How Machine Learning Can Help Marketers

August 4, 2021 Stan

The term “Machine Learning” was first used by Arthur Lee Samuel in 1959 and comes from the theory that computers can learn to perform specific jobs without being programmed to do so, thanks to the recognition of patterns in data. It is an offshoot of Artificial Intelligence that is based on the idea that systems can learn from data, identify models independently and make decisions with human intervention reduced to a minimum. With Marketing Automation’s fast paced highly complex requirements, this is a concept that is starting to get a lot of buzz.

While already highly prevalent in TV/internet subscriptions and social media ads (did you ever notice how when you log into your cable or internet channel it creates content for shows you might like?), this concept is starting to get used more and more in cloud applications. You can image how the idea of future suggestions and automation based on past preferences and learnings could result in huge time & cost savings across the entire marketing spectrum. Deploying these tools can further automate the tasks a Database Administrator or Business Intelligence Analyst would typically do.

Machine Learning is powered by historical data, and generally in marketing we have plenty of that stored, so this is a great way to take advantage of additional benefits to power your marketing to the next level. Deploying AI against your data lake is a great place to start. Benefits of AI include not only cost savings, but often a reduction in risk of human prone errors and a creation of a repeatable predictable process. Additionally, Marketers can use AI to enhance the customer’s journey by tailoring experiences to consumers on a massive scale. Using Artificial Intelligence tactics where automation can make educated guesses at mapping the right content to audiences based on historical patterns, really increases the odds of you being able to better target your communications.

If your company decides to invest in Machine Learning tools, make sure they prioritize the areas where it’s needed, test scenarios, and get executive buy-in early in the process.

Some use cases for Marketers to take advantage of ML are:

Product/content personalization
Email Orchestration & Scheduling (think Send Time Optimization and Fatigue Analysis)
Optimizing email content
Visual/on-line search ads served up (SEO & Programmatic advertising)
Predictive analytics
Next best action decisioning

In conclusion, Machine Learning helps to automate & optimize repetitive tasks and ensure predictable outcomes. Remember that an important factor in launching any new tool is the ability to measure it. It will likely be a worthwhile exercise to measure the success of pre and post implementation and use of AI tools. You’ll want meaningful data to determine if you are getting the right lift from your customizations or need to make tweaks to enhance its success.

We encourage you to give this concept a try. If you’re looking for additional ideas around deploying Machine Learning tools, Relationship One is always here to help.

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