8 Use Cases for Machine Learning in Marketing

Machine learning has made a number of industries more efficient and profitable (marketing included). AI in marketing enables marketers to understand their customers better so they can send them targeted, relevant offers at the right time. Additionally, they can analyse data faster to make better decisions (which saves time and money).

If you’re not already using machine learning in your marketing department, we’ve compiled a number of use cases to show you how AI in marketing can work for you.

What Is Machine Learning?

Machine learning refers to the ability of a computer to learn independently without the need for human intervention. It’s a branch of artificial intelligence (which means that computers can behave in a way that mimics human intelligence).


1. Gain a 360° View of the Customer

Every day, your customers share information about themselves, most of the time without knowing it. They tell you which device they’re using every time they go online, and if you’re paying attention, you can understand their previous history with a company.

Machine learning can assemble all of those disparate pieces of information about your customer and unify it for a 360° view of the customer. With a deep understanding of your customer, you can craft better marketing campaigns that will persuade that person to do business with your company.


2. Data Analysis

Analysing data isn’t as simple as sitting down and staring at a computer screen, then hoping connections jump out at you. You have to prepare the data first. It has to be structured properly, completely accurate, and formatted correctly.

Humans can make mistakes when they prepare data for analysis. A machine, on the other hand, doesn’t make mistakes – it pulls information from the system of record, ‘cleans’ it according to a predetermined set of rules to ensure accuracy, and makes sure it’s formatted correctly.

‘Machines prepare data for analysis quickly and properly.’

In addition, machines analyse more data than humans, and they do it faster, too. There’s only so much information one human can assimilate; that’s not the case for machines. AI in marketing also allows machines to analyse unstructured data, such as images, video, social media content, and audio.


3. Real-Time Analytics

Not only can machine learning analyse large amounts of data quickly, but it can also do so in real-time.

Let’s say you run an A/B test of an ad on Facebook. Machine learning can quickly tell you which campaign performed better, so you can immediately pull the one that isn’t doing as well and focus on the one that’s getting results.



4. Predictive Analytics

Being able to analyse data is the first step in better understanding your customers. What if you knew what customers would do next, though?

That’s where predictive analytics come in. Predictive analytics show the likelihood of a customer taking a particular action. Let’s say Matt purchases a software program for his office. The software vendor’s marketing department uses predictive analytics to forecast that next year he’ll buy a more comprehensive tech support package.

5. Predictive Engagement

Machine learning can also understand who a customer is, where that person is in the customer journey, and what steps a marketer can take to help that person progress to the next stage of the customer journey. When machine learning can tell you what those next steps are, that’s called ‘predictive engagement.’ Predictive engagement is more closely related to the field of personalisation than it is to analytics.

Here’s what predictive analytics looks like in action: you’ve just started looking for HR software. Since you’re at the very earliest stages of your journey, predictive engagement wouldn’t send you a testimonial because that’s not what you’re interested in. What would really help you would be a free demo, which a predictive engagement solution understands and serves up to you.

6. Personalisation at Scale

The idea of automating personalisation sounds like an oxymoron, but AI in marketing can make personalisation more efficient and more effective. Through machine learning, a computer discovers customers’ preferences and tailors offers accordingly.

We’ll illustrate with another example. Margo is a devoted customer of a local Thai restaurant. While she will sometimes try a new dish, Margo gravitates towards the shrimp pad thai. The restaurant’s AI-enabled marketing software picks up on her preference and sends her a coupon for the dish.

7. Personalising the Customer Experience

The term ‘customer experience’ refers to the experience a customer has with a brand. Every time a customer visits your site, that’s part of the customer experience. You want the interactions your customer has with your brand to be positive.

One way to create a more positive customer experience is to personalise it. Personalising the customer experience means that machine learning recognises a customer and remembers that person, adjusting the experiences to his or her preferences. For example, if a customer has already visited a company’s site, that person wouldn’t be prompted to sign up for the corporate newsletter (especially if he didn’t want to sign up for it in the first place).

8. Chatbots

Chatbots can be a powerful customer service tool, yet they also have a role to play for marketers. A chatbot gathers information about customers from every interaction, and it learns what the best answers are for questions.

Marketers can periodically analyse data that chatbots have gathered. That information can tell them what customers and prospects want to know more about, which helps them hone their marketing materials.

Enlighten Designs Helps You Implement AI in Marketing

For over two decades, Enlighten Designs has been delivering amazing digital experiences to its clients. Today, we leverage our expertise and skills to help marketers make better decisions with AI in marketing. To learn how AI in marketing can help you, contact us.


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