There has been trepidation among marketers when it comes to artificial intelligence (AI).
The main concern seems to be, “is it coming for my job?” The reality is AI, and particularly the subset of machine learning, are tools that can help marketers do their jobs more effectively — not replace them altogether.
Understanding AI and Machine Learning
A good starting place for understanding how this works is defining what exactly AI and machine learning mean. Misuse of these terms to date has led to plenty of confusion on what AI entails and what it’s capable of.
AI is not a singular technology. It’s comprised of multiple components: machine learning, robotics, data mining, natural language processing (NLP) and neural networks (computer systems modeled on the human brain and nervous system).
The most important of these for marketers’ purposes is machine learning. Machine learning is a specific process within AI, and refers to the science of self-learning algorithms. At its core, machine learning is about the use of statistics to solve problems using the data from the knowledge discovery process. The driving concept of machine learning is using technology to help humans think better.
So how exactly does machine learning help marketers?
Marketers today are sitting on huge amounts of data. They’ve invested in a marketing stack that captures customer information everywhere from POS systems, to websites and mobile apps. They can also purchase data from popular third-party sources like Facebook.
From Data to Insights
The challenge is to make all that data useful. While some data correlations are obvious to the naked eye — running shoe sales increase during the New Year as consumers make their fitness resolutions or Gigi Hadid wears adidas Superstars, leading to a rise in sales of that shoe — others can be more subtle.
Machine learning’s main advantage for human marketers is the ability to analyze and leverage data in real time, despite enormous volume and complexity.
Like in conventional marketing, machine learning-powered marketing leverages feedback and results from the hits and misses of past marketing recommendations to gauge ROI. The difference is machine learning can identify and demonstrably link multiple causes to effects, and weigh the strength of those correlations.
For example, if a promotional email goes out on Thursday evening, and particular sales are closed based on that email, the machine learning algorithm can reinforce the pathway of correlations that drove that email. Where a sale was not closed, support becomes weaker for the pathways that drove the recommendations that did not produce a sale.
Tracking Hundreds of Factors
The model is always shifting and updating itself automatically. What drives the exact changes and shifts are hard to detect and isolate because hundreds of data points are in play.
While human marketers can track “if A, then B” causal relationships, machine learning-driven marketing can track hundreds of subtle factors that can work in various combinations and lead to a sale — everything from changes in taste, social media memes, fashion trends, seasonal variations, pop culture, and more.
Going back to the sneaker example, a human may make the connection that Gigi Hadid wore adidas Superstars, which led to sales.
Machine learning-driven models would also respond to the change in transactions, but recognize subtle product correlations, such as the particularly strong impact on 20- to 30-year-old women who follow pop culture in social media.
Elusive Correlations = Profitability
Some correlations stick out like a sore thumb — humans easily see it.
But there are also many more understated correlations and patterns humans will never notice.
What we’re finding today — and the reason AI and machine learning are rapidly gaining popularity — is that these more elusive correlations are potentially just as profitable as their more obvious counterparts.
So will AI and machine learning take your job? The answer is no. But marketers leveraging that technology and leveraging the hidden correlations you’re missing? That’s another story.