July  2011, 7(3): 531-558. doi: 10.3934/jimo.2011.7.531

Developing a new data envelopment analysis model for customer value analysis

1. 

Department of Industrial Management, Faculty of Management and Accounting, Islamic Azad University-Karaj Branch, P. O. Box: 31485-313, Karaj, Iran, Iran, Iran

Received  March 2010 Revised  March 2011 Published  June 2011

This paper proposes an application of data envelopment analysis (DEA) to measure the value of customers. In order to distinguish between expectations and needs of profitable and unprofitable customers and to allocate marketing investments among them, customers are compared with each other and ranked in a customer value pyramid. To this end, we use a combination of the Banker, Charnes and Cooper (BCC) model [3], assurance region (AR) model, and cross-efficiency evaluation. A numerical example demonstrates the application of the proposed model in an Iranian manufacturing company.
Citation: Mahdi Mahdiloo, Abdollah Noorizadeh, Reza Farzipoor Saen. Developing a new data envelopment analysis model for customer value analysis. Journal of Industrial & Management Optimization, 2011, 7 (3) : 531-558. doi: 10.3934/jimo.2011.7.531
References:
[1]

D. A. Aaker, V. Kumar and G. S. Day, "Marketing Research,", John Wiley & Sons, (2001). Google Scholar

[2]

J. Anderson and J. Narus, "Business Market Management: Understanding, Creating and Developing Value,", 2nd edition, (2004). Google Scholar

[3]

R. D. Banker, A. Charnes and W. W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis,, Management Science, 30 (1984), 1078. doi: 10.1287/mnsc.30.9.1078. Google Scholar

[4]

D. Bowman and D. Narayandas, Managing customer-initiated contacts with manufacturers: The impact on share of category requirements and word-of mouth behavior,, Journal of Marketing Research, 38 (2001), 281. doi: 10.1509/jmkr.38.3.281.18863. Google Scholar

[5]

A. Charnes, W. W. Cooper and E. Rhodes, Measuring the efficiency of decision making units,, European Journal of Operational Research, 2 (1978), 429. doi: 10.1016/0377-2217(78)90138-8. Google Scholar

[6]

M. T. Chu, J. Z. Shyu and R. Khosla, Measuring the relative performance for leading fables firms by using data envelopment analysis,, Journal of Intelligent Manufacturing, 19 (2008), 257. doi: 10.1007/s10845-008-0079-3. Google Scholar

[7]

R. Colombo and W. Jiang, A stochastic RFM model,, Journal of Interactive Marketing, 13 (1999), 2. doi: 10.1002/(SICI)1520-6653(199922)13:3<2::AID-DIR1>3.0.CO;2-H. Google Scholar

[8]

W. W. Cooper, L. M. Seiford and K. Tone, "Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software,", 2nd edition, (2007). Google Scholar

[9]

J. Deichmann, A. Eshghi, D. Haughton, S. Sayek and N. Teebagy, Application of Multiple Adaptive Regression Splines (MARS) in direct response modeling,, Journal of Interactive Marketing, 16 (2002), 15. doi: 10.1002/dir.10040. Google Scholar

[10]

J. Doyle and R. Green, Efficiency and cross efficiency in DEA: Derivations, meanings and the uses,, Journal of the Operational Research Society, 45 (1994), 567. Google Scholar

[11]

P. Fader, B. Hardie and K. L. Lee, RFM and CLV: Using Iso-value curves for customer base analysis,, Journal of Marketing Research, 42 (2005), 415. doi: 10.1509/jmkr.2005.42.4.415. Google Scholar

[12]

R. Garland, Segmenting retail banking customers,, Journal of Financial Services Marketing, 10 (2005), 179. doi: 10.1057/palgrave.fsm.4770184. Google Scholar

[13]

F. Grönöl and M. Shi, Optimal mailing of catalogs: A new methodology using estimable structural dynamic programming models,, Management Science, 44 (1998), 1249. doi: 10.1287/mnsc.44.9.1249. Google Scholar

[14]

C. Grönroos, From marketing mix to relationship marketing: Towards a paradigm shift in marketing,, Management Decision, 32 (1994), 4. doi: 10.1108/00251749410054774. Google Scholar

[15]

K. Ha, S. Cho and D. Maclachlan, Response models based on bagging neural networks,, Journal of Interactive Marketing, 19 (2005), 17. doi: 10.1002/dir.20028. Google Scholar

[16]

L. K. Hansen and P. R. Salamon, Neural network ensembles,, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (1990), 993. doi: 10.1109/34.58871. Google Scholar

[17]

Z. Huanga, H. Chena, C. J. Hsua, W. H. Chenb and S. Wu, Credit rating analysis with support vector machines and neural networks: A market comparative study,, Decision Support Systems, 37 (2004), 543. doi: 10.1016/S0167-9236(03)00086-1. Google Scholar

[18]

Y. Kim, W. N. Street, G. J. Russell and F. Menczer, Customer targeting: A neural network approach guided by genetic algorithms,, Management Science, 51 (2005), 264. doi: 10.1287/mnsc.1040.0296. Google Scholar

[19]

P. J. Korhonen and M. Luptacik, Eco-efficiency analysis of power plants: An extension of data envelopment analysis,, European Journal of Operational Research, 154 (2004), 437. doi: 10.1016/S0377-2217(03)00180-2. Google Scholar

[20]

J. Liu, F. Y. Ding and V. Lall, Using data envelopment analysis to compare suppliers for supplier selection and performance improvement,, Supply Chain Management: An International Journal, 5 (2000), 143. Google Scholar

[21]

L. Moutinho, B. Curry, F. Davies and P. Rita, "Neural Network in Marketing,", Routledge, (1994). Google Scholar

[22]

P. E. Pfeifer, The optimal ratio of acquisition and retention costs,, Journal of Targeting, 13 (2005), 179. doi: 10.1057/palgrave.jt.5740142. Google Scholar

[23]

D. Pitta, F. Franzak and D. Fowler, A strategic approach to building online customer loyalty: Integrating customer profitability tiers,, Journal of Consumer Marketing, 23 (2006), 421. doi: 10.1108/07363760610712966. Google Scholar

[24]

C. K. Prahalad, "The Fortune at the Bottom of the Pyramid: Eradicating Poverty through Profits,", Wharton School Publishing, (2004). Google Scholar

[25]

W. Reinartz and V. Kumar, The mismanagement of customer loyalty,, Harvard Business Review, (2002), 86. Google Scholar

[26]

T. L. Saaty, "Multicriteria Decision Making: The Analytic Hierarchy Process,", 1988, (1980). Google Scholar

[27]

L. M. Seiford and J. Zhu, Identifying excesses and deficits in Chinese industrial productivity (1953-1990): A weighted data envelopment analysis approach,, Omega, 26 (1998), 279. doi: 10.1016/S0305-0483(98)00011-5. Google Scholar

[28]

L. M. Seiford and J. Zhu, Modeling undesirable factors in efficiency evaluation,, European Journal of Operational Research, 142 (2002), 16. doi: 10.1016/S0377-2217(01)00293-4. Google Scholar

[29]

T. R. Sexton, R. H. Silkman and A. J. Hogan, Data envelopment analysis: Critique and extensions,, in, (1986), 73. Google Scholar

[30]

R. Shabahang, "Financial Accounting,", Iranian Auditing Organization, (2003). Google Scholar

[31]

T. Sueyoshi, J. Shang and W. C. Chiang, A decision support framework for internal audit prioritization in a rental car company: A combined use between DEA and AHP,, European Journal of Operational Research, 199 (2009), 219. doi: 10.1016/j.ejor.2008.11.010. Google Scholar

[32]

R. G. Thompson, F. D. Singleton, J. R. M. Thrall and B. A. Smith, Comparative site evaluations for locating a high-energy physics lab in Texas,, Interfaces, 16 (1986), 35. doi: 10.1287/inte.16.6.35. Google Scholar

[33]

E. M. Van Raaij, The strategic value of customer profitability analysis,, Marketing Intelligence & Planning, 23 (2005), 372. doi: 10.1108/02634500510603474. Google Scholar

[34]

Y.-M. Wang, Y. Luo and L. Liang, Ranking decision making units by imposing a minimum weight restriction in the data envelopment analysis,, Journal of Computational and Applied Mathematics, 223 (2009), 469. doi: 10.1016/j.cam.2008.01.022. Google Scholar

[35]

W. P. Wong and K. Y. Wong, A review on benchmarking of supply chain performance measures,, Benchmarking: An International Journal, 15 (2008), 25. Google Scholar

[36]

Y. P. Yu, and S. Q. Cai, A new approach to customer targeting under condition of information shortage,, Marketing Intelligence & Planning, 25 (2007), 343. doi: 10.1108/02634500710754583. Google Scholar

[37]

V. A. Zeithaml, R. T. Rust and K. N. Lemon, The customer pyramid: Creating and serving profitable customers,, California Management Review, 43 (2001), 118. Google Scholar

[38]

J. Zhu and W. D. Cook, "Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis,", A Problem-Solving Handbook, (2007). Google Scholar

show all references

References:
[1]

D. A. Aaker, V. Kumar and G. S. Day, "Marketing Research,", John Wiley & Sons, (2001). Google Scholar

[2]

J. Anderson and J. Narus, "Business Market Management: Understanding, Creating and Developing Value,", 2nd edition, (2004). Google Scholar

[3]

R. D. Banker, A. Charnes and W. W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis,, Management Science, 30 (1984), 1078. doi: 10.1287/mnsc.30.9.1078. Google Scholar

[4]

D. Bowman and D. Narayandas, Managing customer-initiated contacts with manufacturers: The impact on share of category requirements and word-of mouth behavior,, Journal of Marketing Research, 38 (2001), 281. doi: 10.1509/jmkr.38.3.281.18863. Google Scholar

[5]

A. Charnes, W. W. Cooper and E. Rhodes, Measuring the efficiency of decision making units,, European Journal of Operational Research, 2 (1978), 429. doi: 10.1016/0377-2217(78)90138-8. Google Scholar

[6]

M. T. Chu, J. Z. Shyu and R. Khosla, Measuring the relative performance for leading fables firms by using data envelopment analysis,, Journal of Intelligent Manufacturing, 19 (2008), 257. doi: 10.1007/s10845-008-0079-3. Google Scholar

[7]

R. Colombo and W. Jiang, A stochastic RFM model,, Journal of Interactive Marketing, 13 (1999), 2. doi: 10.1002/(SICI)1520-6653(199922)13:3<2::AID-DIR1>3.0.CO;2-H. Google Scholar

[8]

W. W. Cooper, L. M. Seiford and K. Tone, "Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software,", 2nd edition, (2007). Google Scholar

[9]

J. Deichmann, A. Eshghi, D. Haughton, S. Sayek and N. Teebagy, Application of Multiple Adaptive Regression Splines (MARS) in direct response modeling,, Journal of Interactive Marketing, 16 (2002), 15. doi: 10.1002/dir.10040. Google Scholar

[10]

J. Doyle and R. Green, Efficiency and cross efficiency in DEA: Derivations, meanings and the uses,, Journal of the Operational Research Society, 45 (1994), 567. Google Scholar

[11]

P. Fader, B. Hardie and K. L. Lee, RFM and CLV: Using Iso-value curves for customer base analysis,, Journal of Marketing Research, 42 (2005), 415. doi: 10.1509/jmkr.2005.42.4.415. Google Scholar

[12]

R. Garland, Segmenting retail banking customers,, Journal of Financial Services Marketing, 10 (2005), 179. doi: 10.1057/palgrave.fsm.4770184. Google Scholar

[13]

F. Grönöl and M. Shi, Optimal mailing of catalogs: A new methodology using estimable structural dynamic programming models,, Management Science, 44 (1998), 1249. doi: 10.1287/mnsc.44.9.1249. Google Scholar

[14]

C. Grönroos, From marketing mix to relationship marketing: Towards a paradigm shift in marketing,, Management Decision, 32 (1994), 4. doi: 10.1108/00251749410054774. Google Scholar

[15]

K. Ha, S. Cho and D. Maclachlan, Response models based on bagging neural networks,, Journal of Interactive Marketing, 19 (2005), 17. doi: 10.1002/dir.20028. Google Scholar

[16]

L. K. Hansen and P. R. Salamon, Neural network ensembles,, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (1990), 993. doi: 10.1109/34.58871. Google Scholar

[17]

Z. Huanga, H. Chena, C. J. Hsua, W. H. Chenb and S. Wu, Credit rating analysis with support vector machines and neural networks: A market comparative study,, Decision Support Systems, 37 (2004), 543. doi: 10.1016/S0167-9236(03)00086-1. Google Scholar

[18]

Y. Kim, W. N. Street, G. J. Russell and F. Menczer, Customer targeting: A neural network approach guided by genetic algorithms,, Management Science, 51 (2005), 264. doi: 10.1287/mnsc.1040.0296. Google Scholar

[19]

P. J. Korhonen and M. Luptacik, Eco-efficiency analysis of power plants: An extension of data envelopment analysis,, European Journal of Operational Research, 154 (2004), 437. doi: 10.1016/S0377-2217(03)00180-2. Google Scholar

[20]

J. Liu, F. Y. Ding and V. Lall, Using data envelopment analysis to compare suppliers for supplier selection and performance improvement,, Supply Chain Management: An International Journal, 5 (2000), 143. Google Scholar

[21]

L. Moutinho, B. Curry, F. Davies and P. Rita, "Neural Network in Marketing,", Routledge, (1994). Google Scholar

[22]

P. E. Pfeifer, The optimal ratio of acquisition and retention costs,, Journal of Targeting, 13 (2005), 179. doi: 10.1057/palgrave.jt.5740142. Google Scholar

[23]

D. Pitta, F. Franzak and D. Fowler, A strategic approach to building online customer loyalty: Integrating customer profitability tiers,, Journal of Consumer Marketing, 23 (2006), 421. doi: 10.1108/07363760610712966. Google Scholar

[24]

C. K. Prahalad, "The Fortune at the Bottom of the Pyramid: Eradicating Poverty through Profits,", Wharton School Publishing, (2004). Google Scholar

[25]

W. Reinartz and V. Kumar, The mismanagement of customer loyalty,, Harvard Business Review, (2002), 86. Google Scholar

[26]

T. L. Saaty, "Multicriteria Decision Making: The Analytic Hierarchy Process,", 1988, (1980). Google Scholar

[27]

L. M. Seiford and J. Zhu, Identifying excesses and deficits in Chinese industrial productivity (1953-1990): A weighted data envelopment analysis approach,, Omega, 26 (1998), 279. doi: 10.1016/S0305-0483(98)00011-5. Google Scholar

[28]

L. M. Seiford and J. Zhu, Modeling undesirable factors in efficiency evaluation,, European Journal of Operational Research, 142 (2002), 16. doi: 10.1016/S0377-2217(01)00293-4. Google Scholar

[29]

T. R. Sexton, R. H. Silkman and A. J. Hogan, Data envelopment analysis: Critique and extensions,, in, (1986), 73. Google Scholar

[30]

R. Shabahang, "Financial Accounting,", Iranian Auditing Organization, (2003). Google Scholar

[31]

T. Sueyoshi, J. Shang and W. C. Chiang, A decision support framework for internal audit prioritization in a rental car company: A combined use between DEA and AHP,, European Journal of Operational Research, 199 (2009), 219. doi: 10.1016/j.ejor.2008.11.010. Google Scholar

[32]

R. G. Thompson, F. D. Singleton, J. R. M. Thrall and B. A. Smith, Comparative site evaluations for locating a high-energy physics lab in Texas,, Interfaces, 16 (1986), 35. doi: 10.1287/inte.16.6.35. Google Scholar

[33]

E. M. Van Raaij, The strategic value of customer profitability analysis,, Marketing Intelligence & Planning, 23 (2005), 372. doi: 10.1108/02634500510603474. Google Scholar

[34]

Y.-M. Wang, Y. Luo and L. Liang, Ranking decision making units by imposing a minimum weight restriction in the data envelopment analysis,, Journal of Computational and Applied Mathematics, 223 (2009), 469. doi: 10.1016/j.cam.2008.01.022. Google Scholar

[35]

W. P. Wong and K. Y. Wong, A review on benchmarking of supply chain performance measures,, Benchmarking: An International Journal, 15 (2008), 25. Google Scholar

[36]

Y. P. Yu, and S. Q. Cai, A new approach to customer targeting under condition of information shortage,, Marketing Intelligence & Planning, 25 (2007), 343. doi: 10.1108/02634500710754583. Google Scholar

[37]

V. A. Zeithaml, R. T. Rust and K. N. Lemon, The customer pyramid: Creating and serving profitable customers,, California Management Review, 43 (2001), 118. Google Scholar

[38]

J. Zhu and W. D. Cook, "Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis,", A Problem-Solving Handbook, (2007). Google Scholar

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