Search

Research and practice have called for the incorporation of customer mindset metrics (CMMs) to improve the accuracy of models that predict individual customer profits. However, as CMMs are self-reported data, collected through customer surveys, they are seldom available for a firm's entire customer database and in addition always measured with some degree of error. Their usage in models for individual-level predictions of customer profit has therefore proven challenging. We offer a solution through a new method called multiple overimputation (MO). MO treats missing data as an extreme form of measurement error and imputes the CMMs for both customers with observed, albeit with measurement error, as well as missing values, that are then included as predictors in a model of individual customer profits. Through a simulation study, empirical application in the pharmaceutical industry, and a customer selection exercise, we demonstrate the predictive and economic value of applying MO in the context of CRM., Author(s): Rajkumar Venkatesan [sup.1] , Alexander Bleier [sup.2] , Werner Reinartz [sup.3] , Nalini Ravishanker [sup.4] Author Affiliations: (Aff1) 0000 0000 9136 933X, grid.27755.32, Professor of Business Administration, Darden Graduate [...]

Abstract Many studies use variables from the Compustat database to measure various marketing constructs, yet no clear guidelines detail which metrics correspond with which constructs. Justifications rest mainly on the [...]

Many studies use variables from the Compustat database to measure various marketing constructs, yet no clear guidelines detail which metrics correspond with which constructs. Justifications rest mainly on the ready availability of easy-to-use measures that seem related to a particular construct. As a result, various metrics have been utilized to capture the same construct, and the same metric-such as selling, general, and administrative expenses (SGA)-has been applied to capture vastly different constructs. But using SGA inappropriately can lead to biased estimates, questionable support for the hypotheses, and potentially misleading implications for research and practice. To test the validity of SGA for multiple relevant marketing and sales constructs, this study gathers data on benchmark variables from alternative data sources and applies a multitrait-multimethod (MTMM) approach. Results show that, in general, SGA has been applied too liberally in marketing contexts; SGA is an appropriate operationalization only for some constructs. This article provides guidelines for the proper conceptualization and operationalization of marketing constructs., Author(s): Annette Ptok [sup.1] , Rupinder P. Jindal [sup.2] , Werner J. Reinartz [sup.3] Author Affiliations: (Aff1) 0000 0000 8580 3777, grid.6190.e, Department of Retailing and Customer Management, University of [...]

Manufacturers invest in customer solutions to differentiate their offerings and sustain profitability despite declining margins from goods sales. Notwithstanding strong managerial and academic interest, an examination of whether and explanations for when and why solutions translate into superior performance are lacking. We test hypotheses developed from the resource-based theory and transaction cost economics, supplemented with in-depth theory-in-use interviews, on primary and secondary data collected from 175 manufacturers. From a model that corrects for endogeneity, the findings suggest that, compared with other service offerings, solutions are associated with increased return on sales. This positive profitability effect is enhanced in firms with greater sales capabilities; it is stronger in industries with greater buyer power but weaker in technology-intensive industries. These results caution against the simplistic view of solutions as a universal route to gaining competitive advantage and aid in better identifying the role of solutions in a manufacturer's offering portfolio., Author(s): Stefan Worm [sup.1] , Sundar G. Bharadwaj [sup.2] , Wolfgang Ulaga [sup.3] , Werner J. Reinartz [sup.4] Author Affiliations: (1) 0000 0001 2361 9429grid.413074.5BI Norwegian Business School, Department of [...]

Correction to: J. of the Acad. Mark. Sci. https://doi.org/10.1007/s11747-018-0589-2 The original version of this article unfortunately contained mistakes in Table 10. Data entries were incorrectly aligned under 'Benchmark variables' and [...]

The original version of this article unfortunately contained mistakes in Table 10. Data entries were incorrectly aligned under 'Benchmark variables' and 'Empirical test for SGA or modifications' columns. Please see below correct Table 10., Author(s): Annette Ptok [sup.1] , Rupinder P. Jindal [sup.2] , Werner J. Reinartz [sup.3] Author Affiliations: (Aff1) 0000 0000 8580 3777, grid.6190.e, Department of Retailing and Customer Management, University of [...]

Byline: Werner J. Reinartz (1), V. Kumar (2) Keywords: Store positioning; store location; structural equation modeling Abstract: Four factors have traditionally been identified in influencing store performance: store-, market-, and consumer characteristics and competition. Given partially conflicting and, in some cases, dated findings in the literature we want to re-assess the effects. In particular, past research has usually considered only two out of the four constructs at any time, which is likely to result in erroneous interpretation of results. We draw upon a unique cross-sectional sample of grocery stores with a wide array of store characteristic, store performance, trade area, and consumer demographic variables. Using structural modeling, our prime interest is to assess the differential impact of store attractiveness, market potential, and socio-economic status on two different store performance measures, while controlling for competitive effects. We find that the market potential of a store is by far the most important driver of store sales performance and sales productivity performance. With one exception, the model and the data support the hypothesized relationships about the direction and the strength of the impact of a store's attractiveness, market potential and socio-economic characteristics of the trade area on a store's performance. Author Affiliation: (1) Department of Marketing, University of Houston, College of Business Administration, Houston, TX, 77204-6283 (2) Department of Marketing, University of Houston, College of Business Administration, Houston, TX, 77204-6283 Article History: Registration Date: 14/10/2004