“Machine learning” has graduated into the upper echelon of business buzzwords. That hallowed level where we’ve all used it, but few up us really know what it means. It’s like “The Cloud” or “Synergy.” We sort of get it, but what are the real world applications and ramifications?
The impacts of machine learning are far reaching. Just look at everything IBM’s Watson, a cognitive technology, has done in the past year. It’s taken on everything from fighting cybercrime to perfecting recipes. For the purposes of this article, we’re looking exclusively at machine learning’s impact on the marketing world. But don’t despair, the applications are no less exciting.
What is Machine Learning?
First, let’s start with a quick definition. All the way back in 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed.” Essentially, machine learning is the act of technology learning, improving, and iterating all on it’s own.
So what does this piece of science fiction have to do with marketing? Well, back in the Don Draper era of marketing, the answer would have been “very little.” But nowadays, marketers are inundated with mountains of data from their marketing tools, analytics programs, and their customers. Machine learning provides an opportunity for computers to take on the heavy lifting of analyzing, categorizing, and utilizing all of this data.
Pretty helpful right? That’s why tools like Kahuna have put machine learning at the heart of a new wave of marketing technology. Machine learning makes marketers smarter, faster, and more efficient. So, back to the whole “I’m not really sure what the applications of machine learning are,” problem. Let’s take a look at how marketing tools like Kahuna are using machine learning to put marketers on the cutting edge.
Data is one of the most sought after resources in business today. Companies spend millions and dedicate hundreds of hours to uncovering and tracking as many data points as they possibly can. However, all this data means very little if you can’t uncover the insight it holds. For the average marketer, the data they collect on a customer likely looks something like the following:
They understand where their audience is interacting with their brand and roughly which stage of the buying process a customer is in when the interaction takes place.
However, with machine learning, marketers can take that data and turn it into something more useful:
The data you collect on your users is not a series of independent interactions. Each touchpoint has an impact and relation to another. Machine learning can use the data you collect on customer touchpoints and use it to uncover your customers’ unique journey to a purchase decision. Tools like Kahuna can use historical behavior patterns to determine the time, channel, and message that are best to drive a desired action. Pretty cool right?
We’ve written about this point before, but machine learning is essential to smarter message optimization. Traditionally, marketers have had to test message effectiveness through a standard A/B test:
A small number of messages are sent to each test group. Whichever variant has higher goal achievement is the winner and is used for all of the remaining messages. While these tests are easy to design, they take a long time to conduct and can only test two variants at a time.
With the help of machine learning, the optimization process is faster, more comprehensive and more accurate:
Machine learning takes the logic behind A/B tests and multivariate tests and outsources the number crunching to a computer. Messages are sent on a continual basis, and the computer scales up the send volume of the variants that are performing well, and scales down the under performers. This process is much faster than traditional tests and yields far more accurate results, all with no input from marketers required.
The most powerful advantage of machine learning is it’s ability to scale infinitely. A marketer can exhaustively study the data of a single company and use that knowledge to craft the perfect message, but it’s difficult to do this consistently and impossible to do it for more than one company at a time. Machine learning can apply that level of knowledge to millions of customers every hour.
Because marketers lack that ability to scale, they often resort to demographic information when targeting users. This is essential a best guess at what customers prefer, which looks something like this:
While demographics give a close approximation of what a customer might be interested in, messages crafted for demographics are only relevant to certain customers and even then, only for a period of time. Customer tastes and preferences are constantly evolving.
Machine learning utilizes the behavioral data you collect on each of your customers to craft the perfect message on an individual level. Even better, machine learning conducts this process continually, adapting to evolving customer tastes and preferences.
While the applications of machine learning are infinite, these are just three areas where marketers are already seeing a positive impact from machine learning. Tools like Kahuna seamlessly integrate this technology into the marketing processes marketers are already following. Campaign creation, message optimization, and analytics are all enhanced by machine learning with no added complexity.
If you’re spending more and more time drowning in data, it may be time to invest in tools that help you leverage data to maximum effect.