AI and big data: The savior of brick and mortar stores?

Brick and mortar stores are closing left and right, but artificial intelligence may be able to keep them alive. Kris Hamer, VP Research at RI, considers the current state of retail and how grocery data analytics offer hope of a turnaround.

Traditional US Retailers are on course to close more stores than they did last year. In 2019, there were over 12,000 stores closed by US retail chains, and with over 1,200 already closed in January 2020, the full year number looks set to be more than 14,000.

Compare that to the recession in 2008, when US Retail chains closed more than 6,000 stores. So, with more than double that number, despite the good economic backdrop we have today, it is significant and worthy of attention.

What caused the decline?

There are a lot of factors to consider. One thing is that US malls have lost their luster. Retail space grew twice as fast as the population from 1970 to 2015, according to The Atlantic, citing Cowen and Company research. This eventually spelled doom for malls as the market became oversaturated.

One other reason that caused challenges with brick and mortar stores is the rise of e-commerce. In 2019, e-commerce purchases accounted for 10.2 percent of all retail sales. This figure has risen steadily since 2000 and continues to grow.

From the outside, it is easy to figure out why shoppers decide to purchase online. It is convenient, it is accessible and, with value-added services such as Amazon Prime’s same-day delivery, why do you need to bother yourself with visiting a store at all?

More on e-commerce

If you look deeper into e-commerce, you will realize that convenience is just part of its allure. Retail e-commerce giants like Amazon and Alibaba utilize AI to make their users’ experience more immersive.

Artificial Intelligence (AI) is a general term used to describe the science behind making intelligent machines, especially intelligent computer programs.

With AI, e-commerce has become more intuitive. With just your profile, your choices, and your “likes”, the algorithm behind each online retailer can easily “suggest” which products you will most likely end up buying. Gone are the days where you must manually scroll through the whole product list to find what you are looking for.

The early implementation of AI has caused disruption in the retail landscape and, again, this is one of the reasons leading to the decline in retail. But what if we could use the same technology in brick and mortar stores?

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The future of brick and mortar stores

Before employing AI’s aid, store operators may best focus on the following areas:

  • 1) Value – whatever the trends are, customers would never sacrifice value over price. Why do you think people still buy iPhones even though it is not the cheapest phone out there?
  • 2) Experience – people nowadays look for experience rather than simply going to a store to purchase. Shoppers appreciate a knowledgeable workforce and a tailored buying experience that guides them throughout the purchase process.
  • 3) Convenience – a store operator’s goal is to strive to remove any barriers and friction from the effective shopping experience.

Having developed an operating model that addresses these challenges, turning to the aid of AI to help you iron out inefficiencies in your day-to-day operations would be a smart next step. Machine learning can acquire and leverage point-of-sale data, through the application of intelligent automated algorithms, to help retailers improve their stock replenishment processes to keep products available to the customer. Being alerted to emerging on-shelf availability issues in real-time helps to focus the efforts of store colleagues where they can make the most difference to store sales. With store operating models becoming leaner and leaner, using AI technology to act as the eyes and ears of on-shelf availability performance is a powerful tool in any retailers’ armory.

Furthermore, most fresh food grocery stores have to deal with products that are reaching end of code life and need to be sold quickly. Markdowns in price are the most commonly used method of stoking demand. But the decision about how much to mark down a product by is one that is key to protecting product margin and limiting the number of goods thrown away or donated. Finding that delicate balance is prime for AI technology, that optimizes the simultaneous maximization of the retail price and minimization in waste.

Most retailers benefit from supplier funding to run promotions. The quid pro quo is that in return for the funding, retailers set up their promotions with the right products, in the right place, at the right time. Suppliers regularly visit stores to audit compliance and those that fall foul of the agreement find that funding is withdrawn. AI is an effective tool to control this risk. Identifying signals in sales and set-up data allow retail chains to quickly flag those stores that are set up correctly, and more importantly those that are not. Relying on field management to be in the right place to deal with non-compliance is not enough. They need arming with the information as to where to focus their efforts, powered by AI.

Apply AI to reverse the trend

The retail decline is not inevitable. Whilst the concept of AI is not new, the application is. It is time for retailers to utilize the power of AI to face their competitive threats. This is a way for them to reverse the trend and to avoid at least some of the retail disruption: to make even better decisions, operate more efficiently, and ultimately, to deliver a more positive, memorable experience for their customers.

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Written by Dr Kris Hamer

Kris works with clients to unlock value from data - with particular focus on driving sales and availability, reducing waste and optimizing store assortments. He has a Ph.D. on Retail Performance & KPI linkage at Tesco from Henley Business School, and a first-class degree in Management from the University of Reading.