Managing Director, Marketing Channels Strategy, and Personalization at JPMorgan Chase: Erin McFarland - Growth Comes When You Focus on the Customer, Data, and Experimentation

Erin McFarland is the Managing Director, Marketing Channels Strategy, and Personalization at JPMorgan Chase, the largest bank in the U.S. She is responsible for the optimization of JPMorgan’s industry-leading digital assets and marketing channels that deliver communication to more than seventy million customers. She has spent the last twenty years creating customer-first marketing strategies for national and global brands across healthcare, luxury, hospitality, and finance industries. Erin suggests diving deep into data because data is how customers talk to you, and it helps guide customer acquisition channels. Think of your company as a “caregiver” to your customers and build fault tolerance and stress testing into your technology. This way, you’re ready when they need you. To make life easy for your customers on mobile, automate recurring actions. Create seamless identity management across devices because privacy is a major concern of the modern customer. Cardless transactions are also a big trend in the financial services industry. Erin explains how it’s okay to change careers but not to burn old bridges because networks will matter during such changes. Have a role model that you identify with but don’t look to them for anything beyond inspiration for your career. You have to steer your path based on what you want to change and who it’s for using your strengths. When executing org-wide changes, Erin suggests building for the customer journey, experiencing what it would look like, and figuring out internal dependencies to navigate. Test your ideas because ideas are only assumptions until they’re proven. Erin’s final advice: A leader asking for the opinion of their team members and sharing credit where it’s due validates the team's skills and helps them feel seen. Invite diverse opinions, be curious to learn, and have the guts to pivot when data doesn’t agree with your decision.