Over at Ddubs we’re working on a project that will give us a new capability to promote, but it’s a little complicated and it would be great to get your feedback here to let me know if you understand what I’m even talking about. 🙂
Comments and PMs welcome.
The Expensive Words Edition: We can use Unsupervised Machine Learning to perform Cluster Analysis whereby we can identify customer segments based on their behaviors and actions taken. We then layer over demographics to build a 360-degree image of each segment and create in-depth target market profiles that can be used for marketing and advertising purposes. We can then take those profiles and use them as the outputs in a Supervised Machine Learning experiment to build a model for identifying future members/customers after their initial purchase.
My First Attempt in Plain English: Customers buy things. When they do this they are giving you information about themselves. Sometimes, you can get other information from third-parties about your customers’ likes and activities because they can tie together purchases from credit cards. We can take the purchase info, the likes and habits, and any other actions you’ve tracked your clients doing and figure out what customers are most similar each other. Then, looking at those groups, we can look at their ages, incomes, and other things that they identify with and create a picture of what those different groups look like. You can then take those characteristics and use it for targeting in your advertising campaigns. And moving forward, any time you get a new customer, if you ask the right questions (which we help you identify) we can predict which group the customer belongs to so you can send them customized follow-up messages.
Is this easier? Are there still things that sound complicated and could use a little more explanation?
Thanks for the help!