As usual, NRF 2020 proved to be a “vision” of the future of retail. Walking in this year, you were greeted with the normal hustle and bustle, which is only appropriate for a New York City event, and excitement in the air.
Removing the Risk, Increasing the Payoff in Your Omniretail Inventory.
No Risk, No Reward?
Enthusiastic investors and gamblers have one thing in common: they want to win BIG. However, trying to win big requires greater risk. With a higher risk, the chances of losing are also higher. Both the investor and the gambler dream of winning big, but often want to find ways to minimize the risk of losing.
Assortment Optimization is a powerful tool for retailers to increase profit and improve customer experience and loyalty. It combines cutting-edge machine learning techniques, an agile continuous improvement methodology, and flexible customization to accommodate unique business requirements. Like other related solutions, assortment optimization utilizes machine learning techniques and AI to find the optimal assortment for every SKU in each store location. However, assortment optimization offers other unique benefits.
AI-based systems do not always make better decisions than humans. Find out why, and how to make sure your system is getting the best of both worlds.
Man vs. Machine
When it comes to Assortment Optimization, AI (artificial intelligence) and machine learning play a key part behind the scenes. While a human perspective helps, there are two important factors that make AI very important: scale and automation. For example, a large retailer with 1,000 physical stores and 10,000 products has as many as 10 million assortment decisions to determine which stores should carry which SKUs, what quantities, and when. Even though a significant portion of product offering is chainwide, that still leaves a formidable number of micro-decisions to be made. This amount of decision-making for humans is inefficient and not very profitable.
Assortment optimization is a major focus area for retailers at the moment. Retailers need more accurate SKU rationalization, a better understanding of what to buy in the future, and localized assortments that drive sales. Getting this right is crucial in the market today. There are a few traditional ways that retailers plan out their assortments. They either 1) think through their assortment from a business process standpoint, 2) look at loyalty and shopper data to determine assortment, or 3) think of a brand-new method to determine assortment.
Doesn’t it feel like the rules of how to ‘win at retailretail’ are constantly changing and shifting their focus? First the key was to bring in people to the physical store and shape their customer experience- making it memorable in some way. Then retail seemed to shift its focus to rely heavily on online shopping, leading to several brick and mortar retailers struggling to stay afloat. Today, BOPIS, the latest in retail trends, is throwing a life preserver to the brick and mortar store.
Consumers love buy-online, pick-up-in-store (BOPIS). Retailers love the way BOPIS allows them to drive traffic to stores and leverage their physical footprint as a competitive advantage against online pure-plays. But consumers bring higher expectations to BOPIS than they do to simply shopping in a store: The retailer has explicitly told them the item is available, so the bar is set high.
Unfortunately, for many retailers, BOPIS failures are alienating the very customers they most want to please, hurting them in both the short- and long-term.
For the second year in a row, Retail CIO Outlook Magazine has recognized 4R Systems as a top 10 artificial intelligence solution provider.
Retail CIO Outlook Magazine published their annual listing of the top 10 companies using artificial intelligence to positively impact the retail environment.
By Marsha Shapiro
Having been in technology for almost 30 years, I have the unique experience of watching my two daughters enter similar worlds today and seeing how much, if anything, has changed for women. My oldest daughter is currently in a post college training program for a major cloud infrastructure provider, where only six of the 19 participants are women. My foggy memory is that this has not changed dramatically since I attended a tech consulting program at the start of my career. This ratio is likely why my younger daughter, an engineering student, is being courted for a free weekend in Tahoe just to get her interested in applying for certain internships (the weekend is only for female engineers). So, what, if anything, is being done to encourage women to pursue technology careers today (and is it enough)?