Marketing Analytics Conference Recap
This week, we attended the Marketing Analytics Conference, hosted by the DMA. The event was 2 days packed with great thought leadership and real-life examples how organizations are using analytics as the base for successful strategies. It also provided a setting for executives across many industries to learn from each other and share best practices.
We were very excited to not only participate but also to lead some of the discussion. Our SVP of Product Management, John Funge, led an outstanding a panel on “The Promise of Personalization – Two Approaches in Conversation”. John was joined on stage by Abby Lee, VP, Marketing & Media Strategies, at Re/Max and Justin Boggs, Senior Director, Marketing Analytics at Sephora. They discussed a wide variety of topics about how they address personalization, from the basics such as how they define it to more specific methods they are using to customize their conversations to customers and prospects. It was a fascinating look at two different industries and their challenges of using data to enable better 1-to-1 marketing.
Personalization was just one of the many great themes at the event. The agenda was jam-packed with topics such as data-driven processes and technology, how to reach buyers, developing a data-driven culture, AI, attribution, automation, and more. Like most of the participants at the event, we came away from the session with a ton of ideas to implement in our own businesses when we get back to the office.
After plenty of discussions about marketing analytics and how it is integral for success in the industry, it’s more clear that in order to succeed, you need a good foundation of the right data. That’s where we come in. We can help your organization enhance your own internal 1st party data with more information about the people you already know and supplement that with additional prospects who you don’t. Without the right data, it’s impossible to implement any analytics to drive success. You wouldn’t jump straight to building your house without first pouring the foundation. Likewise, one shouldn’t start trying to implement a sophisticated data-driven program without ensuring you have the right data in place.