Strategy

How stores can meet every neighbourhood’s needs every day

By Laurence Haziot, Global Managing Director and General Manager IBM Consumer Industries 

Why are store closures a new norm in American retail? The answer is complex. Consumer expectations are shifting. E-commerce is growing. More brands are selling directly to consumers.

Retailers, rightly sensing a problem, have been trying out a number of strategies to make their stores more cost-efficient and more essential.

They’ve tried new store formats and concepts. They’ve tested out shorter leases. They’ve worked to reduce check-out lines and to merge physical and digital experiences.

All those approaches can help make brick-and-mortar stores better. But to ensure the future of their stores, retailers will need, first and foremost, to get ahead of their customers’ needs. To do that, they must harness data more effectively. And for that, they need AI.

Retail past, retail present

Not long ago, proprietors lived, produced, and sold their wares to customers in their local neighbourhoods. The key to their success was their knowledge of each customer’s preferences and habits, and their understanding of the everyday factors that influence local demand.

But with the emergence of national and global retail, operational scale has been the focus, and the attention to hyper-local knowledge has been lost along the way. 

To remain competitive going forward, however, retailers will need to maintain their mastery of operational scale, while better acting on the unique, daily needs of every neighbourhood in which they operate. But before they can do that, they need to embrace two fundamental concepts. 

No two neighborhoods are the same

For too long, retailers have made decisions about their assortment based on regional trends. But within a given region, neighbourhoods can vary dramatically.

Even within a given neighbourhood, one block can be vastly different from another. To get the best sense of what a store’s customers might need, retailers need to rely on the most local data available.

That means understanding the demographic breakup of the customers that are going to be near a store at any given hour of any given day. It means understanding what other kinds of business and services—including schools, bars, shopping centers, museums, and stadiums— are in the area. And it means understanding how those factors interact.

If retailers don’t truly grasp their local customers and their local neighbourhoods, they’ll never be able to serve them properly. When retailers treat the neighbourhoods they serve as a special and dynamic places, each of their stores will become essential to those places. 

No two days are the same

Traditionally, retailers have made decisions about their product mix and assortment largely based on historical data. Historical data is one important set of data to consider when choosing how to stock a store on given day. But it can’t be the only set. 

The introduction of just one new factor—bad weather, a local sports event, a traffic jam—can make one business day entirely different from the same business day in previous years. It can affect the number of customers stopping by a store, and it can affect the kinds of items they’ll want to buy, and the quantities in which they’ll want to buy them.

Information on those factors, however, must come from external sources— through news reports, for instance, and social media posts. Much of it is unstructured data, meaning it comes in the form of photos and videos. 

Getting the right mix and quantity of products is crucial for a retailer. Out-of-stocks or excess inventory can drive loss of revenue, costly markdowns or re-distribution of stock. Once retailers acknowledge that no day is like any day that came before it, they can be better prepared to respond to new challenges.

Insights for the here and now
        
Responding effectively to the hourly, daily needs of thousands of distinct neighborhoods requires technology that can manage and analyze complex, diverse sets of data. 

IBM MetroPulse, powered by IBM Watson, senses what is happening on a block-by-block basis in real time through its advanced analytics platform.

With these on-demand signals, retailers can use pre-built algorithms to make precise decisions to better serve their customers in each neighbourhood. This personalisation results in greater revenue, better customer loyalty, and increased operational efficiency.

As the industry becomes more complex, stores will keep their doors open if they accomplish a singular—though often complicated— task: They must provide the right products to the right people at the right time.

Today, the technology to enable that is here. Now, it’s time for retailers to harness it. 

Previous ArticleNext Article