Yogesh Joshi Directory Page

Research Interests

Strategic Marketing Decisions: Product Differentiation, Advertising, Innovation Diffusion, Social Influence.

Selected Working Papers

"Word of Mouth and Advertising: Evidence from U.S. Theatrical Market," with Min Kim.

"Endogenous Consumption and Metered Paywalls," with Chutian Wang and Bobby Zhou.

"The Accuracy of News," with Chutian Wang and Bobby Zhou.

Google Scholar Page

Refereed Journal Publications

Joshi, Yogesh V., Andres Musalem. 2021. When Consumers Learn, Money Burns: Signaling Quality Via Advertising With Observational Learning And Word Of Mouth. Marketing Science 40(1) 168-188.

This research analyzes a firm's investment in advertising that signals quality when consumers learn about quality not only from such advertising but also from interactions with other consumers in the form of observational learning or word of mouth. Further, word-of-mouth interactions may involve underreporting (not everyone shares experiences), positivity (positive experiences are communicated more widely than negative ones), or negativity (negative experiences are communicated more widely than positive ones). The analysis focuses on whether a firm should advertise more or less aggressively in the presence of such consumer interactions compared with their absence and offers four key insights. First, consumer interactions can amplify the signaling effect of advertising, and as a consequence, to prevent mimicking it may be optimal for a high-quality firm to become more aggressive and spend more on advertising to signal quality in the presence of such interactions than without. Second, as underreporting increases, it can be optimal to reduce advertising, sometimes significantly. Third, with increasing positivity, it can be optimal to increase advertising. Fourth, even with increasing negativity, under certain conditions it may still be optimal to increase advertising rather than decrease it.

Ted Matherly, Anastasiya Pocheptsova Ghosh, and Yogesh V. Joshi. 2019. The Freedom of Constraint: How Perceptions of Time Limitations Alleviate Guilt from Two-Phase Indulgent Consumption. Journal of the Association for Consumer Research: Special Issue on Consumer Emotions in the Marketplace 4(2) 147-159.

When purchasing indulgent products, the characteristics of the purchase, such as price discounts and redemption windows (the amount of time available to consume the product), can affect the likelihood of purchase. We show how these characteristics jointly influence consumers' likelihood of purchasing indulgent products and identify the consumer lay theories that drive this effect. Although price discounts can alleviate the anticipated guilt associated with indulgent consumption, we propose that consumers believe the guilt-reducing effects of discounts fade over time. In four experiments, we show that, based on this belief, consumers strategically protect their enjoyment of indulgent products by choosing shorter time periods to consume discounted indulgent purchases, and by preferring discounted indulgent products offered with shorter (vs. longer) redemption windows. We find convergent evidence in sales data from a large discount offers website, observing higher purchase rates for discounted products with shorter redemption windows compared with those for longer ones.

Nam, Hyoryung, Yogesh V. Joshi, P. K. Kannan. 2017. Harvesting Brand Information from Social Tags. Journal of Marketing 81(4) 88-108.

Social tags are user-defined keywords associated with online content that reflect consumers' perceptions of various objects, including products and brands. This research presents a new approach for harvesting rich, qualitative information on brands from user-generated social tags. The authors first compare their proposed approach with conventional techniques such as brand concept maps and text mining. They highlight the added value of their approach that results from the unconstrained, open-ended, and synoptic nature of consumer-generated content contained within social tags. The authors then apply existing text-mining and data-reduction methods to analyze disaggregate-level social tagging data for marketing research and demonstrate how marketers can utilize the information in social tags by extracting key representative topics, monitoring common dynamic trends, and understanding heterogeneous perceptions of a brand.

Joshi, Yogesh V., David J. Reibstein, Z. John Zhang. 2016. Turf Wars: Product Line Strategies in Competitive Markets. Marketing Science 35(1) 128-141.

In this paper we study product line scope and pricing decisions in a horizontally differentiated duopoly. Past research has shown that a firm may offer a broader product line to attract higher demand or charge a higher price (or both), and benefit at the expense of its competitor. We show that such outcomes may be reversed, especially when consumers have relatively high valuation and low heterogeneity in their preferences for the line extension. We find that an equilibrium exists such that only one firm prefers to expand scope but profits may be higher for both firms, even in the absence of market size expansion. This is because a broader scope permits that firm to effectively price discriminate by raising prices for its core customers. The competitor optimally responds by lowering prices to gain share and earn a higher profit. Thus, higher prices for the firm expanding its product line translate into higher demand for the competing firm, thus increasing profit for both. We show that our results hold when firms deploy generic, offensive or defensive strategies during product line expansion.

Trusov, Michael, William M. Rand, Yogesh V. Joshi. 2013. Improving Pre-Launch Diffusion Forecasts: Using Synthetic Networks as Simulated Priors. Journal of Marketing Research 50(6) 675-690.

Although the role of social networks and consumer interactions in new product diffusion is widely acknowledged, such networks and interactions are often unobservable to researchers. What may be observable, instead, are aggregate diffusion patterns for past products adopted within a particular social network. The authors propose an approach for identifying systematic conditions that are stable across diffusions and thus are "transferrable" to new product introductions within a given network. Using Facebook applications data, the authors show that incorporation of such systematic conditions improves prelaunch forecasts. This research bridges the gap between the disciplines of Bayesian statistics and agent-based modeling by demonstrating how researchers can use stochastic relationships simulated within complex systems as meaningful inputs for Bayesian inference models.

Joshi, Yogesh V., David J. Reibstein, Z. John Zhang. 2009. Optimal Entry Timing in Markets with Social Influence. Management Science 55(6) 926-939.

Firms routinely face the challenging decision of whether to enter a new market where a firm's strong presence in an existing market has a positive influence (the leverage effect) on product adoption in the new market, but the reciprocal social influence on the existing market is negative (the backlash effect). In this paper, we show that a firm's optimal entry strategy in this situation cannot be characterized by the familiar "now or never" or "now or at maturity" strategies proposed in the literature. We show that a strong leverage effect does not necessarily provide the justification for a firm to enter a new market, and neither should a strong backlash effect necessarily deter a firm from embracing a new market. The optimal strategy is predicated on a judicious trade-off between the three factors of leverage, backlash, and patience. Thus, an astute manager can always find the opportune time to enter the new market if she takes into account the dynamic and recursive nature of cross-market interaction effects, where leverage enhances the backlash but backlash weakens the leverage in a nonlinear, dynamic fashion. We illustrate that firms stand to benefit from explicit considerations of these effects in deciding whether and when to enter a new market. Furthermore, we explore how the optimal time of entry into the new market relates to the time of peak sales for the existing market, demonstrating that depending on the interactive effects of leverage and backlash, entry could be optimal either before or after peak sales in the existing market.

Musalem, Andres, Yogesh V. Joshi. 2009. How Much Should You Invest in Each Customer Relationship? A Competitive Strategic Approach. Marketing Science 28(3) 555-565.

We analyze firms' decisions to invest in customer relationship management (CRM) initiatives such as acquisition and retention in a competitive context, a topic largely ignored in past CRM research. We characterize each customer by her intrinsic preference towards each firm, the contribution margin she generates for each firm, and her responsiveness to each firm's retention and acquisition efforts. We show that a firm should invest most heavily in retaining those customers that exhibit moderate responsiveness to its CRM efforts. Further, a firm should most aggressively seek to attract those customers that exhibit moderate responsiveness to their provider's CRM efforts and those that are moderately profitable for their current provider. Investing more in customers that are more responsive does not always lead to higher firm profits, because stronger competition for such customers tends to erode the effects of higher CRM efforts of an individual firm. When firms develop a customer relationship over time to generate higher contribution margin or customer responsiveness, we show that such developments may not always be desirable, because sometimes these future benefits may lead to more intense competition and hence lower profits for both firms.

Chen, Yuxin, Yogesh V. Joshi, Jagmohan S. Raju, Z. John Zhang. 2009. A Theory of Combative Advertising. Marketing Science 28(1) 1-19.

In mature markets with competing firms, a common role for advertising is to shift consumer preferences towards the advertiser in a tug-of-war, with no effect on category demand. In this paper, we analyze the effect of such "combative" advertising on market power. We show that, depending on the nature of consumer response, combative advertising can reduce price competition to benefit competing firms. However, it can also lead to a procompetitive outcome where individual firms advertise to increase their own profitability, but collectively become worse off. This is because combative advertising can intensify price competition such that an "advertising war" leads to a "price war." Similar to price competition, advertising competition can result in a prisoner's dilemma where all competing firms make less profit even when the effect of each firm's advertising is to enhance consumer preferences in its favor. Given such procompetitive effects, we further show that cost of combative advertising could be a blessing in disguise—higher unit cost of advertising resulting in lower equilibrium levels of advertising, leading to higher prices and profits. We conduct a laboratory experiment to investigate how combative advertising by competing brands influences consumer preferences. Our experimental analysis offers strong support for our conclusions.

Arora, Neeraj, Xavier Dreze, Anindya Ghose, James D. Hess, Raghuram Iyengar, Bing Jing, Yogesh V. Joshi, V. Kumar, Nicholas Lurie, Scott Neslin, Sajeesh Sajeesh, Meng Su, Niladri Syam, Jacquelyn S. Thomas, Z. John Zhang. 2008. Putting One-to-One Marketing to Work: Personalization, Customization and Choice. Marketing Letters. 19(3/4) 305-321.

The tailoring of a firm's marketing mix to the individual customer is the essence of one-to-one marketing. In this paper, we distinguish between two forms of one-to-one marketing: personalization and customization. Personalization occurs when the firm decides what marketing mix is suitable for the individual. It is usually based on previously collected customer data. Customization occurs when the customer proactively specifies one or more elements of his or her marketing mix. We summarize key challenges and knowledge gaps in understanding both firm and customer choices in one-to-one markets. We conclude with a summary of research opportunities.

Van den Bulte, Christophe, Yogesh V. Joshi. 2007. New Product Diffusion with Independents and Imitators. Marketing Science 26(3) 400-421.

We model the diffusion of innovations in markets with two segments: influentials who are more in touch with new developments and who affect another segment of imitators whose own adoptions do not affect the influentials. This two-segment structure with asymmetric influence is consistent with several theories in sociology and diffusion research, as well as many "viral" or "network" marketing strategies. We have four main results. (1) Diffusion in a mixture of influentials and imitators can exhibit a dip or "chasm" between the early and later parts of the diffusion curve. (2) The proportion of adoptions stemming from influentials need not decrease monotonically, but may first decrease and then increase. (3) Erroneously specifying a mixed-influence model to a mixture process where influentials act independently from each other can generate systematic changes in the parameter values reported in earlier research. (4) Empirical analysis of 33 different data series indicates that the two-segment model fits better than the standard mixed-influence, the Gamma/Shifted Gompertz, and the Weibull-Gamma models, especially in cases where a two-segment structure is likely to exist. Also, the two-segment model fits about as well as the Karmeshu-Goswami mixed-influence model, in which the coefficients of innovation and imitation vary across potential adopters in a continuous fashion.

Balasubramaniam, Mahadevan, Yogesh V. Joshi, Dan Engels, Sanjay Sarma, Zaffar Shaikh. 2001. Tool selection in three-axis rough machining. International Journal of Production Research 39(18) 4215-4238.

An approach to tool selection and sequencing is presented for three-axis rough machining. The trade-off in the selection of tools is as follows: larger tools have reduced access while smaller tools are capable of reduced cutting speed. Furthermore, every tool change incurs a time penalty. The objective of this paper is to select a tool sequence that minimizes the total rough-machining time. In our approach, the removal volume is stratified into 2.5D machining slabs and, for each tool, the area accessible in each slab is computed incrementally, keeping in mind the cutting portion of the tool and the shape of the tool holder and spindle assembly. This reduces the three-axis problem to a series of two-axis problems with complex precedence constraints. Two models are presented to understand this new form of the problem. First, an integer linear programming formulation is discussed to show the complexity of the task. Second, a network flow formulation is presented, by which we show that it is possible to obtain efficiently an approximate solution of the problem. Examples are discussed to illustrate the algorithms discussed.

Book Chapters, Reports and Other Publications

Yogesh Joshi, 2015, Advertising Effects in Social Media, in Consumer Psychology in a Social Media World, Eds. Dimofte, Haugtvedt and Yalch, Routledge: New York.

Anastasiya Pocheptsova and Yogesh Joshi, 2014, "Too Attractive to Pass: a Peculiar Appeal of Shorter Redemption Windows of Daily Deals", in NA - Advances in Consumer Research Volume 42, eds. June Cotte and Stacy Wood, Duluth, MN : Association for Consumer Research, Pages: 86-90.

Daily deals are now widely popular. We show that consumers exhibit suboptimal preference for deals with shorter redemptionwindows for hedonic products and services. We propose that short redemption window is interpreted as a signal of product scarcityand that such interpretation is magnified when consumers need to justify purchase.

Machedon, Radu, William Rand, Yogesh Joshi, 2013, "Automatic Crowdsourcing-Based Classification of Marketing Messaging on Twitter," ASE/IEEE socialcom, 2013 International Conference on Social Computing, pp.975-978.

As the volume of social media communications grow, many different stakeholders have sought to apply tools and methods for automatic identification of sentiment and topic in social network communications. In the domain of social media marketing it would be useful to automatically classify social media messaging into the classic framework of informative, persuasive and transformative advertising. In this paper we develop and present the construction and evaluation of supervised machine-learning classifiers for these concepts, drawing upon established procedures from the domains of sentiment analysis and crowd sourced text classification. We demonstrate that a reasonably effective classifier can be created to identify the informative nature of Tweets based on crowd sourced training data, we also present results for identifying persuasive and transformative content. We finish by summarizing our findings regarding applying these methods and by discussing recommendations for future work in the area of classifying the marketing content of tweets.

Joshi, Yogesh V., Liye Ma, William M. Rand, Louiqa Raschid. 2013. Building the B[r]and: Understanding How Social Media Drives Consumer Engagement and Sales. Marketing Science Institute Report 13-113.

We investigate how activity in digital social media, by new and established brands, relates to engagement with consumers, and eventually, sales. Our dataset includes two years of Twitter activity and offline concerts for several musical bands, and the corresponding social media activity of the bands' followers. In addition to measuring volume (that is, number of tweets sent per unit of time), we use machine learning methods to analyze message sentiment and informational content. We collect A.C. Nielsen sales data for all albums released by these bands. We investigate the characteristics and evolution of consumers' engagement (propensity to tweet in response to a band's tweets as well as propensity to send informational or emotional tweets) using a hidden Markov model. We relate engagement to sales via a generalized diffusion model.

Berger, Jonah, Benjamin Ho, Yogesh V. Joshi. 2011. Identity Signaling with Social Capital: A Model of Symbolic Consumption. Marketing Science Institute Report 11-104.

People consume not only physical goods but also social interaction, and consumer choices depend on both intrinsic motivations (preferences for consumption) and extrinsic motivations (how choices impact social interactions). In this paper, we study how social identity, preference, and utility from social interaction shape consumer behavior and choice in the marketplace. Our contribution is two-fold: first, we present a novel modeling framework that captures heterogeneity in the distribution of intrinsic preferences and social capital in a social network, and demonstrate how rational consumer choice driven by social utility influences the demand for goods. Second, we show how this framework can help explain why trends may emerge out of insular market segments, why some inconspicuous behaviors may become effective signals of identity, and why some very highly popular products may suddenly experience a loss in market interest. We discuss implications of such behaviors for product demand generation, innovation, and advertising.

Reibstein, David J., Yogesh V. Joshi, Paul W. Farris. 2004. Marketing costs and prices: an expanded view. In The Profit Impact of Marketing Strategy Project: Retrospect and Prospects, Eds. Farris and Moore, Cambridge University Press: Cambridge, UK.

More than twenty years ago Farris and Reibstein (1979) published research that demonstrated a strong cross-sectional correlation between relative advertising expenditures and relative prices charged by manufacturers of non-durable consumer goods. Data for that research were taken from the PIMS database. The correlation was demonstrated to survive a number of controls for relative quality and market share. The correlation was also shown to be stronger for later stages in the product life-cycle and for products purchased in relatively small dollar amounts. The research made no claims about the direction of causality from advertising to prices or vice versa. Instead, the paper argued that from the management perspective "consistency" between advertising and pricing was important. In other words, businesses with high (or low) relative prices should generally also have high (or low) levels of relative advertising. The claim for the importance of consistency was buttressed by evidence in the paper that businesses with inconsistent pricing and advertising strategies earned lower ROIs. In this chapter we first review and then extend the earlier Farris and Reibstein (1979) study with new analyses based on the PIMS data. The review is placed in the context of a broader managerial (not necessarily a public policy) concern with the relationship between total marketing costs (not just advertising) and prices. The expanded view of marketing costs includes salesforce and other marketing expenses – budget items with collective dollar values that are typically three to four times advertising budgets.

Balasubramaniam, Mahadevan, Yogesh V. Joshi, Sanjay Sarma, Zaffar Shaikh. 2001. An approach for tool sequence selection for three-axis rough machining. Transactions of the North American Manufacturing Research Institution of SME, 359-366.

Joshi, Yogesh V. 2000. Information Visibility and Its Effect on Supply Chain Dynamics. Auto-ID Labs White Paper, Massachusetts Institute of Technology, Cambridge, MA.

Supply chains are nonlinear dynamic systems, the control of which is complicated by long, variable delays in product and information flows. We present a novel framework for improving the visibility of information in supply chains by reducing the delays in information flow. We first analyze the growth and evolution of production and operations management software over the past three decades, and the current trends in their development, coupled with recent advances in radio frequency technology, wireless communications, data representation methods, and the internet. Information visibility is identified as one of the key elements for successful implementation of any such software. We analyze the dynamics of a supply chain under different scenarios of information visibility and forecasting decisions with the help of simulations. Possible improvements in supply chain costs are identified, provided information visibility. We propose a framework to achieve information visibility in the supply chain using radio frequency tags, tag readers, product identification codes, an object description language, and the internet.

Current Teaching: MBA/MS

Innovation and Product Development

Marketing Research Methods

Marketing Analytics

Past Teaching:

Undergraduate: Customer Centric Innovation, Marketing and Innovation for Entrepreneurs, New Product Marketing

MBA/MS: Instructor for the capstone experiential course in the FTMBA marketing track, New Product Development, MBA Consulting Project, Business Consulting, Innovation Analytics

Doctoral: Mathematical Models in Marketing