Browsing by Author "Kumar, Nanda"
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Item Consumer Stockpiling and Competitive Promotional Strategies(INFORMS) Gangwar, Manish; Kumar, Nanda; Rao, Ram C.; 0000 0000 3471 3835 (Rao, RC); 82000090 (Rao, RC); 307323512 (Kumar, N)An examination of brand prices in several categories reveals that the distribution of prices is multimodal, with firms offering shallow and deep discounts. Another interesting feature of these distributions is that they may have holes in the interior of the support. These pricing distributions do not occur in extant theoretical models of price promotions. We develop a dynamic model of competition in which some price-sensitive consumers stockpile during periods of deep discounts. A game-theoretic analysis of our model generates a multimodal pricing distribution with a hole in the interior of the support. Consumer stockpiling in our model also gives rise to negative serial correlation in prices. This is consistent with our empirical observation of the pricing distribution of several brands across multiple categories in the IRI marketing data set. We generate several interesting insights into firms' optimal promotional strategies and their interplay with the clientele mix, market structure, and other market factors. We find that, in equilibrium, stockpiling by price-sensitive consumers neither harms nor benefits firms when they adopt equilibrium strategies. Interestingly, when price-sensitive consumers stockpile, even increased consumption as a result of stockpiling does not lead to higher profits for firms.Item Content Provision Strategies in the Presence of Content PiracyJohar, M.; Kumar, Nanda; Mookerjee, Vijay S.; 90649574 (Mookerjee, VS); 307323512 (Kumar, N)We consider a publisher that earns advertising revenue while providing content to serve a heterogeneous population of consumers. The consumers derive benefit from consuming content but suffer from delivery delays. A publisher's content provision strategy comprises two decisions: (a) the content quality (affecting consumption benefit) and (b) the content distribution delay (affecting consumption cost). The focus here is on how a publisher should choose the content provision strategy in the presence of a content pirate such as a peer-to-peer (P2P) network. Our study sheds light on how a publisher could leverage a pirate's presence to increase profits, even though the pirate essentially encroaches on the demand for the publisher's content. We find that a publisher should sometimes decrease the delivery speed but increase quality in the presence of a pirate (a quality focused strategy). At other times, a distribution focused strategy is better; namely, increase delivery speed, but lower quality. In most cases, however, we show that the publisher should improve at least one dimension of content provision (quality or delay) in the presence of a pirate. © 2012 INFORMS.Item Empirical Investigation of Habit, Variety-Seeking, and Satiation in Snack Consumption Using Multiple Discrete-Continuous Framework(2020-08) Kamatham, Sriharsha; 0000-0001-6532-4290 (Kamatham, S); Murthi, B.P.S.; Kumar, NandaThis dissertation consists of three research papers examining the role of satiation and state-dependence, choice sets, latent segments in the context of snack consumption. The three chapters specifically examine whether habituation or variety-seeking govern snacking. In the second chapter, we explore variety seeking behavior in a richer context. By using the multiple discrete-continuous extreme value (MDCEV) framework we estimate a model that captures choice of multiple alternatives and quantity consumption. We investigate the effects of satiation and state dependence and use a rich panel data of individual snack consumption to estimate the model estimate the model. We use consumption data of individuals recorded through hand-held devices and model consumers' choices from a variety of snack categories. Using a single framework, we separate the effects of satiation, intrinsic utility, and state dependence. Our modeling approach provides evidence of greater variety seeking in consumers at a brand level than at the category level within a day across time-periods. Across days, we find that category consumption choices are driven by habituation. We find evidence of satiation or diminishing marginal utility, and that satiation varies by snack categories and by dayparts. We show that by accounting for state-dependence and unobserved heterogeneity, the fit for MDCEV model improves tremendously over the base model that doesn’t capture neither of these factors. In the third chapter, we propose a new framework for modeling consideration sets in the MDCEV choice model framework. Using a gradient boosting algorithm from machine learning literature, we predict alternatives that are most likely to be chosen by a consumer at a daypart. In doing so, we reduce the computational burden associated with consideration set enumeration. These consideration sets are constructed as a function of dayparts, prior choices and prior choices, allowing us to predict alternatives that vary across individuals and time of the day. Our modeling approach allows us to estimate bias in parameter estimates, which is an outcome observed when choice models are estimated without inclusion of consideration sets. Using a rich panel data of individual level snack consumption, a setting where multiple discreteness and quantity choices play a role, along with groups of alternatives that are usually considered by individuals based on the time of consumption, we calibrate estimate the parameters of the model. We show that the proposed method provides a superior model fit by about 50% and reduces bias in parameter estimates compared to the base model. Using the proposed approach, we conduct two thought experiments – how does calorie consumption change when the time of consumption of a snack is changed and when a snack with switched with another snack. In the fourth chapter, we uncover latent segments of consumers using their snack consumption behavior using the individual level snack consumption data. We estimate a model of choices and quantity consumption using the multiple discrete-continuous framework with latent segments. Our approach results in a three-segment structure for the snack consumers which are labeled as “old, overweight and inactive”, “male and obese” and “young and active”. Since our model captures both preference for alternatives and quantity choices, we are able to get a better picture of consumption behavior. Latent segment models relied on the multinomial logit framework to uncover segments of consumers purely based on preferences alone. A fundamental assumption of is this model is that consumers face constant marginal utility. However, consumers do face diminishing marginal utility as we consume more of an alternative. Through the MDCEV framework, we relax this assumption and the models enables us to estimate a satiation parameter that captures diminishing marginal utility, thus giving us a complete picture of consumption behavior. To our knowledge, this is the first paper in marketing to show that satiation can also be used an additional dimension for customer segmentation apart from consumer preferences. We find that category consumption is governed by habituation across days in just one of three segments. Within a day, the “male and obese” segment seeks more variety in category consumption over the other segments. We find that all three segments are brand variety-seekers within a day while habituated across days for brand choices. Preference levels for each category varies across segments, while satiation levels also differ across segments. We create profiles for the three segments and find that the calorie consumption varies significantly across the three segments varies by categories. Our results have implications for managers interested in creating optimal consumption bundles and for policymakers interested in addressing over-consumption leading to obesity among US consumers.Item Pricing Models for Online Advertising: CPM vs. CPC(2012-09) Asdemir, K.; Kumar, Nanda; Jacob, V. S. (Varghese S.); 0000 0000 4526 9637 (Jacob, VS); 94021959 (Jacob, VS)Online advertising has transformed the advertising industry with its measurability and accountability. Online software and services supported by online advertising is becoming a reality as evidenced by the success of Google and its initiatives. Therefore, the choice of a pricing model for advertising becomes a critical issue for these firms. We present a formal model of pricing models in online advertising using the principal-agent framework to study the two most popular pricing models: input-based cost per thousand impressions (CPM) and performance-based cost per click-through (CPC). We identify four important factors that affect the preference of CPM to the CPC model, and vice versa. In particular, we highlight the interplay between uncertainty in the decision environment, value of advertising, cost of mistargeting advertisements, and alignment of incentives. These factors shed light on the preferred online-advertising pricing model for publishers and advertisers under different market conditions. © 2012 INFORMS.