Buried in Data? Prescriptive Analytics Helps Fashion Retailers Sift Through the Numbers

Arthur Zaczkiewicz

Updated Mon, June 4, 2018 at 1:23 PM UTC

In today’s consumer-centric retail and fashion market, data is king. And retailers and brands generate a lot of it. From sku-level product information to point of sale data, the numbers can help inform pricing and merchandising strategies.

But sorting through mountains of data and daily reports can strain retailers and brands, and pull them from doing the more creative work required — such as product design and merchandising as well as marketing. Here, Guy Yehiav, chief executive officer of Profitect, shares insights into a solution aimed at helping companies sift through the mounds of data produced via prescriptive analytics.

WWD: What is Profitect?

Guy Yehiav: We’re a software-as-a-service organization that provides retail and CPG companies with prescriptive analytics solutions that enable them to be more efficient, effective and profitable. Profitect’s solution uses machine learning and pragmatic AI to identify patterns in data, translating and distributing them as opportunities in plain-text format. Our prescriptive analytics software was built for retailers by retailers. (A significant portion of our employees came from Fortune 500 retail organizations.) Because of that, we have a unique understanding of the pain points and challenges affecting this market.

Our solution is used by [retailers] such as Ann Taylor, Abercrombie & Fitch, Belk, Finish Line and Walgreens, to name a few. We provide retailers with the right action to take in order to minimize confusion around reports. This allows employees — from the executive level down to store associates — to focus on revenue-producing activities. This has led to significant margin improvements for our customer base and has helped drive Profitect to its second consecutive year of 200 percent-plus year-over-year growth.

WWD: How can fashion retailers benefit from prescriptive analytics?

G.Y.: Fashion retail is an incredibly complex industry, with one particularly frustrating challenge — a short shelf-life. Fashion’s life cycle can fluctuate wildly throughout multiple seasons, and is plagued by endless variables like size, color, consumers’ preferences and even the weather. Because of these factors, it can be quite difficult to identify the root causes and, subsequently, opportunities for improvement.

Along with these numerous factors, retailers must contend with countless data points related to their business, such as stockkeeping unit audits, returns, inventory counts, out-of-stocks, quality and more. Almost every data point has its own report, creating oceans of paperwork that employees must consume, interpret and act on. Prescriptive analytics eliminates that hassle by interpreting the reports for them. The only thing that ends up on your desk is the system’s interpretation of the data, plus any needed action steps. All you have to do is read it and respond. This enables fashion retailers to focus on what they do best: creating exciting new merchandise collections for consumers.

Let me give you a quick example of prescriptive analytics in action. One of our fashion customers used Profitect’s Inventory module to identify a new product with a higher rate of return. Within two days of the product’s release, Profitect’s prescriptive analytics identified damages as the reason for most returns, and directed the retailer to evaluate the quality, check the label, and contact the vendor for an allowance increase or replacement. It turned out that the product’s washing instructions were inaccurate on the label, and would actually damage it. Profitect directed the stores and the vendor to attach updated care labels to the remaining merchandise. Ultimately, product returns fell 78 percent in the following weeks. The prescriptive actions helped maintain sales and save the style.

WWD: What role does AI and machine learning play in your technology, and why is it a plus for retail?

G.Y.: At its core, Profitect’s prescriptive analytics is powered by machine learning — the branch of artificial intelligence (AI) by which machines “learn” to help companies make more effective business decisions. When retailers leverage the power of all three — prescriptive analytics, AI and machine learning — they can stay on top of all aspects of their planning. As I mentioned earlier, countless variables — some controllable, some not — make planning a nightmare for retailers. Working together, the AI, machine learning and prescriptive analytics behind Profitect integrate and synchronize all those variables to create the clearest possible picture of how to ensure maximum sales, efficient allocation and a great customer experience. It’s a clear window into what’s happening with your stores, products and customers.

WWD: How will data analytics continue to evolve and what challenges will prescriptive analytics address in the future of retail?

G.Y.: As advanced as data analytics may seem, prescriptive analytics is just the first generation. I see the future of analytics following the same path as autonomous vehicles. You tell a self-driving car where you want to go, and it takes you there. You tell it what to do and then forget about it. Data analytics is headed in that direction as well. Where it stands right now, some analysis is still required on the part of humans on most analytical platforms.

That future is now. With just a simple command, prescriptive analytics helps retailers get exactly the results they want to see — from analysis to execution. It has enabled businesses to become more efficient and effective in their management of data, as well as actions and outcomes

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Buried in Data? Prescriptive Analytics Helps Fashion Retailers Sift Through the Numbers

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