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How Retailers Are Using Machine Learning to Overcome Covid Challenges.

In 2007 Nassim Nicholas Taleb, a hedge fund manager turned mathematical researcher, published The Black Swan, a book about unpredictable events that result in massive changes in the world. Two years later in 2009, the financial crisis turned the world upside down. As you’re reading this, we’re living through another black swan event. COVID-19 has kept retailers, along with the rest of us, on our toes and scrambling to react to an event which no one prepared for.

Unpredictable Events Cause Big Time Retail Chaos 

COVID-19 has caused chaos in stores. There are multiple sides to the chaos. First, the obvious: shoppers. Panic buying shifts week-to-week from cleaning supplies to meat to flour is causing chaos in the aisles and to supply chains. Who knew that during a pandemic, your brother-in-law would take up baking? This isn’t just limited to essential retailers. Try buying equipment to set up a home gym or office and you’ll witness an ocean of out of stocks both online and in stores. 

Next, there are labor issues contributing to the chaos. Retailers have more things to do with less qualified people to do them. Before COVID, store teams already had full plates with normal duties like processing shipments, maintaining the sales floor, and helping shoppers. Now they’re also responsible for extra cleaning measures, enforcing one way aisles, promoting social distancing in checkout lines, and checking for masks at the door. 

This would be fine if retailers had a wealth of experienced store associates familiar with the nuances of the stores in their specific chain but many retailers lost those exact kinds of employees during government mandated shutdowns. Many of their replacements are new to retail, new to the retailer, or both. 

Last, but certainly not least, store teams are finding that the reports and technology they use to help maintain orderly stores have been thrown off by COVID as well. It seems weird that a pandemic can throw off technology until you remember two things:

  1. A lot of the reports and analytics tools that retailers use rely on historical data. 
  2. Historical data isn’t very meaningful during a Black Swan event where we’re living through a lot of historic firsts as a society. 

If sales of a SKU in August 2020 are dramatically different than August of last year or the last 10 Augusts…does it mean anything? If it does, is there anything your store manager can do about it? Without reliable data, store teams are missing a crucial assist from reporting tools. As a result, they’re missing opportunities to boost sales and letting chronic operational issues grow unchecked. The stakes are higher than losing sales in the moment. Retailers are suffering long term damage to their brand and could be losing customers for good. 

The Icing on the Cake? Higher Standards

This is the worst time in modern history for retailers who are having trouble with store operations. Here’s why: shoppers are tense. For those living in hotbed areas, a trip to the store is a very real risk. You have to put on a mask to protect yourself physically and calculate what items are worth shopping for in a physical store. Food and medicine, yes. Anything else…well, that depends on how safe you feel at the store and how much effort is required to buy what you want in ONE trip. Before Covid, it was annoying to go to the store – only to find that one of your must have items is out of stock or a hassle to find. Now, it’s a dealbreaker. 

So there it is. There’s an unusual amount of chaos at stores due to a pandemic that retailers have no control over. Customer expectations are unusually high and the two levers retailers normally pull (people and technology) have their own issues. Whether they’re veterans or new to retail, your store teams have a lot of work on their plates. The reports they use aren’t working like they should. This is an opportunity and a risk all at once. Retailers who can deliver stores where the products people love are in stock and easy to buy have an opportunity to gain new loyal customers. Those who fail are in danger of losing customers to their competitors. 

What Retailers can do Right Now 

So what can retailers do? The answer is simple. Make sure that the items local customers love are easily buyable and find technology that can help your store teams do this not next quarter but next week. Since technology based on historical data isn’t working well, find technology that can help your store teams with current data. This is a perfect use case for machine learning technology like CB4. 

Whether you’re a grocer carrying 50,000+ SKUs or a convenience store carrying 5,000, it’s impossible for retailers to make every aisle perfect. That’s always been true, but especially now. Luckily no one walks into a store with a shopping list that’s thousands of items long. I walked into Levi’s shortly after it reopened in Brooklyn looking for a single pair of jeans to try on. At most, shoppers are coming in to buy a handful of items, maybe two dozen if you’re buying a few weeks’ worth of groceries. 

CB4 uses patented algorithms that highlight a handful of items with high local demand in each of your stores that aren’t meeting demand due to an operational issue. The items that CB4 highlights are local favorites that might be left in the stock room, be misticketed, missing, or for one reason another simply not buyable. Using our app, store managers receive clear guidance to find and fix the issues and report what was wrong. This results in improved customer experience, increased sales, and a clear ROI, with very little time required.

The key to keeping customers happy is ensuring that at each store, staff is empowered to make products in high local demand easy for shoppers to find and purchase. When your teams are overwhelmed, CB4 keeps them focused on what matters most. Simple fixes like ensuring locally loved products are replenished on the selling floor and correctly signed are usually all it takes.

How CB4 is Different

CB4 alerts your store teams to quickly fix critical issues, help customers, and improve sales. We’re able to do this because we analyze demand as it happens, instead of relying on time-based or cluster comparisons. By using advanced pattern-recognition AI on their POS data, we help retailers see which unique products have the highest unfulfilled demand at every store. This means that when your customers need you the most, your team can contribute and

capitalize today with data you already have.

Real Life Use Case of CB4 During Covid

Here’s a real life example of a retailer using CB4 during the pandemic. One of the grocers we worked with shared an interesting use case.  During the early stage of COVID when stores were especially chaotic, a lot of the store shelf tags were damaged or fell off. This caused clerical errors that typically take a long time to recover from. Store teams were able to use CB4 to uncover the exact SKUs where those issues were causing lost sales and as a result, fix a bunch of shelf tags before the damage grew.

During this unpredictable time, your store teams need more help than ever prioritizing which issues to tackle on the floor. CB4’s app is sophisticated, but simple to use. Store teams receive a weekly set of tasks to fix issues that prevent customers from purchasing the products they want most. These tasks roll up into dashboards so you can act on the chronic, large-scale problems that hurt your bottom line.

To learn more request a demo or check out our product page.


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