What does coding and data have to do with my job?

Man using laptop for analytical work

Chasing the trends is one of the golden rules of marketing (or silver rules if we want to get picky). For example, when programming became popular over the last decade through coding camps, online courses such as Codecademy, and this one video that every intro to computer science teacher loves to show, marketing began to realize the value of coding. Whether it be HTML for websites or JavaScript for Google AdWords, coding has established its place in marketing.

Perhaps one of my most favorite insights about coding is that it is “less about the language, and more about the approach to problem solving.” It’s a cliché I know, it’s yet another concept that intro to CS teachers preach as a fundamental law, but it’s not wrong. Being able to identify the problem and separate it into simple tasks that anyone can follow is something any marketer needs to do. That’s because simple is the name of the game, marketing campaigns aren’t built on confusing the consumer into disgust.

So what does this have to do with data science?

Hand touches phone which holds the many possibilities of marketing there are

Yet despite my thinly veiled ad for computer science courses above there is still some disconnect between marketing and data science. The main cause is that without (sufficient) programming knowledge the marketer makes themself out into a stereotype. Since they don’t know what actually goes into the product they want, they end up losing money doing unnecessary work or lose money trying to convince someone to do impossible work.

Think I’m jumping the gun? Well don’t take my word for it and ask another data scientist. In Digiday’s Confessions series, an anonymous data scientist bluntly states that “Marketers don’t know what they’re asking for when they ask for a data scientist.”It’s an interesting read but in short it states that data science as a concept is confusing and it’s not your fault (entirely). It’s a new field and Marketing Data Science in particular only came about around 2015. The lines between a data analyst, who works in Excel and spreadsheets, and a data scientist, who uses Python/R to do complex statistical analysis, have been blurred.

So if a data scientist is not necessary in my business then why should I care?

Even if that’s true now, basic data science is something worth considering for any business. The speed and performance, micro-targeting, and real-time experimentation is just scratching the surface of what data analysis can do. Remember, when you chase a trend you want to chase it quickly. Data science and analytics are the key to that speed. Just like general programming, the efficiency it brings will soon be undeniable that it will be a basic skill to know to stay competitive.

So where do I start with data science?

Books in analysis are informative and recommended if you decide to major into this sacred art but simply put they are intimidating. Where are the pictures, presentations, and videos? Bootcamps are a thing but not everyone has the time or money dedicated for this venture. For a free and short introduction to the basics, it’s over at Kaggle.

Kaggle is a platform for machine learning, namely in the form of competitions, and in that platform is a multitude of micro classes to be able to compete. From visualization to pandas, you can learn how to apply these concepts quickly. In addition, Kaggle provides clean datasets, ones without many missing or incorrect values, to test out this newfound knowledge. Remember fellow marketers and angry data scientists who think I’m discrediting their craft, the point is to understand the basics of modeling to know what to explore next.

To start somewhere familiar why not check out the average total transactions per user in 2017 through a Google Analytics dataset. Or before that, take some time to admire the results R can produce in measuring ROI through ChrisBow’s analysis. In fact, Kaggle is a way to stay up to date on data marketing developments because of what high-ranking teams are using in their analysis.

In short, data science is worth the hype, even though it is often confused. From communication to price targeting, there is something in your business that can be made more efficient. Whether you pursue it now or in the far future is up to you but just knowing of what you can do with machine learning can change the way you look at data.

I hope this has cleared up a bit of the confusion of all the data science talk you hear today. It would be wonderful if you could leave a comment of any of your personal experiences with data science.

Please feel free to leave comments, suggestions, or simply let us know if you want to see more content like this. And big thanks to Jonathan Aguilera for sharing his take on marketing. I personally am not involved in his opinions. 

Jonathan Aguilera
author

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