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Abstract. Previous research demonstrates that stacked discounts increase retail revenue. For instance, a retailer should sell more products by offering '20% off, plus an extra 25% off' than by offering [...]

Despite considerable advances in the application of advanced analytics across the sport industry, sponsorship revenue forecasting still largely relies on a decades-old methodology, the renewal rate. This paper performs the first application of event history analysis (EHA) approaches to quantitatively analyze the duration of Olympic and World Cup sponsorships, to determine not only the percentage of sponsors who renew, but when sponsorships are most likely to continue, when the probability of a sponsorship ending is highest, and their median lifetimes. Consistent with prior applications of exchange theory to the sponsorship business-to-business relationship, results found sponsorships were most susceptible to dissolution within the first two renewal periods, and sponsorship durations differed significantly based on which methodology was applied. Sponsorship revenue projections varied by as much as $100 million depending on the approach, demonstrating the importance of providing sport managers with advanced analytics to assist in the organization's sponsorship revenue forecasting activities. Keywords: sport sponsorship, revenue forecasting, event history analysis, Olympic Games, World Cup, Introduction One of the more important evolutions in the sport industry over the past decade has been the marked increase in the application of advanced data analytics. Numerous quantitative approaches [...]

Choice models today are ubiquitous across a range of applications in operations and marketing. Real-world implementations of many of these models face the formidable stumbling block of simply identifying the 'right' model of choice to use. Because models of choice are inherently high-dimensional objects, the typical approach to dealing with this problem is positing, a priori, a parametric model that one believes adequately captures choice behavior. This approach can be substantially suboptimal in scenarios where one cares about using the choice model learned to make fine-grained predictions; one must contend with the risks of mis-specification and overfitting/underfitting. Thus motivated, we visit the following problem: For a 'generic' model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal information about these distributions), how may one predict revenues from offering a particular assortment of choices? An outcome of our investigation is a nonparametric approach in which the data automatically select the right choice model for revenue predictions. The approach is practical. Using a data set consisting of automobile sales transaction data from a major U.S. automaker, our method demonstrates a 20% improvement in prediction accuracy over state-of-the-art benchmark models; this improvement can translate into a 10% increase in revenues from optimizing the offer set. We also address a number of theoretical issues, among them a qualitative examination of the choice models implicitly learned by the approach. We believe that this paper takes a step toward 'automating' the crucial task of choice model selection. Key words: nonparametric choice; choice models; revenue prediction; utility preference; preference list; marketing mix History: Received December 14, 2009; accepted December 21, 2011, by Yossi Aviv, operations management. Published online in Articles in Advance January 8, 2013., 1. Introduction A problem of central interest to operations managers is using historical sales data to predict the revenues or sales from offering a particular assortment of products to customers. [...]

Revenue forecasts play an important role in the state government budget and policy process. These forecasts are generally reported to executive and legislative leaders as point estimates, with no acknowledgment of their corresponding risk and uncertainty. The revenue semaphores proposed in this article outline procedures by which revenue uncertainty directly influences the planning process. The goal is not to make forecasts more accurate, but to make discussions of budget and revenue alternatives more comprehensive. Explicitly incorporating risk and uncertainty measures into the budgeting process increases the degree of real-time budgeting and reduces some adverse effects of budget revisions after the fiscal year begins. The discussion occurs in the context of budget control, management, and policy making.

Analyses of bank mergers show that gains come from estimated cost savings rather than from projected revenue enhancements of the merger.

US economists have turned to tax reform in the 1980s and 1990s in an attempt to explain the country's fiscal crisis. Particular focus has been given to the disincentive effects of marginal tax rate increases and its impact on revenue increase, which has been overestimated. Such systematic overestimation of tax reform gains and losses should have yielded a negative relationship between forecast errors and revenue estimates. Inadequate consideration of taxpayer behavior is projected as a vital factor for the excess estimation.

There is a correlation between the earnings forecasts of management and stock price reactions. A hypothesis concerning the informativeness of managers' earnings and revenue forecasts for predicting stock prices was used to examine the motivation and effects associated with revenue forecasts. The hypothesis contends that earnings forecasts are more informative when released without managers' forecasts of revenue, and managers' forecasts of revenues are informative about prices. An analysis of data on annual earnings and revenues and managers' forecasts of annual earnings from the Wall Street Journal Index of Jan 1978 to Dec 1982 revealed that earnings forecasts that were released without revenue forecasts were price informative, while those released with revenue forecasts were not.

Peter DeMarzo, Ilan Kremer, and Andrzej Skrzypacz (2005) analyzed auctions in which bidders compete in securities. They show that a steeper security leads to a higher expected revenue for the seller, and also use this to establish the revenue ranking between standard auctions. In this comment, we obtain the opposite results to DKS's by assuming that a higher return requires a higher investment cost. Given this latter assumption, steeper securities are more vulnerable to adverse selection, and may yield lower expected revenue, than flatter ones. (JEL D44 )

The analysis of data on business enterprises to determine the impact of the continuous usage of a single generic strategy on the long-term revenues of a business is presented.

With respect to Fiji Islands, the correlation between the public revenue and capital expenditure decisions of government and its significance in policy formation are analyzed.