Why retailers can’t get enough data scientists

As the retail apocalypse continues – with an estimated 8,600 closing their doors in 2019 – other retail survivors are upping their game with hard-to-find data scientists

Why retailers can’t get enough data scientists
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Retailers are on the hunt for data scientists, now more than ever. Given the rise in online shopping and the cut-throat competition from e-commerce giant Amazon, smaller retailers have begun closing their physical locations around the world. Dubbed the “retail apocalypse,” 8,600 stores will close in 2019 alone. Studies show that retailers are also forced to shift their sales strategies, offering more personalized online experiences to customers. Given this shift, retailers are actively seeking candidates in the data world who can help capture customer loyalty and keep sales high.

The shift to a more data-centric approach in retail is not necessarily new, though there has been a big push in recent years. Retail giant Target Corporation arguably led the charge when, in 2013, the company hired Paritosh Desai as vice president of business intelligence, analytics and testing. Not only did Desai hire a robust data team, but he also created a data-driven culture company-wide. He established fluidity between the data team and managers by creating an analytics system that managers could use themselves, promoting data-driven decision-making across the board.

Other retailers soon followed suit, though hiring challenges remained. The data scientist shortage hit all industries. An Indeed report from January 2019 showed that there is a 29% increase in demand for data scientists year-over-year and a 344% increase in demand since 2013. However, the supply of skilled data scientist is increasing at a slower pace at 14% across all industries, suggesting a gap between supply and demand.

Retail giants, however, seem to be getting around this shortage likely through high salaries and development opportunities. Last year, Lowe’s announced a plan to invest $500 million annually in technology through the year 2021. The company plans to hire 2,000 software engineers, infrastructure engineers and data analysts. Walmart’s chief data officer recently said that the company has 1,500 data scientists and plans to hire even more, particularly candidates that can help develop voice-activated shopping applications. This team of data scientists already has over 100,000 AI and machine learning-based projects in production, including AI-powered cameras that monitor for theft.

Big data technologies can do so much in retail, so it comes as no surprise that recruitment for these positions has grown in recent years.

Why do retailers recruit data scientists?

While retailers are focusing online, they still have brick-and-mortar stores to promote. One way to do this is by combining the online and offline experience. DIY home projects are likely to involve both, and Lowe’s plans on capitalizing on these customers by adding personalization features through technology. Retailers can monitor in-store activity and push timely offers to these customers to either incentivize in-store purchases or online purchases after their visit. Data teams can also improve recommendation functionality and location-based tools to help increase online and offline sales. A McKinsey report stated that 35% of what customers buy on Amazon (and 75% of what people watch on Netflix) comes from product recommendations based on predictive models and algorithms that analyze transaction data and trends.

Probably the area that data scientists are most valuable in retail is the ability to use data to make decisions that can impact the company’s bottom line. Competition in the retail industry is stiff, not to mention Amazon’s hold, which means retailers need to make smart decisions. Machine learning algorithms detect patterns and correlations in the supply chain and can adjust parameters and values in real-time. The technology can then forecast trends, reducing the risk of stocking items customers simply don’t want or items that could spoil while still on the shelf.

Price optimization is another tool that benefits retailers. By gathering data from multichannel sources, teams can take into consideration items such as location, buying attitudes and competitor pricing to output the ideal price: a price that the customer will pay with the highest possibility of profit. Real-time price optimization attracts and retains customers by offering them good prices but still making a profit.

Retailers can also use big data to understand customer sentiment. With the surge of social media use, there is plenty of data on customers talking about brands online. Data scientists can use data aggregation and use natural language processing to track whether these comments are positive or negative. Understanding this information allows retailers to make structural changes to serve customers better and gain an edge on the competition.

Using the data that’s there

Retailers have vast amounts of data, but still, many smaller retailers do not harness all of it for insightful actions. This thankfully is changing as more data scientists are entering the workforce. The use cases of big data in retail are seemingly endless. Retailers are also using data scientists for fraud prevention, to predict customer lifetime value, claims to monitor and to understand where physical store locations should open and close.

The challenge retailers and other industries face is not just collecting data but using it to offer a better customer experience. Data can improve company performance, manage risk and increase sales, all providing an edge over the competition. Given that, retailers will continue to scour the job market for data science candidates that can help meet these goals.

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