Artificial intelligence and machine learning (AI/ML) technologies are transforming businesses. In fact, the global annual spending on AI by retailers is estimated to reach $7.3 billion by 2022. However, along with this growth comes hype, uncertainty and questions around what AI/ML can really do for retailers today. 

One thing, if anything, is certain: AI/ML in retail is here to stay. 

For those of us who have been in retail a long time know this isn’t the first time data science was used to address retail business challenges. In the early 2000s, data science was applied to common problems within retail merchandising and the supply chain with mixed success. Markdowns were optimized, followed by statistical forecast-driven replenishment, then price and transportation optimization.

The Unified Retailing 2019 Conference hosted by Columbus Consulting yesterday was energizing, as it’s always a fun to meet up with our customers, old colleagues and friends. The speakers were excellent, with frank and interesting conversations around how to win in today’s new retail environment. However, what really struck me throughout the event was the fact that everyone—and I mean everyone—appeared to be dealing with the same type of challenges, such as:

1. How to support an environment where store sales are dropping, e-commerce is flattening, and mobile continues to grow
2. How to win in an environment where there are so many store closures, but just as many store openings from digitally-native brands
3. How to support the “shop anywhere” experience consumers demand
4. How to keep product, experiences and services relevant 

As every speaker discussed different strategies to address each issue, it became apparent over the course of the day these problems haven’t really changed over the last few years. In fact, they’ve only magnified. Despite these struggles, it only solidified my belief—and that of many others in the room—of the following:

If you want to be relevant, you must consider the customer and their preferences first in everything you do.  

The idea behind inventory allocation seems pretty straight-forward: Ensure stores are kept in stock with respect to their ability to sell merchandise. Simple enough, right? Yet, as an allocator, one end-of-season scenario you’re likely familiar with is the aching realization of a missed opportunity — where better inventory allocations in one store could have led to more transactions, fewer markdowns, and improved margins.

Of course, you and I both know the subtle art to successful allocation is far from simple. In fact, a recent U.S. retailer survey estimated inventory misjudgments, including misallocating inventory, account for more than half (53%) of unplanned markdown costs. This issue has prompted retailers, like Lucky Brand, to search for ways to infuse intelligence into their allocation systems and processes.

Mike Relich, COO of Lucky Brand, recently shared his experience leveraging Celect artificial intelligence and machine learning (AI/ML) software to successfully improve retail allocation results. 

Here are the five biggest takeaways:

I’ve spent most of my career in the retail industry. Throughout that time, I witnessed a great deal of change – from the evolution of the stores’ role, to the seismic shift in how retailers sell within the context of convenience, service and compelling experiences. Most noticeably, the fundamental core of how retailers think has changed: Managing at averages will no longer suffice. 

Despite these ongoing transformations, the one constant—and what, for me, is the most enticing aspect of the industry—is the intimate connection retail has with the consumer. With a background in astrophysics, my passion for science and innovation fuels my desire to help retailers use technology and data to make smarter decisions and preserve this crucial connection.  

Knowing how and where to allocate products to the right stores can make or break a retailer. We live in a consumer-centered world. For better or worse, thanks to companies like Amazon, customer delivery expectations have changed so that they expect to get what they want and when they want it at the single click of a button. Often, this product can arrive on their doorstep the very next day.

Not wanting to be left in the dust, many brick-and-mortar stores are utilizing tools enhanced with artificial intelligence to master the subtle art of retail allocation. In today’s retail landscape, companies who do not utilize advanced analytics to allocate their products will be left behind or even fail completely.