MEASURING CUSTOMER LIFETIME VALUE: APPLICATION OF ANALYTIC HIERARCHY PROCESS IN DETERMINING RELATIVE WEIGHTS OF ‘LRFM’
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Published
Dec 19, 2021
Saurabh Pradhan
Gokulananda Patel Pankaj Priya
https://orcid.org/0000-0002-2800-5140
Gokulananda Patel Pankaj Priya
Abstract
ABSTRACT
The limited availability of resources drives retailers to tailor their resources to identified profitable customers. In the present scenario, when the ROI of marketing is being questioned, the satisfaction of the profitable customers is of utmost importance as it drives their loyalty towards the retailer and the retailer’s brand. This research has considered Length of association with customers (L), apart from variables like Recency (R), Frequency (F) and Monetary-value of the purchase (M) in measuring customers’ relative-worth based on the calculation of Customer Lifetime-value (CLV). The contribution of this article lies in calculating weights of these variables – L, R, F, and M and demonstrating the calculation of CLV using weighted LRFM based on data collected from a leading apparel retailer in India. The obtained results for the customer base using the proposed approach is more reliable when compared with traditional non-weighted approaches of RFM based CLV. This methodology will provide a new and better option to retailers for measuring CLV of their customers, thus aiding their decision making about customer-friendly profitable marketing strategies and attaining optimum returns on their investments.
How to Cite
Pradhan, S., Patel, G., & Priya, P. (2021). MEASURING CUSTOMER LIFETIME VALUE: APPLICATION OF ANALYTIC HIERARCHY PROCESS IN DETERMINING RELATIVE WEIGHTS OF ‘LRFM’. International Journal of the Analytic Hierarchy Process, 13(3). https://doi.org/10.13033/ijahp.v13i3.892
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Keywords
Customer Lifetime Value (CLV), AHP, LRFM, RFM
References
Alvandi, M., Fazli, S., & Abdoli, F. S. (2012). K-Mean clustering method for analysis customer lifetime value with LRFM relationship model in banking services. International Research Journal of Applied and Basic Sciences, 3(11), 2294-2302.
Ayoubi, M. (2016). Customer segmentation based on CLV model and neural network. International Journal of Computer Science Issues (IJCSI), 13(2), 31. Doi: https://doi.org/10.20943/01201602.3137
Barzilai, J. (1997). Deriving weights from pairwise comparison matrices. Journal of the Operational Research Society, 48(12), 1226-1232. Doi: https://doi.org/10.1038/sj.jors.2600474
Benoit, D. F., & Van den Poel, D. (2009). Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services. Expert Systems with Applications, 36(7), 10475-10484. Doi: https://doi.org/10.1016/j.eswa.2009.01.031
Black, K. (2011). Business statistics: for contemporary decision making. John Wiley & Sons.
Byson, N. (1995). A goal programming method for generating priorities vectors. Journal of Operational Research Society, Palgrave Macmillan Ltd., Houndmills, Basingstoke, Hampshire, RG21 6XS, England, 641-648.
Chang, H. H., & Tsay, S. F. (2004). Integrating of SOM and K-mean in data mining clustering: An empirical study of CRM and profitability evaluation. Journal of Information Management, 11, 161-203.
Chu, A. T. W., Kalaba, R. E., & Spingarn, K. (1979). A comparison of two methods for determining the weights of belonging to fuzzy sets. Journal of Optimization theory and Applications, 27(4), 531-538. Doi: https://doi.org/10.1007/BF00933438
Coussement, K., Van den Bossche, F. A., & De Bock, K. W. (2014). Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees. Journal of Business Research, 67(1), 2751-2758. Doi: 10.1016/j.jbusres.2012.09.024
Crawford, G., & Williams, C. (1985). A note on the analysis of subjective judgment matrices. Journal of Mathematical Psychology, 29(4), 387-405. Doi: https://doi.org/10.1016/0022-2496(85)90002-1
Golany, B., & Kress, M. (1993). A multicriteria evaluation of methods for obtaining weights from ratio-scale matrices. European Journal of Operational Research, 69(2), 210-220. Doi: https://doi.org/10.1016/0377-2217(93)90165-J
Goodman, J. (1992). Leveraging the customer database to your competitive advantage. (Retail/Database). Direct Marketing, Dec, 1, 4.
Greenberg, P. (2001). CRM at the speed of light: Capturing and keeping customers in Internet real time. McGraw-Hill Professional.
Gupta, S. et al. (2006). Modeling customer lifetime value. Journal of Service Research, 9, 139-150. Doi: http://dx.doi.org/10.1177/1094670506293810
Haenlein, M., Kaplan, A. M., & Beeser, A. J. (2007). A model to determine customer lifetime value in a retail banking context. European Management Journal, 25(3), 221-234. Doi: http://dx.doi.org/10.1016/j.emj.2007.01.004
Haenlein, M., Kaplan, A. M., & Schoder, D. (2006). Valuing the real option of abandoning unprofitable customers when calculating customer lifetime value. Journal of Marketing, 70(3), 5-20.
Hawkes, V. A. (2000). The heart of the matter: The challenge of customer lifetime value. CRM Forum Resources.
Hidalgo, P., Manzur, E., Olavarrieta, S., & Farias, P. (2008). Customer retention and price matching: The AFPs case. Journal of Business Research, 61(6), 691-696. Doi: http://dx.doi.org/10.1016/j.jbusres.2007.06.046
Hiziroglu, A., & Sengul, S. (2012). Investigating two customer lifetime value models from segmentation perspective. Procedia-Social and Behavioral Sciences, 62, 766-774. Doi: http://dx.doi.org/10.1016/j.sbspro.2012.09.129
Ho, W. (2008). Integrated analytic hierarchy process and its applications–A literature review. European Journal of Operational Research, 186(1), 211-228. Doi: https://doi.org/10.1016/j.ejor.2007.01.004
Hong, T., & Kim, E. (2012). Segmenting customers in online stores based on factors that affect the customer’s intention to purchase. Expert Systems with Applications, 39(2), 2127-2131. Doi: http://dx.doi.org/10.1016/j.eswa.2011.07.114
Hosseini, S. M. S., Maleki, A., & Gholamian, M. R. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37(7), 5259-5264. Doi: http://dx.doi.org/10.1016/j.eswa.2009.12.070
Hu, Y. H., & Yeh, T. W. (2014). Discovering valuable frequent patterns based on RFM analysis without customer identification information. Knowledge-Based Systems, 61, 76-88. Doi: https://doi.org/10.1016/j.knosys.2014.02.009
Hughes, A. (1994). Strategic database marketing: The masterplan for starting and managing a profitable, customer-based marketing program. 1st ed. Chicago, IL: Probus Publishing Co., Chicago, IL.
Islam, R., & bin Mohd Rasad, S. (2006). Employee performance evaluation by the AHP: A case study. Asia Pacific Management Review, 11(3).
Jain, D., & Singh, S. S. (2002). Customer lifetime value research in marketing: A review and future directions. Journal of Interactive Marketing, 16(2), 34. Doi: http://dx.doi.org/10.2224/sbp.2012.40.7.1057
Kumar, N. V., & Ganesh, L. S. (1996). A simulation-based evaluation of the approximate and the exact eigenvector methods employed in AHP. European Journal of Operational Research, 95(3), 656-662.
Kumar, V., Venkatesan, R., Bohling, T., & Beckmann, D. (2008). Practice prize report—The power of CLV: Managing customer lifetime value at IBM. Marketing Science, 27(4), 585-599.
Lin, S. Y., Wei, J. T., Weng, C. C., & Wu, H. H. (2011). A case study of using classification and regression tree and LRFM model in a pediatric dental clinic. International Proceedings of Economic Development and Research—Innovation, Management and Service, 14, 131-135.
Liu, D. R., & Shih, Y. Y. (2005). Integrating AHP and data mining for product recommendation based on customer lifetime value. Information & Management, 42(3), 387-400. Doi: 10.1016/j.im.2004.01.008
Ma, M., Li, Z., & Chen, J. (2008). Phase-type distribution of customer relationship with Markovian response and marketing expenditure decision on the customer lifetime value. European Journal of Operational Research, 187(1), 313-326. Doi: http://dx.doi.org/10.1016/j.ejor.2007.03.018
Malthouse, E. C., & Blattberg, R. C. (2005). Can we predict customer lifetime value?. Journal of Interactive Marketing, 19(1), 2-16. Doi: https://doi.org/10.1002/dir.20027
Miglautsch, J. R. (2000). Thoughts on RFM scoring. Journal of Database Marketing & Customer Strategy Management, 8(1), 67-72. Doi: https://doi.org/10.1057/palgrave.jdm.3240019
Parvaneh, A., Abbasimehr, H., & Tarokh, M. J. (2012). Data mining application in retailer segmentation based on LRFM variables: case study. Global Journal on Technology, 1.
Pradhan, S. (2021). Excel Calculations & Tabular Data for the article titled "Measuring Customer Lifetime Value: Application of Analytic Hierarchy Process in determining Relative Weights of ‘LRFM’" (Mendeley Data; Version V4) [Data set]. Mendeley Data. Doi: http://dx.doi.org/10.17632/48ngxh788s.4.
Reinartz, W. J., & Kumar, V. (2000). On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing, 64(4), 17-35. Doi: https://doi.org/10.1509%2Fjmkg.64.4.17.18077
Reinartz, W., Thomas, J. S., & Kumar, V. (2005). Balancing acquisition and retention resources to maximize customer profitability. Journal of Marketing, 69(1), 63-79. Doi: http://dx.doi.org/10.1509/jmkg.69.1.63.55511
Rosset, S., Neumann, E., Eick, U., Vatnik, N., & Idan, Y. (2002, July). Customer lifetime value modeling and its use for customer retention planning. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 332-340). Doi: https://doi.org/10.1145/775047.775097
Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return on marketing: Using customer equity to focus marketing strategy. Journal of Marketing, 68(1), 109-127. Doi: http://dx.doi.org/10.1509/jmkg.68.1.109.24030
Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281. Doi: https://doi.org/10.1016/0022-2496(77)90033-5
Saaty, T. L. (1990a). How to make a decision: the analytic hierarchy process. European journal of Operational Research, 48(1), 9-26. Doi: https://doi.org/10.1016/0377-2217(90)90057-I
Saaty, T. L. (1990b). Eigenvector and logarithmic least squares. European Journal of Operational Research, 48(1), 156-160. Doi: https://doi.org/10.1016/0377-2217(90)90073-K
Saaty, T. L. (2003). Decision-making with the AHP: Why is the principal eigenvector necessary. European Journal of Operational Research, 145(1), 85-91.
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98.
Saaty, T. L., & Hu, G. (1998). Ranking by eigenvector versus other methods in the analytic hierarchy process. Applied Mathematics Letters, 11(4), 121-125. Doi: https://doi.org/10.1016/S0893-9659(98)00068-8
Safari, F., Safari, N., & Montazer, G. A. (2016). Customer lifetime value determination based on RFM model. Marketing Intelligence & Planning, 4(4), 446-461. Doi: https://doi.org/10.1108/MIP-03-2015-0060
Shih, Y. Y., & Liu, D. R. (2008). Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands. Expert Systems with Applications, 35(1-2), 350-360.
Stone, B. (1994). Successful direct marketing methods. New York, NY: NTC Business Books, McGraw-Hill Education.
Takeda, E., Cogger, K. O., & Yu, P. L. (1987). Estimating criterion weights using eigenvectors: A comparative study. European Journal of Operational Research, 29(3), 360-369.
Taslicali, A. K., & Ercan, S. (2006). The analytic hierarchy and the analytic network processes in multicriteria decision making: A comparative study. Journal of Aeronautics and Space Technologies, 2(4), 55-65.
Tukel, O. I., & Dixit, A. (2013). Application of customer lifetime value model in make-to-order manufacturing. The Journal of Business and Industrial Marketing, 28(6), 468-474.
Wu, H. H., Lin, S. Y., & Liu, C. W. (2014). Analyzing patients’ values by applying cluster analysis and LRFM model in a pediatric dental clinic in Taiwan. The Scientific World Journal, 2014.
Yoseph, F., & Heikkila, M. (2018, December). Segmenting retail customers with an enhanced RFM and a hybrid regression/clustering method. In 2018 International Conference on Machine Learning and Data Engineering (iCMLDE) (108-116). IEEE. Doi: https://doi.org/10.1109/iCMLDE.2018.00029
Zahedi, F. (1986). A simulation study of estimation methods in the analytic hierarchy process. Socio-Economic Planning Sciences, 20(6), 347-354.
Ayoubi, M. (2016). Customer segmentation based on CLV model and neural network. International Journal of Computer Science Issues (IJCSI), 13(2), 31. Doi: https://doi.org/10.20943/01201602.3137
Barzilai, J. (1997). Deriving weights from pairwise comparison matrices. Journal of the Operational Research Society, 48(12), 1226-1232. Doi: https://doi.org/10.1038/sj.jors.2600474
Benoit, D. F., & Van den Poel, D. (2009). Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services. Expert Systems with Applications, 36(7), 10475-10484. Doi: https://doi.org/10.1016/j.eswa.2009.01.031
Black, K. (2011). Business statistics: for contemporary decision making. John Wiley & Sons.
Byson, N. (1995). A goal programming method for generating priorities vectors. Journal of Operational Research Society, Palgrave Macmillan Ltd., Houndmills, Basingstoke, Hampshire, RG21 6XS, England, 641-648.
Chang, H. H., & Tsay, S. F. (2004). Integrating of SOM and K-mean in data mining clustering: An empirical study of CRM and profitability evaluation. Journal of Information Management, 11, 161-203.
Chu, A. T. W., Kalaba, R. E., & Spingarn, K. (1979). A comparison of two methods for determining the weights of belonging to fuzzy sets. Journal of Optimization theory and Applications, 27(4), 531-538. Doi: https://doi.org/10.1007/BF00933438
Coussement, K., Van den Bossche, F. A., & De Bock, K. W. (2014). Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees. Journal of Business Research, 67(1), 2751-2758. Doi: 10.1016/j.jbusres.2012.09.024
Crawford, G., & Williams, C. (1985). A note on the analysis of subjective judgment matrices. Journal of Mathematical Psychology, 29(4), 387-405. Doi: https://doi.org/10.1016/0022-2496(85)90002-1
Golany, B., & Kress, M. (1993). A multicriteria evaluation of methods for obtaining weights from ratio-scale matrices. European Journal of Operational Research, 69(2), 210-220. Doi: https://doi.org/10.1016/0377-2217(93)90165-J
Goodman, J. (1992). Leveraging the customer database to your competitive advantage. (Retail/Database). Direct Marketing, Dec, 1, 4.
Greenberg, P. (2001). CRM at the speed of light: Capturing and keeping customers in Internet real time. McGraw-Hill Professional.
Gupta, S. et al. (2006). Modeling customer lifetime value. Journal of Service Research, 9, 139-150. Doi: http://dx.doi.org/10.1177/1094670506293810
Haenlein, M., Kaplan, A. M., & Beeser, A. J. (2007). A model to determine customer lifetime value in a retail banking context. European Management Journal, 25(3), 221-234. Doi: http://dx.doi.org/10.1016/j.emj.2007.01.004
Haenlein, M., Kaplan, A. M., & Schoder, D. (2006). Valuing the real option of abandoning unprofitable customers when calculating customer lifetime value. Journal of Marketing, 70(3), 5-20.
Hawkes, V. A. (2000). The heart of the matter: The challenge of customer lifetime value. CRM Forum Resources.
Hidalgo, P., Manzur, E., Olavarrieta, S., & Farias, P. (2008). Customer retention and price matching: The AFPs case. Journal of Business Research, 61(6), 691-696. Doi: http://dx.doi.org/10.1016/j.jbusres.2007.06.046
Hiziroglu, A., & Sengul, S. (2012). Investigating two customer lifetime value models from segmentation perspective. Procedia-Social and Behavioral Sciences, 62, 766-774. Doi: http://dx.doi.org/10.1016/j.sbspro.2012.09.129
Ho, W. (2008). Integrated analytic hierarchy process and its applications–A literature review. European Journal of Operational Research, 186(1), 211-228. Doi: https://doi.org/10.1016/j.ejor.2007.01.004
Hong, T., & Kim, E. (2012). Segmenting customers in online stores based on factors that affect the customer’s intention to purchase. Expert Systems with Applications, 39(2), 2127-2131. Doi: http://dx.doi.org/10.1016/j.eswa.2011.07.114
Hosseini, S. M. S., Maleki, A., & Gholamian, M. R. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37(7), 5259-5264. Doi: http://dx.doi.org/10.1016/j.eswa.2009.12.070
Hu, Y. H., & Yeh, T. W. (2014). Discovering valuable frequent patterns based on RFM analysis without customer identification information. Knowledge-Based Systems, 61, 76-88. Doi: https://doi.org/10.1016/j.knosys.2014.02.009
Hughes, A. (1994). Strategic database marketing: The masterplan for starting and managing a profitable, customer-based marketing program. 1st ed. Chicago, IL: Probus Publishing Co., Chicago, IL.
Islam, R., & bin Mohd Rasad, S. (2006). Employee performance evaluation by the AHP: A case study. Asia Pacific Management Review, 11(3).
Jain, D., & Singh, S. S. (2002). Customer lifetime value research in marketing: A review and future directions. Journal of Interactive Marketing, 16(2), 34. Doi: http://dx.doi.org/10.2224/sbp.2012.40.7.1057
Kumar, N. V., & Ganesh, L. S. (1996). A simulation-based evaluation of the approximate and the exact eigenvector methods employed in AHP. European Journal of Operational Research, 95(3), 656-662.
Kumar, V., Venkatesan, R., Bohling, T., & Beckmann, D. (2008). Practice prize report—The power of CLV: Managing customer lifetime value at IBM. Marketing Science, 27(4), 585-599.
Lin, S. Y., Wei, J. T., Weng, C. C., & Wu, H. H. (2011). A case study of using classification and regression tree and LRFM model in a pediatric dental clinic. International Proceedings of Economic Development and Research—Innovation, Management and Service, 14, 131-135.
Liu, D. R., & Shih, Y. Y. (2005). Integrating AHP and data mining for product recommendation based on customer lifetime value. Information & Management, 42(3), 387-400. Doi: 10.1016/j.im.2004.01.008
Ma, M., Li, Z., & Chen, J. (2008). Phase-type distribution of customer relationship with Markovian response and marketing expenditure decision on the customer lifetime value. European Journal of Operational Research, 187(1), 313-326. Doi: http://dx.doi.org/10.1016/j.ejor.2007.03.018
Malthouse, E. C., & Blattberg, R. C. (2005). Can we predict customer lifetime value?. Journal of Interactive Marketing, 19(1), 2-16. Doi: https://doi.org/10.1002/dir.20027
Miglautsch, J. R. (2000). Thoughts on RFM scoring. Journal of Database Marketing & Customer Strategy Management, 8(1), 67-72. Doi: https://doi.org/10.1057/palgrave.jdm.3240019
Parvaneh, A., Abbasimehr, H., & Tarokh, M. J. (2012). Data mining application in retailer segmentation based on LRFM variables: case study. Global Journal on Technology, 1.
Pradhan, S. (2021). Excel Calculations & Tabular Data for the article titled "Measuring Customer Lifetime Value: Application of Analytic Hierarchy Process in determining Relative Weights of ‘LRFM’" (Mendeley Data; Version V4) [Data set]. Mendeley Data. Doi: http://dx.doi.org/10.17632/48ngxh788s.4.
Reinartz, W. J., & Kumar, V. (2000). On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing, 64(4), 17-35. Doi: https://doi.org/10.1509%2Fjmkg.64.4.17.18077
Reinartz, W., Thomas, J. S., & Kumar, V. (2005). Balancing acquisition and retention resources to maximize customer profitability. Journal of Marketing, 69(1), 63-79. Doi: http://dx.doi.org/10.1509/jmkg.69.1.63.55511
Rosset, S., Neumann, E., Eick, U., Vatnik, N., & Idan, Y. (2002, July). Customer lifetime value modeling and its use for customer retention planning. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 332-340). Doi: https://doi.org/10.1145/775047.775097
Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return on marketing: Using customer equity to focus marketing strategy. Journal of Marketing, 68(1), 109-127. Doi: http://dx.doi.org/10.1509/jmkg.68.1.109.24030
Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281. Doi: https://doi.org/10.1016/0022-2496(77)90033-5
Saaty, T. L. (1990a). How to make a decision: the analytic hierarchy process. European journal of Operational Research, 48(1), 9-26. Doi: https://doi.org/10.1016/0377-2217(90)90057-I
Saaty, T. L. (1990b). Eigenvector and logarithmic least squares. European Journal of Operational Research, 48(1), 156-160. Doi: https://doi.org/10.1016/0377-2217(90)90073-K
Saaty, T. L. (2003). Decision-making with the AHP: Why is the principal eigenvector necessary. European Journal of Operational Research, 145(1), 85-91.
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98.
Saaty, T. L., & Hu, G. (1998). Ranking by eigenvector versus other methods in the analytic hierarchy process. Applied Mathematics Letters, 11(4), 121-125. Doi: https://doi.org/10.1016/S0893-9659(98)00068-8
Safari, F., Safari, N., & Montazer, G. A. (2016). Customer lifetime value determination based on RFM model. Marketing Intelligence & Planning, 4(4), 446-461. Doi: https://doi.org/10.1108/MIP-03-2015-0060
Shih, Y. Y., & Liu, D. R. (2008). Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands. Expert Systems with Applications, 35(1-2), 350-360.
Stone, B. (1994). Successful direct marketing methods. New York, NY: NTC Business Books, McGraw-Hill Education.
Takeda, E., Cogger, K. O., & Yu, P. L. (1987). Estimating criterion weights using eigenvectors: A comparative study. European Journal of Operational Research, 29(3), 360-369.
Taslicali, A. K., & Ercan, S. (2006). The analytic hierarchy and the analytic network processes in multicriteria decision making: A comparative study. Journal of Aeronautics and Space Technologies, 2(4), 55-65.
Tukel, O. I., & Dixit, A. (2013). Application of customer lifetime value model in make-to-order manufacturing. The Journal of Business and Industrial Marketing, 28(6), 468-474.
Wu, H. H., Lin, S. Y., & Liu, C. W. (2014). Analyzing patients’ values by applying cluster analysis and LRFM model in a pediatric dental clinic in Taiwan. The Scientific World Journal, 2014.
Yoseph, F., & Heikkila, M. (2018, December). Segmenting retail customers with an enhanced RFM and a hybrid regression/clustering method. In 2018 International Conference on Machine Learning and Data Engineering (iCMLDE) (108-116). IEEE. Doi: https://doi.org/10.1109/iCMLDE.2018.00029
Zahedi, F. (1986). A simulation study of estimation methods in the analytic hierarchy process. Socio-Economic Planning Sciences, 20(6), 347-354.
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