Using Machine Learning in Retail Marketing

What is Machine Learning?

Machine Learning (ML) is a subset of Artificial Intelligence (AI) geared to improve data analysis and model building via pattern recognition. Through ML, computers can get better at identifying patterns over time and make their own informed decisions as they ingest more data. Let’s examine ways machine learning is helping businesses improve their marketing efforts to drive sales, increase Return on Investment (ROI), and improve the customer shopping experience.

Predictive Analytics and Insights

For marketers, the end goal of ML is to send timely, personalized, relevant messages and product recommendations that truly resonate with consumers and incite them to make initial or repeat purchases. Tools like a Predictive Marketing Automation Platform (PMA) utilize ML to apply algorithms and statistical models that target specific buyer segments. From there, they proactively analyze (and ultimately aim to predict) not just their behavior, but their intentions as well. In the age of Big Data, this necessitates a degree of speed, aptitude, and accuracy that is simply beyond the scope of human capacity. Thanks to advances in ML, the whole process can be implemented autonomously in real-time more efficiently than ever before.

The Buyer Lifecycle

Precise targeting and forecasting requires an intricate understanding of where each customer in your base lands within the Buyer Lifecycle (BLC). ML analyzes email behaviors, past purchases, web browsing/purchasing habits, and external data sources to calculate how engaged certain customers and prospects are at any given time and divide them into segments. For example, if a customer has not bought from you in a while, an automatic message can be deployed in an attempt to persuade him/her to come back to your brand and buy again. This actively reduces churn rates and funnels more customers back into your active customer base. On the other hand, there are customers that spend more money, more often than the rest. These are known as Most Valuable Buyers (MVBs). ML can acknowledge these spending patterns and send special “VIP only” messages both acknowledging and rewarding MVBs for their loyalty – an effective form of positive reinforcement.

Customer Acquisition

ML can also help you calculate a prospective customer’s potential future lifetime value (FLTV) and autonomously engage them through multiple acquisition channels. ML capabilities can both determine customer value and autonomously respond through multiple acquisition channels. They also help you decide how much you can spend on acquisition to successfully meet your goals.

Improving the Customer Experience

These days, most consumers don’t just desire a personalized shopping experience – they actually expect it. Econsultancy recently referenced a consumer survey that found 64 percent of respondents now expect an individualized experience, while 85 percent consider an individualized experience to be important. Rather than sift through immense piles of data to try to get to know your customers better and generate personalized relationships, ML can swiftly cater to a customer’s wants and needs almost instantaneously. This can come in the form of chatbots that respond directly to customer queries for a more streamlined shopping experience, or recommendation engines that make suggestions and guide customers towards favorable products they’re more likely to buy based upon an automated analysis of past browsing/buying behavior.


This is the age of one-to-one marketing, where reaching buyers on a personal level via the proper channel is becoming more essential than ever. According to a recent global survey by Google and the Massachusetts Institute of Technology Sloan Management Review (MIT SMR), 74 percent of respondents said they believe their organization’s current goals would be better achieved with greater investment in machine learning and automation. The timeline to reach customers has changed dramatically. Given the way people shop today, you have to be able to respond to both their inquiries and behaviors almost instantaneously. While ML will not magically do everything for you, it is certainly a driving force in the marketing industry that is helping retailers acquire, retain, and satisfy customers at a higher, more consistent rate than ever before.