Conference Sessions - Jesse H. Jones Graduate School of ...

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TC14 ■ TC14 Champions Center VI Consumer Responses to Pricing Cluster: Special Sessions Invited Session Chair: Anja Lambrecht, London Business School, Regent’s Park, London, NW1 4SA, United Kingdom, alambrecht@london.edu 1 - Starting Prices as Catalysts for Consumer Response to Customization Marco Bertini, London Business School, Regent’s Park, London, NW1 4SA, United Kingdom, mbertini@london.edu, Luc Wathieu Consumers often make decisions about products that require a certain degree of customization. From the perspective of the firm, providing customization is costly, but it can lead to greater value creation and surplus extraction. However, success of a customization strategy is contingent on the presence of customers who are responsive and prepared to pay for this benefit. The goal of this research is to propose and test a theoretical link between the presence of a starting (base) price and consumer engagement in customization: starting prices make consumers realize that the good has a strong component of customization. Specifically, we propose that starting prices act to split the total expense into a common component and a component that purchasers can attribute to their idiosyncratic tastes. This perception catalyzes consumer response to customization, as it causes positive process evaluations and a greater willingness to pay for the difference between the starting price and the final price. 2 - Free vs. Fee: Pricing of Online Content Services Kanishka Misra, London School of Business, Regent’s Park, London, NW1 4SA, United Kingdom, kmisra@london.edu, Anja Lambrecht Online content providers face the challenge whether to offer content for free, against a fee, or some hybrid of the two. We hypothesize that this varies with the demand for content and the distribution of customer valuation of the service. We develop a model and provide empirical evidence for when firms should price online content. We use empirical data from a website on the amount of content that is free or offered against a fee as well as demand (click stream) of the site across time. Our results suggest that the amount of content offered against a fee varies and increases when content is more attractive to a larger number of customers due to specific demand shocks. 3 - Private Label Response to National Brand Promotions: A Field Experiment Eric Anderson, Hartmarx Professor of Marketing, Northwestern University, Kellogg School of Management, 2001 Sheridan Road, Evanston, IL, 60208, United States of America, eric-anderson@kellogg.northwestern.edu, Karsten Hansen, Duncan Simester Recently there has been an active debate about whether retailers react to trade promotions by lowering the prices of competing products. A simple category pricing model suggests that if a promotion on one item affects demand for other items it will generally be profitable to change the prices of all affected items. We report on the results of a large-scale field experiment that investigated how private label brands should be priced when a national brand is promoted. Our experiment focuses on 28 “copycat” private label brands and the corresponding national brand sold in over 6,000 stores of a national retailer. We experimentally vary the price level of the copycat private label brand when the national brand is promoted. Our econometric model integrates historical and experimental data in a unified framework. The findings confirm that shielding is an effective strategy for preserving the sales volume and profit contribution of the private label items. It can lead to higher profits not just on the private label items, but also in aggregate. The size of the shielding discount is important. If the shielding discounts are too large they erode too much margin, while small shielding discounts may not preserve enough private label demand. 4 - Paying with Money or with Effort: Pricing when Customers Anticipate Hassle Anja Lambrecht, London Business School, Regent’s Park, London, NW1 4SA, United Kingdom, alambrecht@london.edu, Catherine Tucker For many services, customers subscribe to long-term contracts. We suggest that rather than evaluating multi-period service contracts at the contract-level, customers use period-level bracketing. This means they evaluate the contract as the sum of distinct per-period utilities. This has important consequences when utility varies over the course of the contract, for example due to “hassle costs.” If customers use period-level bracketing, they will value a lower price more in periods where they have hassle than in other periods. We explore this using data from a field experiment for web hosting services. The field experiment had 2 hassle cost priming conditions (present, absent) x 2 discount conditions (offered, not offered). We find that a lower price in the initial period is more attractive to customers when they expect their hassle costs to be high at set-up. In six lab experiments, we support and extend the field experiment’s findings. Importantly, we find evidence for period-level bracketing when customers have hassle costs independently of whether hassle costs occur in the first, an intermediate or the last period of a contract. We rule out alternative explanations, such as hyperbolic discounting. Our findings suggest that in setting prices, firms should consider the timing of hassle costs faced by customers. MARKETING SCIENCE CONFERENCE – 2011 26 ■ TC15 Champions Center V CRM I: Customer Lifetime Value Contributed Session Chair: Zainab Jamal, Hewlett Packard Labs, 1501 Page Mill Road, Palo Alto, CA, United States of America, zainab.jamal@hp.com 1 - Payments as a Virtual Lock-in: Customers’ Profitability over Time in the Presence of Payments Irit Nitzan, Tel Aviv University, The Recanati Graduate School, of Business Administration, Tel Aviv, 69978, Israel, iritnitz@tau.ac.il, Barak Libai, Danit Ein-Gar In this project, we demonstrate how customer defection is affected by the mere use of a payment mechanism. We show that offering customers an opportunity to pay over time leads them to perceive payments as switching costs. As a result, customers change their defection behavior, and consequently their lifetime value to the firm. We use a combination of aggregate data from a cellular company and experiments to exhibit how customers may perceive payments as switching costs, and how this changes their behavior and profitability over time. Our results are very relevant for the current public discussion on customers’ switching costs in industries such as the cellular industry, and are of key importance to firms given the immediate effect of retention on the bottom line. 2 - A New Model Proposal to Churn Management Omer Faruk Seymen, Sakarya University, Sakarya University Esentepe Kampusu UZEM, Sakarya, Turkey, ofseymen@sakarya.edu.tr, Abdulkadir Hiziroglu Predicting customer churn in the scope of relationship management has been receiving great attention by companies. Acquiring a new customer costs five times more than retaining an existing customer. To reduce the retention cost, prediction of churners has to be as accurate as possible so that retention campaigns and allocating the resources should be appropriate. In churn management, the majority of these models are utilized to predict churn customers on the next period. In this study, a new churn model is proposed within this context that not only next period churners can be detected but possible churners in further periods who could be seen “loyal” for next period can also be identified. Detecting these types of customers might allow companies to allocate their resources efficiently and to reduce the cost associated with the retention programs. In the proposed model, historical data from a supermarket retailer which contains millions of transaction data belong to one million customers are used. We proposed that customers visit the company within a time route and this could bring out a churn route pattern which could be derived from churner customers’ behaviors. This findings will be examined by rule based algorithm, neural network, decision trees and regression. The model is tested on 8340 customers and its results were assessed using the historical data. The results of the proposed model were compared to Bayesian network that is a well-known churn predictive model. Comparison results showed that route pattern of churners obtained from their RFM datas have a great importance to realize customer churn behavior, so the proposed model could be useful for marketing practitioners. 3 - Hazards of Ignoring Involuntary Customer Churn Zainab Jamal, Hewlett Packard Labs, 1501 Page Mill Road, Palo Alto, CA, United States of America, zainab.jamal@hp.com, Randolph Bucklin We study the impact of ignoring involuntary churn (when a firm terminates the subscription of a customer) on the estimation and diagnosis of voluntary churn (when the customer terminates the relationship). The presence of competing events has been shown to bias the estimates of the hazard rates and survival times of the main event. In our case, the main event is voluntary churn and the competing event is involuntary churn. We estimate a bivariate Weibull survival model that captures the dependency between the two event times and has been proposed in the literature as an approach to the problem of dependent competing risk. We compare this model with a benchmark model – a univariate Weibull model with only voluntary churn. We estimate the models using maximum likelihood techniques. We find significant differences in the prediction of voluntary churn rates. Also the impact of covariates on the voluntary churn rates is different across the two models. Further, the bivariate Weibull survival model does better in predicting the customers who are more likely to churn. An added dimension of the study is that the key covariates that influence voluntary churn rate impact involuntary churn rate differently. Our study highlights the need to incorporate involuntary churn when modeling the voluntary churn process.
Thursday, 3:30pm - 5:00pm ■ TD01 Legends Ballroom I Choice II: Effects on ... Contributed Session Chair: Linda Court Salisbury, Assistant Professor, Boston College, CSOM, Fulton 441, 140 Commonwealth Ave., Chestnut Hill, MA, 02467, United States of America, salisbli@bc.edu 1 - Incorporating State Dependence in Aggregate Market Share Models Polykarpos Pavlidis, PhD Candidate, Simon Graduate School of Business, University of Rochester, Rochester, NY, 14627, United States of America, pavlidisp2@simon.rochester.edu, Dan Horsky, Minjae Song An empirical investigation of individual household level data in twenty CPG categories (IRI Marketing Data set, 2008) uncovers that state dependence is a significant determinant of consumers’ choices in all twenty product categories. In light of this finding, we examine whether and how one can incorporate state dependence in a demand model of frequently purchased goods estimated with market level data. Aggregated data is more often available and is frequently used in applied research in marketing and economics. The incorporation of consumer choice dynamics in these market share models can potentially increase their applicability and improve their predictive behavior. 2 - Complexity Effects on Choice Experiment-based Model Performance Benedict Dellaert, Erasmus University Rotterdam, P.O. Box 1738, Rotterdam, 3000 DR, Netherlands, dellaert@ese.eur.nl, Bas Donkers, Arthur van Soest Understanding what drives choice experiment-based model performance helps firms to better evaluate future marketing actions when using such models. This research investigates choice experiment complexity (i.e., the number of alternatives, number of attributes, and utility similarity between the most attractive alternatives) as a key factor to affect choice model performance both within and between complexity conditions. The results show that complexity has a negative direct effect on choice model performance. However, this effect is contingent on consumers’ decision time used relative to the number of normative elementary information processes (EIPs) that the decision task requires, which we take as an approximation of the degree of simplification in the consumer’s decision process. When consumers spend less time per normative EIP, this increases choice model performance within a given choice complexity condition. However, it decreases choice model performance between complexity conditions. We also introduce a practical modeling approach that captures these direct and indirect effects of choice complexity and hence enables predictions between different choice complexity conditions. 3 - The Interplay of Reference Dependence and Choice Set Formation in Replacement Decisions Lianhua Li, PhD Student, University of Alberta, Faculty of Business, Edmonton, AB, Canada, lianhua@ualberta.ca, Paul Messinger, Joffre Swait The literature presents evidence supporting the expectation of a strong preference for the status quo good in replacement decisions. Prospect theory predicts this tendency; however, prospect theory ignores possible choice set formation effect, assuming that all goods (status quo and new goods) will be evaluated, using the status quo as reference point, and then compared to reach a final choice. Nonetheless, several explanations for the exaggerated preference for status quo goods in prior literature predict the possibility of choice set formation revolving around status quo products. We therefore hypothesize that replacement decisions are subject to both referencedependence and choice set formation effect. To test this hypothesis, we develop and compare three choice models: a pure reference-dependence model, a pure choice set formation model, and a hybrid model that accounts for both reference-dependence and choice set formation. Using experimental choice data, we find that the hybrid model greatly outperforms the first two models, which strongly suggests the coexistence of reference-dependence and choice set formation in replacement decisions. We also show that omitting either process leads to estimation bias in the parameters of the included process. Just as the reference-dependence model based on prospect theory has led to improved understanding and prediction of choice, we believe “upgrading” it to include choice set formation effect will further enhance these benefits. 4 - Does Choice Set Formation Drive the Diversification Effect? A Model and Experimental Evidence Linda Court Salisbury, Assistant Professor, Boston College, CSOM, Fulton 441, 140 Commonwealth Ave., Chestnut Hill, MA, 02467, United States of America, salisbli@bc.edu, Fred M. Feinberg Diversification – exhibiting greater variety as multiple choices are made together, in advance of consumption – is a robust and important phenomenon (e.g., purchasing groceries, queuing Netflix films). Studies of diversification tend to focus on choice alone, conceptualizing it as inherently comparative. This precludes the possibility that diversification is driven largely by choice set formation, wherein each item needs to 27 MARKETING SCIENCE CONFERENCE – 2011 TD02 surpass a (latent) threshold to even be compared with others available. We examine diversification via an experimental choice sequence task and dual-component stochastic model (choice set formation; choice-from-set). The experiment manipulates two key diversification drivers in prior literature: whether choices are made all at once (“simultaneously”) vs. separately (“sequentially”), and the relative attractiveness of available options. We find that diversification may be primarily a phenomenon of choice set formation: people do penalize their previously chosen option, but only for simultaneous choices, and only in the set formation portion of the model. Thus, prior model-based evidence of variety-seeking in diversification may have been misinterpreted as a discounting of an item’s features at the stage of choice, as opposed to a failure to ‘consider’ the option at all. That is, failing to methodologically incorporate choice set formation may erroneously suggest that consumers are using a diversification choice heuristic when they may instead be diversifying the alternatives in their ‘consideration set.’ Furthermore, results suggest that the expected number of items thus ‘considered’ is substantially greater in multiple-, versus single-, item choice. ■ TD02 Legends Ballroom II Online Advertising - II Cluster: Internet and Interactive Marketing Invited Session Chair: Harald van Heerde, University of Waikato, Private Bag 3105, Hamilton, 3240, New Zealand, heerde@waikato.ac.nz 1 - Connecting Social Media with Television Advertising and Online Search Yanwen Wang, PhD, Emory University, 1300 Clifton Rd, Atlanta, GA, 30033, United States of America, yanwen_wang@bus.emory.edu In an attempt to muscle through turbulent economic times, the U.S. automobile industry has embraced social media. Ford pioneered the practice of hiring syndicated bloggers to serve as brand advocates, while much of BMW’s recent success is attributed to its engagement with consumers across Facebook, Twitter, and YouTube. While there is research looking at the impact of social media on firm outcomes – be they sales or other metrics like stock-returns – little, if any, research is undertaken on understanding how social media impact consumers’ online search for automobiles. Understanding the impact of social media on consumer search is very important as online search informs consumer’s consideration-sets, which in turn affect their final choice of an automobile. By combining three unique datasets, namely: (i) consumerlevel click-stream search data for automobiles, (ii) consumer-directed television advertising and (iii) social media (volume and sentiments of buzz spanning blogs, newspaper articles, newsgroups, online forums, etc.), this study sets out to answer the following questions. One, how does the volume and sentiment of online buzz impact the volume of a consumer’s online branded search? Two, how do these effects compare with the impact of television advertising on online search? Three, are there synergies between online buzz and television advertising on online search? How do these effects systematically vary across consumers and geographic markets? After calibrating our model with these aforementioned data, we offer insights for advertising and targeted advertising interventions to undo negative buzz generated through product recalls. 2 - Investigating Advertisers’ View of Online and Print Media: Complements or Substitutes? Shrihari Sridhar, Assistant Professor of Marketing, Michigan State University, Eli Broad College of Business, N300 Marketing Department, East Lansing, MI, 48824, United States of America, sridhar@bus.msu.edu, S. Sriram Faced with declining profits, print newspapers have turned hybrid, catering to both print and online audiences. As a multi-channel platform, the hybrid newspaper needs readers and advertisers across both print and online channels to be on board. This requires knowledge not only about the interrelatedness in demand between readers and advertisers, but also about whether advertisers perceive the print and online media-channels to be complements or substitutes. Extant research has investigated whether readers find online and print newspapers to be complements or substitutes. But less is known about how advertisers, who provide 80% of newspaper revenue, choose to allocate their media dollars between the online and print version of a multi-channel platform. From a newspaper’s standpoint, understanding advertisers’ preferences for allocating advertising expenses between print and online media would be useful to develop targeted pricing and salesforce strategies. We use advertiser-level data from a large hybrid newspaper in the US from 2005-2010, pertaining to the amount of media dollars purchased across three outlets (print-weekday, print-Sunday and online) by quarter. We estimate a multiple discrete-continuous extreme value model (MDCEV) of advertisers’ simultaneous choice (i.e., which combination of media to advertise in) and the corresponding expenditure in each medium. While performing this analysis, we control for temporal variation in print and online readership as well as salesforce efforts directed at each advertiser. We subsequently investigate how this decision is affected by advertisers’ baseline utility for each medium, growth in the readership base across channels and also by their perceived substitutability/complementarity across channels.
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