Machine learning – the future is now for retailers

Retailers are finding machine learning technology to be highly applicable to current business issues.

Machine learning (ML), a subset of artificial intelligence (AI) that analyzes patterns in data to “learn” and adapt in a manner similar to humans, is reaching an inflection point in retail. This once bleeding-edge solution may not yet be a routine retail solution, but is popping up more frequently in the retailer enterprise.

Here are a few reasons why so many retailers are studying up on the benefits of ML:

Better reactions
In the past few years, retail has found itself facing a number of high-impact events that even the best currently available AI-based predictive systems could not foresee. These include the COVID-19 pandemic, global supply chain disruption, and an ongoing labor shortage.

[Read more: Survey: Supply chain disruption to continue; here’s the impact and actions taken]

ML technology provides two key advantages in dealing with this type of unpredictable disruption. First, as soon as an event like a pandemic occurs, ML solutions can begin detecting and analyzing data patterns to recommend responses that are much more constructive than reactions based on human intuition alone.

Second, once an “unpredictable” event such as COVID-19 or global supply chain disruption has occurred, ML technology can recognize otherwise undetectable data patterns to sense when a similar situation may be developing. And even if something like a pandemic recoccurs without advance notice, ML systems can rely on previous learnings to provide retailers with a much stronger initial response.

Generation gap, part 2
When the baby boomers came of age in the turbulent 1960s, the massive disconnect between the behaviors, attitudes and desires of that emerging cohort compared to their parents was dubbed the “Generation Gap.” Conventional wisdom holds that as boomers maintained a young, progressive outlook on life, this gap largely disappeared as Gen X and millennials grew up with little to rebel against.

Guess what? Gen Z has arrived as arguably the most important generation of consumers, and there is a significant gap between how they live their lives and how older consumers do. This gap is not predicated not so much on sex, drugs and rock n roll, but on how they use technology.

For example, a recent survey from Morning Consult indicates that 42% of respondents have used mobile wallet payments. However, this includes only 21% of baby boomers and 36% of Gen Xers have. These percentages rise much higher among millennials (53%), and especially Gen Zers (62%).

It’s no surprise that a generation that played with toy smartphones as toddlers and came of age in the era of pandemic-inspired touchless commerce would gravitate toward mobile wallet payments. Gen Z consumers are also much more likely to rely on social media influencers and consider a retailer’s sustainability when making purchase decisions.

Properly meeting the needs of Gen Z customers at every touchpoint goes far beyond having a mobile and social presence, or hosting young shopper market research panels. ML technology enables retailers to effectively sense and respond to demand signals they may not fully understand.

Automation requires humanity
Finally, as automation spreads across the enterprise, ML is playing a crucial role in ensuring that processes continue becoming more effective and efficient, just as they do when humans manually performing them learn on the job.

For example, in the case of case of Just Walk Out frictionless shopping technology, Amazon deploys sensors, optics, and ML algorithms. As a result, the company has reduced the number of cameras required in stores enabled with this solution to make them more cost-effective, smaller, and capable of running deep networks locally. Just Walk Out sensors and algorithms have also evolved to detect a broad range of products and differences in shopping behavior in full-sized grocery stores.

Also, consider how Walgreens is leveraging the ML capabilities of Blue Yonder’s inventory management technology to improve the accuracy of inventory, shrink, and shipping. The drugstore giant uses ML to see what the probable fulfillment rate is, as well as to support the sustained increase in customer usage of omnichannel shopping features, such as buy-online-pickup-in-store (BOPIS), curbside pickup, and same-day delivery, which began during the COVID-19 pandemic.