How Machine Learning can improve customer experience

Machine learning is a promising technology for improving the customer experience. Why? It’s simple: because it can predict customer behaviors. Prediction as a capability is the Holy Grail for foreseeing each customer need and personalizing products and services accordingly. From the consumer’s perspective, when ML’s ethical pitfalls are avoided, prediction can be the ultimate antidote to the information overload that we all face every day. By deploying ML to predict which content is most relevant for each individual, customers can receive better recommendations, less junk mail, very little inbox spam, and higher quality search results, among many other things. These improvements to customer experience aren’t only a nice-to-have, pleasant side-effect of profit-driven ML deployments. They pursue the raison d’etre of any company — to serve customers — and will ultimately translate into further benefits for the business. After all, a happier customer is a more loyal customer, and a higher customer retention rate means a higher customer growth rate.

Machine learning (ML) — technology that learns from experience (data) to predict the behavior of each individual — is well known for improving the bottom line by running major operations more effectively. But did you know that it can also measurably improve the customer experience?

ML generates actionable predictions for individual customers, and those predictions can drive how each customer is served. In this way, ML can target a marketing campaign to customers who are more likely to respond, or disallow credit card transactions that are likely to be fraudulent. It can move likely spam out of the email inbox, or display the property (Airbnb), search result (Google), product (Amazon and Netflix), or romantic partner (Match.com) that’s most likely to be of interest to a customer.

Despite these clear value propositions, ML isn’t yet deployed nearly as widely and seamlessly as it could be. The problem is that the world largely focuses on how advanced and impressive the core technology is, which distracts from focusing intensely on its tangible value proposition — the precise ways in which it can render business processes more effective. As a result, most ML projects fail to deploy, never realizing their intended business value. But as decision makers increasingly recognize that ML can have a huge impact on the customer experience — in addition to the bottom line — companies will begin to shift their focus to generating concrete value with ML, ultimately accelerating and expanding its use.

How ML Helps to Improve the Customer Experience

Why is ML such a promising technology for improving the customer experience? It’s simple: It can predict customer behaviors. Prediction as a capability is the Holy Grail for foreseeing each customer need and personalizing products and services accordingly. From the consumer’s perspective, when ML’s ethical pitfalls are avoided, prediction can be the ultimate antidote to the information overload that we all face every day. By deploying ML to predict which content is most relevant for each individual, customers can receive better recommendations, less junk mail, very little inbox spam, and higher quality search results, among other things.

This has far-reaching potential. ML’s predictions can enhance the customer experience across lines of business and across industries. By way of illustration, here are seven established business applications of ML, each delivering an impact to the bottom line (the leftmost column) — as well as an impact to the customer experience (the rightmost column):