As consumer expectations grow for more personalized, relevant, and assistive experiences, machine learning is becoming an invaluable tool to help meet those demands. It’s helping marketers create smarter customer segmentations, deliver more relevant creative campaigns, and measure performance more effectively. In fact, 85% of executives believe AI will allow their companies to obtain or sustain a competitive advantage.1
We created this guide to help you optimize your machine learning marketing efforts — whether you’re just starting out or you want to discover more benefits of machine learning.
A quick machine learning guide for marketers
At its core, machine learning is a way to quickly label and analyze huge data sets. People can do this on their own, but a machine helps do it faster and on an infinitely larger scale. In fact, 66% of marketing leaders agree automation and machine learning will enable their team to focus more on strategic marketing activities.2
But machines can’t learn on their own — they need the help of a human. Dive in to the interactive machine learning quiz below for a machine learning 101.
Getting started: It’s easier than you think
Why the first step is taking a step back
Now that you know what machine learning is, you might be asking yourself how to get started using it. We’ve seen many marketers dive head-first into building a machine learning program from the ground up. But that’s a tricky business. It requires a lot of upfront investment. And can take years to perfect.
Instead of jumping in too quickly, take a step back. Companies, including Google, are already doing the heavy lifting by integrating machine learning into existing and new marketing products, helping you gain deeper insights from your data without additional effort from your team. All you need to do is make sure your organization is set up to get the most value out of these products.
We’ve outlined three key considerations every marketer should make to prepare their organization for machine learning.
Define your machine learning marketing goal upfront.
Much like us, machines work best when they are given clearly defined goals. Your goal, or output, works as a framework. It helps a data scientist build your machine learning models and identify the right data to use when training your model. Make sure your goal is quantifiable, and measurable. Doing this upfront will help you define and measure the success of your model.
An algorithm is only as good as its data.
Here’s a golden rule to remember: a machine learning algorithm is only as good as the data it’s fed. So, to use machine learning effectively, you must have the right data for the problem you’re trying to solve. And not just a few data points. Machines need a lot of data to learn — think hundreds of thousands of data points. Your data will need to be formatted, cleaned, and organized for your algorithm, and you will need two datasets: one to train the model and one to evaluate its performance.
Assemble a diverse team with the right mindset.
Marketing teams can identify the best use cases for machine learning, but data scientists and analysts are critical when it comes to implementation. That’s why assembling a cross-functional team is essential to the success of any machine learning program. But to get the most out of machine learning at your organization, you need the right team and the right mindset. The latter requires a cultural shift that prioritizes and rewards experimentation, measurement, and testing throughout your organization.
How machine learning powers better marketing
A deep dive into key benefits and opportunities
There are countless ways that machine learning can help your business. Explore the marketing applications below to learn what products can help you optimize campaigns, and see how brands are already using machine learning to boost their marketing efforts.
Source: Think with Google