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My name’s Emily Bollman. I’m the digital marketing manager at SnapAV in Charlotte, North Carolina. We are a B-to-B home and audio-visual manufacturer and distributor. We are currently the Goliath in our industry, so it is hard for us to find new customers, so the way that we grow is to increase the spend of our current customers and get them buying more of our products that they don’t already buy. Personalization is the foundation for our company. We try and make every touch, every experience with our dealer very personalized. They have certain buying behaviors and they are creatures of habit, so we have to know what those are so that we can take care of them properly. The way we use Dynamic Yield to get our customers to buy products they don’t already buy is that we use our knowledge of our customer base. We look at a product that would work well with another. We are a little different at SnapAV. When we built audiences inside of Dynamic Yield, we didn’t go with the recommended 20 or 30. We actually made hundreds and hundreds of audiences to the tune of around 380. We use those audiences to understand their purchase behavior. One of the ways that we use Dynamic Yield successfully is in our product recommendations. It’s great for us. We used to do it manually, and Dynamic Yield allowed us to have all of our product pages populated with recommendations. They update when a product goes legacy. We don’t have to manually go and take something out. The operational savings that it gave our company was huge. With Dynamic Yield, our recommended products click-through rate went up about 40-60% given the day, so that was our manual strategy where we thought we were being as smart as we could by recommending a product, but when we let machine learning make the decisions for us, the engagement soared. I would recommend Dynamic Yield because it is intuitive to use, it’s simple to hook up with the platform that you’re using, and the support is outstanding.
Emily Bollman, Digital Marketing Manager at SnapAV chats about how they makes every interaction as personal as possible by analyzing and recommending items based on the buying behaviors of current customers.