Oded Netzer

Alternative Models for Capturing the Compromise Effect

The compromise effect denotes the finding that brands gain share when they become the intermediate rather than extreme option in a choice set. Despite the robustness and importance of this phenomenon, choice modelers have neglected to incorporate the compromise effect in formal choice models and to test whether such models outperform the standard value maximization model. In this article, the authors suggest four context-dependent choice models that can conceptually capture the compromise effect. Although the models are motivated by theory from economics and behavioral decision research, they differ with respect to the particular mechanism that underlies the compromise effect (e.g., contextual concavity versus loss aversion). Using two empirical applications, the authors (1) contrast the alternative models and show that incorporating the compromise effect by modeling the local choice context leads to superior predictions and fit compared with the traditional value maximization model and a stronger (naive) model that adjusts for possible biases in utility measurement, (2) generalize the compromise effect by demonstrating that it systematically affects choice in larger sets of products and attributes than has been previously shown, (3) show the theoretical and empirical equivalence of loss aversion and local (contextual) concavity and (4) demonstrate the superiority of models that use a single reference point over “tournament models” in which each option serves as a reference point. They discuss the theoretical and practical implications of this research as well as the ability of the proposed models to predict other behavioral context effects.

Extending Compromise Effect Models to Complex Buying Situations and Other Context Effects

Building on the work of Dhar, Menon, and Maach (2004), this commentary describes how the compromise effect models developed in the work of Kivetz, Netzer, and Srinivasan (2004) can be extended to predict complex (business-to-business) purchase decisions and additional behavioral context effects. The authors clarify their general modeling approach and outline how it applies to choices among solutions (augmented products) and group decision making. They then hypothesize about the influence of business-to-business and technology markets on various context effects (e.g., compromise and asymmetric dominance). They show how the models incorporate various context effects and discuss ideas for further research.

Adaptive Self-Explication of Multiattribute Preferences

The authors propose a Web-based adaptive self-explicated approach for multiattribute preference measurement (conjoint analysis) with a large number (ten or more) of attributes. The proposed approach overcomes some of the limitations of previous self-explicated approaches. The authors develop a computer-based self-explicated approach that breaks down the attribute importance question into a ranking of attributes followed by a sequence of constant-sum paired comparison questions. In the proposed approach, the questions are chosen adaptively for each respondent to maximize the information elicited from each paired comparison question. Unlike the traditional self-explicated approach, the proposed approach provides standard errors for attribute importance. In two studies involving digital cameras and laptop computers described on 12 and 14 attributes, respectively, the authors find that the ability to correctly predict validation choices of the proposed adaptive approach is substantially and significantly greater than that of adaptive conjoint analysis, the fast polyhedral method, and the traditional self-explicated approach. In addition, the adaptive self-explicated approach yields a significantly higher predictive validity than a nonadaptive fractional factorial constant-sum paired comparison design.

Extracting Product Comparisons from Discussion Boards

In recent years, product discussion forums have become a rich environment in which consumers and potential adopters exchange views and information. Researchers and practitioners are starting to extract user sentiment about products from user product reviews. Users often compare different products, stating which they like better and why. Extracting information about product comparisons offers a number of challenges; recognizing and normalizing entities (products) in the informal language of blogs and discussion groups require different techniques than those used for entity extraction in the more formal text of newspapers and scientific articles. We present a case study in extracting information about comparisons between running shoes and between cars, describe an effective methodology, and show how it produces insight into how consumers view the running shoe and car markets.

Dynamic Targeted Pricing in B2B Relationships

We model the multifaceted impact of pricing decisions in business-to-business B2B relationships that are governed by trust. We show how a seller can develop optimal intertemporal targeted pricing strategies to maximize profits over time while taking into consideration the impact of pricing decisions on short-term profit margin, reference price formation, and long-term relationships. Our modeling framework uses a hierarchical Bayesian approach to weave together a multivariate nonhomogeneous hidden Markov model, buyer heterogeneity, and control functions to facilitate targeting, capture the evolution of trust, and control for price endogeneity. We estimate our model on longitudinal transactions data from a retailer in the industrial consumables domain. We find that buyers in our data set can be best represented by two latent states of trust toward the seller---a “vigilant” state that is characterized by heightened price sensitivity and a cautious approach to ordering and a “relaxed” state with purchase behaviors that are consistent with high relational trust. The sellers pricing decisions can transition buyers between these two states. An optimal dynamic and targeted pricing strategy based on our model suggests a 52% improvement in profitability compared with the status quo. Furthermore, a counterfactual analysis examines the sellers optimal pricing policy under fluctuating commodity prices.

Using text mining to analyze user forums

Product discussion boards are a rich source of information about consumer sentiment about products, which is being increasingly exploited. Most sentiment analysis has looked at single products in isolation, but users often compare different products, stating which they like better and why. We present a set of techniques for analyzing how consumers view product markets. Specifically, we extracted relative sentiment analysis and comparisons between products, to understand what attributes users compare products on, and which products they prefer on each dimension. We illustrate these methods in an extended case study analyzing the sedan car markets.

The Shape of Marketing Research in 2021

It’s humbling when I think back 10 years: no broadband, no social media, no smartphones, no 50-inch LED TVs, no DVRs, no e-readers, no iPods, and Google hadn’t had its IPO. The term “co-creation” barely was taking off—now my company is into “crowdsourcing” [Howe, 2006; Whitla, 2009]. In the last decade, many industries went through what Andy Grove labeled “strategic-inflection points”— those moments when the balance of forces shifted from the old structure and the old ways of doing business and competing, to new ones [Grove, 1996]: the music business, the book business, the publishing business, even the original Internet leader, AOL. Will my business be next? What will be the “normal” 10 years from now? What will be the “next big things?” I do know that “digitization of everything” will be the mantra. I am certain the rate of change will keep accelerating—after all, Facebook went from nothing to 500 million users in just 6 years. And we finally realized that we marketers are not in control anymore. I know that Internet access anywhere will be a given; that geo-marketing will be pervasive; that retail environments will be transformed by digital technologies; that smartphone capabilities will be far more advanced; that RFID will have a big impact (even though I can’t tell how big); that privacy will be even more of an issue. And none of this even touches changes that won’t be driven by technology: the global economic balance of power will shift substantially in the next decade, driven by the BRIC economies and led by China. I also know that all of this is only the tip of the iceberg—I just can’t see the eight-ninths beneath the surface yet. The basics of marketing don’t change: I still need to identify, develop, and market products and services that satisfy customer needs even as they keep me ahead of the competition. I must do a better job in several ways. I need to be better at anticipating the future, at sensing consumer and customer needs, at being faster to market, at communicating and interacting with consumers and customers, at understanding and delivering against consumer needs around the world, and at recognizing potential inflexion points that could either bring great potential or destroy my business. I wonder what the “new normal” will be...

Idea Generation, Creativity, and Prototypicality

We explore the use of big data tools to shed new light on the idea generation process, automatically “read” ideas to identify promising ones, and help people be more creative. The literature suggests that creativity results from the optimal balance between novelty and familiarity, which can be measured based on the combinations of words in an idea. We build semantic networks where nodes represent word stems in a particular idea generation topic, and edge weights capture the degree of novelty versus familiarity of word stem combinations (i.e., the weight of an edge that connects two word stems measures their scaled co-occurrence in the relevant language). Each idea contains a set of word stems, which form a semantic subnetwork. The edge weight distribution in that subnetwork reflects how the idea balances novelty with familiarity. Based on the “beauty in averageness” effect, we hypothesize that ideas with semantic subnetworks that have a more prototypical edge weight distribution are judged as more creativ...

Beyond the Target Customer: Social Effects of Customer Relationship Management Campaigns

Customer relationship management (CRM) campaigns have traditionally focused on maximizing the profitability of the targeted customers. The authors demonstrate that in business settings characterized by network externalities, a CRM campaign that is aimed at changing the behavior of specific customers propagates through the social network, thereby also affecting the behavior of nontargeted customers. Using a randomized field experiment involving nearly 6,000 customers of a mobile telecommunication provider, they find that the social connections of targeted customers increase their consumption and become less likely to churn, due to a campaign that was neither targeted at them nor offered them any direct incentives. The authors estimate a social multiplier of 1.28. That is, the effect of the campaign on first-degree connections of targeted customers is 28% of the effect of the campaign on the targeted customers. By further leveraging the randomized experimental design, the authors show that, consistent with a network externality account, the increase in activity among the nontargeted but connected customers is driven by the increase in communication between the targeted customers and their connections, making the local network of the nontargeted customers more valuable. These findings suggest that in targeting CRM marketing campaigns, firms should consider not only the profitability of the targeted customer but also the potential spillover of the campaign to nontargeted but connected customers.

The Functional Alibi

Spending money on hedonic luxuries often seems wasteful, irrational, and even immoral. We propose that adding a small utilitarian feature to a luxury product can serve as a functional alibi, justifying the indulgent purchase and reducing indulgence guilt. We demonstrate that consumers tend to inflate the value, and usage frequency, of utilitarian features when they are attached to hedonic luxuries. Using a mixed-method approach, combining archival data (an analysis of over 1,000 online reviews of handbags) with studies conducted in the field and laboratory, we establish the functional alibi effect and show that it is mediated by guilt and more likely to occur when the luxury purchase is perceived as frivolous and expensive, and when the purchase is for oneself rather than a gift. We explore the effect of adding a functional alibi in a variety of marketing contexts, and we examine various consumer populations representing diverse demographics.