wharton

Feb 8: Shawndra Hill to present on Talkographics: Using What Viewers Say Online to Calculate Audience Affinity Networks for Social TV-based Recommendations

February 6, 2013 By Sunil Wattal

Shawndra Hill
Assistant Professor, OIM Dept
The Wharton School, University of Pennsylvania

February 8, 2013
Speakman Hall 200, 1000am – 1130am
Seminar Title : Talkographics: Using What Viewers Say Online to Calculate Audience Affinity Networks for Social TV-based Recommendations

Abstract
Viewers of TV shows are increasingly taking to online sites like Facebook and Twitter to comment about the shows they watch as well as to contribute content about their daily lives. We present a novel recommendation system (RS) based on the user-generated content (UGC) contributed by TV viewers via the social networking site Twitter. In our approach, a TV show is represented by all of the tweets of its viewers who follow the show on Twitter. These tweets, in aggregate, enable us to reliably calculate the affinity between TV shows and to describe how and why certain shows are similar in terms of their audiences in a privacy friendly way. This paper’s two main contributions are: 1) a new methodology for collecting data from social media — including information about product networks (or how shows are connected through users on a social network), geographic location, and user-contributed text comments — which can be used to generate affinity networks and test them; and 2) a new privacy friendly UGC-based RS that relies on all publicly-available text contributed by viewers, as opposed to only pre-selected keywords extracted from the UGC associated with the shows, a specific ontology or taxonomy, which makes our approach more flexible and generalizable than those used in any prior research. We show that our approach predicts remarkably well the TV shows that Twitter users follow. We also explain why the approach works so well: First, we show that the UGC reflects the demographics, geographic location, and psychographics of viewers, and coin the term talkographics to refer to descriptions of a TV show’s viewers — or in general any product’s audience — that are revealed by the words used in text messages sent by Twitter-using TV viewers; second, we show that Twitter text can represent many complex nuanced combinations of the demographic, geographic, and psychographic features of the audience; third, we show that we can use talkographic profiles to first calculate similarities between TV shows, then use these similarities reliably in RSs; we also show that our approach can be combined with a product association network approach to achieve even better recommendations; finally, we show that our text-based approach performs best for shows for which there is a demographic bias to the viewing audience compared to those that do not have a demographic bias. To demonstrate that our RS is generalizable, we apply the same approach to followers of clothing and automobile retailers.

Dec 7: Kartik Hosanagar to present on Will the Global Village Fracture into Tribes: Recommender Systems and their Effects on Consumers

November 29, 2012 By Sunil Wattal

Kartik Hosanagar
Associate Professor, OIM Dept
The Wharton School, University of Pennsylvania

December 7, 2012
Speakman Hall 200, 1000am – 1130am
Seminar Title : Will the Global Village Fracture into Tribes: Recommender Systems and their Effects on Consumers

Abstract
Personalization is becoming ubiquitous on the World Wide Web. Such systems use statistical techniques to infer a customer’s preferences and recommend content best suited to him (e.g., “Customers who liked this also liked…”). A debate has emerged as to whether personalization has drawbacks. By making the web hyper-specific to our interests, does it fragment internet users, reducing shared experiences and narrowing media consumption? We study whether personalization is in fact fragmenting the online population. Surprisingly, it does not appear to do so in our study. Personalization appears to be a tool that helps users widen their interests, which in turn creates commonality with others. This increase in commonality occurs for two reasons, which we term volume and product mix effects. The volume effect is that consumers simply consume more after personalized recommendations, increasing the chance of having more items in common. The product mix effect is that, conditional on volume, consumers buy a more similar mix of products after recommendations.

Please click here for a copy of the paper

Eric Clemons to speak on How Information Changes Consumer Behavior And How Consumer Behavior Determines Corporate Strategy

October 24, 2009 By Sunil Wattal

How Information Changes Consumer Behavior And How Consumer Behavior Determines Corporate Strategy

Eric Clemons

Professor of Operations and Information Management
The Wharton School
University of Pennsylvania

November 6, 2009

Alter Hall 405, 1000am – 1130am

Abstract

Information availability has increased consumers’ informedness, the degree to which they know what is available in the marketplace, with precisely which attributes and at precisely what price. This informedness has altered the demand side of market behavior: customers now discount more heavily when comparable products are available from competitors and when products do not meet their wants, needs, cravings and longings, but they no longer discount as heavily when purchasing unfamiliar products. Changes in the demand side are producing comparable changes in the supply side: firms earn less than their expectations when competing in traditional mass market fat spots, while earning far more than previously when entering newly created resonance marketing sweet spots. We trace the impact of hyperdifferentiation and resonance marketing on strategy, with a clear progression from a limited number of fat spots, through reliance upon line extensions, and ultimately to fully differentiated market sweet spots.

For a copy of the full paper, click here.