Shachar Reichman

Crowd-Squared: Amplifying the Predictive Power of Search Trend Data

Big Data generated by crowds provides a myriad of opportunities for monitoring and modeling peoples intentions, preferences, and opinions. A crucial step in analyzing such “big data�? is selecting the relevant part of the data that should be provided as input to the modeling process. In this paper, we offer a novel, structured, crowd-based method to address the data selection problem in a widely used and challenging context: selecting search trend data. We label the method “crowd-squared,�? as it leverages crowds to identify the most relevant terms in search volume data that were generated by a larger crowd. We empirically test this method in two domains and find that our method yields predictions that are equivalent or superior to those obtained in previous studies (using alternative data selection methods) and to predictions obtained using various benchmark data selection methods. These results emphasize the importance of a structured data selection method in the prediction process, and demonstrate the utility of the crowd-squared approach for addressing this problem in the context of prediction using search trend data.

Moneyball for Academics: Network Analysis for Predicting Research Impact

How are scholars ranked for promotion, tenure and honors? How can we improve the quantitative tools available for decision makers when making such decisions? Can we predict the academic impact of scholars and papers at early stages using quantitative tools?Current academic decisions (hiring, tenure, prizes) are mostly very subjective. In the era of “Big Data,” a solid quantitative set of measurements should be used to support this decision process.This paper presents a method for predicting the probability of a paper being in the most cited papers using only data available at the time of publication. We find that highly cited papers have different structural properties and that these centrality measures are associated with increased odds of being in the top percentile of citation count.The paper also presents a method for predicting the future impact of researchers, using information available early in their careers. This model integrates information about changes in a young researcher’s role in the citation network and co-authorship network and demonstrates how this improves predictions of their future impact.These results show that the use of quantitative methods can complement the qualitative decision-making process in academia and improve the prediction of academic impact.

OR Forum—Tenure Analytics: Models for Predicting Research Impact

Tenure decisions, key decisions in academic institutions, are primarily based on subjective assessments of candidates. Using a large-scale bibliometric database containing 198,310 papers published 1975–2012 in the field of operations research (OR), we propose prediction models of whether a scholar would perform well on a number of future success metrics using statistical models trained with data from the scholar’s first five years of publication, a subset of the information available to tenure committees. These models, which use network centrality of the citation network, coauthorship network, and a dual network combining the two, significantly outperform simple predictive models based on citation counts alone. Using a data set of the 54 scholars who obtained a Ph.D. after 1995 and held an assistant professorship at a top-10 OR program in 2003 or earlier, these statistical models, using data up to five years after the scholar became an assistant professor and constrained to tenure the same number of candidates as tenure committees did, made a different decision than the tenure committees for 16 (30%) of the candidates. This resulted in a set of scholars with significantly better future A-journal paper counts, citation counts, and h -indexes than the scholars actually selected by tenure committees. These results show that analytics can complement the tenure decision-making process in academia and improve the prediction of academic impact.

When Online Engagement Gets in the Way of Offline Sales - A Natural Experiment

In recent years, billions of dollars are spent, by both online and offline retailers, on website design aimed at increasing consumers’ online engagement. We study the relationship between online engagement and offline sales, utilizing a quasi-experimental setting whereby a leading premium automobile brand launched a new interactive website gradually across markets, allowing for a treatment-control comparison. The paper provides first evidence of a causal effect of online engagement on offline sales, with the high-engagement website leading to a decline of approximately 12% in car sales. This negative effect is due to substitution between online and offline engagement, as evidenced by high-engagement website visitors’ decreased tendency to seek out personal contact with a car dealer and proceed to offline engagement – a necessary stage in the car purchase funnel. An analytical model of the online-to-offline sales funnel is developed, generalizing our findings and highlighting the conditions under which online engagement will substitute for offline engagement, and may decrease offline sales. Taken together, our findings suggest that while online engagement serves as a means for both product information provision and consumer persuasion, it may fall short in achieving the latter goal compared to the offline channel. For pure offline products, hands-on engagement is a necessary step toward purchase. Thus, increasing consumers’ online engagement may not be an optimal strategy if it has the potential to halt progression down the sales funnel and reduce offline engagement.