Let’s Break it DownThe pathway to AI is a broad and linear continuum that begins with simple, rule-based decision-making. To qualify as authentic AI, software development must progress through multiple stages of programming, data science and complex system analysis. Let’s take a closer look at these levels of decision-making sophistication from the most basic to the most autonomous.
- Rule-based decision-making uses “yes or no” data to allow different actions or change control flow. “If this, then that” logic falls into this early category.
- Statistical reasoning applies simple regression to numerical data to predict scores on one variable from the scores on another variable. This makes it possible to detect outliers, predict value and estimate whether existing trends will continue.
- Machine learning (ML) is a technique that teaches software to learn on its own, without being explicitly programmed to do so. Simple ML requires supervision, or human expertise, to review, validate and adjust program actions.
- ArtificiaI Intelligence (AI) means a device or system can sense its environment and take actions without human intervention to maximize its chance for success with a particular goal.
Machine Learning Offers the Most PotentialOf the four, machine learning (ML) offers the greatest revenue-generation potential for commerce marketers. Software developers can apply it to build tools that can analyze millions of data points about a shopper’s preferences and behaviors to create a more personal experience. And consumers are ready for it. Research shows consumers want personalization, but they also want to feel in control of their experiences. According to Oracle Retail 2025, three in five consumers (58%) had a positive attitude about the idea of having their grocer suggest a shopping list for their approval based on purchase history and social and environmental data.
Personalize Your Marketing
Are you prepared to meet this growing demand? Consider how you can apply these advanced software techniques to your marketing program to add personalized elements to your communications with shoppers. Machine learning powers personalized product recommendations that, essentially, provide a one-to-one shopping experience. When you pair them with cart and browse recovery programs, you can automate highly personalized customer experiences, while recapturing lost revenue and generating new sales. Here’s a fictional example of machine learning in action that demonstrates how such techniques can easily create a more personal experience between the shopper and the retailer.
By personalizing visual results and recommendations, ML makes the consumer experience more convincing and immersive. And as technology advances and consumer use of it increases, we can expect to see a corresponding surge in their demand for an even more convenient, personalized experience. But with so much advanced technology available, it’s important to remember that you are the expert on your business. Even commerce marketing automation that uses ML requires your expertise to direct and “teach” it how to achieve your business outcomes. You must review the results of your campaigns and make changes over time to boost your chances for success.