74 research outputs found
The value of a "free" customer
We study the problem of a firm that faces asymmetric information about the productivity of its potential workers. In our framework, a worker’s productivity is either assigned by nature at birth, or determined by an unobservable initial action of the worker that has persistent effects over time. We provide a characterization of the optimal dynamic compensation scheme that attracts only high productivity workers: consumption –regardless of time period– is ranked according to likelihood ratios of output histories, and the inverse of the marginal utility of consumption satisfies the martingale property derived in Rogerson (1985). However, in the case of i.i.d. output and square root utility we show that, contrary to the features of the optimal contract for a repeated moral hazard problem, the level and the variance of consumption are negatively correlated, due to the influence of early luck into future compensation. Moreover, in this example long-term inequality is lower under persistent private informationCustomer lifetime value, CRM, Dynamic programming, GMM Estimation
A Dynamic Model of Sponsored Search Advertising
Sponsored search advertising is ascendant Jupiter Research reports
expenditures rose 28% in 2007 to 22 retail price of the software products advertised on the considered
search engine, this implies a conversion rate (sales per click) of about
1.1%, well within common estimates of 1-2% (gamedaily.com). Hence our
approach appears to yield valid estimates of advertiser click
valuations. Another finding is that customers appear to be segmented by
their clicking frequency, with frequent clickers placing a greater
emphasis on the position of the sponsored advertising link. Estimation
of the policy simulations is in progress
Advertiser Learning in Direct Advertising Markets
Direct buy advertisers procure advertising inventory at fixed rates from
publishers and ad networks. Such advertisers face the complex task of choosing
ads amongst myriad new publisher sites. We offer evidence that advertisers do
not excel at making these choices. Instead, they try many sites before settling
on a favored set, consistent with advertiser learning. We subsequently model
advertiser demand for publisher inventory wherein advertisers learn about
advertising efficacy across publishers' sites. Results suggest that advertisers
spend considerable resources advertising on sites they eventually abandon -- in
part because their prior beliefs about advertising efficacy on those sites are
too optimistic. The median advertiser's expected CTR at a new site is 0.23%,
five times higher than the true median CTR of 0.045%.
We consider how pooling advertiser information remediates this problem.
Specifically, we show that ads with similar visual elements garner similar
CTRs, enabling advertisers to better predict ad performance at new sites.
Counterfactual analyses indicate that gains from pooling advertiser information
are substantial: over six months, we estimate a median advertiser welfare gain
of \$2,756 (a 15.5% increase) and a median publisher revenue gain of \$9,618 (a
63.9% increase)
RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients
Rheumatoid arthritis (RA) is a chronic inflammatory disorder with poorly defined aetiology characterised by synovial inflammation with variable disease severity and drug responsiveness. To investigate the peripheral blood immune cell landscape of early, drug naive RA, we performed comprehensive clinical and molecular profiling of 267 RA patients and 52 healthy vaccine recipients for up to 18 months to establish a high quality sample biobank including plasma, serum, peripheral blood cells, urine, genomic DNA, RNA from whole blood, lymphocyte and monocyte subsets. We have performed extensive multi-omic immune phenotyping, including genomic, metabolomic, proteomic, transcriptomic and autoantibody profiling. We anticipate that these detailed clinical and molecular data will serve as a fundamental resource offering insights into immune-mediated disease pathogenesis, progression and therapeutic response, ultimately contributing to the development and application of targeted therapies for RA.</p
Choice Models and Customer Relationship Management
Customer relationship management (CRM) typically involves tracking individual customer behavior over time, and using this knowledge to configure solutions precisely tailored to the customers' and vendors' needs. In the context of choice, this implies designing longitudinal models of choice over the breadth of the firm's products and using them prescriptively to increase the revenues from customers over their lifecycle. Several factors have recently contributed to the rise in the use of CRM in the marketplacePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47023/1/11002_2005_Article_5892.pd
A Dynamic Model of Sponsored Search Advertising
Sponsored search advertising is ascendant---Jupiter Research reports expenditures rose 28% in 2007 to 22 retail price of the software products advertised on the considered search engine, this implies a conversion rate (sales per click) of about 1.1%, well within common estimates of 1-2% (gamedaily.com). Hence our approach appears to yield valid estimates of advertiser click valuations. Another finding is that customers appear to be segmented by their clicking frequency, with frequent clickers placing a greater emphasis on the position of the sponsored advertising link. Estimation of the policy simulations is in progress.Sponsored Search Advertising, Two-sided Market, Dynamic Game, Structural Models, Empirical IO, Customization, Auctions
Market Roll-Out and Retailer Adoption for New Brands
This paper proposes a descriptive model of the spatial and temporal evolution of retail distribution for new packaged goods. The distribution model postulates separate processes for local market entry by manufacturers, and adoption by retailers given entry. Of special interest is whether retail adoption occurs along a competitive network with retailers as nodes and overlapping trade areas of these retailers as links. The model is calibrated on data covering the introduction of two very successful new brands in the frozen pizza category. For these brands, manufacturers sequentially enter markets based on spatial proximity to markets already entered (spatial evolution), and on whether chains in these markets adopted previously elsewhere (market selection). A retail chain adopts new brands based on the adoption timing of competing chains within its trade territory (competitive contagion) and on the fraction of its trade area in which the new brand is available (trade area coverage). The effects of market selection and of trade area coverage create dependencies between market entry and retail adoption. Because of these dependencies the attraction of a particular market as a lead market depends on its location in the geographic structure of the U.S. retail trade.spatial diffusion, network diffusion, retail distribution, new product research
The impact of collinearity on regression analysis: the asymmetric effect of negative and positive correlations
The purpose of this paper is to ascertain how collinearity in general, and the sign of correlations in specific, affect parameter inference, variable omission bias, and their diagnostic indices in regression. It is found that collinearity can reduce parameter variance estimates and that positive and negative correlation structures have an asymmetric effect on variable omission bias. It is also shown that the effects of collinearity are moderated by the relationship between the dependent variable and the regressors, a consideration not incorporated into most commonly used collinearity diagnostics. The formulae derived enable researchers to assess the sensitivity of regression results to the underlying correlation structure in the data.
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