At its simplest, machine learning (ML) refers to the capacity for a program to automatically improve, or “learn,” as it ingests data to accomplish a specific task or set of tasks. While ML is a subset of artificial intelligence, it’s often used interchangeably with AI and conflated with predictive analytics or algorithms.
The many uses and applications of machine learning create a lot of confusion about what the term really means, especially at a time when complex algorithms have been able to generate seemingly “intelligent” outcomes for quite some time.
Machine learning goes a step beyond algorithms or predictive analytics, and its applications are growing exponentially in tandem with the number of new and existing companies investing in its development. Despite machine learning’s advanced nature, it may come as a surprise to smaller and midmarket retailers that ML is by no means out of reach.
What follows are three ways that retailers of any size can use machine learning to drive more value from the data that they already have at their disposal.
Reengage Lagging Customers
A central tenet of business is that it’s much cheaper to sell to existing customers than acquire new ones. Most retailers have troves of customer transaction and engagement data on hand, which is perfect for uncovering hidden connections that can drive more sales.
Normally, it would be very difficult – not to mention inefficient – for a retailer to try and guess when a customer is likely to move out of the funnel and stop engaging. However, machine learning can use a limited number of data points to surface customers that are at risk of leaving, allowing retailers to re-engage with a special offer or other personalized communication.
This article originally appeared on multichannelmerchant.com. To read the full article, click here.