John C. Hower Professor at The Wharton School
The Wharton School
Kartik Hosanagar is a Professor at The Wharton School of the University of Pennsylvania. Kartik's research work focuses on the digital economy, in particular Internet media, Internet marketing and ecommerce.
Kartik has been recognized as one of the world's top 40 business professors under 40. He has received several teaching awards including the MBA and Undergraduate Excellence in Teaching awards at the Wharton School. His research has received several awards including the best paper award at the Consortium on Technology Policy and Management. Kartik is a cofounder of Yodle Inc, a venturebacked firm that has been listed among the top 50 fastest growing private firms in the US. He has served on the advisory board of Milo Inc (acq by eBay) and is involved with other startups as either an investor or board member. His past consulting and executive education clients include Google, Nokia, American Express, Citi and others.
Kartik graduated at the top of his class with a Bachelors degree in Electronics and a Masters in Information Systems from Birla Institute of Technology and Sciences (BITS, Pilani), India, and he has an MPhil in Management Science and a PhD in Management Science and Information Systems from Carnegie Mellon University.
Enabling Technologies Course Weblog, Facebook page and Twitter Page
Jing Peng, Ashish Agarwal, Kartik Hosanagar, Raghuram Iyengar (Under Review), Network Overlap and Content Sharing on Social Media Platforms.
Abstract: Social media platforms allow users to connect and share content. The extent of information diffusion may depend on the characteristics of users’ connections, such as the overlap among users’ connections. We investigate the impact of network embeddedness (i.e., number of common followees, common followers, and common mutual followers between two users) on the information diffusion in directed networks. To accommodate the empirical observation that a user may receive the same information from several others, we propose a new hazard model that allows an event to have multiple causes. By analyzing the diffusion of sponsored ads on Digg and brandauthored tweets on Twitter, we find that the effect of embeddedness in directed networks varies across different types of “neighbors”. The number of common neighbors are not always conducive to information diffusion. Moreover, the effects of common followers and common mutual followers are negatively moderated by the novelty of information, which shows a boundary condition for previous finding on embeddedness in undirected networks. For marketing managers, these findings provide insights on how to target customers in a directed network at the micro level.
Panos Markopoulos and Kartik Hosanagar (Working), A Model of Product Design and Information Disclosure Investments.
Vibhanshu Abhishek, Kartik Hosanagar, Peter Fader (Working), The Long Road to Online Conversion: A Model of MultiChannel Attribution.
Ashish Agarwal, Kartik Hosanagar, Michael B. Smith (Under Revision), Sponsored Search: Do Organic Results Help or Hurt.
Young Jin Lee, Yong Tan, Kartik Hosanagar (Under Revision), Do I Follow My Friends or the Crowds? Examining Informational Cascades in Online Movie Reviews.
Soumya Sen, Roch A. Guerin, Kartik Hosanagar (Under Revision), Shared or Dedicated Infrastructure? On the Impact of Reprovisioning.
Nitin Bakshi, Kartik Hosanagar, Christophe Van den Bulte (Working), New Product Diffusion with Two Interacting Segments or Products.
Vibhanshu Abhishek, Kartik Hosanagar, Peter Fader (2015), Aggregation Bias in Sponsored Search Data: The Curse and The Cure, Marketing Science, 34, pp. 5977.
Abstract: There has been significant recent interest in studying consumer behavior in sponsored search advertising (SSA). Researchers have typically used daily data from search engines containing measures such as average bid, average ad position, total impressions, clicks and cost for each keyword in the advertiser's campaign. A variety of random utility models have been estimated using such data and the results have helped researchers explore the factors that drive consumer click and conversion propensities. However, virtually every analysis of this kind has ignored the intraday variation in ad position. We show that estimating random utility models on aggregated (daily) data without accounting for this variation will lead to systematically biased estimates specifically, the impact of ad position on clickthrough rate (CTR) is attenuated and the predicted CTR is higher than the actual CTR. We demonstrate the existence of the bias analytically and show the effect of the bias on the equilibrium of the SSA auction. Using a large dataset from a major search engine, we measure the magnitude of bias and quantify the losses suffered by the search engine and an advertiser using aggregate data. The search engine revenue loss can be as high as 11% due to aggregation bias. We also present a few data summarization techniques that can be used by search engines to reduce or eliminate the bias.
Kartik Hosanagar, Yong Tan, Peng Han (Working), Dynamic Referrals in PeertoPeer Media Distribution.
Vibhanshu Abhishek, Peter Fader, Kartik Hosanagar (Under Revision), Media Exposure through the Funnel: A Model of MultiStage Attribution.
Abstract: Consumers are exposed to advertisers across a number of channels. As such, a conversion or a sale may be the result of a series of ads that were displayed to the consumer. This raises the key question of attribution: which ads get credit for a conversion and how much credit does each of these ads get? This is one of the most important questions facing the advertising industry today. Although the issue is well documented, current solutions are often simplistic; for e.g., attributing the sale to the most recent ad exposure. In this paper, we address the problem of attribution by developing a Hidden Markov Model (HMM) of an individual consumer's behavior based on the concept of a conversion funnel. We apply the model to a unique dataset from the online campaign for the launch of a car. We observe that different ad formats, e.g. display and search ads, affect consumers differently based on their states in the decision process. Display ads usually have an early impact on the consumer, moving him from a disengaged state to an state in which he interacts with the campaign. On the other hand, search ads have a pronounced effect across all stages. Further, when the consumer interacts with these ads (e.g. by clicking on them), the likelihood of a conversion increases considerably. Finally, we show that attributing conversions based on the HMM provides fundamentally different insights into ad effectiveness relative to the commonly used approaches for attribution. Contrary to the common belief that display ads as are not useful, our results show that display ads affect early stages of the conversion process. Furthermore, we show that only a fraction of online conversions are driven by online ads.
OIDD314 ENABLING TECHNOLOGIES
Conducting business in a networked economy invariably involves interplay with technology. The purpose of this course is to improve understanding of technology (what it can or cannot enable) and the business drivers of technologyrelated decisions in firms. We will be discussing some of the new and most disruptive technologies right now to stimulate thought on new applications for commerce and new ventures, as well as their implications to the tech industry as a whole. Topics include social media, online advertising, big data, and cloud computing. ,The course will take a layered approach (from network infrastructure) to data infrastructure to applications infrastructure, or direct enablers of commerce) to first, understanding and then, thinking about technology enablers. Network infrastructure layers include fundamentals of wired and wireless infrastructure technologies such as protocols for networking, broadband technologies for last (DSL, Cable etc) and other miles (advances in optical networking) and digital cellular communications. Data infrastructure layers include usage tracking technologies, search technologies and data mining. Direct application layers include personalization technologies (CRM), design technologies for content and exchanges, software renting enablers, application service provision, agents and security mechanisms. Finally some emberging technology enablers (such as bluetooth, biometrics and virtual reality) are identified and discussed.
OIDD662 ENABLING TECHNOLOGIES
This course is about understanding emerging technology enablers with a goal of stimulating thinking on new applications for commerce. No prerequisite or technical background is assumed. The class is selfcontained (mainly lecturebased) and will culminate in a classdriven identification of novel businesses that exploit these enablers. ,No prerequisite or technical background is assumed. Students with little prior technical background can use the course to become more technologically informed. Those with moderate to advanced technical background may find the course a useful survey of emerging technologies. The course is recommended for students interested in careers in consulting, investement banking and venture capital in the tech sector.
“Goes above and beyond the call of duty” award recipient, 2010 One of ten faculty nominated by the MBA student body for the Helen Kardon Moss Anvil Award, 2010 MBA Excellence in Teaching Award, 2007 Wharton Class of 2009 “Goes above and beyond the call of duty” award recipient, 2007
To Pique Interest, StartUps Try a Digital Velvet Rope, The New York Times 07/17/2011 EBay bids for durability in changing digital world, USA Today 03/31/2008 Clicks are great but calls are better, Forbes 04/23/2007 An arduous path to green cards, Philadelphia Inquirer 04/08/2006 Can India conquer world with IT only?, Economics Times 11/22/2005
Knowledge @ Wharton
- Mobile Money in India: Does Digitalization Follow Demonetization?, Knowledge @ Wharton 05/30/2017
- Is Privacy Still a Big Deal Today?, Knowledge @ Wharton 05/25/2017
- Social Media Endorsements: Where Will Marketers Draw the Line?, Knowledge @ Wharton 05/23/2017
- Can Netflix and Amazon Disrupt India’s Streaming Video Market?, Knowledge @ Wharton 05/04/2017
- Funding Flipkart: Can India’s Internet ‘Unicorn’ Take on Amazon?, Knowledge @ Wharton 04/25/2017
- Has the ‘Dream Run’ for Indian IT Ended?, Knowledge @ Wharton 04/14/2017
- The Democratization of Machine Learning: What It Means for Tech Innovation, Knowledge @ Wharton 04/13/2017
- The Death of the Daily Deal, Knowledge @ Wharton 03/28/2017
- How Facebook’s Big Bet on Video Could Change TV, Knowledge @ Wharton 03/21/2017
- Why Innovation Is Key for India to Surge Ahead, Knowledge @ Wharton 01/13/2017
- How Will Demonetization Affect Business in India in 2017?, Knowledge @ Wharton 01/05/2017
- Walmart vs. Amazon: Is India the Next Battleground?, Knowledge @ Wharton 12/05/2016
- Can Snapchat Avoid Twitter’s Slowing Growth Trap?, Knowledge @ Wharton 10/21/2016
- Why Samsung Could Get Burned in the Android Market, Knowledge @ Wharton 10/18/2016
- Does Apple Need a New Blockbuster to Thrive?, Knowledge @ Wharton 09/13/2016
- Does More Trouble Lie Ahead for Indian Startups?, Knowledge @ Wharton 08/29/2016
- Can Verizon Unlock Yahoo’s ‘Hidden Value’?, Knowledge @ Wharton 07/26/2016
- How “Pokemon Go” Took Augmented Reality Mainstream, Knowledge @ Wharton 07/21/2016
- Why India’s Leading Fashion Etailer Abandoned Its Apponly Strategy, Knowledge @ Wharton 06/24/2016
- Why Millennials and China Are Key to Comcast’s DreamWorks Deal, Knowledge @ Wharton 05/04/2016
- Yodle: How a Startup Went from Birth to Buyout, Knowledge @ Wharton 04/06/2016
- Acting Legend Kamal Haasan Looks to the Future of Indian Movies, Knowledge @ Wharton 03/31/2016
- Leveraging the Internet of Things for Competitive Advantage, Knowledge @ Wharton 03/22/2016
- Can an Apponly Ecommerce Model Succeed in India?, Knowledge @ Wharton 03/16/2016
- How Facebook Lost Face in India, Knowledge @ Wharton 02/15/2016
- Netflix in India: Will It Be a Blockbuster?, Knowledge @ Wharton 01/22/2016
- How Can Barnes & Noble Avoid Borders’ Fate?, Knowledge @ Wharton 12/18/2015
- ‘Recommended for You’: How Well Does Personalized Marketing Work?, Knowledge @ Wharton 12/04/2015
- How Stockholm Became a ‘Unicorn Factory’, Knowledge @ Wharton 11/09/2015
- Why YouTube Red’s Launch Is a ‘Watershed Moment’ for Google, Knowledge @ Wharton 11/04/2015
- Will Twitter Remain in Facebook’s Shadow?, Knowledge @ Wharton 09/09/2015
- Digital Transformation: Learning to Take the Fight to Pureplay Rivals, Knowledge @ Wharton 09/08/2015
- Can Microsoft Gain a Mobile Edge — Finally?, Knowledge @ Wharton 08/18/2015
- Online Groceries in India: Will Consumers Bite?, Knowledge @ Wharton 05/07/2015
- A La Carte TV Is Coming — but Will Consumers Be Better Off?, Knowledge @ Wharton 10/24/2014
- Alibaba’s Next Move: Grow Abroad, or Go Deeper into China?, Knowledge @ Wharton 10/01/2014
- Will Microsoft Change the Game with Its Mojang Acquisition?, Knowledge @ Wharton 09/23/2014
- India’s Union Budget Builds Slowly Toward Reform, Knowledge @ Wharton 07/17/2014
- Will a Change in Leadership Turn Around Infosys?, Knowledge @ Wharton 06/19/2014
- Year One and Counting: Amazon Aims to Strike It Big in India, Knowledge @ Wharton 06/16/2014
- Pay TV Consolidation: Who Are the Potential Winners and Losers?, Knowledge @ Wharton 05/29/2014
- Mandate for Modi: A Business Agenda, Knowledge @ Wharton 05/19/2014
- India’s New Wave of Private Equity Investments, Knowledge @ Wharton 04/25/2014
- A Reality Check on Facebook’s Oculus Purchase, Knowledge @ Wharton 04/02/2014
- How the Music Industry Could Use Streaming to Reinvent Itself, Knowledge @ Wharton 03/21/2014
- Quick Take: What’s Up with Facebook’s WhatsApp Deal?, Knowledge @ Wharton 02/21/2014
- Can Raghuram Rajan Reverse the Indian Economy’s Decline?, Knowledge @ Wharton 09/16/2013
- Will Microsoft’s Reorganization Pay Off?, Knowledge @ Wharton 07/17/2013
- The redBus Acquisition: A Boost for India’s Startup Ecosystem, Knowledge @ Wharton 07/03/2013
- Entrepreneurs in India Find Challenges — and Niches, Knowledge @ Wharton 06/20/2013
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