Written by Luqmaan Abdul-Cader, Tyler Heintz, Saman Sabeti

Topic 19 -- Retail Data Revolution with John Squire and Steve Goetz

This topic covered the broad new range of uses for data to improve the retail industry. John Squire and Steve Goetz gave us several interesting insights on how they use all of the new data collected from retail customers to improve customer experience, operational efficiency, and overall profits. The theme of both talks seemed to paint a very optimistic future of retail, and how the growing use of the internet for shopping is not a direct threat to the industry, but rather a reason for it to grow and adapt. We’ll go into more detail on their individual talks here:

John Squire
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John Squire is the Co-Founder and Chief Executive Officer of Dynamic Action, an advanced analytics solution which delivers strategic solutions for market action and decision.

In today’s market, it is evident for retailers around the globe that customer acquisition year over year rates and customer conversion year over year rates are at decline, and that it is only getting harder for these rates to increase. This translates to a total decline in customer loyalty, making it harder for retailers to win over and maintain customers. John mentioned that on an international level, foreign retailers are starting to make changes and improvements in order to help increase and protect their profit margins, making their market platforms more efficient as a whole. However, North America has yet to catch up, with lower year over year rates.

In order to help solve this issue, Dynamic Action provides a platform which brings together cloud retail systems and enterprise applications in order to better coordinate data analysis. They do so by utilizing big data technologies to figure out best how to take aggregate market data and use it to help retailers run their businesses better. John mentioned that there are four main questions are posed by Dynamic Action when addressing data: The descriptive - What happened? The diagnostic - Why did it happen? The predictive - What will happen? And finally, the prescriptive - What action do I take? By answering these questions, Dynamic Action is able to take both human and data input and convert that information to decision support and ultimately to decision automation, which allows for a maximization of productivity. John further explained that using artificial intelligence driven systems and algorithms, Dynamic Action firsts identifies possible issues and opportunities. They then prioritize what actions to take based on potential profit and revenue. Finally, the different departments collaborate and share the best options. After explaining such, John further went on to explain how Dynamic Action incorporates Amazon’s three C’s: customers, competitors, and corporation.

Dynamic Action helps their clientele by providing them with merchandising data, customer data, web analytics, exposure, and inventory data. With almost ten billion dollars in transactions upto 2016 and many large clients such as Cole Haan, Brooks Brothers, Eddie Bauer, Sur La Table, Dynamic Action has become very successful in today’s market.

Steve Goetz
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Steve Goetz is the Director of Strategic Partnerships at Macy's, one of the world’s largest retail sellers, both online and in-store.

Macy’s is a 150 year old company that has built its current size and success off of a terrific in-store buying experience and curated selection of items. However, as eCommerce sites Amazon grew over the last decade and people began to doubt the future of retail, Macy’s has had to adapt very quickly to a growing environment of high customer expectations, demands of quick product turnaround, and highly competitive pricing. Consequently, Macy’s has grown their online presence substantially and began using both their online and in-store data collected to optimize the customer experience in both points of sale.

Steve focused on many of the uses of Macy’s data to improve the efficiency of customer returns and overall product sizing. He used a jarring, but clever question to open up the topic: Would you let a company see you naked, to get all of your body’s proper dimensions, in order to get perfectly sized cloths? To many, the instinctive answer is “No, of course not, that is too private to share with a company!” yet we share this same information with a tailor, masseuse, or doctor without any privacy concerns.

This illuminated an interested question for us as well as a few of our classmates: Why do we trust individual people far more than we trust companies, institutions, or technology? People and companies often have the same goal of using your information to improve your customer experience, whether it’s a tailor making your best fit cloths or a computer algorithm shouldn’t really matter. This discussion ranged throughout the class as different people gave different input as to what data they would be comfortable sharing for benefits in the retail shopping process.

Steve soon rounded off the debate and began talking more about his experience with applying data to the field of product returns. Returns is an area of the retail business that is vastly important to the customer experience, yet incurs a great cost overall. Every return made requires expensive back-shipping, a process of verification and classification to see if the product is still sellable and if so at what price point, and repackaging. All of these procedures are expensive, often more so than the product itself. This high cost of returns makes reducing their frequency one of the most valuable operations done in retail. Steve discussed how Macy’s uses everything known about customers at large to help make sure every customer gets the exact product for them in terms of size, color, etc with the percentage of returns. Macy’s also uses these aggregate data points to predict how much stock of which sizes, colors, etc should be held, essentially using return frequency to predict future demand.

This was just one application of large scale data use in retail, but it demonstrated how widespread the improvements that data can foster are. Steve themed his talk around how giving these retail companies access to our data can improve the experience for us as customers and for them as retailers.