News and Insights


29 May

How AI Can Help Retailers Eliminate Shoppers’ Frustrations – and Create a Winning Customer Experience


Understandably, retail customers get irritated when they make a trip to a store only to find that the products they seek aren’t available – particularly if they received an email promo offer of a discounted price for these items.

Empty ShelfHowever, this is happening much more often than it should. In fact, nearly one-half of shoppers say that stock availability is their biggest frustration when going to brick and mortar malls, according to recent survey of an estimated 1,000 U.S. consumers that we’ve conducted. About seven of ten survey respondents said they have received an email voucher from a store, but found that the product was out of stock once they got there.

Technology, of course, can help eliminate these issues, especially innovation in the form of artificial intelligence (AI) and machine learning (ML). Currently, 40 percent of retail/consumer products companies have adopted AI intelligent automation tools, according to research from IBM, and this is expected to double within three years. What’s more, 85 percent of retailers plan to use these solutions for supply chain planning by 2021.

More than one-third of the participants in our survey believe that an app which would give updates on stock levels would enhance the customer experience (CX). Clearly, such an app would meet with greater success if AI/ML were driving the information provided, by playing a lead role in the forecasting of this data. Through AI/ML, marketing, sales and supply teams can collaborate to understand well-established shopping patterns. How much of a particular product are we selling on a typical weekday? How about on Fridays, and weekends? What are the peak buying times during the day? If our store is located in, say, Minnesota, how do winter storms impact business?

Once these baseline indicators are determined, the teams can dig deeper to improve the accuracy and range of AI/ML-enabled forecasting. In a major metro region, for example, how much do cold cut sub sandwich sales spike at a deli on Sundays when the local NFL team plays? Perhaps in the same shopping center, how many more team T-shirts and ballcaps are sold at a department store on the following Monday, after a victory?

To pursue story lines outside of pro sports, what impact will an anticipated surge in home sales have on demand for paint, hammers, nails, saws, lawn mowers, grass trimmers, etc.? Which specific days does children’s clothing move faster leading up to the beginning of the school year?

Women in TechIn other words, the capacity to effectively forecast here is limited solely by the extent of our creative curiosity in developing queries. We ask questions about the future covering virtually any dynamic – day of the year, time of day, weather, events, trends, etc. – and AI/ML takes the guesswork out of the answers. Armed with the information, retail marketing, sales and supply teams work closely with their product partners to meet what will most likely be the customer demand for any specific day, or even hour.

Thus, when that promo alert pops into customers’ in-boxes, we know they will arrive at our store to find what they came for (along with other items which – thanks to predictive AI/ML – we’re pretty sure they’ll want to buy as well). Ultimately, this allows us to establish a critical building block of outstanding CX: trust. At REPL, we work closely with our retail clients to achieve these goals, ensuring they not only meet the challenges of the digital revolution, but thrive within them. If this sounds like something you’d like to discuss further, then please contact us.

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