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SUMMARY:Seminar by Prof. Anindya Ghose\, New York University
DTSTART:20160527T120000
DTEND:20160527T133000
DTSTAMP:20260427T202858Z
UID:0e7761e4f6de131887b4fc32557591f6a3711b146c70c5a18a1f5fb0
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Anindya Ghose\, New York University\nMeasuring The Effec
 tiveness of Mobile Marketing: Evidence From Field ExperimentsAbstract:\nTh
 e explosive growth of smartphones and location-based services (LBS) has co
 ntributed to the rise of mobile advertising.  In this talk\, we will pres
 ent results from multiple studies that are designed to measure the effecti
 veness of mobile marketing promotions.  In the first field study where we
  exploit a quasi-natural experiment we examine the role of contextual crow
 dedness on the redemption rates of mobile coupons.  We find that people b
 ecome increasingly engaged with their mobile devices as their context get 
 more crowded\, and in turn become more likely to respond to targeted mobil
 e messages.  These studies causally show that mobile advertisements based
  on real-time static geographical location and contextual information can 
 significantly increase consumers’ likelihood of redeeming a geo-targeted
  mobile coupon.  However\, beyond the location and contextual information
 \, the overall mobile trajectory of each individual consumer can provide e
 ven richer information about consumer preferences. In a second study\, we 
 propose a new mobile advertising strategy that leverages full information 
 on consumers’ offline moving trajectories.  To examine the effectivenes
 s of this new mobile trajectory-based advertising strategy\, we designed a
  large-scale randomized field experiment in one of the largest shopping ma
 lls in the world. Using machine learning techniques\, we find that mobile 
 trajectory-based advertising can lead to highest redemption probability\, 
 fastest redemption behavior\, and highest satisfaction rate from customers
  at the focal advertising store.  Our studies help firms better understan
 d the question of which kinds of mobile advertising are most effective and
  how machine learning techniques can be combined with statistical models a
 nd field experiments to offer the right product to the right audience at t
 he right time on the right channel.Bio\nAnindya Ghose (Ph.D.\, Carnegie Me
 llon) is a professor of IT and a professor of Marketing at NYU's Leonard N
 . Stern School of Business\, and the Director of the Center for Business A
 nalytics at Stern. He is a NEC Faculty Fellow. In 2014\, he was selected b
 y Poets & Quants as one of the “Top 40 Business School Professors Under 
 40 in the World” and by Analytics Week as one the "Top 200 Thought Leade
 rs in Big Data and Business Analytics". He is also the youngest ever recei
 pient of the INFORMS ISS Distinguished Fellow award. His work has been pub
 lished in leading journals in Marketing\, Economics\, IS\, and Computer Sc
 ience. He has received 14 best paper awards. He is a Marketing Science Ins
 titute Young Scholar and a National Science Foundation CAREER award winner
 . He has published more than 80 papers in premier scientific journals and 
 peer reviewed conferences\, and has given more than 200 talks internationa
 lly. He is a frequent keynote speaker in executive gatherings and thought 
 leading events globally. He has been awarded 14 grants from Google\, Micro
 soft\, Adobe and several other corporations. He teaches courses on digital
  marketing and business analytics at the undergraduate\, MBA\, EMBA\, MSBA
 \, and Executive Education level in various parts of the world including t
 he US\, India\, China\, Europe\, and South Korea. He serves as a Senior Ed
 itor in ISR and an Associate Editor in Management Science. He has consulte
 d in various capacities for Berkeley Corporation\, CBS\, Dataxu\, Facebook
 \, NBC Universal\, OneVest\, Samsung\, Showtime\, and 3TI World\, and coll
 aborated with Adobe\, Alibaba\, China Mobile\, Google\, IBM\, Indiegogo\, 
 Microsoft\, Recobell\, Travelocity and many other leading Fortune 500 firm
 s on realizing business value from IT investments\, internet marketing\, b
 usiness analytics\, mobile marketing\, digital analytics\, social media\, 
 and other areas. He serves as Chief Data Scientist of 3TI World\, Scientif
 ic Advisor to OneVest and a Scientific Expert with Cornerstone Research on
  litigation consulting. His research has been profiled numerous times in t
 he BBC\, Bloomberg TV\, CNBC\, China Daily\, The Economist\, Financial Tim
 es\, Fox News\, Forbes\, Knowledge@Wharton\, Korean Broadcasting News Comp
 any\, Los Angeles Times\, Marketplace Radio\, MSNBC\, National Public Radi
 o\, NBC\, Newsweek\, New York Times\, New York Daily\, NHK Japan Broadcast
 ing\, Reuters\, Time Magazine\, Washington Post\, and the Wall Street Jour
 nal. His biggest passion is high altitude mountaineering and in his spare 
 time he climbs in the Andes\, Himalayas\, Alps\, and other mountains acros
 s the world.
LOCATION:EPFL\, ODY 4.03\, VIP Room http://plan.epfl.ch/?zoom=19&recenter_
 y=5863800.12869&recenter_x=731560.22521&layerNodes=fonds\,batiments\,label
 s\,information\,parkings_publics\,arrets_metro\,transports_publics&floor=4
 &q=ODY_4.03
STATUS:CONFIRMED
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