Machine learning is on the verge of transforming the marketing sector. In many ways, it’s already started. According to Gartner, 30% of companies will use machine learning in one part of their sales process by 2020.
What’s more, these companies are using machine learning to get ahead of competitors by tackling some of marketing’s toughest challenges, such as personalization, instant customer support, and big data.
In other words, machine learning isn’t just for computer scientists. Marketers should sit up and take notice. Below, I’ve covered five ways you can use machine learning to supercharge your digital marketing efforts.
What Is Machine Learning?
Before we get into the marketing side of things, let’s take a second to establish what artificial intelligence and machine learning are.
Artificial intelligence is simply any form of intelligence demonstrated by a machine instead of the natural intelligence displayed in humans and animals. When most people think of artificial intelligence, they think specifically of computers that replicate some level of human intelligence, like a chess-playing computer I mentioned in the introduction.
Machine learning is a branch of artificial intelligence that enables systems to find new and better solutions automatically by learning from mistakes and experiences. The more data and experience an algorithm has access to, the better it becomes in the future.
Machine learning systems can largely be divided into two subsets: guided and unguided. Guided systems are supplied with data sets and solutions by humans in the first instance. They are taught which patterns to look for initially and will then get better at identifying those patterns going forward.
Unguided systems are given access to unsorted and disparate data sets and are left to decipher patterns independently without guidance from humans. Unguided systems will create an algorithm and then look for ways to improve it going forward.
Using Machine Learning to Improve Your Marketing
We know that marketing teams don’t want for lack of data. Marketers struggle with making sense of all the data they have at their fingertips and then putting that data to use. This analysis is where machine learning comes in.
The primary reason to add machine learning to your marketing stack is that it can make sense of vast amounts of data much faster and much more effectively than humans.
This process can use data to identify patterns and make predictions almost instantly. Marketers can then use these insights to optimize a huge portion of their workflow, from running more tests and improving their website’s UX to personalizing the customer experience and automating consumer engagement.
The long and short of it is machine learning can be used to improve just about every part of your digital marketing efforts. Below we discuss five of the most important ways.
Analyze Data Sets
However you use machine learning in your marketing efforts, the process will probably begin by analyzing data sets.
For instance, machine learning can be used to analyze and find user activity patterns on your website. Rather than sifting through data in your Google Analytics profile yourself, an algorithm could do the job in seconds, predicting future user behavior and identifying patterns that you can use to optimize your site.
Sure, humans are perfectly capable of analyzing data themselves, but you can’t do it half as fast or accurately as AI-powered solutions.
Marketers can also use machine learning to gain a better understanding of their customer base.
Take customer segmentation, for instance. Dividing up your audience into different groups can make your marketing efforts much more effective, but it’s time-consuming to do so yourself. On the other hand, a machine learning algorithm could automatically segment your customer base based on actions and behavioral patterns that you couldn’t hope to identify.
Create and Optimize Content
You don’t need me to reiterate the importance of content in your digital marketing efforts. However, you may need clarification on how machine learning can improve what you write and publish and why using it in your content marketing strategy is essential.
For starters, machine learning can help your articles rank higher in search engine results. It’s one thing to be a great writer; it’s another to write in a way that pleases Google, so it rewards you in the SERPs. You need to make sure you use all relevant keywords, discuss every relevant topic, and cover all of your bases in general.
That’s pretty hard to do without smart content creation tools like Frase.io, which uses machine learning to compare your content against Google’s top results and make sure you hit all of the relevant points.
Second, you use algorithms to write content for you. Phrasee is an AI-powered copywriting tool that uses machine learning to create email subject lines and push notifications that its algorithm believes will drive the highest ROI.
You can even use AI to help you curate content for your customers. Curata offers a machine learning content curation software that helps marketers find and publish the most relevant and engaging content for their audiences.
Personalization matters for consumers. Research by Accenture shows 91% of consumers prefer brands that remember who they are and provide relevant offers and recommendations as a result. What’s more, if they don’t get a personalized experience, over half of consumers are only too happy to switch to a competitor.
Here’s the good news: machine learning lets you deliver the most personalized customer experience possible. You can employ an algorithm that tracks user behavior on a granular level, learns what products they like, and creates a personalized homepage and recommendation list as a result.
Amazon, for instance, uses AI algorithms that take into account the purchase history of users, the items in their cart, and their viewing habits to offer the product recommendations that are most likely to convert.
The same algorithm could also generate personalized offers for every customer and email them to consumers when they are most likely to purchase.
Improve Marketing Automation
Better personalization is one way machine learning can transform how your brand engages customers, but it’s not the only way. It can also better automate your marketing efforts and significantly improve customer engagement as a result.
Let’s say you automatically send an email to customers when they sign up for your newsletter or abandon their cart. While most brands will send a generic email, companies that adopt machine learning can tailor content and offers based on that consumer’s browsing history.
If they looked at your brand’s range of dog toys before signing up for your newsletter, a relevant offer on chew toys would make them much more likely to re-engage with your brand.
For SaaS brands, AI-powered marketing automation tools can analyze much larger and disparate data sets to better segment leads. This allows sales reps to prioritize those leads that are much more likely to convert.
Marketing automation is incredibly powerful. According to Invesp, marketing automation leads to an over 14% increase in sales productivity and an over 12% reduction in marketing overhead.
It’s entirely possible to do this without machine learning, but AI makes your automation efforts much more personalized and much more powerful.
Chatbots are a powerful customer service tool. Eight out of ten consumers who have engaged with them report a positive experience. If you run an online business, they are all but essential.
With chatbots, you don’t have to have a human on-hand to respond to consumers. Instead, machine learning-powered chatbots can automatically answer consumer queries with a scarily-high rate of accuracy. That’s because your chatbot will learn from your website’s content and the conversations it has with consumers to constantly improve the answers it provides.
Because the chatbot is continuously learning and improving itself, it will deliver an even better customer experience with more conversations. You may want to have your chatbot pass an incredibly complicated query onto a human at first, but soon the bot will become so effective there’ll be no need for a human to interject. Eventually, you’ll have a smart enough chatbot to upsell the consumer, not just answer their questions.
Consumers probably won’t be able to tell they’re speaking to a robot, either. Some chatbots, like IntelliTicks, employ another branch of AI, Natural Language Processing (NLP), to have human-level conversations with customers.
What’s more, data gathered by AI-powered chatbots can be analyzed by another machine learning algorithm to generate insights that marketers can use to optimize their efforts going forward.
What is the Future of Machine Learning?
Things move fast in the world of machine learning. Expect advances in marketing AI to happen rapidly.
Improved algorithms are in development right now, for instance. These algorithms don’t need input from humans at the start, making them much easier and faster for marketers to implement.
Personalization will become even more powerful, too. Machine learning algorithms will become better at discerning what consumers want for one, but the ways they can be integrated with online stores will improve, too. Soon, marketers will be able to customize every part of their sites for individual users, much like social media timelines are personalized for every user.
Finally, expect big advances in mobile machine learning. AI-powered digital assistants will become a more prominent part of our life, and marketers will have to develop strategies to contend with this. Mobile applications will also be able to integrate machine learning features in the same way websites can right now.
Don’t get overwhelmed, however. Before you start worrying about what the future holds, work your way through the suggestions I’ve made above first. You’ll then be ready for whatever occurs in the future.
It’s clear: machine learning can transform your digital marketing efforts.
Don’t rush into it, however. Adopting solutions without first understanding how the technology works and its role in your company will typically do more harm than good.
Machine learning is powerful, but it isn’t a silver bullet. Adopt one solution at a time, however, and you’ll be fine.
Which machine learning strategy are you going to implement first?
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