Presentation – Standing On The Bridge Between Marketing and Finance, April 28th
The East Bay Chapter of the Berkeley-Haas Alumni Network is privileged to host a presentation by Dr. David Cavander, the Principal Scientist for Digital Marketing at Adobe.
As an economist and expert in quant marketing, Dr. Cavander will give his insights on the following hot topics:
– How do you determine the optimal Return on Marketing Investment, using Quantitative Analysis?
– How do you determine the optimal mix between Owned, Paid and Earned Media, and how are they synergistic?
– What have we learned over the last 60+ years about Marketing?
– How do you make a brand more likable?
– How is machine learning changing the future of Digital Marketing?
If you want your company to be on the cutting edge of Marketing, this presentation will give you insights on how to get there.
Networking – if time permits, there will be networking activities prior to the presentation with additional opportunities following the presentations and Q&A
Date: Thursday, April 28, 2016
Time: 6:00 pm – 8:00 pm
Berkeley-Haas School of Business
Map of Haas School of Business: http://www.haas.berkeley.edu/haas/building/level1.html
Parking: New Maxwell Parking Garage, directly across Piedmont Ave., just north of the football stadium.
BART: Downtown Berkeley station, about a 15-minute walk to Haas.
Map of campus: http://www.berkeley.edu/map
Cost: $20 for alumni and guests, $10 for students
Light refreshments will be served.
Limited seating, so register early.
(Please note that registration fees are transferable, but non-refundable)
About the Presenter:
Dr. Cavander founded MarketShare Partners, an advertising econometrics firm in Los Angeles in 2005 as Chief Science Officer. He joined Adobe in Product Development in 2012. In ten years, he’s worked with over 300 brands globally. His career also spans executive roles with Eli Lilly, Mars Inc., Accenture and Best Buy.
For more info on David Cavander: Profile
Questions? Contact Dean Suzuki, firstname.lastname@example.org