If you’re a marketer, you probably aren’t too concerned about what’s “under the hood” of the martech tools you’ve adopted. You want it to be easy to use, and able to drive the results you expect – whether its creating and deploying offers, increasing engagement or improving sales.
Today, though, artificial intelligence (AI) and machine learning (ML) are all the rage, and everyone is touting how these technologies make their solution a game changer.
But do you truly know what AI and ML is? Or are you being caught up in the buzz about what you should have, even if you’re not sure what it is and how it works?
Not sure? We’ve put together this primer to help you gain a basic understanding of AI and ML, how they work together, and why it’s important to distinguish between the two when you’re evaluating new marketing technologies.
AI is sometimes referred to as machine intelligence, since it can be loosely understood as incorporating human intelligence into machines. Machines are considered “intelligent” if they can complete tasks based on a set of problem-solving rules, or algorithms. As a result, artificial intelligence is a very broad concept that can range from “Good Old-Fashioned AI” to futuristic technologies like deep learning.
Many marketing platforms claim to use AI. But in reality, what AI actually is to them can vary greatly – from a simple algorithm, search capability or chatbot to something more sophisticated, complex and powerful.
Machine learning is a subset of, and a technique for realizing, AI. With ML, a computer is empowered with the ability to learn through a method of training algorithms to make decisions. This training requires massive amounts of data, in which the algorithm can keep learning more and more about the information it processes.
ML is an absolute necessity for brands interested in designing highly personalized offerings. ML enables processing of vast amounts of data, from info collected by the data science and IT teams, to sales, customer service and customer loyalty sources. So, instead of programming a specific set of instructions, the software is “trained” as it takes in all this information, and continues to “learn” as more data is added. This is ideal for marketing applications, in which the machine can evaluate customers interactions in real time, and enable marketers gain better insights into each individual so they can tweak offers to get better results.
These unique capabilities make ML essential if you’re trying to go beyond segmentation and microsegmentation marketing to achieve true 1:1 personalization. ML-enabled solutions can help you deliver the right offer to each customer, at just the right moment.
This kind of personalization is often a big challenge for marketers because offer creation has long been a manual process that cannot scale to address hundreds of thousands, or even millions, of customers. ML assists in automating the process so that 1:1 offers can be achieved, even at some of the largest brands.
Integrating ML into your marketing toolbox can help you automate processes, such as identifying relationships between disparate data. Then, as more data is gathered with every customer interaction, ML gets smarter, enabling you to quickly test and analyze alternatives, and fine-tune offers to deliver the most impactful customer journey to each person.
If you’d like to learn more about AI and ML in marketing, and how they can help you deliver effective personalized offers more quickly, download our new white paper, “Bridging the Gap Between Data Science and Marketing: How to Achieve Personalization at Scale with AI and ML.”