Machine learning is the biggest thing to happen in marketing for years – probably since the mobile web turned everything on its head. However, there’s still a lot of confusion surrounding the technology and a perception that it’s beyond the reach of most marketers.
In this blog, we’re going to briefly explain what machine learning is in plain English and provide some working examples of how marketers can use the technology to get better results today. (If you’re after a more in-depth article, this one over at IBM is a good one.)
What is machine learning?
Machine learning uses algorithms to process data, spot patterns in datasets and use them to make decisions, predictions or take certain actions. By repeating this process over and again, these algorithms are able to measure how patterns change over time and improve the quality of their output, which is where the “learning” element comes in.
For example, a leading retail brand might input consumer data into analgorithm to see how national holidays, weather patterns, school terms, sporting events and all kinds of other influences affect buying habits.
These insights can reveal what really impacts consumer spending and how the impact of these influences vary over time – five years, 10 years, 100 years. And, once this algorithm has enough data, it will be able to predict how similar influences will impact consumer spending before they even happen.
Why does this matter? Because it helps businesses make more profitable decisions and adapt to challenging conditions more effectively.
What can machine learning do for marketers?
Machine learning powers a growing number of tools marketers use on a daily basis – so you’re already using the technology one way or another. To get the most out of it, though, you’re going to want to get involved in creating your own algorithms, designed to solve problems your business/marketing team is facing.
Here are some of the ways we’re using machine learning at Vertical Leap:
- Identify new search ops: Find out how we use Apollo Insights to identify hidden search marketing opportunities.
- Buyer intent models: Identify users with the highest purchase intent and make them our priority to maximise ROI.
- Selling more products: We helped Sarah Raven discover where to spend her marketing budget, resulting in 194% more sales.