Four Ways Machine Learning Can Boost Revenue And Increase Operating Efficiencies At Retail

Introduction

When it comes to retail commerce, machine learning is no longer just a curious technology experiment. There’s no question of the benefits that come with effectively applying ML to a retailer’s eCommerce site and its business and operational systems—the keyword being “effectively.” eCommerce giants have already heavily tapped this resource, as have many retail chains that operate both online and brick-and-mortar operations. However, unlike titans Amazon and Netflix, most retailers don’t have an army of programmers, data engineers, and data scientists to lean on. Even retailers with sizable IT departments don’t often have nor want to hire a large team of ML specialists.

Delivering A Personalized Shopping Experience

Yet, the retailers that don’t wade into the ML pond may soon find themselves falling behind the competition. Customers want richer, more innovative, personalized online shopping experiences that make finding what they want faster and more efficient. Retailers need to deliver those experiences, boost revenue, and increase operating efficiencies, all at the same time. ML can help by:

• Enabling retailers to present product recommendations that are more relevant to each shopper, boosting both customer satisfaction and sales revenue
• Provide more accurate fraud detection, saving retailers money while reducing false positives and the accompanying customer frustration
• Segmenting retail customers using far more than demographics, so the retailer can optimize its marketing campaigns (and budget) while showing customers more of what they want
• Optimizing retail inventory and supply chain management, reducing waste while ensuring the stores have the merchandise customers want, when they want it
• And that’s just the beginning…

Where to Start?

The conundrum, of course, is that without a staff of ML and AI experts, not to mention data engineers, where does a retailer start?

The fact is, they no longer need one. SaaS vendors like AWS, Microsoft, and Google now provide complete suites of ML algorithms that retailers can use, right out of the box, as well as the managed cloud infrastructure on which to host and run them. You can also accelerate your ML initiatives—and the benefits you and your customers derive from them— by leveraging a trusted technology partner. A technology partner can help plan, execute, and deploy the solution, not to mention training your existing staff to use and maintain it. You’ll still need a couple of savvy programmers that understand your company’s retail systems and data, so they can create a data pipeline that feeds the ML algorithms. (The right partner technology partner can get them up to speed with how to do that, too.)

Conclusion

By following the steps we’ve outlined in our white paper, Getting Started with Machine Learning in Retail, avoiding the pitfalls we described, and selecting a savvy partner, any retailer can start enjoying the benefits of ML sooner than later.

JBS Custom Software Solutions has years of experience delivering retail ML solutions on managed cloud platforms like AWS and using AWS Fraud Detector, AWS Personalize, and AWS SageMaker. For more information on how we can apply this experience to optimize the customer experience, revenue, and operational efficiencies for retailers of any size, contact us today.