×

Do CRM systems cause one-to-one marketing effectiveness? (English) Zbl 1426.62323

Summary: This article provides an assessment of the causal effect of customer relationship management (CRM) applications on one-to-one marketing effectiveness. We use a potential outcomes based propensity score approach to assess this causal effect. We find that firms using CRM systems have greater levels of one-to-one marketing effectiveness. We discuss the strengths and challenges of using the propensity score approach to design and execute CRM related observational studies. We also discuss the applicability of the framework in this paper to study typical causal questions in business and electronic commerce research at the firm, individual and economy levels, and to clarify the assumptions that researchers must make to infer causality from observational data.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis

Software:

pscore; Stata

References:

[1] Agodini, R. and Dynarski, M. (2004). Are experiments the only option? A look at dropout prevention programs. Rev. Econom. Statist. 86 180–194.
[2] Angrist, J. D. and Krueger, A. B. (1999). Empirical strategies in labor economics. In Handbook of Labor Economics 3A (O. Ashenfelter and D. Card, eds.) 1277–1366. North-Holland, Amsterdam.
[3] Banker, R. D., Kauffman, R. J. and Mahmood, M. A. (1993). Measuring the business value of IT: A future oriented perspective. In Strategic Information Technology Management : Perspectives on Organizational Growth and Competitive Advantage (R. D. Banker, R. J. Kauffman and M. A. Mahmood, eds.) 595–605. Idea Group Publishing, Harrisburg, PA.
[4] Bapna, R., Goes, P. and Gupta, A. (2001). Insights and analysis of online auctions. Comm. ACM 44 (11) 42–50.
[5] Bapna, R., Jank, W. and Shmueli, G. (2005). Consumer surplus in online auctions. Working paper, Dept. Operations and Information Management, Univ. Connecticut. Available at www.sba.uconn.edu/users/rbapna/research.htm.
[6] Barua, A. and Mukhopadhyay, T. (2000). Information technology and business performance: Past, present, and future. In Framing the Domains of IT Management : Projecting the Future... Through the Past (R. W. Zmud, ed.) 65–84. Pinnaflex, Cincinnati, OH.
[7] Becker, S. O. and Ichino, A. (2002). Estimation of average treatment effects based on propensity scores. The Stata Journal 2 358–377.
[8] Benjamin, D. J. (2003). Does 401(k) eligibility increase saving? Evidence from propensity score subclassification. J. Public Economics 87 1259–1290.
[9] Bhagwati, J., Panagariya, A. and Srinivasan, T. N. (2004). The muddles over outsourcing. J. Economic Perspectives 18 (4) 93–114.
[10] Boulding, W., Staelin, R., Ehret, M. and Johnston, W. J. (2005). A customer relationship management roadmap: What is known, potential pitfalls, and where to go. J. Marketing 69 (4) 155–166.
[11] Brynjolfsson, E. and Hitt, L. M. (1998). Beyond the productivity paradox. Comm. ACM 41 (8) 49–55.
[12] Connolly, M. (2003). The end of the MBA as we know it? Academy of Management Learning and Education 2 365–367.
[13] Dehejia, R. H. and Wahba, S. (1999). Causal effects in nonexperimental studies: Re-evaluating the evaluation of training programs. J. Amer. Statist. Assoc. 94 1053–1062.
[14] Dehejia, R. H. and Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. Rev. Econom. Statist. 84 151–161.
[15] Dehning, B., Richardson, V. J., Urbaczewski, A. and Wells, J. D. (2004). Reexamining the value relevance of e-commerce initiatives. J. Management Inform. Syst. 21 55–82.
[16] Dehning, B., Richardson, V. J. and Zmud, R. W. (2003). The value relevance of announcements of transformational information technology investments. MIS Quarterly 27 637–656.
[17] Fornell, C., Mithas, S., Morgeson, F. and Krishnan, M. S. (2006). Customer satisfaction and stock prices: High returns, low risk. J. Marketing 70 (1) 3–14.
[18] Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly .
[19] Harvard Management Update (2000). A crash course in customer relationship management. Harvard Management Update 5 (3) 12 pages.
[20] Heckman, J. J. (2005). The scientific model of causality (with discussion). Sociological Methodology 35 1–162.
[21] Holland, P. (1986). Statistics and causal inference (with discussion). J. Amer. Statist. Assoc. 81 945–970. JSTOR: · Zbl 0607.62001 · doi:10.2307/2289064
[22] Im, K. S., Dow, K. E. and Grover, V. (2001). Research report: A reexamination of IT investment and the market value of the firm—An event study methodology. Information Systems Research 12 103–117.
[23] Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Rev. Econom. Statist. 86 4–29.
[24] Jank, W. and Shmueli, G. (2006). Functional data analysis in electronic commerce research. Statist. Sci. 21 155–166. · Zbl 1426.62375 · doi:10.1214/088342306000000132
[25] Kauffman, R. J. and Weill, P. (1989). An evaluative framework for research on the performance effects of information technology investments. In Proc. International Conference on Information Systems , Association for Information Systems 377–388. Boston.
[26] Kohli, A. K. and Jaworski, B. J. (1990). Market orientation: The construct, research propositions, and managerial implications. J. Marketing 54 (2) 1–18.
[27] Koppius, O. R. and Van Heck, E. (2002). Information architecture and electronic market performance in multi-dimensional auctions. Working paper, Erasmus Univ.
[28] Lucas, H. C. (1975). Performance and the use of an information system. Management Sci. 21 908–919.
[29] Lucas, H. C. (1993). The business value of information technology: A historical perspective and thoughts for future research. In Strategic Information Technology Management : Perspectives on Organizational Growth and Competitive Advantage (R. D. Banker, R. J. Kauffman and M. A. Mahmood, eds.) 359–374. Idea Group Publishing, Harrisburg, PA.
[30] Lucas, H. C. and Nielsen, N. R. (1980). The impact of the mode of information presentation on learning and performance. Management Sci. 26 982–993.
[31] Mithas, S. and Jones, J. L. (2006). Do auction parameters affect buyer surplus in e-auctions for procurement? Production and Operations Management .
[32] Mithas, S., Jones, J. L., Krishnan, M. S. and Fornell, C. (2005). A theoretical integration of technology adoption and business value literature: The case of CRM systems. Working paper, Ross School of Business, Univ. Michigan.
[33] Mithas, S. and Krishnan, M. S. (2004a). Causal effect of CRM systems on cross selling effectiveness and sales-force productivity by bounding a matching estimator. In Proc. Ninth Annual INFORMS Conference on Information Systems and Technology (H. Bhargava, C. Forman, R. Kauffman and D. J. Wu, eds.) 1–32. Denver, CO.
[34] Mithas, S. and Krishnan, M. S. (2004b). Returns to managerial and technical competencies of information technology professionals: An empirical analysis. Working paper, Ross School of Business, Univ. Michigan.
[35] Mithas, S., Krishnan, M. S. and Fornell, C. (2005). Why do customer relationship management applications affect customer satisfaction? J. Marketing 69 (4) 201–209.
[36] Mithas, S. and Whitaker, J. (2006). Effect of information intensity and physical presence need on the global disaggregation of services: Theory and empirical evidence. Working paper, Smith School of Business, Univ. Maryland.
[37] Peppers, D., Rogers, M. and Dorf, B. (1999). Is your company ready for one-to-one marketing? Harvard Business Review 77 (1) 3–12.
[38] Pfeffer, J. and Fong, C. (2003). Assessing business schools: A reply to Connolly. Academy of Management Learning and Education 2 368–370.
[39] Rai, A., Patnayakuni, R. and Patnayakuni, N. (1997). Technology investment and business performance. Comm. ACM 40 (7) 89–97.
[40] Rosenbaum, P. R. (1999). Choice as an alternative to control in observational studies (with discussion). Statist. Sci. 14 259–304. · Zbl 1059.62699 · doi:10.1214/ss/1009212410
[41] Rosenbaum, P. (2002). Observational Studies , 2nd ed. Springer, New York. · Zbl 0985.62091
[42] Rosenbaum, P. R. and Rubin, D. B. (1983a). Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. J. Roy. Statist. Soc. Ser. B 45 212–218.
[43] Rosenbaum, P. R. and Rubin, D. B. (1983b). The central role of the propensity score in observational studies for causal effects. Biometrika 70 41–55. JSTOR: · Zbl 0522.62091 · doi:10.1093/biomet/70.1.41
[44] Rosenbaum, P. R. and Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. J. Amer. Statist. Assoc. 79 516–524.
[45] Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educational Psychology 66 688–701.
[46] Rubin, D. B. (1977). Assignment to treatment group on the basis of a covariate. J. Educational Statistics 2 1–26.
[47] Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Ann. Internal Medicine 127 757–763.
[48] Rubin, D. B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services and Outcomes Research Methodology 2 169–188.
[49] Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. J. Amer. Statist. Assoc. 100 322–331. · Zbl 1117.62418 · doi:10.1198/016214504000001880
[50] Rubin, D. B. and Waterman, R. P. (2006). Estimating the causal effects of marketing interventions using propensity score methodology. Statist. Sci. 21 206–222. · Zbl 1426.62325 · doi:10.1214/088342306000000259
[51] Rust, R. T. and Kannan, P. K. (2003). E-service: A new paradigm for business in the electronic environment. Comm. ACM 46 (6) 36–42.
[52] Sambamurthy, V., Bharadwaj, A. and Grover, V. (2003). Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary firms. MIS Quarterly 27 237–263.
[53] Srinivasan, R. and Moorman, C. (2005). Strategic firm commitments and rewards for customer relationship management in online retailing. J. Marketing 69 (4) 193–200.
[54] Venkatraman, N. (2004). Offshoring without guilt. Sloan Management Review 45 (3) 14–16.
[55] Winship, C. and Morgan, S. L. (1999). The estimation of causal effects from observational data. Annual Review of Sociology 25 659–706.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.