If CMOs aren’t already using Machine Learning (ML), most of them are either looking for a solution or are trying to learn more about it. The fear and uncertainty around AI and ML within marketing departments is beginning to recede as the applicaiton and benefits of ML become better understood.
But there’s still a need to level set expectations. Some inadvertently look at ML as a solution rather than a tool. There is no magical, instant solution.
For marketing, it’s all about making great personalization and customer experiences more achievable. Personalization is foundational to creating great customer experiences, because it helps create relevance through experiences that resonate. This happens by recognizing behaviors which we can then define as patterns. Enter ML.
Machine Learning recognizes patterns which could be patterns of buying behaviors, patterns of content that moves people to purchase, patterns that move people to renew subscriptions, and so forth. As data sets grow, Machine Learning can help marketing identify patterns the naked eye might miss or take a long time to find. Machine Learning might surface patterns you never suspected and thus never knew you were looking for.
There are a few simplified observations you should know about ML:
- You need a clear objective
- You need to have a plan for collecting enough high-quality data
- You need to analyze data
- It’s complicated to do from scratch…
This is where having a marketing tools with ML features is very useful.
Personally I’m excited by the potential of Sitecore Cortex. Here you have a very complete marketing technology stack which allows tight integrations with other platforms. xDB (Experience Database) along with xConnect allows you to collect data from endless sources. Once data from throughout this stack is analyzed by Sitecore Cortex, you have the ability to make fast incremental adjustments to campaigns and other personalization — and the ability to scale toward hyper-personalization or individualization. In one platform.
So should CMOs care about ML? Absolutely. Just don’t forget. No data = nothing to learn. And this hyper-analyzation of data still needs human activation to make it relevant.