Grocery retailers have a common phrase – “If it isn’t on the shelf, customers can’t buy it.” Their business is driven by creating trust that when the customer wants an item, they will be able to find it.

Whether the retailers are investigating a new trend or ensuring staples are always on the shelves, they spend a lot of time thinking about the products they stock. And it got us wondering – how do grocery stores leverage data to make this important decision?

Traditionally, retailers have many factors to consider when determining which items to stock, including:

  • In-person traffic: As shoppers return to their pre-pandemic habits, stores are getting busier. Assessing and understanding foot traffic is essential to keeping shelves full. Retailers use various tools to understand what’s happening in their stores, from foot tracking counting systems and consumer predictive analytics to forecasting services. They then use this data to determine what needs to be ordered and when.
  • Seasonal items: Knowing which items aren’t selling is just as important. Grocery retailers operate on slim margins and the business relies on keeping the right items in stock at the right time. For example, yeast is an important part of many winter holiday baking traditions and will sell quickly in November and December. Stores can stock less yeast during the warm summer months when people do not bake as often.
  • Shelf-life expectancy: Fresh products are a must for any grocer. And they are laser focused on ensuring available items are sold well before a printed expiration date or any visible signs the product is going bad. Proper shelf life expectancy management also ensures the stores place effective orders to keep shelves full of the freshest items.
  • Customer behavior: With hundreds of people walking through a grocery store every day, it can be difficult to identify each individual’s needs and preferences. Are they cooking for one or four? Are they cooking at all? How many have families? Do they prefer healthy foods or comfort foods? Every piece of information helps create a clearer picture of the customer, which grocery retailers can use to anticipate buying behaviors and stock the items their customers expect.

Rethinking the traditional grocery shopping experience

Kroger is a national chain of grocery stores, with about 2,800 stores nationwide. In this footprint, the company engages with millions of customers every day. And each of those customers is a great individual data point.

As more customers gravitate to using self-checkout (SCO) machines, Kroger looked for new ways to gather metrics that will help them improve the customer experience. The grocer turned to IBM Technology Support Services to uncover insights from the more than 25,000 SCO machines.

The solution IBM created was custom-built to Kroger’s needs and meticulously collected and organized maintenance, repair, and restore time data from all the vendors who service Kroger’s SCO stations. The resulting report gives the Kroger team a clear a view of all stores nationwide, with the ability to generate insights by division, geography and even by store.

“We can tell Kroger what the better performing stores are versus the lower performing stores,” said Joy Donaldson, Retail Delivery Executive with IBM Technology Support Services, “and if there’s anything in the data that can help them improve the performance.”

And even seemingly small insights can result in big changes to the customer experience.

“Share as much information with your provider as you can,” said Donaldson. “No matter how minute that information is, it benefits everyone. The more data we have, the more we can do data analysis, determine trends and make predictions.”

Next, Kroger plans to work with IBM to expand the current report with data insights from its help desk.

Learn more about how IBM Technology Support Services can help your business create exceptional customer experiences with the power of data.

Was this article helpful?
YesNo

More from Artificial intelligence

Bringing your own custom foundation model to watsonx.ai

3 min read - Since its release in July 2023, IBM watsonx.ai™ has been enabling businesses to train, validate, tune and deploy AI models. With its cutting-edge generative AI and machine learning (ML) features and capabilities, watsonx.ai is designed to help businesses capitalize on the opportunities of generative AI and foundation models, while allowing them to mitigate risks and drive trust, performance and flexibility of AI outcomes. IBM's approach to AI is based on four core pillars: open, trusted, targeted and empowering. Our AI…

How the Masters uses watsonx to manage its AI lifecycle

5 min read - At the Masters®, storied tradition meets state-of-the-art technology. Through a partnership spanning more than 25 years, IBM has helped the Augusta National Golf Club capture, analyze, distribute and use data to bring fans closer to the action, culminating in the AI-powered Masters digital experience and mobile app. Now, whether they’re lining the fairways or watching from home, fans can more fully appreciate the performance of the world’s best golfers at the sport’s most prestigious tournament. In a continuous design thinking…

IBM Tech Now: April 8, 2024

< 1 min read - ​Welcome IBM Tech Now, our video web series featuring the latest and greatest news and announcements in the world of technology. Make sure you subscribe to our YouTube channel to be notified every time a new IBM Tech Now video is published. IBM Tech Now: Episode 96 On this episode, we're covering the following topics: IBM Cloud Logs A collaboration with IBM watsonx.ai and Anaconda IBM offerings in the G2 Spring Reports Stay plugged in You can check out the…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters