Yogesh V. Joshi

A Theory of Combative Advertising

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 prisoners dilemma where all competing firms make less profit even when the effect of each firms 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.

Optimal Entry Timing in Markets with Social Influence

Firms routinely face the challenging decision of whether to enter a new market where a firms 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 firms 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.

Improving Prelaunch Diffusion Forecasts: Using Synthetic Networks as Simulated Priors

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.

Tool selection in three-axis rough machining

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.

Harvesting Brand Information from Social Tags

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.

Turf Wars: Product Line Strategies in Competitive Markets

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.

Automatic Crowdsourcing-Based Classification of Marketing Messaging on Twitter

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.

Underpromising and Overdelivering: Strategic Implications of Word of Mouth

We analyze a firms optimal communication strategy for setting consumer expectations when consumers are uncertain about product quality and word of mouth is prevalent in the market. We derive three main results: [i] Extant signaling theory argues that advertising should be costly for it to be informative of product quality. We show that in the presence of negative word of mouth, even costless advertising can serve as an informative signal for quality. [ii] Conventional wisdom suggests that as the consequences of negative word of mouth become stronger, a firm should become more cautious in setting high consumer expectations, to prevent future disappointment. We show that this need not always be the case: interestingly, when negative word of mouth is prevalent and its consequences become stronger, a firm might become more aggressive in setting high expectations given consumer rationality. [iii] Disconfirmation (defined as a stronger shift in beliefs when experiences are inconsistent with messages) serves as an adequate mechanism for preventing quality misrepresentation by a firm in its communications to consumers. But when markets are characterized by confirmation effects (a tendency to discount experiences inconsistent with messages) and future sales are important, we observe firms might entirely misrepresent their quality.

Co-Investment Network Dynamics in Crowdfunded Ventures

We examine the relationship between investment behavior of individual investors on crowdfunding platforms and the fundraising outcomes in crowdfunded ventures. Extant research has identified two main drivers for crowdfunding investments: irrational herding behavior of investors driven simply by observation of other investors’ actions; and rational herding behavior where investors take into account venture characteristics along with observed actions of other investors in making their own decisions. Here, we focus on investment characteristics of a network of co-investors in a focal venture on its crowdfunding growth at three stages -- early, middle, and late. Using data on crowdfunding of music artists’ ventures, we find that in addition to the factors identified in previous research, network characteristics such as the cliquishness of co-investors as well as the breadth of their co-investments in other artists play a significant role in determining the growth of investments in crowdfunded music ventures. We also show that this role is particularly important in the late phase, as compared to the early phase, of investment growth.

Does Word of Mouth Reduce Advertising That Signals Quality

We analyze a firm’s optimal communication strategy when dissipative advertising can be used as a signal of unobserved quality for an experience good, consumers share experiences via word of mouth, and word of mouth can be biased. We study the impact of two distinct empirically documented behavioral biases in word of mouth: negativity and positivity. In terms of the first of these biases, a priori, one might expect that with more negative opinions being shared, it should be easier for a low quality firm to be exposed and hence a high quality firm may need a smaller investment to separate itself in the eyes of rational consumers. Surprisingly, we show that with more negativity bias, a high quality firm becomes more aggressive in signaling its quality. This is because when negative word of mouth is prevalent and consumers hear about a negative experience, they are more likely to be forgiving while updating their quality beliefs. This yields important benefits to a low quality firm, and as a consequence, to effectively achieve separation and prevent the low type from mimicking, a high quality firm needs to increase its advertising spending. Such firm behavior crucially relies on followers being aware of the existence and magnitude of this bias; and is reversed otherwise. Similar results hold for positivity bias, when biases arise due to under-reporting, and when a firm can rely on prices to signal quality to consumers along with advertising. Overall, our analysis suggests that as bias in word of mouth increases, it is optimal for a high quality firm to shift to a more aggressive communication strategy.We analyze a firm’s optimal communication strategy when dissipative advertising serves as a signal of quality for an experience good, and consumers share experiences via word of mouth which can be positively or negatively biased. With negativity bias, a priori one might expect that when more negative opinions are shared, a high quality firm should spend less on advertising, since a low type would gain less from mimicking the high type. Surprisingly, we show that a higher advertising spending is needed instead. This is because when negative word of mouth is prevalent and consumers hear about a negative experience, they are forgiving while updating their quality beliefs. This behavior benefits the low quality firm; hence to prevent a low type from mimicking, a high type increases its advertising spending. Analogous results hold for positivity bias, and when a firm uses prices to signal its quality along with advertising.