retail-data-science-weekly

Hello, happy Friday and welcome to a new weekly series – Retail and Data Science Weekly, brought to you by Bold Metrics. It’s no secret that Data Science is changing the retail world and the most innovative retailers are seeing powerful results as their investment in Data Science has increased.

One of the fastest growing retailers, Stitch Fix, filed publicly for it’s IPO this week, sending ripples through the retail world and putting an even larger spotlight on the role that Data Science can play in a company’s growth in the current market.

Stitch Fix runs a large data-science operation, which the company says helps it make more accurate styling choices for customers, and also helps it create its own clothing that matches up with current trends. You can be sure that the company will pitch this to investors, and will make an argument that it should be valued higher than a typical retail company as a result.

“Our data science capabilities fuel our business,” Stitch Fix says in the filing. “These capabilities consist of our rich and growing set of detailed client and merchandise data and our proprietary algorithms.” (Source – recode)

While some retailers have dipped their toes in the Data Science world, by hiring a handful of Data Scientists, Stitch Fix went all in early boasting a team of over 80 Data Scientists. This may sound like a lot but according to the CEO, it’s what fuels their business.

Here’s the challenge. Companies like Stitch Fix build their entire business around Data Science, it was core from the beginning. For large brands and retailers this isn’t the case. It wasn’t that long ago that some of the worlds largest retailers launched mobile responsive sites and some brands are still playing catch-up when it comes to having stand-alone apps.

It’s safe to say that with Stitch Fix going public, and their recent comments about the role that Data Science has played in their success even more retailers are going to be rushing to hire Data Scientists…which poses a new problem, scarcity.

They have been called “unicorns,” because they are so hard to find. Their position has been called the “sexiest job of the 21th century.” They are data scientists, a job which – in a world where big data now permeates every industry and requires the right people to extract knowledge and insights – not surprisingly topped Glassdoor’s list for the 25 Best Jobs in America for 2016.  (Source – CIO.com)

There is now a scramble to hire Data Scientists, and at the same time, many engineers are now rushing to take Data Science power courses that help them get this skill on their resume in as little as a month. Here’s the real challenge. With such a strong demand for Data Scientists, particularly within retail, and so many people now re-branding themselves as Data Scientists there’s a real quality gap and finding someone that can deliver beyond the title is more important than ever.

It’s a bit like the concept of a Full Stack developer. While there are true Full Stack developers out there, many software engineers will call themselves full stack when they’re likely more front-end or back-end developers. Of course there’s nothing wrong with this, just like there’s nothing wrong with taking a one-month Data Science intensive and then calling yourself a Data Scientist, but for retailers who are new to hiring these positions finding someone that can do what they really need.

Yes, one can learn R and Hadoop and “claim” to be a data scientist, but that’s far from the truth. By comparison, one can also take a few medical classes and claim to be a doctor or watch a few courtroom TV shows and claim to be a lawyer. The difference is that the disciplines of medicine and law are “professionalized.” As a result, they are able to guard their gates by setting standards on who can call themselves a “doctor” or a “lawyer.” In data science, we cannot do that as of yet. (Source – Forbes)

So here’s the real question. What can retailers do to play catch-up with companies like Stitch Fix when it comes to Data Science? In many cases the key is finding a true technical leader, and this person likely won’t be a Data Scientist themselves, they will be a team builder, someone that knows how to hire the right people with the right skills. At the same time there’s a careful balance between building in-house and using technologies built by amazing Data Scientists that can plug in and begin making an impact right away.

It all sounds complicated, and it is in so many ways, but one thing is certain after this week – more retailers than ever now realize that getting Omni-channel right is just table stakes, leveraging the power of Data Science, either in-house or through third parties is one of the critical components to growing and as company, and most importantly – providing the best experience for your customers.

At the end of the day, what makes good Data Science so beautiful is that all the math, data, and machine learning going on behind the scenes is invisible to the customer, what they get is simply a better experience.

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