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.
Given a data set that includes the date of first and last purchase, total lifetime spend and total number of orders for a group of customers, a machine learning system can accurately guess which customers are unlikely to return. This is information that almost every retailer has on hand, and that often goes underutilized.
Boost Average Order Size
Given a large enough data set of purchases, machine learning can also draw connections between products that are often bought together and make smarter recommendations. For example, a retailer might manually have their website recommend belt purchases when consumers are buying pants, but that may not actually be the best combination.
Amazon is especially good at making smart recommendations based on aggregated order data, and it’s part of the reason why many consumers never leave Prime when searching for a product online. Fortunately, most retailers have large amounts of order history data – including items that are purchased in the same cart – at their disposal and can put it to use for a similar effect.
The value of machine learning here lies in spotting connections between products that would otherwise go completely unnoticed, and then surfacing those connections for the retailer to take action. This can come in the form of automated recommendations that increase the chances of consumers purchasing additional products, or as follow up marketing activities like an email offer for one of the associated items.
Reduce Marketing Waste
Machine learning’s capacity to “learn,” or improve without direct human input, means that systems can identify trends in real-time and adapt accordingly. This is particularly useful when it comes to marketing.
For example, marketers typically need to plan campaigns ahead of key seasons or holidays – Christmas, back to school, summer, etc. This, however, requires a degree of guesswork in terms of what consumers will respond to.
With machine learning, systems can analyze live sales data and determine which products are receiving the best consumer response, allowing marketers to quickly adapt their own tactics, particularly on more nimble channels. Marketers can spend more time and budget promoting products that work well together – see the example above as well – to the consumers that are most likely to purchase.
The value of machine learning, and the reason why retailers of all sizes need to start exploring it, ultimately lies in deriving more insights from the one resource most retailers have plenty of: data, which can provide a major bottom-line lift to the overall business.
By Paul Mandeville, chief product officer at QuickPivot