ࡱ> cslmnonmlkjihgfed@ Q<bjbjFF ,,X+2LLL.z$TN4N4N44L27,$j=zL4LLMV9\4m]`bbbbbb$ŜR>cUV>c>ct t LM [ggg>c(t $L6Mg>c`gg$gM^= PhN4fcgq0gc`g4D4t t gh ^:C_g#``g ^ ^ ^$ D_g  Copyright by Doug Walker, 2008 ALL RIGHTS RESERVED INCORPORATING COMPETITOR DATA INTO CUSTOMER RELATIONSHIP MANAGEMENT A Dissertation Presented to the Faculty of the C.T. Bauer College of Business University of Houston In Partial Fulfillment Of the Requirements for the Degree Doctor of Philosophy by Doug Walker May, 2008 INCORPORATING COMPETITOR DATA INTO CUSTOMER RELATIONSHIP MANAGEMENT APPROVED: ________________________________________________ James D. Hess, Bauer Professor of Marketing Science Chairperson of Committee ________________________________________________ Michael Ahearne, Associate Professor of Marketing ________________________________________________ Niladri B. Syam, Associate Professor of Marketing ________________________________________________ Christian J. Murray, Associate Professor of Economics ________________________________________________ Arthur D. Warga, Dean C. T. Bauer College of Business ACKNOWLEDGEMENTS My sincerest thanks to my dissertation committee chairman, Jim Hess; the members of my committee, Mike Ahearne, Niladri Syam, and Chris Murray; and the rest of the faculty, students and staff - none of this would have been possible without you. To my wife, Kim, and my daughters, Jessica and Rachael, thank you for your love, encouragement, patience, sacrifice, and prayers. I love you with all of my heart. INCORPORATING COMPETITOR DATA INTO CUSTOMER RELATIONSHIP MANAGEMENT Abstract Fueled by technological innovation, customer relationship management (CRM) research and practices have been driven primarily by the exponential growth in customer transaction data held by firms. Consideration of the competition has largely been lost in this flood of firm-focused data. The practice of CRM seems to have strayed from its market orientation roots. Academic leaders in the field of CRM have called for research incorporating competitor data. Researchers are beginning to answer that call. Few would argue that the availability of competitor data enhances CRM decision making, including the allocation of marketing effort. However, since in most contexts competitor data is difficult and expensive to acquire, how important is it to the firm? This study shows that in a pharmaceutical context the firms marketing effort allocation decisions would fundamentally change based on the availability of competitor data to be used in the analysis. Specifically, when the firm does not consider the competitions marketing efforts and customers perceptions of the competing brands, the estimates of response to the firms marketing efforts are biased for a sizeable minority of the firms customers, leading to a misallocation of the firms resources. Since this type of data is typically available for only a portion of the firms customers, it must be imputed for the rest of the customers in the database. A data augmentation method that imputes a composite of the data collected via a survey for customers that did not participate in the survey is presented. Results using this method outperforms a model using firm data only and a model using firm data and survey data on the perceived characteristics of each brand, even if the perceived drug characteristics are known for all of the customers. TABLE OF CONTENTS List of Tables vii List of Figures viii Introduction 1 Literature Review 4 Omitted Variables 11 Model 13 Alternative Models 22 Data 29 Estimation 36 Results 40 Data Augmentation 48 Future Research 52 Contributions 54 Appendix: Physician Survey 55 References 56 LIST OF TABLES Table 1: Correlations Among Detailing Levels Across Brands 13 Table 2: Variables Included in Alternative Models Based on Data Availability 24 Table 3: Comparison of Category Representation for Respondents and Non-Respondents 32 Table 4: Comparison Between Survey Respondents and Non-Respondents 33 Table 5: Competitor Detailing Model Based on Survey Data 35 Table 6: Firm Model Results 40 Table 7: Effectiveness Model Results 41 Table 8: Effectiveness and Competitor Effort Model Results 42 Table 9: Physician Class Assignments Comparison: Firm vs. Effectiveness and Competitor Effort Model 43 Table 10: Physician Class Assignments Comparison: Effectiveness vs. Effectiveness and Competitor Effort Model 44 Table 11: Comparison of Class Profiles Between Models 45 Table 12: Reallocation Results for Each Alternative Model 48 Table 13: Accuracy of Segment Assignment Using Augmented Data for 50 Physicians in Holdout Sample 51 LIST OF FIGURES Figure 1: Graphical Representation of the Databases Used in the Study 31  SEQ Hd \* MERGEFORMAT 1. INTRODUCTION Customer relationship management (CRM) has naturally evolved within firms that embrace the concept of a market orientation, where the firm is focused on generating customer-focused market intelligence, disseminating that intelligence, and responding to it  ADDIN EN.CITE Kohli1990898917Kohli, Ajay K.Jaworski, Bernard J.Market Orientation: The Construct, Research Propositions, and Managerial ImplicationsJournal of MarketingJournal of Marketing1-18542MARKET orientationMARKETING -- ManagementINDUSTRIAL managementRESEARCHINDUSTRIAL organizationMARKETING researchBUSINESS planningMARKETING strategyRESEARCH, IndustrialBUSINESS logisticsRESEARCH1990American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=9602205182&site=ehost-live (Kohli and Jaworski 1990). The emphasis firms place on analytical CRM, which utilizes customer databases, has exploded in recent years as improving technology allows firms to collect, store, and analyze customer data ever more efficiently and less expensively than ever before. The evolution of CRM has been driven by the customer data firms have chosen to use to guide marketing activities. Early segmentation efforts focused primarily on demographic differences among the firms customers. Firms made the implicit assumption that customers that are similar demographically will respond to a particular marketing appeal in a similar manner  ADDIN EN.CITE Kotler199491see 916Philip KotlerGary ArmstrongPrinciples of Marketing6th1994Englewood Cliffs, New JerseyPrentice-Hall, Inc.(see Kotler and Armstrong 1994). Next, firms began to consider transactional data in addition to customer demographics to inform marketing effort decisions. Both researchers and practitioners became interested in the recency, frequency, and monetary value (RFM) of a customers purchase history  ADDIN EN.CITE Drozdenko200292see 926Drozdenko, R. G.Drake, P. D.Optimal Database Marketing: Strategy, Development, and Data Mining2002Thousand Oaks, CaliforniaSage Publications Inc(see Drozdenko and Drake 2002). Appreciation of an estimated lifetime value of a customer (LTV), or customer lifetime value (CLV), gained in prominence  ADDIN EN.CITE Berger199877e.g. 7717Berger, Paul D.Nasr, Nada I.Customer Lifetime Value: Marketing Models and ApplicationsJournal of Interactive MarketingJournal of Interactive Marketing17-30121MARKETING1998WinterJohn Wiley & Sons, Inc. / Businesshttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=348356&site=ehost-live (e.g. Berger and Nasr 1998). Interest in CRM exploded as improving technology allowed the creation of extensive customer databases, documenting not only a customers purchases, but the marketing efforts directed at the customer as well. In fact, firms were now able to capture virtually all of the interactions between the customer and the firm, regardless of which party initiated the contact. Statistically based methods, such as latent class modeling  ADDIN EN.CITE Wedel200094946Wedel, MichelKamakura, Wagner A.Market Segmentation: Conceptual and Methodological Foundations2nd2000BostonKluwer Academic Publishers(Wedel and Kamakura 2000) and concomitant variable methods, where segments defined by transactional variables can be described using demographic variables  ADDIN EN.CITE Gupta199493e.g. 9317Gupta, SachinChintagunta, Pradeep K.On Using Demographic Variables to Determine Segment Membership in Logit Mixture ModelsJournal of Marketing ResearchJournal of Marketing Research128-136311PROBABILITIESMARKET segmentationECONOMETRIC modelsESTIMATION theoryLOGITSMEMBERSHIPMETHODOLOGYHOUSEHOLDS1994American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=9411156708&site=ehost-live (e.g. Gupta and Chintagunta 1994), generated interest. Although CRM has its roots in market orientation, up until now, a key component of market orientation, the competition, had been largely ignored  ADDIN EN.CITE Boulding2005373717Boulding, W.Staelin, R.Ehret, M.Johnston, W. J.A Customer Relationship Management Roadmap: What Is Known, Potential Pitfalls, and Where to GoJournal of MarketingJournal of Marketing155-666942005(Boulding et al. 2005). Kohli and Jaworski  ADDIN EN.CITE Kohli1990898917Kohli, Ajay K.Jaworski, Bernard J.Market Orientation: The Construct, Research Propositions, and Managerial ImplicationsJournal of MarketingJournal of Marketing1-18542MARKET orientationMARKETING -- ManagementINDUSTRIAL managementRESEARCHINDUSTRIAL organizationMARKETING researchBUSINESS planningMARKETING strategyRESEARCH, IndustrialBUSINESS logisticsRESEARCH1990American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=9602205182&site=ehost-live (1990) emphasize the consideration of key exogenous factors, including the competition, during intelligence generation in a market-oriented firm. Narver and Slater  ADDIN EN.CITE Narver1990909017Narver, John C.Slater, Stanley F.The effect of a market orientation on business profitabilityJournal of MarketingJournal of Marketing20-35544MARKET orientationCORPORATIONS -- ValuationMARKETING researchMARKETINGBUSINESS forecastingORGANIZATIONAL effectivenessINDUSTRIAL managementCOMPETITIVE advantageBUSINESS enterprises -- FinanceEconomic aspects1990American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=9102183223&site=ehost-live (1990) concur, identifying competitor orientation as a basic component of market orientation. The primary reason the competition had been widely ignored then was the same as it is today, data availability. Firms did not have easy access to data detailing customer interaction with the competition, as they did for their own interactions with the customer. However, firms in one industry, pharmaceuticals, do enjoy access to sales data for all brands of ethical drugs at the individual physician level. (This level of data accessibility is rare outside of the U.S.) Now, the firm cannot only consider the purchase history of each customer and the marketing effort directed at each customer, but the size of the customer in terms of their total category demand within a particular category. Although the response models had become more comprehensive with the inclusion of competitor sales data, an important limitation still remained. The marketing effort of the competition was still being largely ignored, introducing an omitted variable problem that could potentially impact the estimates of the response parameters  ADDIN EN.CITE Manchanda20048817Manchanda, PuneetChintagunta, Pradeep K.Responsiveness of Physician Prescription Behavior to Salesforce Effort: An Individual Level AnalysisMarketing LettersMarketing Letters129-145152-32004(Manchanda and Chintagunta 2004). Gonul et al.  ADDIN EN.CITE Gonul20019917Gonul, Fusun F.Carter, FranklinPetrova, ElinaSrinivasan, KannanPromotion of Prescription Drugs and Its Impact on Physicians' Choice BehaviorJournal of MarketingJournal of Marketing79-90653PHYSICIANSMEDICINE -- Formulae, receipts, prescriptionsPATIENTSDRUGS -- Marketing2001American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4856485&site=ehost-live (2001) and Venkataraman and Stremersch  ADDIN EN.CITE Venkataraman2007818117Venkataraman, SriramStremersch, StefanThe Debate on Influencing Doctors' Decisions: Are Drug Characteristics the Missing Link?Management ScienceManagement Science1688-17015311PHYSICIANSDECISION makingMARKETINGDRUGS -- Side effectsPOLITICAL planningSAMPLING2007INFORMS: Institute for Operations Researchhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=27875318&site=ehost-live (2007) utilize datasets containing brand level data on competitor marketing effort for small panels of physicians. Venkataraman and Stremersch  ADDIN EN.CITE Venkataraman2007818117Venkataraman, SriramStremersch, StefanThe Debate on Influencing Doctors' Decisions: Are Drug Characteristics the Missing Link?Management ScienceManagement Science1688-17015311PHYSICIANSDECISION makingMARKETINGDRUGS -- Side effectsPOLITICAL planningSAMPLING2007INFORMS: Institute for Operations Researchhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=27875318&site=ehost-live (2007) also consider the effectiveness and side effects of the brands in the category. However, these are composite measures based on clinical trials and labeling, not as perceived by the physicians. Moon et al.  ADDIN EN.CITE Moon2007838317Moon, SangkilKamakura, Wagner A.Ledolter, JohannesEstimating Promotion Response When Competitive Promotions Are UnobservableJournal of Marketing ResearchJournal of Marketing Research503-515443MARKETING researchSALES promotionMARKETING -- Mathematical modelsSPECIAL events -- MarketingCONSUMERSPSYCHOLOGYMARKOV processesMONTE Carlo methodMATHEMATICAL modelshidden Markov modelhierarchical Bayes analysismissing data problemsales forecastingsales promotion2007American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=25685153&site=ehost-live (2007) incorporate unobserved competitor marketing effort into their analysis via a hidden Markov model. We are unaware of any study to date that focuses on the magnitude of the bias in response to the firms marketing efforts resulting from omitting competitor marketing efforts and physician perceptions of drug characteristics. Equally important, the studies that did incorporate competitor data did not contemplate that this data was available for only a portion of the firms customers. Augmenting the database to include all of the firms customers will also be addressed in this study. Ultimately, physician response can more completely be portrayed as a function of customer demographics, firm sales, firm marketing effort, competitor sales, competitor marketing effort, and physician perceptions of the drugs. This study is unique in that competitor data is considered not for only a small sample of the firms customers, but for all of the firms customers via a survey and data augmentation. Additionally, physician perceptions of drug characteristics are examined, again via a survey and data augmentation. Bias in the estimated response to the firms marketing efforts when this data is ignored will be carefully investigated. Specifically, the analysis will attempt to determine whether the firms marketing allocation decisions would be fundamentally different based on the availability of competitor data.  SEQ Hd \* MERGEFORMAT 2. LITERATURE REVIEW In this section, we will consider several research streams that are relevant to this research. First, we will discuss the papers in the vast CRM literature that incorporate competitor data. Second, since the context of this study involves the marketing of a category of ethical drugs, we will review relevant papers in the pharmaceutical sales literature. Third, since the exclusion of competitor data when estimating response can be thought of as a missing data issue, we will look at topics in that literature relevant to this study. Finally, we will discuss applicable data augmentation methods. Competitor Data in CRM CRM researchers are not ambivalent to the importance of considering the competition when making CRM decisions. Numerous studies, concentrating only on firm-specific data, demonstrate CRM can enhance firm profits, at least in the short run  ADDIN EN.CITE Cao200547e.g. 4717Cao, YongGruca, Thomas S.Reducing Adverse Selection Through Customer Relationship ManagementJournal of MarketingJournal of Marketing219-229694ADVERSE selection (Insurance)CUSTOMER relationsMARKETINGRISK (Insurance)SALES promotionCUSTOMER relationship managementcustomer relationship managementadverse selectioncross-sellingdirect marketingbivariate probit2005American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=18302825&site=ehost-live Ryals2005484817Ryals, LynetteMaking Customer Relationship Management Work: The Measurement and Profitable Management of Customer RelationshipsJournal of MarketingJournal of Marketing252-261694BUSINESSCONSUMERSCUSTOMER relationsINDUSTRIAL managementPUBLIC relationsCUSTOMER relationship managementcustomer relationship managementcustomer lifetime valuecustomer strategiescustomer retention2005American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=18302827&site=ehost-live (e.g. Cao and Gruca 2005; Ryals 2005). However, Boulding and colleagues  ADDIN EN.CITE Boulding200537, pg. 1613717Boulding, W.Staelin, R.Ehret, M.Johnston, W. J.A Customer Relationship Management Roadmap: What Is Known, Potential Pitfalls, and Where to GoJournal of MarketingJournal of Marketing155-666942005(2005, pg. 161) state that a failure to integrate competition into a firms CRM activities potentially puts it at serious risk. Bell and his co-authors  ADDIN EN.CITE Bell2002727217Bell, DavidDeighton, JohnReinartz, Werner J.Rust, Roland T.Swartz, GordonSeven Barriers to Customer Equity ManagementJournal of Service ResearchJournal of Service Research77-8551CUSTOMER relationsMARKETING2002Sage Publications Inc.http://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=7017200&site=ehost-live (2002) concur, emphasizing that the learning gained from examining a firms own customers is incomplete without considering prospective customers. In a pharmaceutical context, Manchanda et al.  ADDIN EN.CITE Manchanda2005888817Manchanda, PuneetWittink, DickChing, AndrewCleanthous, ParisDing, MinDong, XiaojingLeeflang, PeterMisra, SanjogMizik, NatalieNarayanan, SridharSteenburgh, ThomasWieringa, JaapWosinska, MartaXie, YingUnderstanding Firm, Physician and Consumer Choice Behavior in the Pharmaceutical IndustryMarketing LettersMarketing Letters293-308163/4PHARMACEUTICAL industryCONSUMER behaviorHEALTH care industryPERSONAL care industryBUSINESS enterprisesQUESTIONS & answersMETHODOLOGYPHYSICIANS (General practice)EDUCATION -- Researchnew productspatient compliancepharmaceutical marketingpharmaceutical pricingphysician networksresponse models2005Springer Science & Business Media B.V.http://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=19558523&site=ehost-live (2005) consider the lack of competitor detailing data to be a major issue. These comments seem relevant to shared customers where the firm enjoys varying shares of those customers total category requirements. The firms share-of-wallet for each customer is one competitor-oriented measure that has received some attention from CRM researchers. Researchers have conceptualized that knowing the firms share-of-wallet can be of value in segmenting a firms customers  ADDIN EN.CITE Reinartz200370e.g. 7017Reinartz, Werner J.Kumar, V.The Impact of Customer Relationship Characteristics on Profitable Lifetime DurationJournal of MarketingJournal of Marketing77-99671BUSINESS forecastingCONSUMER affairs departmentsCUSTOMER relationsDECISION makingECONOMIC forecastingMARKETING -- Decision makingPROFITPUBLIC relationsMONETARY incentives2003American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=8987845&site=ehost-live (e.g. Reinartz and Kumar 2003). The basic premise, which is quite intuitive, is that the firm should focus on customers with substantial category demand, but of which the firm has a small share  ADDIN EN.CITE Anderson2003767617Anderson, James C.Narus, James A.Selectively Pursuing More of Your Customer's BusinessMIT Sloan Management ReviewMIT Sloan Management Review42-49443BUSINESS planningKNOWLEDGE managementPROFITABILITYMARKETING strategyCUSTOMER relationship management2003SpringSloan Management Reviewhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=9547973&site=ehost-live (Anderson and Narus 2003). There is some empirical support for this approach  ADDIN EN.CITE Reinartz2005717117Reinartz, WernerThomas, Jacquelyn S.Kumar, V.Balancing Acquisition and Retention Resources to Maximize Customer ProfitabilityJournal of MarketingJournal of Marketing63-79691CUSTOMER relationsMARKETINGPROFITABILITYRESOURCE managementCUSTOMER retention2005American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=15403072&site=ehost-live (Reinartz, Thomas, and Kumar 2005). Share-of-wallet has commonly been conceptualized as a measure of customer loyalty and used as a proxy for competitor effort  ADDIN EN.CITE Bowman200468e.g. 6817Bowman, DouglasNarayandas, DasLinking Customer Management Effort to Customer Profitability in Business MarketsJournal of Marketing ResearchJournal of Marketing Research433-447414CORPORATE profitsCUSTOMER servicesMARKETINGCUSTOMER relationship management2004American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=15403002&site=ehost-live Reinartz2005717117Reinartz, WernerThomas, Jacquelyn S.Kumar, V.Balancing Acquisition and Retention Resources to Maximize Customer ProfitabilityJournal of MarketingJournal of Marketing63-79691CUSTOMER relationsMARKETINGPROFITABILITYRESOURCE managementCUSTOMER retention2005American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=15403072&site=ehost-live (e.g. Bowman and Narayandas 2004; Reinartz et al. 2005). Share-of-wallet has been found to positively impact customer profitability  ADDIN EN.CITE Reinartz2005717117Reinartz, WernerThomas, Jacquelyn S.Kumar, V.Balancing Acquisition and Retention Resources to Maximize Customer ProfitabilityJournal of MarketingJournal of Marketing63-79691CUSTOMER relationsMARKETINGPROFITABILITYRESOURCE managementCUSTOMER retention2005American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=15403072&site=ehost-live (Reinartz et al. 2005) and has been theorized to mediate the effect of customer retention on profits  ADDIN EN.CITE Zeithaml1985737317Zeithaml, Valarie A.The New Demographics and Market FragmentationJournal of MarketingJournal of Marketing493BUSINESSMENGROCERY tradeMANUFACTURESMARKET segmentationSHOPPINGSUPERMARKETSDEMOGRAPHYMARITAL statusPOPULATIONUNITED States1985SummerAmerican Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=5002043&site=ehost-live (Zeithaml 1985). Several papers have taken the findings that share-of-category requirements are predictive of customer profitability as incentive to devise methods to estimate the share-of-wallet for a firms customers. The underlying assumption, of course, is that knowing this information will result in better informed CRM decisions. Bhattacharya et al.  ADDIN EN.CITE Bhattacharya1996696917Bhattacharya, C. B.Fader, Peter S.Lodish, Leonard M.DeSarbo, Wayne S.The Relationship Between the Marketing and Share of Category RequirementsMarketing LettersMarketing Letters5-1871BRAND choiceCONSUMER behaviorCONSUMER satisfactionCONSUMERS' preferencesPRODUCT managementRELATIONSHIP marketingmarketing mixshare of category requirementsscanner databrand loyalty1996Springer Science & Business Media B.V.http://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=7579181&site=ehost-live (1996) looked at the relationship between share-of-category requirements and the marketing mix. They found a small but significant relationship, but cautioned against making causal claims. Du, Kamakura and Mela  ADDIN EN.CITE Du2007535317Du, Rex YuxingKamakura, Wagner A.Mela, Carl F.Size and Share of Customer WalletJournal of MarketingJournal of Marketing94-113712CONSUMERS -- ResearchCUSTOMER relationsCUSTOMER servicesMARKETING researchCUSTOMER relationship managementCONSUMER profilingcustomer relationship managementshare of walletshare-of-category requirementslist augmentationdatabase marketing2007American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=24279399&site=ehost-live (2007) prescribe a larger investment in large category-demand, low category-share customers, and propose a database augmentation method that estimates share-of-wallet. Pharmaceutical Sales This study incorporates sales effort in the form of detailing, but does not investigate salespeople. In fact, the analysis focuses on the customers. In the context of ethical drug sales, the customers are the physicians. Although a review of sales research in general is not appropriate, a summary of the pharmaceutical sales literature will be of value in presenting the context for this study. Gonul et al.  ADDIN EN.CITE Gonul20019917Gonul, Fusun F.Carter, FranklinPetrova, ElinaSrinivasan, KannanPromotion of Prescription Drugs and Its Impact on Physicians' Choice BehaviorJournal of MarketingJournal of Marketing79-90653PHYSICIANSMEDICINE -- Formulae, receipts, prescriptionsPATIENTSDRUGS -- Marketing2001American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4856485&site=ehost-live (2001) utilize a physician-level database that includes prescription writing, detailing, and sampling by brand for a small panel of physicians. Their response model accommodates physician heterogeneity over three latent classes via the intercept term, but assumes the impact of detailing and sampling on prescription writing is constant across brands and physicians. The multinomial logit model does not allow for consideration of persistence in physician prescription writing behavior over time. The public-policy motivated findings suggest detailing and sampling serve primarily an informative role. Using a fixed-effects model, physician-specific effects are considered by Mizik and Jacobson  ADDIN EN.CITE Mizik2004868617Mizik, NatalieJacobson, RobertAre Physicians "Easy Marks"? Quantifying the Effects of Detailing and Sampling on New PrescriptionsManagement ScienceManagement Science1704-17155012PHARMACEUTICAL industryMARKETINGSAMPLES (Commerce)SALES managementPHYSICIANSREGRESSION analysisMANAGEMENT scienceMARKETING strategyDRUGS -- PrescribingDRUGS -- Marketingpharmaceutical marketingsalesforce effectiveness2004INFORMS: Institute for Operations Researchhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=15666973&site=ehost-live (2004). The authors also include lagged prescriptions to allow for physician preferences to persist over time. Competitor marketing effort is excluded from their database. Their analysis shows that detailing and sampling do impact prescription behavior, although the effects are small. Using Bayesian methods, Manchanda and Chintagunta  ADDIN EN.CITE Manchanda20048817Manchanda, PuneetChintagunta, Pradeep K.Responsiveness of Physician Prescription Behavior to Salesforce Effort: An Individual Level AnalysisMarketing LettersMarketing Letters129-145152-32004(2004) are able to investigate physicians response to detailing at the individual physician level. They focus on the total number of prescriptions written in a particular drug category and find that detailing does positively influence the number of prescriptions written, although, as expected, at a decreasing marginal rate. The marketing efforts of the competition are not considered. A discussion of the potential benefits of reallocating details is included in the study. The potential endogeneity inherent in a pharmaceutical sales response model is analyzed by Manchanda, Rossi and Chintagunta  ADDIN EN.CITE Manchanda20045517Manchanda, PuneetRossi, Peter E.Chintagunta, Pradeep K.Response Modeling with Nonrandom Marketing-Mix VariablesJournal of Marketing ResearchJournal of Marketing Research467-478414DIRECT marketingMARKETING -- Mathematical modelsPHYSICIANSSALES prospectingCUSTOMER relationship management2004American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=15403005&site=ehost-live (2004). They model the number of prescriptions written in the category as a function of detailing, but then make detailing dependent on the parameters of the response function. They report that accounting for reverse causality results in better model fit. Substantive findings include an apparent over-detailing of high volume physicians. The authors suggest that their results may be due to the effects of latent competitor sales efforts. In their study competitor effort is unaccounted for, although it may actually be partially controlled for implicitly, since the individual specific intercepts represent unobserved heterogeneity in Bayesian analysis. Venkataraman and Stremersch  ADDIN EN.CITE Venkataraman2007818117Venkataraman, SriramStremersch, StefanThe Debate on Influencing Doctors' Decisions: Are Drug Characteristics the Missing Link?Management ScienceManagement Science1688-17015311PHYSICIANSDECISION makingMARKETINGDRUGS -- Side effectsPOLITICAL planningSAMPLING2007INFORMS: Institute for Operations Researchhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=27875318&site=ehost-live (2007) consider the interaction between the characteristics of each drug in a category and the marketing efforts exerted for each of those drugs. Specifically, the authors incorporate a measure of each brands effectiveness and the corresponding side effects. Effectiveness and side effects are not measured based on the perceptions of each physician, but rather they are summary statistics derived from a meta-analysis of clinical trials and drug labeling, respectively. Generally, their results suggest effective drugs with few side effects benefit more from marketing effort. Missing Data Customer databases for most firms consist primarily of firm-specific data. In other words, competitor data related to the customers in the database are missing. Imagine a rectangular customer database with customers on the rows and variables relating to those customers on the columns. The missing data literature deals primarily with situations where some of the values in any particular column are missing. If a firm has firm-specific data, but no competitor data, entire columns of data could be considered to be missing, not just some of the values in the columns. Little can be done to impute the missing values when this is the case. However, since enhancement of the customer database for some customers via a survey is part of this research, the projection of values for variables collected in the survey for those customers not included in the survey involves methods used to address missing data. Fortunately, assuming the participants in the survey are randomly selected, the mechanism that produced the missing data is the easiest to address. Even so, an understanding of the key issues in missing data analysis is appropriate. Little and Rubin  ADDIN EN.CITE Little200266666Little, Roderick J. A.Rubin, Donald B.Statistical Analysis With Missing Data2nd2002Hoboken, New JerseyJohn Wiley & Sons, Inc.(2002) discuss the importance of discovering the mechanism that leads to missing data, since the mechanism determines the appropriate methodological response. The authors list three missing data mechanisms, with the key issue being if the actual value of the missing data is the reason it is missing. Using their notation, consider a complete rectangular data set Y, with each element in the dataset represented as yij, where i is the row and j is the column. Also, consider a matrix M of the same dimensions, where the value for element mij is 1 if the value is observed and 0 if it is missing. Data are called missing completely at random (MCAR) if the conditional distribution of M is dependent only on some unobserved parameters, EMBED Equation.3 , but not on the values of the data Y, expressed as  EMBED Equation.3  for all Y, EMBED Equation.3 . ( SEQ Eq \* MERGEFORMAT 1) If the observed elements in Y are labeled Yobs and the missing elements are labeled Ymis, data are considered to be missing at random (MAR) if  EMBED Equation.3  for all Ymis, EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 2) indicating that the reason the data are missing is dependent only on the values of the elements in Y that are observed. Finally, missing elements in Y are not missing at random (NMAR) if the distribution of the matrix M is dependent on the actual values in Y that are missing. Missing data enters this study in two ways. First, for comparison purposes, the typical scenario where the firm has no competitive data for any of their customers will be considered. Second, survey data will be missing for some physicians due to non-response. If the mechanism behind any non-response is unrelated to the values that would have been entered on the survey if completed, but rather is due to observed values in the existing database, the missing data mechanism will be MAR. The non-response will be categorized as NMAR if the non-response is dependent on values related to the survey items. MCAR is the simplest missing data mechanism to address, while NCAR is the most difficult. Database Augmentation Database augmentation techniques are firmly entrenched in the missing data literature. In fact, augmentation is a special case of imputation, a common technique for handling missing data  ADDIN EN.CITE Kamakura2003555517Kamakura, Wagner A.Wedel, MichelList Augmentation with Model Based Multiple Imputation: A Case Study Using a Mixed-Outcome Factor ModelStatistica NeerlandicaStatistica Neerlandica46-57571DIRECT marketingESTIMATION theoryMARKETINGMULTIPLE imputation (Statistics)factor analysissimulated likelihoodmultiple imputation2003Blackwell Publishing Limitedhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=9815715&site=ehost-live (Kamakura and Wedel 2003). Typically, a firm will conduct a survey or purchase data for a random sample of their customer database. This data, along with data already existing for customers included in the survey, will be analyzed to discover relationships between the survey data and the internal data. Predictive models based on these relationships will then be developed to estimate the values for the surveyed variables for those customers not included in the survey. The objective is to leverage the survey data in such a way that informs decision making concerning all of the customers in the database. Du et al.  ADDIN EN.CITE Du2007535317Du, Rex YuxingKamakura, Wagner A.Mela, Carl F.Size and Share of Customer WalletJournal of MarketingJournal of Marketing94-113712CONSUMERS -- ResearchCUSTOMER relationsCUSTOMER servicesMARKETING researchCUSTOMER relationship managementCONSUMER profilingcustomer relationship managementshare of walletshare-of-category requirementslist augmentationdatabase marketing2007American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=24279399&site=ehost-live (2007) demonstrate a database augmentation method in a banking context. They use survey data on share-of-wallet for a variety of banking product categories, along with internal data on customers income and tenure, to estimate share-of-wallet for customers excluded from the survey. Their method simultaneously imputes whether the customer has an external balance in a category, and then if they do, the size of the external balance. Their method does not consider competitor brand shares. Sub-sampling, whether in the form of surveys or test markets, creates a need for database augmentation. Methods for imputing missing values can be as simple as entering the mean level for the observed values for a variable where the value is unobserved. At the other extreme, sophisticated methods designed to account for large proportions of missing data and a variety of measurement scales for the missing values have been developed  ADDIN EN.CITE Kamakura200355e.g. 5517Kamakura, Wagner A.Wedel, MichelList Augmentation with Model Based Multiple Imputation: A Case Study Using a Mixed-Outcome Factor ModelStatistica NeerlandicaStatistica Neerlandica46-57571DIRECT marketingESTIMATION theoryMARKETINGMULTIPLE imputation (Statistics)factor analysissimulated likelihoodmultiple imputation2003Blackwell Publishing Limitedhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=9815715&site=ehost-live (e.g. Kamakura and Wedel 2003). All of these methods involve imputing values based on models utilizing the information found in not only the survey data but the existing internal data as well. Little and Rubin  ADDIN EN.CITE Little200266666Little, Roderick J. A.Rubin, Donald B.Statistical Analysis With Missing Data2nd2002Hoboken, New JerseyJohn Wiley & Sons, Inc.(2002) give three criteria to guide imputations. First, the imputation should be conditioned on observed variables. Second, when possible, the procedure should be multivariate to preserve correlations between missing variables. Third, imputed values should be drawn from a predictive distribution rather than just imputing means to avoid overstating a central tendency. Additionally, the authors encourage the use of multiple imputation, as opposed to single imputation, to account for imputation uncertainty. Multiple imputation involves drawing a series of complete datasets for analysis, with parameter estimates being the mean from each of the analyses and the standard errors including imputation uncertainty.  SEQ Hd \* MERGEFORMAT 3. OMITTED VARIABLES The theoretical foundation of this study rests on the premise that excluding variables from a model will bias the parameter estimates related to the variables that are included in the model. However, two conditions must be met for omitted variable bias to exist. Assume the following regression model, with subscripts suppressed,  EMBED Equation.3  ( SEQ Eq \* MERGEFORMAT 3) If the variable Z is excluded from the model, all of the parameters in the model could be biased as long as  is not equal to zero and at least one of the variables remaining in the model, W or X, is correlated with Z. The extent and direction of the bias cannot be determined as long as there are two or more variables remaining in the model, but the magnitude of the bias is a function of the size of  and the degree to which Z is correlated with the variables remaining in the model. Relevance of Excluded Variables The first condition that must be met for bias to exist concerns the relevance of the omitted variables in the model. Both conceptual and empirical research has solidified the importance of including competitor variables in models where the impact of a firms marketing efforts on customer response is being investigated. Researchers looking at market orientation, the foundation of CRM  ADDIN EN.CITE Kohli199089e.g. 8917Kohli, Ajay K.Jaworski, Bernard J.Market Orientation: The Construct, Research Propositions, and Managerial ImplicationsJournal of MarketingJournal of Marketing1-18542MARKET orientationMARKETING -- ManagementINDUSTRIAL managementRESEARCHINDUSTRIAL organizationMARKETING researchBUSINESS planningMARKETING strategyRESEARCH, IndustrialBUSINESS logisticsRESEARCH1990American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=9602205182&site=ehost-live Narver1990909017Narver, John C.Slater, Stanley F.The effect of a market orientation on business profitabilityJournal of MarketingJournal of Marketing20-35544MARKET orientationCORPORATIONS -- ValuationMARKETING researchMARKETINGBUSINESS forecastingORGANIZATIONAL effectivenessINDUSTRIAL managementCOMPETITIVE advantageBUSINESS enterprises -- FinanceEconomic aspects1990American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=9102183223&site=ehost-live (e.g. Kohli and Jaworski 1990; Narver and Slater 1990), and CRM researchers in marketing  ADDIN EN.CITE Bell200272e.g. 7217Bell, DavidDeighton, JohnReinartz, Werner J.Rust, Roland T.Swartz, GordonSeven Barriers to Customer Equity ManagementJournal of Service ResearchJournal of Service Research77-8551CUSTOMER relationsMARKETING2002Sage Publications Inc.http://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=7017200&site=ehost-live Boulding2005373717Boulding, W.Staelin, R.Ehret, M.Johnston, W. J.A Customer Relationship Management Roadmap: What Is Known, Potential Pitfalls, and Where to GoJournal of MarketingJournal of Marketing155-666942005(e.g. Bell et al. 2002; Boulding et al. 2005) have all emphasized the importance of considering the competition when making marketing allocation decisions. Empirical researchers analyzing physician response that have had access to panel data including competitor marketing effort, have found those competitor variables to be significant in modeling response to the firms marketing efforts  ADDIN EN.CITE Gonul20019917Gonul, Fusun F.Carter, FranklinPetrova, ElinaSrinivasan, KannanPromotion of Prescription Drugs and Its Impact on Physicians' Choice BehaviorJournal of MarketingJournal of Marketing79-90653PHYSICIANSMEDICINE -- Formulae, receipts, prescriptionsPATIENTSDRUGS -- Marketing2001American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4856485&site=ehost-live Venkataraman2007818117Venkataraman, SriramStremersch, StefanThe Debate on Influencing Doctors' Decisions: Are Drug Characteristics the Missing Link?Management ScienceManagement Science1688-17015311PHYSICIANSDECISION makingMARKETINGDRUGS -- Side effectsPOLITICAL planningSAMPLING2007INFORMS: Institute for Operations Researchhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=27875318&site=ehost-live (Gonul et al. 2001; Venkataraman and Stremersch 2007). Correlation Between Excluded Variables and Variables Remaining in Model In addition to the omitted variables being relevant, they must also be correlated with at least one variable remaining in the model for bias to exist. In the pharmaceutical context, Manchanda and Chintagunta  ADDIN EN.CITE Manchanda20048817Manchanda, PuneetChintagunta, Pradeep K.Responsiveness of Physician Prescription Behavior to Salesforce Effort: An Individual Level AnalysisMarketing LettersMarketing Letters129-145152-32004(2004) speculate that the addition of competitor detailing in their model may fundamentally change their findings. Mizik and Jacobson  ADDIN EN.CITE Mizik2004868617Mizik, NatalieJacobson, RobertAre Physicians "Easy Marks"? Quantifying the Effects of Detailing and Sampling on New PrescriptionsManagement ScienceManagement Science1704-17155012PHARMACEUTICAL industryMARKETINGSAMPLES (Commerce)SALES managementPHYSICIANSREGRESSION analysisMANAGEMENT scienceMARKETING strategyDRUGS -- PrescribingDRUGS -- Marketingpharmaceutical marketingsalesforce effectiveness2004INFORMS: Institute for Operations Researchhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=15666973&site=ehost-live (2004) argue that the exclusion of competitor detailing in their model does not create bias in their response parameters because they speculate that the correlations between detailing among firms is very low. They back this assertion by looking at the correlations between detailing levels for all brands in a dataset and category external to their study and find them to be low. This argument could be misleading in two ways. First, the correlations among detailing for the brands in a category may vary across categories. A correlational analysis of the detailing levels among the four brands in this study show relatively high degrees of correlation, as shown in Table  REF table_correlations_detailing \h 1. This is consistent with commonly reported practice in the pharmaceutical industry, along with conversations with the focal firm in this study, where baseline detailing levels are set based on the total category demand of each physician.  Second, omitted competitor detailing does not necessarily have to be correlated with the included firm detailing variable for the parameter estimate for firm detailing to be biased. All parameter estimates in the model can potentially be biased if any variable included in the model is correlated with a relevant omitted variable  ADDIN EN.CITE Wooldridge200240406Wooldridge, Jeffrey M.Econometric Analysis of Cross Section and Panel Data2002Cambridge, MassachusettsThe MIT Press(Wooldridge 2002). Even if the correlation between the focal firms detailing and competitor detailing is low, competitor detailing would be expected to be correlated with a lagged dependent variable appearing in the model.  SEQ Hd \* MERGEFORMAT 4. MODEL Demand for Products in a Category Each firm, of course, is interested in maximizing profits. Obviously, this maximization applies across all of the firms products, but even with the available data pertaining to a single product in a particular category, a consideration of the firms profit function is worthwhile. A physicians total category demand for a particular class of drugs, over some defined time period, can be conceptualized as follows. Each physician has a limited, and generally fixed, number of appointment slots available to see patients. This number will be represented as n. Obviously, this number will vary across physicians for a variety of reasons. For example, the time spent with each patient, on average, may depend to some extent on whether or not the physician is employed by a health maintenance organization (HMO). A certain proportion, q, of each physicians patients will be diagnosed with a condition that could be treated with a drug from the category in question. Again, this proportion would be expected to vary by physician. For instance, a cardiologist would be expected to prescribe heart medication to a higher proportion of their patients than would a family practice doctor. Of those patients diagnosed with a particular condition, a physician would treat a certain percentage of them, h, with a drug from the category being considered. This percentage would likely vary across physicians due to several reasons, for example, years in practice. Therefore, using the indicated notation presented above, the expected number of prescriptions for a particular drug category and physician would be the product of the number of patients seen in a period, the proportion of those with a condition potentially treatable by drugs in the category, and the percentage of those with the condition where drugs in the category are the best treatment option, or n q h. Obviously, these values could change over time. For example, if a physician is enjoying a growing practice, more patients will be seen and n will increase. Greater specialization over time in conditions treatable by drugs in the category would increase the proportion of patients seen that will be diagnosed with the relevant condition, so q will increase. Finally, positive experience with drugs in the category or evolving best practices could result in a greater percentage of those with the condition being treated with drugs in the category, increasing h. Each firms marketing mix could certainly impact the total category demand for a category of drugs, primarily by increasing the percentage of patients diagnosed with the condition being treated with a drug from the category, represented by h. This impact would most likely be seen relatively early in the life cycle of the category. In a mature category, firms marketing efforts would be less likely to alter a physicians total category demand, but rather would influence each brands share of prescriptions for the physician. In the category being studied and over the time period of the data, total category demand both in aggregate and by physician are generally constant. With this conceptualization of total category demand as a foundation, several aspects of the ethical drug market have led to reasonable simplifications in the profit function in previous research  ADDIN EN.CITE Manchanda20045e.g. 517Manchanda, PuneetRossi, Peter E.Chintagunta, Pradeep K.Response Modeling with Nonrandom Marketing-Mix VariablesJournal of Marketing ResearchJournal of Marketing Research467-478414DIRECT marketingMARKETING -- Mathematical modelsPHYSICIANSSALES prospectingCUSTOMER relationship management2004American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=15403005&site=ehost-live (e.g. Manchanda et al. 2004). First, the costs of producing an ethical drug are primarily sunk. In fact, the marginal costs are so small compared to the sales price that they are typically assumed to be zero. Second, expenditures on the sales force (detailing) dominate other marketing expenditures. In a representative drug category, 80% of total marketing expenditures pertain to detailing  ADDIN EN.CITE Manchanda20048817Manchanda, PuneetChintagunta, Pradeep K.Responsiveness of Physician Prescription Behavior to Salesforce Effort: An Individual Level AnalysisMarketing LettersMarketing Letters129-145152-32004(Manchanda and Chintagunta 2004). The response to changes in price, typically a key variable in analyzing demand, is of much less importance in the ethical drug market. Price is only indirectly salient to the patient and far less important to the physician than the appropriateness of a particular drug for each patient. Therefore, in an ethical drug context, detailing is the critical variable. Third, although the cost of a detail can certainly vary from one visit to the next and over physicians, the cost will not be nearly as variable as say, for example, different advertising campaigns. Therefore, the marginal cost of a detail is typically assumed to be constant. The resulting simplified profit function for the firm is  EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 4) where j = physician, r = revenue from a prescription, S = number of prescriptions (or scripts), c = marginal cost of a detail, and D = number of details. We assume the number of prescriptions written is some function of detailing. Since the total category demand in this category is essentially constant, we can concentrate on the impact of detailing on market share, rather than on brand demand. Ideally, once this functional relationship is specified, elasticities can be calculated, allowing for the determination of a superior allocation of details. Additional variables could certainly be added and a more sophisticated cost function could be applied, but regardless, it is evident that within this context, the main consideration is the impact of detailing on prescription share. Generally speaking, we are considering a linear model very loosely of the form  EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 5) where Sh is the share of total category prescriptions,  represents the intercept, and  is the impact of detailing on prescription share. Estimation of the parameter  allows for persistence in prescription writing over time. In the general model, marketing effort, D in this case, and lagged share, Sht-1, will incorporate all brands in the category. Attempting to precisely specify this relationship will be the central modeling task in this research. General Model Previous research investigating the impact of marketing on prescription writing behavior has utilized a variety of modeling approaches. Three primary considerations guide the modeling choice in this research. First, the objective is to model market share. Linear models are immediately ruled out due to the restricted range of the dependent variable. Second, the estimation of the elasticity of market share relative to marketing variables is of primary concern. Multiplicative log-linear models with the natural log of market share as the dependent variable suffer from the problem of constant elasticity. For example, the elasticity of market share relative to a particular variable should approach zero as market share approaches one. Exponential log-linear models, again with the natural log of market share serving as the dependent variable, are similarly problematic. In addition, the exponential model implies that elasticity should increase indefinitely as the value of the variable increases. Finally, the relationship between the relevant independent variables and the resulting elasticity in market share must be consistent theoretically with how the variables are expected to impact market share. A random utility model, such as the multinomial logit, implies that the elasticity of market share increases as the independent variable increases from low levels, reaches a peak, and then declines  ADDIN EN.CITE Cooper198863636Cooper, Lee G.Nakanishi, MasaoMarket-Share Analysis: Evaluating Competitive Marketing Effectiveness1988BostonKluwer Academic Publishers(Cooper and Nakanishi 1988). Market share elasticity, considering the key variables in this study (specifically detailing), is expected to decline monotonically as the level of the variables increase, making the multinomial logit approach not well suited for this research. The proposed model for the share of prescriptions written for drug i by physician j in period t begins with a general model for brand share, mijt. Kotler  ADDIN EN.CITE Kotler197145456Kotler, PhilipMarketing Decision Making: A Model Building Approach1971New YorkHolt, Rinehart and Winston(1971) considers the well-known multiplicative competitive interaction (MCI) model to be the fundamental theorem of market share, represented as  EMBED Equation.3  ( SEQ Eq \* MERGEFORMAT 6) where Mijt is the marketing effort for drug i directed at physician j in period t and the denominator represents the combined marketing effort for all of the brands  ADDIN EN.CITE Cooper198863636Cooper, Lee G.Nakanishi, MasaoMarket-Share Analysis: Evaluating Competitive Marketing Effectiveness1988BostonKluwer Academic Publishers(Cooper and Nakanishi 1988). The MCI model is appropriate given the three considerations discussed earlier, providing a model for market share that allows for monotonically decreasing market share elasticity over the range of the independent variables. Although statistically equivalent, Cooper and Nakanishi  ADDIN EN.CITE Cooper198863636Cooper, Lee G.Nakanishi, MasaoMarket-Share Analysis: Evaluating Competitive Marketing Effectiveness1988BostonKluwer Academic Publishers(1988) describe how marketing effort in the MCI model can alternatively be conceptualized as the attraction consumers feel for each particular brand. In this paper, relative marketing effort is of primary concern. However, physician perceptions of each drugs characteristics will also be included in the model, along with lagged share to account for persistence in prescription writing behavior. Therefore, the attraction conceptualization is more appropriate in this research. Typically, the MCI model is specified using a multiplicative function of relevant variables. Suppressing all but the subscript for brand, i, marketing effort can be expressed as  EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 7) where i is a parameter for the constant effect of brand i, Xyi is the value of the yth variable Xy for brand i, y is a parameter corresponding to variable Xy, and i is an error term. Our objective is to build upon the MCI model in equation ( REF mci \h 6) to produce a model that is linear in its parameters and that represents the share of total category prescriptions written by physician j for the focal brand in period t. To minimize notational complexity and therefore improve expositional clarity, we will demonstrate this transformation assuming two particular marketing mix variables are relevant in the model. Once the transformation is complete, we will express it in its general form. Expanding equation ( REF mci \h 6) produces an initial brand share model,  EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 8) where i = brand, j = physician, t = period, D = detailing, A = ads read in journal and ij = the constant effect of brand i with respect to physician j in a category with K brands. The parameters  and  represent the effects of detailing and promotional activities, respectively. The parameters  and  can vary by brand, physician, or both, addressing heterogeneity in physician response to marketing effort. The specification in equation ( REF mci2 \h 8) is referred to as the differential-effects MCI model  ADDIN EN.CITE DeSarbo2002222217DeSarbo, Wayne S.Degeratu, Alexandru M.Ahearne, Michael J.Saxton, M. KimDisaggregate Market Share Response ModelsInternational Journal of Research in MarketingInternational Journal of Research in Marketing253-266193INDUSTRIAL concentrationMARKET shareMARKETING -- ManagementMARKETING researchMARKETING strategyMARKET penetrationFinite mixturesPrescription drugsMaximum likelihoodMarket share models2002http://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=7482086&site=ehost-live (DeSarbo et al. 2002). The model shown in equation ( REF mci2 \h 8) could be estimated directly using non-linear techniques. However, the estimation will be much simpler and the derivation of the elasticities much clearer by transforming equation ( REF mci2 \h 8) into an equation that is linear in its parameters. First, a logarithmic transformation generates  EMBED Equation.3 . ( SEQ Eq \* MERGEFORMAT 9) Next, a log-centering operation is required. The first of two steps in this process are to sum equation ( REF logtransform \h 9) across all brands, i = (1, , k), then divide by the number of brands, k, producing  EMBED Equation.3  ( SEQ Eq \* MERGEFORMAT 10) where  EMBED Equation.3 , EMBED Equation.3 , and EMBED Equation.3  represent the geometric means of brand share, detailing, and promotional activities, respectively. The second step in the log-centering operation requires subtracting equation ( REF logcenter1 \h 10) from equation ( REF logtransform \h 9), resulting in  EMBED Equation.3  ( SEQ Eq \* MERGEFORMAT 11) This can also be written as  EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 12) where * = ij  1j, * = ijt  1jt and d = 1, if m = i and 0 otherwise  ADDIN EN.CITE <EndNote><Cite><Author>DeSarbo</Author><Year>2002</Year><RecNum>22</RecNum><record><rec-number>22</rec-number><ref-type name="Journal Article">17</ref-type>DeSarbo, Wayne S.Degeratu, Alexandru M.Ahearne, Michael J.Saxton, M. KimDisaggregate Market Share Response ModelsInternational Journal of Research in MarketingInternational Journal of Research in Marketing253-266193INDUSTRIAL concentrationMARKET shareMARKETING -- ManagementMARKETING researchMARKETING strategyMARKET penetrationFinite mixturesPrescription drugsMaximum likelihoodMarket share models2002http://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=7482086&site=ehost-live (DeSarbo et al. 2002). Since the left hand side is now a ratio, either brand shares or the actual number of brand prescriptions can be used, resulting in  EMBED Equation.3 . ( SEQ Eq \* MERGEFORMAT 13) Focusing on brand 1 (the focal brand), we transform equation ( REF logcenter2c \h 13) into  EMBED Equation.3 . ( SEQ Eq \* MERGEFORMAT 14) This baseline model, fully specified, introduces a number of challenging, but addressable, econometric issues that will be discussed in full. It also provides us a convenient platform for testing several nested models that allow investigation into the value of various types of competitor data. Applying a general notation, reorganizing terms, and allowing for a variety of variables produces  EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 15) where Xy is the yth variable.  SEQ Hd \* MERGEFORMAT 5. ALTERNATIVE MODELS The firms ability to accurately estimate customer response to their marketing efforts can be limited by data availability. When relevant variables are omitted from the model, response parameters can be biased and segment assignment can be compromised, leading to sub-optimal allocation of marketing resources. The extent of the problem depends on the size of the effects the omitted variables have on the dependent variable and the degree to which the omitted variables are correlated with those variables included in the model. The construction of alternative models that represent common omitted variable situations will allow these problems to be investigated. We will estimate and compare various specifications of the general model shown in equation ( REF mean_model2 \h 15). The notation used in equation ( REF mean_model2 \h 15) is intentionally general to allow for a clear exposition of the separation of focal firm and competitor variables that follows. Actual variables used in the models will be presented explicitly in the presentation of each alternative model. The term representing marketing effort and other attraction variables will be manipulated to produce the alternative specifications that will be considered in the analysis. The firm may or may not have data on the marketing effort of the competitors, by brand and by physician. Additionally, the firm may or may not be aware of physicians perceptions of the effectiveness and side effects of the brands in the category. The resulting three specifications (one of which is the general model) are shown in Table  REF table_data_availability_by_model \h 2. Each alternative specification presented in Table  REF table_data_availability_by_model \h 2 is nested within the general model. However, this is not clearly evident in equation ( REF mean_model2 \h 15). To produce a representation of the general model where each alternative specification is just the general model with omitted variables, the terms in equation ( REF mean_model2 \h 15) must be expanded and manipulated algebraically, since the values for each marketing variable for brand 1, EMBED Equation.3 , also appear in the geometric mean that makes up the denominator, EMBED Equation.3 . First, equation ( REF mean_model2 \h 15) can be rewritten as  EMBED Equation.3 . ( SEQ Eq \* MERGEFORMAT 16) Rearranging terms, expanding, and relaxing the restriction that the effect of focal firm variables on the focal firms market share is equivalent to the effect of corresponding competitor variables produces the general model with the focal brand variables distinct from the competitor variables as follows  SHAPE \* MERGEFORMAT   EMBED Equation.3  EMBED Equation.3  intercept,  EMBED Equation.3  attraction for focal brand,  EMBED Equation.3  attraction for competitor brands,  EMBED Equation.3  and error term. ( SEQ Eq \* MERGEFORMAT 17) The presentation of the general model as shown in equation ( REF general_model_exp3 \h 17) is beneficial. Producing the alternative specifications in Table  REF table_data_availability_by_model \h 2 now requires only the omission of specific terms from equation ( REF general_model_exp3 \h 17). Therefore, it will be clearly evident that each alternative specification is nested within the general model. Terms can then be recombined, as appropriate, for each alternative model. The actual variables included in the models, rather than the general notation shown in equation ( REF general_model_exp3 \h 17), will be presented explicitly in the following presentation of the alternative models.  EMBED Equation.3 Firm Model The Firm Model is the sparsest of the alternatives, with the assumption that the firm has no knowledge of competitor marketing effort or physician perceptions of brand characteristics. This specification is consistent with the data typically available in a firms database. Dropping the unobserved terms from equation ( REF general_model_exp3 \h 17) and inserting variable names and covariates produces  EMBED Equation.3   EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 18) where Years = years in practice, Female = sex, with 1 being female, OBG = dummy, with 1 being OB/GYN specialist, Urol = dummy, with 1 being urology specialist, and Det = detailing. The terms remaining in the firm-focused model are virtually assured of being correlated with the omitted terms, resulting in biased parameter estimates. The omitted variables in this model include competitor detailing and physician perceptions of effectiveness and side effects for each brand. This anticipated bias is indicated by the superscript associated with the parameters.  EMBED Equation.3 Effectiveness Model The Effectiveness Model assumes no knowledge of competitor effort, but does contend the firm is aware of the physician perceptions of the effectiveness and side effects for each brand. This specification builds upon the data typically available in a firms database consistent with the firm model presented above. However, it also assumes managers and salespeople are aware of physicians perceptions of the characteristics of each drug in the category. This assumption may or may not be reasonable; however, it presents the opportunity to examine the value of a model that excludes competitor marketing effort while considering physician perceptions of the competing brands. In contrast to the firm model, the specification of this model requires adding both focal firm and competitor variables for perceived brand characteristics to the firm model in equation ( REF alt1_model \h 18), while still excluding competitor detailing, producing  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 19) where Eff = perceived effectiveness of the brand, and SE = quality of the side effect profile for the brand.  EMBED Equation.3 Competitor Effort and Effectiveness Model The Competitor Effort and Effectiveness Model is the fully specified model presented in equation ( REF general_model_exp3 \h 17). The assumption for the model is that the firm has augmented their database to include not only physician perceptions of brand characteristics, but also competitor detailing and the frequency with which physicians view journal ads for each of the brands in the category. The explicit presentation of the general model is  EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3   EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 20) where JA = journal ads read by the physician.  SEQ Hd \* MERGEFORMAT 6. DATA Firm records and survey data were combined to produce the dataset used in this study. The firm records are at the physician level and come from the internal database of a pharmaceutical firm competing in a large ethical drug category. The survey data consists of the responses from a subset of the physicians in the firms database. The four brands included in the study comprise about 90% of the market. Primary and Secondary Data The initial data provided by the firm consists of the names, contact information, and internal rankings for each physician assigned to the category by the firm. The firms internal rankings place each physician in one of eleven categories roughly based on their total category demand. Since the bottom two categories had very limited or no activity in the category year-to-date, they were dropped from consideration, resulting in a population of physicians numbering 7,101. Fax numbers were secured for these physicians. The survey was completed by a subset of these 7,101 physicians. The survey data provides variables related to brand-specific detailing by the competition and physicians perceptions of the characteristics of each brand (effectiveness in treating each of three common symptoms, as well as side effects). Detailing by the focal firm is also included. By surveying variables included in the firms data, the accuracy of the physicians responses can be considered. All 7,101 physicians were invited to participate in the survey in exchange for a small honorarium. The survey was delivered via fax to each physicians primary practice location. The initial solicitation yielded 393 useable responses. Non-respondents were offered an additional opportunity to participate. One hundred-fifteen useable responses resulted from the second offer, resulting in a total of 508 respondents and a response rate of 7.2%. The firm then provided additional data for 5,358 of the 7,101 physicians, including all of the physicians practicing in the dominant specialty in this category, as well as for any physician in a different specialty that responded to the survey. This includes a monthly panel (January 2007 October 2007) of brand-specific data on prescriptions written, along with data on detailing and sampling for the focal firm. The specialty, number of years in practice, and sex of each physician was also provided. The dataset resulting from combining the firm and survey data is graphically represented in Figure  REF figure_graphic_data \h 1. The firm roughly categorizes physicians based on their total category demand, with higher prescribing physicians belonging to a higher numbered category. Some physicians in the data are unclassified. Table  REF table_respond_vs_nonrespond \h 3 shows the percentage of respondents coming from each category and the corresponding breakdown of non-respondents by category. Generally speaking, the sample appears to have been drawn proportionally from each category, with the possible exceptions of under-sampling in the lowest category demand groups (categories 3 and 4) and over-sampling from category 9. This suggests that the sample may overemphasize high prescribing physicians unless the unclassified group is made up of higher than average prescribers.   Since detailing and prescription writing data is available for some physicians in each category, the average figures can be multiplied by the percentage representation in each category to produce weighted averages that can be used for comparison. These results appear in Table  REF table_comparison_responsegroups \h 4. Since the over-sampled unclassified group is made up of physicians that are detailed more and prescribe more than physicians in category 9, the net effect is a similar set of weighted averages, indicating the sample is a reasonable representation of the population. Incorporation of Survey Competitor Detailing Data Although the firm provided panel data for brand prescription writing and firm detailing over a number of months, the data collected via the survey was taken at a particular point in time. The analysis could be conducted as a cross-sectional analysis, incorporating the survey data and considering only one period of prescription and firm detailing data. This approach would effectively throw away the majority of the available data. A second approach would be to use the survey data to get some idea of the extent of competitor detailing and apply that knowledge over time, allowing all of the prescription and firm detailing data to be used. The latter approach will be used in this study.  The survey intentionally did not identify the focal firm. Firm data was collected along with competitor data, allowing the survey data to be compared to the firms records to assess the physicians ability to accurately report detailing activity for the focal firm. The necessary assumption to incorporate the survey data on competitor detailing into the dataset over time is that the accuracy with which each physician is able to report the detailing efforts of the focal firm will be similar to the accuracy in reporting competitor detailing. The objective is not to predict the number of competitor details for each physician during each time period. A survey taken at a single point in time is unable to provide the insight for that task. However, using the survey data for focal firm detailing along with the firms record of detailing over time, a mean level of detailing for each physician coupled with a distribution of those details over time allows for model estimation using a multiple imputation approach. Multiple imputation requires using several randomly drawn datasets from the representative distribution of the missing data in order to account for what is essentially measurement error in the imputed data. In this case, competitor detailing over time is missing data. The objective is to produce a dataset generator that can produce randomly drawn datasets using physician reported competitor detailing levels, along with the model derived from the firm detailing records and the survey data for focal firm detailing. The proposed relationship between survey reported detailing and actual details is  EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 21) where d is a dummy variable accounting for seasonal variations in detailing levels, assuming a value of 1 if m = t and 0 otherwise. A transformation produces a function that is linear in its parameters and allows a convenient link in count data functions,  EMBED Equation.3 . ( SEQ Eq \* MERGEFORMAT 22) In effect, this mean of competitor detailing is a function of the survey reported number of details per period with an adjustment for month. Note the notation indicating the intercept will vary over physicians. Detailing was initially assumed to be distributed Poisson. Estimating the model using Poisson regression, however, indicated the data was overdispersed, with  EMBED Equation.3  ADDIN EN.CITE Cameron199874746Cameron, A. ColinTrivedi, Pravin K.Regression Analysis of Count Data1998CambridgeCambridge University Press(Cameron and Trivedi 1998). Typically, overdispersed count data is estimated using a negative binomial model. However, since this model is to be used for making random draws, a simpler alternative exists. With panel data, a Poisson model allowing for random effects essentially is similar to the negative binomial with the overdispersion parameter varying across groups  ADDIN EN.CITE Greene200797976Greene, William H.LIMDEP Version 9.0 Econometric Modeling Guide22007Econometric Software, Plainview, NY(Greene 2007). Conducting random draws using the parameter estimates from the random effects Poisson model is convenient. The results from estimating a Poisson regression model with random effects as shown in equation ( REF ln_detailinlg_model \h 22) are presented in Table  REF table_comp_det_model \h 5. These results, coupled with the survey responses concerning competitive detailing, are used to produce the set of datasets to be used in the multiple imputation.  Incorporation of Other Survey Variables In addition to competitive detailing, the survey was also used to collect data on physicians perception of the effectiveness of each brand in the category, along with the side effect profile of each brand. Brand effectiveness was measured using a set of three five-point scales, focusing on the three common symptoms associated with the condition leading to the prescribing of drugs in the category. To account for measurement error, one of the three ratings is randomly drawn for each physician for each dataset constructed for the multiple imputation. Side effects are reported to be minimal in this category. Therefore, a single item, five-point scale was considered adequate to measure the physicians perceptions of the side effect profile. Likewise, the number of times per period the physician sees a journal ad for a particular brand of drug in the category was measured with a single item. Each brand regularly has ads appearing in the major journals. Finally, years-in-practice was not available for all physicians. The existing data on this variable fit a lognormal distribution. Missing values were randomly drawn from this distribution for each dataset created.  SEQ Hd \* MERGEFORMAT 7. ESTIMATION A number of econometric issues need to be accounted for to accurately estimate each of the alternative models shown in equations ( REF alt1_model \h 18), ( REF alt2_model_a \h 19), and ( REF alt3_model_a \h 20). First, econometric concerns common in this type of data, endogeneity and heterogeneity, must be addressed. Second, multiple imputation require the estimation of the model using a set of datasets. The results can then be used to calculate parameter estimates and standard errors. Endogeneity Two endogenous variables appear in the models. First, the lagged dependent variable is by definition correlated with the error term in the model and therefore must be treated as an endogenous variable. Second, detailing cannot be assumed to be exogenous. Detailing levels are set by the firm, at least partly based on market share results observed in the past. Instruments will be used to account for endogeneity in this study, specifically lagged values of share and detailing. Effective instruments must be shown to be valid and exogenous. Validity pertains to whether the instrument is correlated with the endogenous variable after accounting for all of the other exogenous variables in the model. For all three alternative models (see Table  REF table_data_availability_by_model \h 2), the instruments appear to be valid. Lagged share was regressed on the instrument, share lagged two periods, along with all of the exogenous variables in the model. For each of the three alternative models, the t-statistic for the parameter attached to the instrument was significant (respectively, t = 27.4, t = 26.3, and t = 26.1). Similarly, lagged detailing provides valid instruments for firm detailing. Detailing lagged one, two, and three periods is used in each model. For the Effectiveness and Competitor Effort Model, the t-statistics for the three lagged instruments are t = 14.4, 12.6, and 8.8, respectively. The results are similar for the two other alternative models. Not only must instruments be valid, but also exogenous. Two over-identifying restrictions allow the exogeneity of the instruments in the model to be tested using a Sargan test  ADDIN EN.CITE Sargan1958969617Sargan, J. D.The Estimation of Economic Relationships using Instrumental VariablesEconometricaEconometrica393-4152631958JSTOR(Sargan 1958). First, the model is estimated using the instruments. The residuals from that estimation are then regressed on all of the exogenous variables. The number of observations times the resulting R2, NR2, is distributed 2 with two degrees of freedom, equivalent to the number of over-identifying restrictions. The null hypothesis is that all instruments are exogenous, so a failure to reject indicates exogenous instruments. For each of the three models, the Firm Model, the Effectiveness Model, and the Effectiveness and Competitor Effort Model, the result is a failure to reject the null. NR2 is 1.65, 1.63, and 1.34 respectively for the three models, all below the value of 2 with two degrees of freedom, 5.99 ( = 0.05). Therefore, the instruments for all three alternative models are effective in addressing the endogeneity inherent in the models. Heterogeneity Physician response to the various variables in the models can certainly be expected to vary across physicians. Heterogeneity must be addressed. The nature of the data allow a number of reasonable alternatives. A fixed effects approach is a consideration whenever panel data is being analyzed. In this case, the impact of unobserved, time invariant effects can be estimated for each physician. One strength of the fixed effects approach is that the unobserved effects can be correlated with the time varying variables. A key disadvantage is parameter estimates are possible only for time varying variables. In this research, there are variables of interest that do not vary over time, or are assumed not to vary over the time periods analyzed in this study. Therefore, a fixed effect approach is problematic. Estimating random effects is another alternative with panel data. The key distinction between fixed and random effects is with random effects, the unobserved effects must be assumed to be orthogonal to the variables included in the model. Unlike with fixed effects, parameter estimates for time invariant variables can be estimated. Random effects is a reasonable alternative if time invariant variables that are correlated with time variant regressors can be observed and included in the model. Unfortunately, with both fixed and random effects, only heterogeneity in the unobserved effects across physicians can be addressed. Given distributional assumptions, Bayesian approaches potentially allow all parameter estimates to vary across individuals. Likewise, a latent class approach allows heterogeneous response for all parameters estimated, not across individuals, but across groups of individuals. A latent class approach requires no distributional assumptions. Given a specified number of points of support, or latent classes, a latent class estimation produces a discrete, finite sample distribution of the parameters. Latent class analysis can also be informative managerially, producing often managerially relevant segments based on response. Considering the objectives of this study, comparing parameter estimates to investigate bias, reallocation of marketing resources based on segmentation, and incorporating survey data, a latent class approach is particularly attractive. As discussed earlier in the section describing the data to be used in the study, multiple datasets were constructed to be used in the multiple imputation. Six datasets were analyzed in this study, an adequate number to produce relevant inferences using complete data methods  ADDIN EN.CITE Little200266666Little, Roderick J. A.Rubin, Donald B.Statistical Analysis With Missing Data2nd2002Hoboken, New JerseyJohn Wiley & Sons, Inc.(Little and Rubin 2002). Using instruments for endogenous variables and latent class analysis to address heterogeneity of response, each of the six datasets were used to estimate each of the three alternative models. Information criteria were used to determine the appropriate number of latent classes. The Bayesian (or Schwartz) information criterion (BIC) indicates a three latent class model for all six datasets. The Akaike information criterion (AIC), which penalizes additional parameterization less than does the BIC, is minimized with five latent classes in all cases. All things equal, parsimony suggests the three class model.  SEQ Hd \* MERGEFORMAT 8. RESULTS The results from the estimation of each of the three alternative models appear in Tables  REF table_results_firmmodel \h 6,  REF table_results_effectmodel \h 7, and  REF table_results_ECEmodel \h 8. The Effectiveness Model fits significantly better than the Firm Model (LL = 25.11 >  EMBED Equation.3  = 18.55), The Effectiveness and Competitor Effort Model fits significantly better than the Effectiveness Model (LL = 16.19 >  EMBED Equation.3  = 14.68), and the Effectiveness and Competitor Effort Model fits significantly better than the Firm Model (LL = 41.30 >  EMBED Equation.3  = 29.62).  Interestingly, there are similarities in the latent classes across the three models. Most noticeably, each model assigns a positive coefficient for firm detailing for latent  Class 1, indicating there are a group of physicians that are currently being under-detailed. Similarly, the detailing levels appear to be adequate for physicians in Class 2. Class 3 physicians appear to be over-detailed; however, the negative coefficient is not significant for the Effectiveness and Competitor Effort Model. This result for the Firm Model is consistent with previous research that did not account for drug effectiveness and competitor detailing. It also confirms the speculation in Manchanda and Chintagunta  ADDIN EN.CITE Manchanda20048817Manchanda, PuneetChintagunta, Pradeep K.Responsiveness of Physician Prescription Behavior to Salesforce Effort: An Individual Level AnalysisMarketing LettersMarketing Letters129-145152-32004(2004) that the finding of an over-detailed group may be the result of omitting competitor effort from the analysis. There are some interesting differences in parameters within classes across the models, particularly the effects of lagged share and drug effectiveness. These differences, along with competitor detailing, which appears only in the Effectiveness and Competitor Effort Model, suggest the underlying biases found in the Firm Model and the Effectiveness Model. Although the parameter estimates for firm detailing are similar within classes but across models, the assignment of physicians to the various classes differ significantly across models. Table  REF table_classification_comparison_firmmode \h 9 and Table  REF table_classification_comparison_effmodel \h 10 show the extent of the misclassification of physicians, with Table  REF table_classification_comparison_firmmode \h 9 comparing the Firm Model to the Effectiveness and Competitor Effort Model and Table  REF table_classification_comparison_effmodel \h 10 comparing the Effectiveness Model to the Effectiveness and Competitor Effort Model. The bias in parameter estimates for the misclassified physicians are now evident. The Firm Model produces significantly biased parameter estimates for firm detailing for 35.6% of the physicians in the sample. The Effectiveness Model fares better, producing significantly biased firm detailing parameters for 9.1% of the physicians in the sample.  Description of the Latent Classes A comparison of the average levels of key variables shown in Table  REF table_class_profiles \h 11 gives insight into the variance in the classifications between the Firm Model and Effectiveness and Competitor Effort Model (ECE Model). Both the Firm Model and the ECE Model assign physicians with high detailing levels, high firm brand prescriptions, and favorable perceptions of the effectiveness of the firms brand to Class 1. However, the ECE Model assigns higher category prescribers to Class 1 than does the Firm Model. There are more distinctions between the models for Class 2. The Firm Model indicates firm detailing should remain constant for very high category prescribers where the firm has a low share, in spite of a high level of competitive detailing and low perceptions of the effectiveness of the brand. In contrast, the ECE Model indicates maintaining the level of detailing for low share physicians, but at a relatively lower level of detailing and for physicians that have a favorable perception of the brand. Finally, the Firm Model suggests lowering the level of detailing for low category prescribers where the share is low, even though the perception of the brand is high. The ECE Model indicates physicians that hold a low perception of brand effectiveness should be detailed less. These differences are consistent with the differences in class assignment between the two models. Reallocation  The implications of reallocating details based on the results of the three models can now be investigated. The parameters in the models are essentially elasticities. Since the key actionable variable, detailing, is a non-negative integer and the estimates are valid at the current level of detailing, reallocation based on adding or subtracting a single detail is appropriate. The expected increase in prescriptions for the focal brand due to an increase or decrease of a single detail can be derived using any of the three alternative models, since only the firm detailing term common to all models figures into the partial derivative. The equations for all three alternative models can be written as  EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 23) where C consists of everything else on the right hand side for any of the three alternative models. Manipulating equation ( REF starting_eq_taking_partial_deriv \h 23) algebraically produces  EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 24) where the notation  EMBED Equation.3  indicates the change in additional terms on the right hand side, so  EMBED Equation.3 . ( SEQ Eq \* MERGEFORMAT 25) Therefore, with a single additional detail,  EMBED Equation.3 , ( SEQ Eq \* MERGEFORMAT 26) where  EMBED Equation.3  is the number of prescriptions after adding the detail. Solving for the new number of prescriptions produces  EMBED Equation.3 . ( SEQ Eq \* MERGEFORMAT 27) Therefore, the change in prescriptions resulting from the additional detail is  EMBED Equation.3 . ( SEQ Eq \* MERGEFORMAT 28) A similar result for removing a detail produces the following change in prescriptions,  EMBED Equation.3 . ( SEQ Eq \* MERGEFORMAT 29) Applying a reallocation of detailing plan to a dataset based on each of the three alternative models allows a comparison of the relative value of the models to the firm. The results presented above in Tables  REF table_results_firmmodel \h 6,  REF table_results_effectmodel \h 7, and  REF table_results_ECEmodel \h 8 indicate a class of over-detailed physicians and a class of under-detailed physicians for each of the three models, respectively. The simple reallocation plan considers the relative sizes of the under-detailed segment, Class 1, and the over-detailed segment, Class 3, for each model. If Class 1 consists of more physicians than does Class 3, one detail is assumed to be taken from each physician in Class 3 and reallocated to a randomly selected group of Class 1 physicians. If Class 1 consists of fewer physicians than does Class 3, one detail is assumed to be reallocated to each physician in Class 1 from a randomly selected group of Class 3 physicians. The results of this exercise are presented in Table  REF table_reallocatio_results \h 12. With the net revenue for each prescription filled at over $100 and a conservative estimate of resulting monthly refills of six months, the discounted value of each incremental new prescription is around $600. A reallocation plan based on the ECE Model could be expected to net the firm additional revenue of around $880,000, nearly $350,000 more than a plan based on the Firm Model results. Biased response estimates are costly to the firm.  SEQ Hd \* MERGEFORMAT 9. DATA AUGMENTATION Previous pharmaceutical research that has incorporated competitor detailing (and even drug effectiveness) have analyzed panel data for a portion of the physicians active in the category  ADDIN EN.CITE Gonul20019917Gonul, Fusun F.Carter, FranklinPetrova, ElinaSrinivasan, KannanPromotion of Prescription Drugs and Its Impact on Physicians' Choice BehaviorJournal of MarketingJournal of Marketing79-90653PHYSICIANSMEDICINE -- Formulae, receipts, prescriptionsPATIENTSDRUGS -- Marketing2001American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4856485&site=ehost-live Venkataraman2007818117Venkataraman, SriramStremersch, StefanThe Debate on Influencing Doctors' Decisions: Are Drug Characteristics the Missing Link?Management ScienceManagement Science1688-17015311PHYSICIANSDECISION makingMARKETINGDRUGS -- Side effectsPOLITICAL planningSAMPLING2007INFORMS: Institute for Operations Researchhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=27875318&site=ehost-live (Gonul et al. 2001; Venkataraman and Stremersch 2007). Neither study attempted to apply their findings to physicians outside of the panel. Consistent with these studies, firms cannot expect to acquire competitor detailing data or perceptions of brand effectiveness for more than a portion of the physicians active in the category. When a survey is used to collect the additional data, non-response virtually assures data will be missing for some (likely a majority) of the physicians. The firm is not only interested in understanding how segments of physicians respond to their marketing efforts based on the analysis of a sample of their customers. Ultimately, they want to identify the relevant segments, and then determine segment membership for all of the physicians active in the category. Ideally, an analysis of a sample of the physicians can determine the segments that exist in the category. Then, data augmentation can be used to assign physicians outside of the sample to the segments. Typically, data augmentation is done to predict particular missing values. For example, in Du et al.  ADDIN EN.CITE Du2007535317Du, Rex YuxingKamakura, Wagner A.Mela, Carl F.Size and Share of Customer WalletJournal of MarketingJournal of Marketing94-113712CONSUMERS -- ResearchCUSTOMER relationsCUSTOMER servicesMARKETING researchCUSTOMER relationship managementCONSUMER profilingcustomer relationship managementshare of walletshare-of-category requirementslist augmentationdatabase marketing2007American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=24279399&site=ehost-live (2007), bank customers were surveyed to determine share-of-wallet in various product categories. Data augmentation was then used to predict share-of-wallet for a holdout sample based on the analysis of the calibration sample. Similarly, Kamakura and Wedel  ADDIN EN.CITE Kamakura2003555517Kamakura, Wagner A.Wedel, MichelList Augmentation with Model Based Multiple Imputation: A Case Study Using a Mixed-Outcome Factor ModelStatistica NeerlandicaStatistica Neerlandica46-57571DIRECT marketingESTIMATION theoryMARKETINGMULTIPLE imputation (Statistics)factor analysissimulated likelihoodmultiple imputation2003Blackwell Publishing Limitedhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=9815715&site=ehost-live (2003) developed an augmentation method to predict particular variables, such as share-of-wallet and customer satisfaction. Here, the ultimate objective is not to predict the value of particular variables collected via a survey for those customers not included in the survey, but rather to assign physicians not included in the survey to segments. The augmentation approach used in this study is unique is three ways. First, the missing data relates directly to competitor data, commonly considered to be a critical and common type of missing data  ADDIN EN.CITE Boulding200537e.g. 3717Boulding, W.Staelin, R.Ehret, M.Johnston, W. J.A Customer Relationship Management Roadmap: What Is Known, Potential Pitfalls, and Where to GoJournal of MarketingJournal of Marketing155-666942005(e.g. Boulding et al. 2005). Second, since the ultimate objective is to assign physicians to segments rather than impute individual values for missing data, the method involves a multivariate draw of missing values based on a multiplicative composite of all of the key missing survey variables, consistent with the modeling approach. Third, segment membership probabilities are not modeled as a function of observed variables, but instead are the result of using a complete data analysis technique incorporating both physicians with survey data and those with imputed data. A dataset was first drawn and analyzed as discussed in the estimation section above and segment assignments were noted for all 508 physicians in the sample. Next, parameter estimates to be used for drawing competitor detailing datasets as shown in equation ( REF ln_detailinlg_model \h 22) were re-estimated using a randomly selected 458 of the 508 physicians in the sample. Competitor detailing was drawn for each of the 458 physicians based on these estimates. All variables associated with the survey were assumed to be unknown for the remaining 50 physicians, including competitor detailing, drug effectiveness measures, side effect measures, and journal ads read. The product of the competitor detailing, firm brand effectiveness, and competitor brand effectiveness variables was then calculated for each of the 458 physicians in the analysis sample. This composite was then regressed on the variables in the dataset that were observed for all physicians, specifically the four demographic variables, firm detailing, and lagged share. These estimates were used to calculate the predicted composite variable of survey related variables for the 50 physicians in the holdout sample. Missing data for the 50 physicians in the holdout sample was then imputed to be the same as the physician in the calibration sample with the composite variable value closest to that of each physician in the holdout sample. The now complete dataset was analyzed for the ECE Model as before. Segment assignments based on the data with imputed values could then be compared with segment assignments based on an analysis of the full sample to determine the accuracy of the augmentation system. The results are presented in Table  REF table_augmentation_classifications \h 13.  The augmentation approach appears to be quite effective for this particular dataset and holdout sample. Of the 50 physicians in the sample, only two were misclassified when augmented data was used. Two physicians assigned to the under-detailed segment (Class 1) should have been assigned to the over-detailed segment (Class 3). The 96% success rate, as compared to the results shown in Tables  REF table_classification_comparison_firmmode \h 9 and  REF table_classification_comparison_effmodel \h 10, indicate surveying a portion of physicians to acquire data on competitor detailing and drug brand effectiveness then augmenting the database to make segment assignments outperforms both the Firm Model which uses data already in the firms database and the Effectiveness Model which requires collecting brand effectiveness data from all of the firms customers.  SEQ Hd \* MERGEFORMAT 10. FUTURE RESEARCH Generalizability Ethical drug manufacturers typically possess data on prescriptions written for all of the competing brands at the physician level. Access to this type of competitor data is extremely rare in other industries, and even within the pharmaceutical industry outside of the U. S. In this study, brand sales are never considered to be omitted variables due to lack of data availability. However, in most industries, firm databases typically do not include this data. Future research could focus on generalizing the approach used in this study, allowing it to be applied in industries where competitor sales are unknown. The primary modification would be to move from an analysis of market share to an investigation of brand sales. In effect, this would require a model for total category demand. Combining the market share model with a total category demand model would indirectly produce a brand sales model  ADDIN EN.CITE Leeflang200064646Leeflang, Peter S. H.Wittink, Dick R.Wedel, MichelNaert, Philippe A.Building Models for Marketing Decisions2000BostonKluwer Academic Publishers(Leeflang et al. 2000). With that change, the sparsest model would include marketing effort and sales data only for the focal firm. The collection and value of competitor brand sales data could then be considered similarly to brand effectiveness and competitor effort data in this study. Endogeneity In this study, endogenous variables were not modeled explicitly, but rather were addressed using instrumental variables. The investigation of the antecedents of these variables, as well as potential causal relationships among these variables, could lead to valuable future research. The relationship between detailing and physician perceptions of the relative effectiveness of the brands is of particular interest. Only one study explicitly includes brand effectiveness in the model  ADDIN EN.CITE Venkataraman2007818117Venkataraman, SriramStremersch, StefanThe Debate on Influencing Doctors' Decisions: Are Drug Characteristics the Missing Link?Management ScienceManagement Science1688-17015311PHYSICIANSDECISION makingMARKETINGDRUGS -- Side effectsPOLITICAL planningSAMPLING2007INFORMS: Institute for Operations Researchhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=27875318&site=ehost-live (Venkataraman and Stremersch 2007), although the assumption is that all physicians have identical perceptions. This assumption suggests perceived effectiveness is solely the result of experience with the drug. However, detailing has been found to have a mostly informative effect in some studies  ADDIN EN.CITE Gonul20019e.g. 917Gonul, Fusun F.Carter, FranklinPetrova, ElinaSrinivasan, KannanPromotion of Prescription Drugs and Its Impact on Physicians' Choice BehaviorJournal of MarketingJournal of Marketing79-90653PHYSICIANSMEDICINE -- Formulae, receipts, prescriptionsPATIENTSDRUGS -- Marketing2001American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4856485&site=ehost-live (e.g. Gonul et al. 2001), possibly suggesting detailing may be an antecedent to physician perceptions of brand effectiveness. If perceived effectiveness proved to fully mediate the effects of detailing on prescription writing, understanding the relative effectiveness of the brands as perceived by the physicians would make competitor detailing data redundant. Alternatively, perceptions of drug effectiveness could drive detailing levels if the manufacturer actively targeted physicians with low perceptions of the effectiveness of their brand. Although not directly investigated in this study, the data suggest that both detailing and perceived effectiveness impact prescription writing. If perceived effectiveness fully mediated the effect of detailing, the correlation between detailing and perceived effectiveness would be expected to be very high. In fact, the correlation between physician-reported effectiveness and detailing is only 0.18, especially low considering common source bias likely exists. The correlation between detailing and market share, as well as effectiveness and market share, are 0.33 and 0.35, respectively. Coupled with the low correlation between detailing and effectiveness, the data indicate both detailing and effectiveness are valuable in explaining prescription writing. Consistent with the findings in this study, and since a survey would be required to acquire either physician perception or competitor detailing data, firms would be well advised to collect both types of competitor data when possible.  SEQ Hd \* MERGEFORMAT 11. CONTRIBUTIONS CRM research has been built primarily by considering customer databases consisting of transactional data between the firm and its customers. Interactions between the firms customers and competing firms have largely been ignored. Our research answers the call for incorporating competitor data into CRM. When the competition is ignored, estimates of the impact of marketing efforts on firm sales can be biased, leading to poor marketing allocation decisions. In our sample, ignoring the physicians perceptions of the effectiveness of the various brands and the level of competitor detailing results in statistically significant bias in the estimated response to the firms detailing efforts for 36% of the physicians. A reallocation of detailing based on the results of a model that includes physician brand effectiveness perceptions and competitor detailing indicate a 9% increase in prescriptions written. Since competitor data can typically be acquired only for a subset of the firms customers, a data augmentation method is presented and shown to outperform analyses utilizing only firm data, accurately segmenting 96% of the physicians where competitor data is unavailable. Obviously, this research investigates a single drug category in a single firm. Future research needs to replicate these results across categories and firms. Applying this approach to firms in data poor industries will provide challenging and potentially valuable research opportunities.  SEQ Hd \* MERGEFORMAT 12. APPENDIX: PHYSICIAN SURVEY   SEQ Hd \* MERGEFORMAT 13. REFERENCES  ADDIN EN.REFLIST Anderson, James C. and James A. 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(2002), Econometric Analysis of Cross Section and Panel Data. Cambridge, Massachusetts: The MIT Press. Zeithaml, Valarie A. (1985), "The New Demographics and Market Fragmentation," Journal of Marketing, 49 (3).   The exponent -1/k has been changed to 1/k so the anticipated signs of the parameters associated with the competitor variables will be negative, easing interpretation.  We will assume the firm always knows the number of brands in the category, K, which seems reasonable, particularly in this context.  Operationally, detailing and prescriptions will always have 1 added to their values as in commonly done in this context to avoid the undefined ln(0).  The superscripts correspond to the model numbers in Table  REF table_data_availability_by_model \h 2.  A reproduction of the survey, with the category details removed to protect the identity of the focal firm, appears in the Appendix.  Since firm data was available for non-respondents only in the dominant specialty, the comparison of respondents to non-respondents is restricted to that specialty. Results are assumed to be similar for other specialties.  As mentioned along with the presentation of the general model, for practical reasons 1 is added to prescriptions and detailing when estimating the models. This addition has been suppressed in previous equations, but must now be expressed explicitly for the forthcoming calculations. PAGE   PAGE iii  PAGE 39 CATEGORY w/ EFFORT Logit  ADDIN EN.CITE Gonul20019917Gonul, Fusun F.Carter, FranklinPetrova, ElinaSrinivasan, KannanPromotion of Prescription Drugs and Its Impact on Physicians' Choice BehaviorJournal of MarketingJournal of Marketing79-90653PHYSICIANSMEDICINE -- Formulae, receipts, prescriptionsPATIENTSDRUGS -- Marketing2001American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4856485&site=ehost-live (Gonul et al. 2001) Hidden Markov  ADDIN EN.CITE Moon2007838317Moon, SangkilKamakura, Wagner A.Ledolter, JohannesEstimating Promotion Response When Competitive Promotions Are UnobservableJournal of Marketing ResearchJournal of Marketing Research503-515443MARKETING researchSALES promotionMARKETING -- Mathematical modelsSPECIAL events -- MarketingCONSUMERSPSYCHOLOGYMARKOV processesMONTE Carlo methodMATHEMATICAL modelshidden Markov modelhierarchical Bayes analysismissing data problemsales forecastingsales promotion2007American Marketing Associationhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=25685153&site=ehost-live (Moon, Kamakura, and Ledolter 2007) NBD  ADDIN EN.CITE Venkataraman2007818117Venkataraman, SriramStremersch, StefanThe Debate on Influencing Doctors' Decisions: Are Drug Characteristics the Missing Link?Management ScienceManagement Science1688-17015311PHYSICIANSDECISION makingMARKETINGDRUGS -- Side effectsPOLITICAL planningSAMPLING2007INFORMS: Institute for Operations Researchhttp://ezproxy.lib.uh.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=27875318&site=ehost-live (Venkataraman and Stremersch 2007) Table  SEQ Tb \* MERGEFORMAT 13: Accuracy of Segment Assignment Using Augmented Data for 50 Physicians in Holdout Sample Class Assignment Using Data AugmentationClass Assignment Using Full SampleClass 1Class 2Class 3Class 1 10 of 10 physicians 100% accuracy for Class 10 physicians0 physiciansClass 2 0 physicians19 of 19 physicians 100% accuracy for Class 20 physiciansClass 3 2 of 21 physicians 9.5% missed for Class 30 physicians19 of 21 physicians 90.5% accuracy for Class 3 Table  SEQ Tb \* MERGEFORMAT 12: Reallocation Results for Each Alternative Model Firm ModelEffectiveness ModelECEMDetails Reallocated (% of Total)416 (11.8%)496 (14.0%)518 (14.7%)Additional Prescriptions for 508 Physicians in Sample (% Increase)64 (5.4%)99 (8.3%)105 (8.8%)Prescription Increase Per Physician0.130.190.21Projected Increase in Prescriptions for 7101 Physicians in Population89513841468 Table  SEQ Tb \* MERGEFORMAT 11: Comparison of Class Profiles Between Models  Firm ModelEffectiveness and Competitor Effort ModelClass 1 Detail MoreFirm details Average competitor details Firm brand prescriptions Total category prescriptions Perceived effectiveness of focal brand Perceived effectiveness of competitor brands1.44 1.18 0.76 6.18 3.65 3.781.40 1.22 0.72 8.71 3.64 3.81Class 2 Detail SameFirm details Average competitor details Firm brand prescriptions Total category prescriptions Perceived effectiveness of focal brand Perceived effectiveness of competitor brands1.12 1.28 0.22 10.17 3.38 3.880.89 1.12 0.09 5.65 3.57 3.93Class 3 Detail LessFirm details Average competitor details Firm brand prescriptions Total category prescriptions Perceived effectiveness of focal brand Perceived effectiveness of competitor brands0.84 1.05 0.06 2.51 3.54 3.861.09 1.18 0.17 4.33 3.32 3.78 Table  SEQ Tb \* MERGEFORMAT 10: Physician Class Assignments Comparison: Effectiveness vs. Effectiveness and Competitor Effort Model Class Assignment Using Effectiveness ModelClass Assignment Using Effectiveness and Competitor Effort ModelClass 1 Parameter for firm detailing = 0.20Class 2 Parameter for firm detailing = -0.01Class 3 Parameter for firm detailing = -0.18Class 1 Parameter for firm detailing = 0.18196 physicians 38.6% of sample tdiff = -0.216 physicians 1.2% of sample tdiff = 1.69*9 physicians 1.8% of sample tdiff = 3.84*Class 2 Parameter for firm detailing = -0.037 physicians 1.4% of sample tdiff = -2.01*132 physicians 26.0% of sample tdiff = -0.2313 physicians 2.6% of sample tdiff = 1.87*Class 3 Parameter for firm detailing = -0.1111 physicians 2.2% of sample tdiff = -2.43*10 physicians 2.0% of sample tdiff = -0.91124 physicians 24.4% of sample tdiff = 0.70 Table  SEQ Tb \* MERGEFORMAT 9: Physician Class Assignments Comparison: Firm vs. Effectiveness and Competitor Effort Model Class Assignment Using Firm ModelClass Assignment Using Effectiveness and Competitor Effort ModelClass 1 Parameter for firm detailing = 0.22Class 2 Parameter for firm detailing = -0.01Class 3 Parameter for firm detailing = -0.17Class 1 Parameter for firm detailing = 0.18148 physicians 29.1% of sample tdiff = -0.4663 physicians 12.4% of sample tdiff = 1.89*0 physicians 0.00% of sample tdiff = 4.15*Class 2 Parameter for firm detailing = -0.036 physicians 1.2% of sample tdiff = -3.13*67 physicians 13.2% of sample tdiff = -0.2179 physicians 15.6% of sample tdiff = 2.03*Class 3 Parameter for firm detailing = -0.1133 physicians 6.5% of sample tdiff = -3.37*48 physicians 9.4% of sample tdiff = -0.9564 physicians 12.6% of sample tdiff = 0.67 Table  SEQ Tb \* MERGEFORMAT 8: Effectiveness and Competitor Effort Model Results ModelClass 1Class 2Class 3Log likelihood-1285.25Avg. Membership %44.428.926.7Est.SEEst.SEEst.SEIntercept-0.09 0.17 0.14 0.14-0.27* 0.21Lagged Share 0.83* 0.06 0.27* 0.13-0.00 0.12Firm Detailing 0.18* 0.07-0.03 0.05-0.11o 0.07Competitor Detailing-0.11* 0.07-0.05 0.06-0.04 0.06Focal Brand Effectiveness 0.22* 0.09-0.01 0.09 0.05 0.09Competitor Brand Effectiveness-0.24* 0.13-0.10 0.19-0.13 0.20Focal Brand Side Effects 0.01 0.06 0.06o 0.05 0.04 0.06Competitor Brand Side Effects 0.10 0.09-0.26* 0.11-0.05 0.15Focal Brand Journal Ads 0.10* 0.04 0.06o 0.04 0.20* 0.05Competitor Brand Focal Ads-0.09* 0.05 0.00 0.06-0.16* 0.05Years in Practice 0.00 0.00 0.00o 0.00 0.00o 0.00Sex (female)-0.14* 0.04 0.07o 0.05 0.16* 0.04OBG/GYN 0.03 0.06-0.05 0.04 0.05 0.05Urologist-0.07 0.06-0.61* 0.10 0.00 0.05Sigma 0.51* 0.01 0.23* 0.02 0.24* 0.02* = significant at 0.1 o = significant considering only variance within datasets Table  SEQ Tb \* MERGEFORMAT 7: Effectiveness Model Results ModelClass 1Class 2Class 3Log likelihood-1301.44Avg. Membership %38.332.629.7Est.SEEst.SEEst.SEIntercept-0.25 0.23-0.02 0.26-0.07 0.12Lagged Share 0.74* 0.32 0.24o 0.42 0.44* 0.29Firm Detailing 0.20* 0.11-0.01 0.09-0.18* 0.07Focal Brand Effectiveness 0.24o 0.22 0.22o 0.36 0.01 0.16Competitor Brand Effectiveness-0.11 0.30-0.48o 0.53-0.06 0.20Focal Brand Side Effects 0.04 0.08-0.03 0.07-0.01 0.06Competitor Brand Side Effects-0.02 0.12 0.05 0.17 0.03 0.07Years in Practice 0.00 0.00 0.00 0.00 0.00o 0.00Sex (female)-0.11o 0.07-0.02 0.12 0.08* 0.04OBG/GYN 0.02 0.07 0.05 0.10-0.04 0.05Urologist-0.13o 0.08-0.26o 0.23-0.12* 0.16Sigma 0.50* 0.02 0.30* 0.05 0.21* 0.03* = significant at 0.1 o = significant considering only variance within datasets Table  SEQ Tb \* MERGEFORMAT 6: Firm Model Results ModelClass 1Class 2Class 3Log likelihood-1326.55Avg. 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Missing MissingRows represent physicians. Columns consist of variables in the indicated categories. Table  SEQ Tb \* MERGEFORMAT 2: Variables Included in Alternative Models Based on Data Availability Variable(1) Firm Model(2) Effectiveness Model(3) Competitor Effort and Effectiveness ModelLagged ShareyesyesyesFocal Brand DetailingyesyesyesCompetitor DetailingnonoyesFocal Brand Journal Ads ReadnonoyesCompetitor Journal Ads ReadnonoyesPerceived Effectiveness of Focal BrandnoyesyesPerceived Effectiveness of Competitor BrandsnoyesyesPerceived Profile of Focal Brand Side EffectsnoyesyesPerceived Profile of Competitor Brand Side EffectsnoyesyesYears in PracticeyesyesyesSexyesyesyesSpecialtyyesyesyes Table  SEQ Tb \* MERGEFORMAT 1: Correlations Among Detailing Levels Across Brands Brand BBrand CBrand DFocal Brand0.390.420.41Brand B0.520.56Brand C0.64 000! $Ifgdekd $$IfTlh֞!   {{{{{{ t044 laT00!0(0.050;0 $Ifgde $$Ifa$gde0 0!0&0(0-0.03050:0;0<0K0P0R0W0X0]0^0c0d0i0k0p0q0r000000000000000000000000000000000000000000011 1 1 1111"1#1(1)1.1/14161;1<1=1C1H1J1O1P1h"haVH* hJhaV h=phaVhaV[;0<0K0! $IfgdekdZ $$IfTlh֞!   {{{{{{ t044 laTK0R0X0^0d0k0q0 $Ifgde $$Ifa$gdeq0r00! $Ifgdekd $$IfTlh֞!   {{{{{{ t044 laT0000000 $Ifgde $$Ifa$gde000! $Ifgdekd~$$IfTlh֞!   {{{{{{ t044 laT0000000 $Ifgde $$Ifa$gde000! $Ifgdekd$$IfTlh֞!   {{{{{{ t044 laT000001 1 $Ifgde $$Ifa$gde 1 11! $Ifgdekd$$IfTlh֞!   {{{{{{ t044 laT11#1)1/161<1 $Ifgde $$Ifa$gde<1=1C1! $Ifgdekd4$$IfTlh֞!   {{{{{{ t044 laTC1J1P1W1]1d1j1 $Ifgde $$Ifa$gdeP1U1W1\1]1b1d1i1j1k1111111122222222#4$4;4<4=4>444444444455 5 5 5555555 5$5(5,5.5/5155595=5?5@5B5G5I5N5P5Q5T5Y5[5`5c5d5h5l5p5t555 h-JshaVh\mHnHujhaVUh haVCJaJ h=phaV hJhaVhaVPj1k11!$a$gd)Ekd$$IfTlh֞!   {{{{{{ t044 laT11111111B~kdX$$IfTl0   t0644 laT$ H!$d$Ifa$gdF28 H!$d$IfgdF28gd$a$gd1122 222Wkd$$IfTlF  8 t06    44 laT H!$d$IfgdF282252<2A2mWWW H!$d$IfgdF28kd@$$IfTlF  8 t06    44 laTA2B2I2P2U2mWWW H!$d$IfgdF28kd$$IfTlF  8 t06    44 laTU2V2]2d2i2mWWW H!$d$IfgdF28kd>$$IfTlF  8 t06    44 laTi2j2q2x2}2mWWW H!$d$IfgdF28kd$$IfTlF  8 t06    44 laT}2~2222mWWW H!$d$IfgdF28kd<$$IfTlF  8 t06    44 laT22222mWWW H!$d$IfgdF28kd$$IfTlF  8 t06    44 laT22222mWWW H!$d$IfgdF28kd:$$IfTlF  8 t06    44 laT2222.3/303mec[V@ H!$d$Ifgd<gd$a$gd$a$gd>kd$$IfTlF  8 t06    44 laT03<3L3M3p3v3|3R? 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KA?H1Z @؀깡jx|K2B* R vfRv,L ! ~ Ay +?klD:]b m6HgV%ˊg!5#>Fno]`f(P@penR~.C?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcefghijkruwvxzy{|}~Root Entry" FP4ityData d(WordDocument!ObjectPool$|hP4i_1240235115FhhOle CompObjfObjInfo "%(+./2567:=>?@ABEHIJKLMNORUVWXYZ[\_dinqrstuvwz}~ FMicrosoft Equation 3.0 DS Equation Equation.39q O~  FMicrosoft Equation 3.0 DS Equation Equation.39qEquation Native )_1240234733 FhhOle CompObj fObjInfo Equation Native  o_1240234744@FhhOle  S8I~ fM|Y,()=fM|() FMicrosoft Equation 3.0 DS Equation Equation.39q )d CompObj fObjInfoEquation Native )_12402358161FhhOle CompObjfObjInfoEquation Native  FMicrosoft Equation 3.0 DS Equation Equation.39qqk fM|Y,()=fM|Y obs ,()_1267901385FhhOle CompObjfObjInfo FMicrosoft Equation 3.0 DS Equation Equation.39qE8} Y=+W+X+Z+. FMicrosoft Equation 3.0 DS Equation Equation.39qEquation Native a_1241265951FhhOle CompObj fObjInfo! Equation Native !j_1267352930Yc$FhhOle #N  j =rS j "cD j FMicrosoft Equation 3.0 DS Equation Equation.39q'a~ Sh t =CompObj#%$fObjInfo&&Equation Native '}_1240201794E)Fhh+D+Sh t"1 + FMicrosoft Equation 3.0 DS Equation Equation.39q+ˆ\k m ijt =M ijt M kjtk=1K " ,Ole )CompObj(**fObjInfo+,Equation Native -_1267430010w.FhhOle 0CompObj-/1fObjInfo03 FMicrosoft Equation 3.0 DS Equation Equation.39qp^~ M i =exp i + i ()X yi y y=1Y "Equation Native 4_1241339352h63FhhOle 8CompObj249f FMicrosoft Equation 3.0 DS Equation Equation.39q¢Wk m ijt =e  ij + ijt D ijt ij A ijt ij e ObjInfo5;Equation Native <_12413393898FhhOle C kj + kjt D kjt kj A kjt kj k=1K " [] FMicrosoft Equation 3.0 DS Equation Equation.39q,Y lnm iCompObj79DfObjInfo:FEquation Native G_1241339407"=Fhhjt ()= ij + ijt + ij lnD ijt ()+ ij lnA ijt ()"lnexp kj + kjt ()D kjt kj A kjt kj k=1K " ()Ole PCompObj<>QfObjInfo?SEquation Native T FMicrosoft Equation 3.0 DS Equation Equation.39q0," ln2m jt ()=" j +" jt + ij ln2D jt ()+ ij ln2A jt ()"lne  kj + kjt D kjt kj A kjt kj k=1K " (), FMicrosoft Equation 3.0 DS Equation Equation.39q_1240204707' BFhhOle ]CompObjAC^fObjInfoD`+pO 2m FMicrosoft Equation 3.0 DS Equation Equation.39q 2DEquation Native a-_1239081243GFhhOle bCompObjFHcfObjInfoIeEquation Native f-_1241339439LFhhOle gCompObjKMhfObjInfoNjEquation Native k-_1241339457JTQFhh FMicrosoft Equation 3.0 DS Equation Equation.39qk 2A FMicrosoft Equation 3.0 DS Equation Equation.39qOle lCompObjPRmfObjInfoSoEquation Native p­d lnm ijt 2m jt ()= ij "" j + ij lnD ijt 2D jt ()+ ij lnA ijt 2A jt ()+ ijt "" jt ._1241339478VFhhOle xCompObjUWyfObjInfoX{ FMicrosoft Equation 3.0 DS Equation Equation.39qY" lnm ijt 2m jt ()= 1j + mj*m=2K " d mj + ij Equation Native |:_1241339496O[FhhOle CompObjZ\flnD ijt 2D jt ()+ ij lnA ijt 2A jt ()+ 1jt + mjt*m=2K " d mj FMicrosoft Equation 3.0 DS Equation Equation.39qObjInfo]Equation Native :_1267362088`FhhOle "% lnS ijt 2S jt ()= 1j + mj*m=2K " d mj + ij lnD ijt 2D jt ()+ ij lnA ijt 2A jt ()+ 1jt + mjt*m=2K " d mj FMicrosoft Equation 3.0 DS Equation Equation.39q'pl lnS 1jt 2S jt ()=CompObj_afObjInfobEquation Native _1267362098^reFhh 1j + 1j lnD 1jt 2D jt ()+ 1j lnA 1jt 2A jt ()+ 1jt FMicrosoft Equation 3.0 DS Equation Equation.39qOle CompObjdffObjInfogEquation Native X'<l lnS 1jt 2S jt ()= 1j + y,1j lnX y,1jt 2X y,jt () y=1Y " + 1jt_1241271833mjFhhOle CompObjikfObjInfol FMicrosoft Equation 3.0 DS Equation Equation.39q*x X y,1jt FMicrosoft Equation 3.0 DS Equation Equation.39qEquation Native F_1241271841oFhhOle CompObjnpfObjInfoqEquation Native F_1267363996tFhhOle *Pu 2X y,jt FMicrosoft Equation 3.0 DS Equation Equation.39q'¨V4~ lnS CompObjsufObjInfovEquation Native _1267364878yFh0h1jt 2S jt ()= 1j + y,1j lnX y,1jt X y,1jt X y,kjtk=2K " () 1K () y=1Y " + 1jtOle CompObjxzfObjInfo{Equation Native y FMicrosoft Equation 3.0 DS Equation Equation.39q']m lnS 1jt 2S jt () FMicrosoft Equation 3.0 DS Equation Equation.39q_1267364004;~F0h0hOle CompObj}fObjInfo'"Pn = 1j FMicrosoft Equation 3.0 DS Equation Equation.39qHtu + y,1j lnX y,1jt Equation Native >_1261568472F0h0hOle CompObjfObjInfoEquation Native _1267432692,F0h0hOle () K"1K () y=1Y " FMicrosoft Equation 3.0 DS Equation Equation.39q,| + y,cj lnX y,kjtCompObjfObjInfoEquation Native _1267364714F0h0hk=2K " () 1K () y=1Y " FMicrosoft Equation 3.0 DS Equation Equation.39q'&@k + 1jtOle CompObjfObjInfoEquation Native B_1241967232F0h0hOle CompObjfObjInfo FMicrosoft Equation 3.0 DS Equation Equation.39qPZ 1)#*# FMicrosoft Equation 3.0 DS Equation Equation.39qEquation Native 8_1267432449F0h0hOle CompObjfObjInfoEquation Native _1267432460F0h0hOle `s lnS 1jt 2S jt ()= 1j1)#*# + 1,1j1)#*# Years+ 2,1j1)#*# Female+ 3,1j1)#*# OBG+ 4,1j1)#*# Urol FMicrosoft Equation 3.0 DS Equation Equation.39qma< + 1,1j1)#*# lnS 1jt"1 2S jt"1 ()+CompObjfObjInfoEquation Native _1241967267F0h0h 2,1j1)#*# lnDet 1jt () 34 ()+ 1jt1)#*# FMicrosoft Equation 3.0 DS Equation Equation.39qOle CompObjfObjInfoEquation Native 8     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