These roundtable sessions are designed with you in mind - providing a great opportunity for experts to share how to create business impact with data and analytics. Working in groups of 10 participants, attendees will get advice on how to overcome common issues and share struggles and lessons learned.
#1 What happens when a marketing team turns data-driven - Jessica Hose, Director, Marketing Analytics, Thrivent Financial
When your distribution channel is primarily face-to-face, how important is data-driven marketing, when the sale happens offline? A marketing team turned data-driven and saw outstanding results, showing that the work to change the culture, people, and processes will produce the results we all hear should happen. And all this happened in just 3 years.
#2 The Implicit and Explicit Effectiveness of Advertising on Twitter - Michelle Grushko, Twitter & Jeff Zifrony, Twitter
Using Conjoint analysis, we aimed to understand the influence of exposure to insurance advertisements on Twitter. Typically, insurance advertisers rely on self-reported purchase intent measures though brand surveys to understand the effectiveness of their advertisements. Low success rates on this metric led us to use conjoint analysis where behavioral choices led users to make a decision on willingness to pay for an insurance premium. This provided insight into how important a brand is when deciding on which policy to choose. Based on exposure alone, seeing an advertisement on Twitter led to a $7 increase in willingness to pay.
#3 Overcoming Today's Biggest Data Governance Challenges - Joe Christopher, Vice President, Analytics, Blast Analytics & Marketing
Data Governance is imperative to bring order to the chaos, and ensure your organization is making data-driven decisions that will create a competitive advantage for your organization.The good news is that customer acquisition teams, product owners, customer support, and others are increasingly becoming data-driven in order to better understand their users. They are starting to ask more complex questions and expect the relevant data to be available to them. The bad news is that too many self-service analytics consumers are using bad data or misinterpreting the data.