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Machine learning in marketing

Machine learning models the data and enables users to make decisions based on the data, helping to create more insightful user interfaces. There are multiple ways machine learning is revolutionizing business data science. Newer ML is replacing human labor and basic algorithms (like decision trees) in machine learning-based data science applications, helping to analyze and understand complex patterns and determining the optimal use of resources. That allows you to process data in a way that enables you to evaluate the overall quality of the data and discover marketing trends, as well as to automate processes. Think about it; how many tasks does a human being in marketing carry out everyday? What are the different types of machine learning and how do they help companies improve their performance?

At our institute, we do first class research, development and experimentation in machine learning, together with international researchers and practitioners, for which we list examples below. Keep reading if you are interested.

NLP based machine learning in marketing

The algorithms for evaluating marketability, including those for brands, are deep and have been for a very long time. They are built using NLP and ML techniques, which are based on machine learning and artificial intelligence. Using machine learning, marketers can use data to help categorize the huge variety of factors that can impact a brand's marketing potential; from content to messaging to social media. The best marketers are the ones who can use machine learning to categorize and prioritize the huge variety of marketing opportunities. With data at hand, the best marketers can create hypotheses about the marketing value created by each post, and test them against the data to make sure they are on the same page. This way, the best marketers can answer customer questions and get direct feedback to make sure their marketing strategy is working.

Think about it; how many marketing projects have you launched with a big budget and only managed to raise a small amount of that total from qualified prospects? Huge types of marketing achievements are rare because the software built to analyze and prioritize the massive variety of marketing messages and metrics is only just coming into its own. Machine learning is the only way that marketers can bring together the data about the marketing performance of brands and use it to help categorize and prioritize the marketing messages that are relevant to a prospect. Through experimentation, the more data that is brought together, the more accurate, and up-to-date the insights will be.

How ML funnel based attribution works

Machine learning is being used in a number of different industries to help categorize and prioritize the huge variety of marketing messages and metrics. One of the most prominent of these is direct marketing, using marketing automation tools used to send email to prospects. These tools use a machine learning approach to categorize and prioritize the messages that a mailserver can send to prospects, based on previous activity. The machine learning models used in these tools allow the tool to learn things about prospects, based on past interactions, rather than having to remember every single thing that happened between them and generate hypotheses based on it. This way, machine learning is able to learn on the go and find patterns and correlations that go beyond the obvious lines and symbols.

For example, direct marketing automation uses machine learning powered tools that categorize and rank marketing messages into broad categories like offers, demos, tools, resources, tips, and tricks. These tools are used by marketers all over the world to send marketing automation emails to prospects. These tools allow to personalize the email with a subject line and text that best describes the capabilities of each product and service. The tool also lets you make personalized offers to channel groups, prospects or customers. Using machine learning, direct marketing can find meaningful connections between marketing messages and actions taken by the user, helping marketers create more meaningful personalized offers.

The machine learning approach to marketing automation tools is being adopted by many marketing departments to analyze the marketing performance of nimbler marketing systems and come up with actionable marketing ideas. The machine learning approach to actionable marketing systems is a direct result of the machine learning used in direct marketing tools. The machine learning models in these tools allow marketers to be able to see into the minds of potential customers and customers of the same brand, and learn from interactions to figure out what action to take next.

We develop the underlying software models that underlie up-to-date machine learning-based products and services and test machine learning applications (e.g., decision trees / tree search) in use right now.

Talk to us about how machine learning can boost your product marketing or to find out which marketing automation products, tools and services are currently being used by practitioners.

Leave your email, or submit the contact form via your web browser to get notified of new research articles, the latest algorithms, or to engage in a research collaboration. We will contact you as soon as your submission has been received, helping you find the best marketing automation products, resources and tools to help to process data in a way that allows you for example to evaluate the performance of your marketing, to use machine learning to learn users and prospects’ behavior based on online logs of what they say and do, and to make better decisions.