Machine learning (ML) is used in marketing to accurately analyze large amounts of data and provide key information about a brand’s customer base, the success of their current marketing strategies, and how best to optimize them to both gain and retain clients.

ML is a type of artificial intelligence that uses algorithms to analyze large sets of data. This can sound quite overwhelming when starting off and you may assume it’s something only computer scientists need to deal with.

But in actual fact, machine learning is used everyday by the majority of people and makes life a lot simpler (think automated chatbots, social media algorithms, and home devices such as Amazon Alexa, etc.).

Why should you be using machine learning?

In the past decade, the marketing industry has been dominated by machine learning. Machine learning has proven highly beneficial to big companies such as Amazon, Google and Facebook. 61% of marketers say the use of artificial intelligence is the most important part of their data strategy.

Basically, ML provides you with an accurate and precise analysis of your data within minutes. No marketer wants to spend endless hours analyzing data trying to identify key patterns and what campaigns are working best for their brand. ML will do all the messy data analysis for you, so you can focus your time and energy on the tasks that you actually want to do!

This article will cover the main applications of machine learning in marketing that will save you time and ultimately optimize your marketing strategy.

How AI will take your B2B marketing to the next level
In marketing, AI can be used for a variety of tasks that are fairly time-consuming for a human, such as data analysis or research. The big brains at Forbes have said it’s going to change the face of B2B marketing forever and transform digital marketing. So let’s find out why!

Predicting customer lifetime value

Customer lifetime value (LTV) is essentially looking at the worth of your customer base and identifying which customers are going to stay with you the longest.

Usually, LTV is calculated after the customer has left by looking at the total profit you have gained from the customer and the amount of time they have been with you.

If you use ML you can predict LTV before the customer leaves you.  The right type of algorithm can analyse the behavior of each one of your customers to give an accurate prediction of their lifetime value. This information allows you to direct your marketing and advertising campaigns to the customers who are likely to stay with you for longer.

Improving customer service

ML is a highly intelligent application that can greatly enhance the service provided to your customers. 75% of businesses that use artificial intelligence and machine learning have seen an increase in customer satisfaction by more than 10%, which is pretty great!

Chatbots are a key application of ML for improving customer service. They provide instant answers 24/7 and have the ability to pass the customer over to a human operator if needed. You can also use chatbots for outbound marketing by sending automated messages to customers once they’ve purchased recommending products that they might be interested in.

Predictive targeting

ML can be used to ensure your budget is being spent on the right people: the people who are highly likely to purchase your product/service.

ML uses an algorithm to create an ideal customer profile, based on the behaviors of your current customers such as their interactions with your website and the type of content they have engaged with the most, as well as the demographics of their business such as company size and industry. This allows you to identify a target audience that has a high probability of buying your product/service. You can then target your ads to this specific audience increasing both your ROI and revenue.

Lead scoring

Wasting time and energy on leads that are never going to be converted into a sale is something that we all hate doing. Luckily by using ML, you can reduce the chances of this happening.

Propensity models are great for scoring leads based on the probability of them making a purchase. Without ML, these models are often developed by importing data into spreadsheets and then analyzing it, which is time-consuming and often very ineffective.

ML can generate these propensity models for you within minutes and give you accurate results about the probability of each lead buying a product/service you offer. This means your sales team can focus their time on the leads with the highest probability of being converted into an actual sale!

It’s important to note that the more data you have, the more effective and accurate the algorithm becomes. It’s recommended to have at least 300 customers that have already brought from you to have enough data to generate the most accurate probabilities of leads.

Predicting churn

Predicting when and why customers are going to leave your brand will allow you to reach out and offer a solution to their problem or an incentive to stay, like discounts, reducing the chance of churn.

However, an inaccurate prediction can send you on the dangerous path of giving the wrong customers discounts, meaning you will not only fail to retain the correct customers, but also lose revenue.

ML can reduce the chance of this by providing you with an accurate and well-analyzed model that looks at customer behavior to work out which customers are most likely to churn. You can also identify what factors are causing the customers to leave allowing you to adjust your product/service accordingly to keep your churn rate low.

Personalization

Personalizing your content to meet the individual needs of each customer is a vital part of your marketing strategy. It can give you the upper hand when a customer is deciding to buy from your brand or not. Research shows that more than half of customers will decide to go with a competitor if personalization isn’t part of their journey with a brand!

Machine learning can easily personalize your content for each of your customers. It can send personalized emails, create a personalized homepage, and generate offers that are relevant to your customers’ needs based on their behaviors and past purchases.

Product recommendations

Recommending products/services through ads is more effective than just waiting for customers to find you organically. In fact, 91% of smartphone users who bought or who are planning to purchase a product have said that it was after seeing an ad that was relevant to them.

Tools such as TensorFlow and Google Cloud use ML to create recommendation algorithms based on customer behaviors. They can also recommend to you what type of products the market is currently demanding so you can tailor your current and future products/services to meet that specific demand.

Marketing automation

Marketing automation should be incorporated into every brand’s strategy. There is a 451% higher rate of qualified leads in brands that use marketing automation tools to deal with customer experience than brands that don’t!

And yet again, machine learning is the gold dust that will enable you to easily and effectively automate all of your marketing campaigns from Google ads to weekly email newsletters to discount systems.

Something like SALESmanago Copernicus is a great tool to use to automate your marketing. It will analyze your customer data and automate your marketing to provide personalized content tailored to the specific needs of your customers.

B2B marketing automation
There’s a lot of moving parts to B2B marketing, all of which you’ve got to keep track of. Many of these are fiddly processes that are as time-consuming as they’re dull, especially when you’d much rather be doing something more productive/creative/generally life-affirming.

Create and optimize your content

Do you struggle to come up with content for your brand? Not sure which type of content is working best for attracting new customers and retaining current ones?

ML can be used to research and identify which types of content are performing well at the moment for you and can also recommend content that is working well for your competitors. You can also use ML to carry out A/B testing, meaning you can try out various types of content and see which one is working the best by looking at the resultant data.

ML tools like Phrase and Frase.io are great for content creation and optimization. Phrase can write engaging content for email campaigns and push notifications and Frase.io can help with your SEO strategy. It uses ML to analyze your current content and tell you exactly what you need to do in order to rank higher on search engine results pages.

You can also curate your content using ML. Curata and Vestorly are two really useful tools that will allow you to do this. They will send the right type of content to the right type of person at the right time meaning the probability of the customer purchasing is high helping you to increase your ROI.

Final thoughts

Gaining instant data that is accurately analyzed using machine learning will allow you to better optimize your marketing efforts and deliver a more personalized experience to your clients.

Machine learning has already revolutionized the marketing industry and will continue to do so as it becomes more and more advanced. This means by using machine learning now, you will also be investing in the future of your business too.
How have you been applying machine learning to your marketing? Got questions on how to do it? Share them with the B2B Marketing Alliance Community!