If I, as a buyer, pay with a credit card in a shop, earn cashback points, use my loyalty card for discounts, or leave behind “footprints” online shopping as I surf different supply sites – more and more data is gathered.
Added to that is information from social networks from fans, likes, and retweets. And now there’s fitness data from gadgets, provided it’s shared by the users or the apps come from the major players in the sports business.
Drawing the conclusions that are truly important from this mountain is an art that’s in high demand:
For companies, a lot depends on the right answers to these questions! Corresponding, they need more and more specialists in the combo of statistics, IT, and market research: the data mining managers.
Our career experts Andy Gugenheimer (head of sportyjob.com and cooperation partner of the ISPO JOB MARKET) and Gunther Schnatmann (recruitment consultant specializing in marketing and online) explain what experience applicants need to get in on this promising field with the major sports brands.
Mathematics and statistics are the foundations for properly evaluating data. That is why applicants for a job as a data mining manager need to be well-versed with methods like regression (creating connections) and cluster analysis (determining similarity structures). They need to master explorative procedures (that is, creating hypotheses) for detecting patterns and identifying abnormalities.
“The people that are interested in data mining manager jobs often have purely digital skills from programming, but that isn’t enough,” warns expert Andy Gugenheimer.
IT skills aren’t everything, but it's all nothing without them. Or, to explain it differently: Special programs are necessary to analyze and statistically process the gathered data. Gugenheimer: “Applicants should be able to utilize the right software for every issue and every analysis task. The more you know and have a command of, the better!”
In-demand analytical software would be, for example, SPSS and SAS. Those also well-versed in database programs, that is the “goldmine”, will score extra points. This includes MySQL, SQL, and Oracle.
It gets down to the nitty gritty when the rough data evaluations are available and the right conclusions need to be drawn from them. This is where data mining experts need to be constantly asking new questions.
For example: What customer groups entered what search words on Google before they got to our online shop? How long were sales offers for running shoes noticed, by what kind of customers? What influence did viewing time and use of bonus cards have on the purchase of workout clothes? What age groups do intensive running training, and are underrepresented in the customer club? And so on and so on.
Considering questions and combining answers to newer and newer questions is the core of the task. Derived from that is the analysis of the answers – what does that mean for my products, my product range, my pricing?
Ultimately, the candidate must be faced with the task of thinking, always considering new questions. Only that way will the treasure trove of data bring true benefits for the company.
Of course, customer behavior was researched before Big Data and data mining, too: through surveys, test markets, and sales analyses. This is market research, and it still continues to exist today, now with a strong IT background. That is why market research is an alternative gateway to data mining that’s more established in marketing.
People from market research are familiar with the basics of data analysis and the issues regarding buyer behavior. Gugenheimer: “Those who want to use market research to get into data analysis and data mining will, however, have to have acquired the necessary software skills and keep pace with the digital times – that is why there good opportunities for lateral entry, especially if the applicants have already done market research in the sports sector!”
So, data mining employees can research and analyze, ask questions, and combine answers together. But in the end, the results of their work are new mountains of figures, Excel tables, and diagrams with a ton of points and lines. At first glance, it will all be Greek to most colleagues in Development, Marketing, and Sales. They need results prepared in a comprehensible way.
That is why sporting goods manufacturers, in particular, take a look at where the core business has a lot to do with brands emotions, at the “translation skills” of their analytical experts for the brand and sales managers.
For example, an Adidas job description for a Data Analytics supervisor phrases the requirements thusly: “Simplify everything by helping business owners with easy-to-use dashboards and insights.”
That is, simplify everything so your colleagues can easily use the results. This is where applicants should be able to break down the needs of individual divisions in the company (“We need to concentrate more on the target group XY, because...”).
They also need to be able to represent it in a comprehensible way with simple graphics, clear presentations, and formulations that get right to the point.
There are a good deal of requirements for the supposedly simple task of data evaluation. The more of them an applicant fulfills on their own, the higher their chances for one of the highly coveted positions.
Those still in school or getting their bearings can, of course, adapt to the existing additional skills. There are classes on “Clear Presenting,” and programming languages can be self-taught.
And one thing is true of the basic math and statistics requirements: It’s best to dive into these fields while still in school, whether you’re studying business informatics or business administration. They’re a bit unpopular with many people, but later you can use them to snatch the exciting digital jobs.