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Data Science, Female Technologists, and the Future of Retail

This article is more than 6 years old.

Having worked in traditional brick and mortar and then at a TV shopping network early in my career, I remain fascinated by the radical changes remaking the retail industry. I’m especially intrigued by the innovations that have driven these changes over the years - from the early traveling salesmen to catalogs to department stores and now online and web selling. With their customers having ordered over one billion items this past holiday season, Amazon is indeed the new Sears. But what does the future hold?

Much of this innovation and change derives from the introduction of new technologies, and the resulting pace of change is dynamic. For example, one year ago the use of artificial intelligence (AI) in online commerce was barely discussed. Today, it is considered the next big thing. In truth, the technology to support it is still early and it is more representative of the pace of change than actual evolution.

One technological development that is in its prime and that will also help pave the way for widespread use of AI is data science and machine learning. Ecommerce generates an incredible amount of data about customers, including their buying preferences, where they live, and when they shop.

Companies like Etsy and Amazon are examples of retailers using data to better attract buyers, match sellers, and complete transactions. Both mine the data to identify product preferences or where transactions break down to improve the next experience. Rating systems and reviews are a rich source for this data and help drive many site and product changes.

Anita Andrews provides a unique window into this world. A political scientist by training, she began her career working with technology initiatives in the New York City Mayor’s office. Among them were efforts to use technology in support of policy issues, like crime reduction and the networking of job centers to make job searches across the five boroughs easier and more integrated. It was there that she learned about the power of data to reduce crime or make it easier for people to find jobs, and where the hook was set for a move to the tech sector.

Inspired by the use of technology to reduce unemployment, improve quality of life, and enable completely new policies and solutions that simply were not possible before, she decided to pursue a Masters in Computer Science. Degree attained, she left public service to enter the consumer internet space. She was blown away by the amount of data that was available and realized that this was the key to advancing R&D, conversion, customer service, and more in the sector. 

Andrews founded her own successful consultancy helping companies use this data for product design and marketing optimization. After this company was acquired, she took a position with startup RJ Metrics, and joined Magento two years ago as part of its acquisition of the company.

Now, Andrews is the Director of Analytics Services for Magento, an online commerce platform that powers many of the retailer and ecommerce shops you know and love. In her role, she leads a team that crunches the numbers generated from these sites and applies data science to refine and grow those businesses. Her position is both technical and customer facing at once.

In speaking with Andrews, she was quick to point out that the use of data in the retail space is not new. Those supermarket coupons you receive when checking out at Safeway or Kroger are not randomly generated. Data collection has been occurring at brick and mortar stores through the use of loyalty programs for years.

But the reason that teams like Andrews’ exist is because the amount of data generated by ecommerce window shopping and transactions is exponentially larger than a trip to the supermarket. Anita and her team can help clients optimize a shopping experience or a future offer campaign based on the influx of data, such as where you logged on, what device you used, how long you stayed, if you looked at other items while shopping, whether you put something in your cart and then removed it, or which credit card brand you used. Most of these data points are simply impossible to collect from a physical store visit.

While there is definitely a creep factor to some of this, Andrews points out the tradeoffs in favor of the consumer can be enormous. Discounts, discovery, and improved experience are near term benefits, but ultimately, these insights can lead to a completely new level of personalization and convenience for shoppers. And for a company, this can lead to efficiency in inventory, shipping, experience and more that return additional benefits to the consumer. 

When asked whether the career path for data science had become more defined since she entered the field, Andrews said that it’s more important to have a passion for data and retail experience than a degree in Computer Science. The ability to multi-task and balance competing points of view are important when analyzing data streams because a heavy bias can lead to a misinterpretation of the data.

Andrews did point out that data science is a terrific career path for other women because it combines technical proficiency with people skills. In her role, Andrews must manage both data and teams. Women who are relationship-oriented, facts-driven, optimistic, and gritty will thrive in the data sciences. And according to a recent report by Glassdoor, a data scientist is the best job in America today because of the combination of job openings, job satisfaction and salary.

Looking ahead, Andrews expects data science and tech enabled commerce to pave the way for AI. She predicts that as more and more players use machine learning to grow their businesses, then the table will be set for faster processing that will power AI-enabled selling, more customer-centric product searches, and superior automated help with customer service inquiries. She also cautioned that the growth of AI will demand strong oversight so that we can balance the role of automation versus human intervention in the retail experience.

As a self-avowed retail industry geek, I am excited about the prospect of new technologies speeding, automating, and streamlining the future of ecommerce. But Andrews’ disclaimer shows that even as data and AI become more central to the selling process, there will always be a need for humans to help contextualize it for business realities and peculiarities. This duality is paving the way for a new generation of retail professionals that can mix data fluency with business savvy to make our online shopping experiences even better.