Customer Segmentation Model in E-commerce Using Clustering Techniques and LRFM Model: The Case of Online Stores in Morocco
Given the increase in the number of e-commerce sites,
the number of competitors has become very important. This means
that companies have to take appropriate decisions in order to meet the
expectations of their customers and satisfy their needs. In this paper,
we present a case study of applying LRFM (length, recency,
frequency and monetary) model and clustering techniques in the
sector of electronic commerce with a view to evaluating customers’
values of the Moroccan e-commerce websites and then developing
effective marketing strategies. To achieve these objectives, we adopt
LRFM model by applying a two-stage clustering method. In the first
stage, the self-organizing maps method is used to determine the best
number of clusters and the initial centroid. In the second stage, kmeans
method is applied to segment 730 customers into nine clusters
according to their L, R, F and M values. The results show that the
cluster 6 is the most important cluster because the average values of
L, R, F and M are higher than the overall average value. In addition,
this study has considered another variable that describes the mode of
payment used by customers to improve and strengthen clusters’
analysis. The clusters’ analysis demonstrates that the payment method is
one of the key indicators of a new index which allows to assess the
level of customers’ confidence in the company's Website.
[1] Interbank Electronic banking Centre, Morocco, “Activité monétique 1er
trimestre 2015 au Maroc”, https://www.cmi.co.ma/PDF/Mon%C3%
A9tique%20Marocaine%20au%2031%20Mars%202015.pdf
[2] G. C O’Connor, B O’Keefe, Viewing the web as a marketplace: the case
of small companies, Decision Support Systems, vol. 21(3), 1997, pp.
171–183.
[3] H. H. Wu, S. Y. Lin, C. W. Liu, Analyzing Patients’ Values by Applying
Cluster Analysis and LRFM Model in a Pediatric Dental Clinic in
Taiwan, Hindawi Publishing Corporation The Scientific World Journal,
Vol 2014, Article ID 685495, 2014, 7 pages.
[4] C. H. Cheng, Y. S. Chen, Classifying the segmentation of customer
value via RFM model and RS theory, Expert Systems with Applications,
Vol.36, N.3, 2009, pp.4176–4184.
[5] S. Irvin, Using lifetime value analysis for selecting new customers,
Credit World, Vol.82, No.3, 1994, pp. 37-40.
[6] J. T. Wei, S. Y. Lin, C. C. Weng, H. H. Wu, A case study of applying
LRFM model in market segmentation of a children’s dental clinic.
Expert Systems with Applications Vol.39, No.5, 2012, pp. 5529–5533.
[7] H. H. Chang, S. F. Tsay, Integrating of SOM and K-man in data mining
clustering: An empirical study of CRM and profitability evaluation,
Journal of Information Management, Vol.11, No.4, 2004, pp. 161–203.
[8] W. J. Reinartz, V. Kumar, On the profitability of long-life customers in a
noncontractual setting: An empirical investigation and implications for
marketing. Journal of Marketing, Vol.64, No.4, 2000, pp. 17–35.
[9] A. M. Hughes, Boosting response with RFM. Marketing Tools, Vol.3,
No.3, 1996, pp. 4-7.
[10] G. M. Marakas, Decision Support Systems in the 21st Century, Second
Edition. Prentice Hall, Upper Saddle River, NJ, 2003.
[11] A. X. Yang, How to develop new approaches to RFM segmentation,
Journal of Targeting, Measurement and Analysis for Marketing, Vol.13,
No.1, 2004, pp. 50-60.
[12] S. Golsefid, M.Ghazanfari, S. Alizadeh,'Customer Segmentation in
Foreign Trade based on Clustering Algorithms Case Study: Trade
Promotion Organization of Iran'. World Academy of Science,
Engineering and Technology, International Science Index 4,
International Journal of Mechanical, Aerospace, Industrial, Mechatronic
and Manufacturing Engineering, Vol.1, No.4, 2007, pp. 230 - 236.
[13] C. H. Wang, Apply robust segmentation to the service industry using
kernel induced fuzzy clustering techniques, Expert Systems with
Applications, Vol.37, No.12, 2010, pp. 8395-8400.
[14] J. T. Wei, S. Y. Lin, and H. H. Wu, A review of the application RFM
model, African Journal of Business Management, Vol.4, No.19, 2010,
pp. 4199–4206.
[15] A. M. Hughes, Strategic database marketing. Chicago, Probus
Publishing Company, 1994.
[16] R. Kahan, Using database marketing techniques to enhance your one-toone
marketing initiatives, Journal of Consumer Marketing, Vol.15, No.5,
1998, pp. 491-493.
[17] E. C. Chang, H. C. Huang, H. H Wu, Using K-means method and
spectral clustering technique in an outfitter’s value analysis, Quality &
Quantity, Vol.44, No.4, 2010, pp. 807–815.
[18] S. M. S. Hosseini, A. Maleki, M. R. Gholamian, 2010, Cluster analysis
using data mining approach to develop CRM methodology to assess the
customer loyalty, Journal of Expert Systems with Applications, Vol.37,
No.7, 2010, pp. 5259–5264.
[19] I. C. Yeh, K. J. Yang, T. M. Ting, Knowledge discovery on RFM model
using Bernoulli sequence, Expert Systems with Applications, Vol.36,
No.3, 2009, pp. 5866–5871.
[20] H. C. Chang, H. P. Tsai, Group RFM analysis as a novel framework to
discover better customer consumption behavior, Expert Systems with
Applications, Vol.38, No.12, 2011, pp.14499–14513.
[21] L. H. Li, F. M. Lee, W. J. Liu, The timely product recommendation
based on RFM method, Proceedings of the International Conference on
Business and Information, 2006, Singapore.
[22] S. Chow, R. Holden, Toward an understanding of loyalty: The
moderating role of trust, Journal of Management issues, Vol.9, No.3,
1997, pp. 275–298.
[23] S. Huang, E. C. Chang, H. H. Wu, A case study of applying data mining
techniques in an outfitter’s customer value analysis, Expert Systems with
Applications, Vol.36, No.3, 2009, pp 5909–5915.
[24] J. T. Wei, S. Y. Lin, C. C. Weng, H. H. Wu, Customer relationship
management in the hairdressing industry: An application of data mining
techniques, Expert Systems with Applications, Vol.40, No.18, 2013, pp.
7513–7518.
[25] S. Wang, Cluster analysis using a validated self-organizing method:
Cases of problem identification, Intelligent Systems in Accounting,
Finance and Management, Vol.10, No.2, 2001, pp. 127–138.
[26] K. E. Fish, P. Ruby, An artificial intelligence foreign market screening
method for small businesses, International Journal of Entrepreneurship,
Vol.13, 2009, pp. 65–81.
[27] D. Ordonez, C. Dafonte, M. Manteiga, B. Arcay, Hierarchical Clustering
Analysis with SOM Networks, World Academy of Science, Engineering
and Technology, International Science Index 45, Vol.4, No.9, 2010, pp.
213 - 219.
[28] L. Churilov, A. Bagirov, D. Schwartz, K. Smith, D. Michael, Data
mining with combined use of optimization techniques and selforganizing
maps for improving risk grouping rules: Application to
prostate cancer patients, Journal of Management Information Systems,
Vol.21, No.4, 2005, pp. 85–100.
[29] A. Sindhuja, V. Sadasivam, Automatic Detection of Breast Tumors in
Sonoelastographic Images Using DWT, World Academy of Science,
Engineering and Technology, International Science Index 81,
International Journal of Biological, Biomolecular, Agricultural, Food
and Biotechnological Engineering, Vol.7, No.9, 2013, pp. 590-596.
[30] D. Birant, Data Mining Using RFM Analysis, Knowledge-Oriented
Applications in Data Mining, ISBN: 978-953-307-154-1, 2011, InTech,
DOI: 10.5772/13683, Available from: http://www.intechopen.com/
books/knowledge-oriented-applications-in-data-mining/data-miningusing-
rfm-analysis
[31] G. Punj, D. W. Steward, (1983), Cluster analysis in marketing research:
review and suggestions for applications, Journal of Marketing Research,
Vol.20, No.2, 1983, pp. 134-148.
[32] R. J. Kuo, L. M. Ho, C. M. Hu, Integration of self-organizing feature
map and K-means algorithm for market segmentation, Computers &
Operations Research, Vol.29, No.11, 2002, pp. 1475-1493.
[33] J. Han, M. Kamber, Data Mining: Concepts and Techniques, CA:
Morgan Kaufmann, San Francisco, 2006.
[34] P. N. Tan, M. Steinbach, V. Kumar, Introduction to data mining,
Pearson education, 2005.
[35] O. Krivobokova, 'Evaluating Customer Satisfaction as an Aspect of
Quality Management'. World Academy of Science, Engineering and
Technology, International Science Index 29, Vol.3, No5, 2009, pp. 482 -
485.
[36] R. M. Schindler, T. M. Kibarian, Increased Consumer Sales Response
through Use of 99 Ending Prices, Journal of Retailing, Vol.72, No.2,
1996, pp. 187-199.
[37] N. GUEGUEN, 100 petites expériences en psychologie du
consommateur pour mieux comprendre comment on vous influence,
Paris: Dunod, 2005.
[38] H. Isaac, P. Volle, E-Commerce De la stratégie à la mise en oeuvre
opérationnelle, Pearson Education, 2008.
[39] S. Bellman, G. Lohse, E. Johnson, Predictors of online buying behavior,
Communications of the ACM, Vol.42, Vo.12, 2000, pp. 32-38.
[1] Interbank Electronic banking Centre, Morocco, “Activité monétique 1er
trimestre 2015 au Maroc”, https://www.cmi.co.ma/PDF/Mon%C3%
A9tique%20Marocaine%20au%2031%20Mars%202015.pdf
[2] G. C O’Connor, B O’Keefe, Viewing the web as a marketplace: the case
of small companies, Decision Support Systems, vol. 21(3), 1997, pp.
171–183.
[3] H. H. Wu, S. Y. Lin, C. W. Liu, Analyzing Patients’ Values by Applying
Cluster Analysis and LRFM Model in a Pediatric Dental Clinic in
Taiwan, Hindawi Publishing Corporation The Scientific World Journal,
Vol 2014, Article ID 685495, 2014, 7 pages.
[4] C. H. Cheng, Y. S. Chen, Classifying the segmentation of customer
value via RFM model and RS theory, Expert Systems with Applications,
Vol.36, N.3, 2009, pp.4176–4184.
[5] S. Irvin, Using lifetime value analysis for selecting new customers,
Credit World, Vol.82, No.3, 1994, pp. 37-40.
[6] J. T. Wei, S. Y. Lin, C. C. Weng, H. H. Wu, A case study of applying
LRFM model in market segmentation of a children’s dental clinic.
Expert Systems with Applications Vol.39, No.5, 2012, pp. 5529–5533.
[7] H. H. Chang, S. F. Tsay, Integrating of SOM and K-man in data mining
clustering: An empirical study of CRM and profitability evaluation,
Journal of Information Management, Vol.11, No.4, 2004, pp. 161–203.
[8] W. J. Reinartz, V. Kumar, On the profitability of long-life customers in a
noncontractual setting: An empirical investigation and implications for
marketing. Journal of Marketing, Vol.64, No.4, 2000, pp. 17–35.
[9] A. M. Hughes, Boosting response with RFM. Marketing Tools, Vol.3,
No.3, 1996, pp. 4-7.
[10] G. M. Marakas, Decision Support Systems in the 21st Century, Second
Edition. Prentice Hall, Upper Saddle River, NJ, 2003.
[11] A. X. Yang, How to develop new approaches to RFM segmentation,
Journal of Targeting, Measurement and Analysis for Marketing, Vol.13,
No.1, 2004, pp. 50-60.
[12] S. Golsefid, M.Ghazanfari, S. Alizadeh,'Customer Segmentation in
Foreign Trade based on Clustering Algorithms Case Study: Trade
Promotion Organization of Iran'. World Academy of Science,
Engineering and Technology, International Science Index 4,
International Journal of Mechanical, Aerospace, Industrial, Mechatronic
and Manufacturing Engineering, Vol.1, No.4, 2007, pp. 230 - 236.
[13] C. H. Wang, Apply robust segmentation to the service industry using
kernel induced fuzzy clustering techniques, Expert Systems with
Applications, Vol.37, No.12, 2010, pp. 8395-8400.
[14] J. T. Wei, S. Y. Lin, and H. H. Wu, A review of the application RFM
model, African Journal of Business Management, Vol.4, No.19, 2010,
pp. 4199–4206.
[15] A. M. Hughes, Strategic database marketing. Chicago, Probus
Publishing Company, 1994.
[16] R. Kahan, Using database marketing techniques to enhance your one-toone
marketing initiatives, Journal of Consumer Marketing, Vol.15, No.5,
1998, pp. 491-493.
[17] E. C. Chang, H. C. Huang, H. H Wu, Using K-means method and
spectral clustering technique in an outfitter’s value analysis, Quality &
Quantity, Vol.44, No.4, 2010, pp. 807–815.
[18] S. M. S. Hosseini, A. Maleki, M. R. Gholamian, 2010, Cluster analysis
using data mining approach to develop CRM methodology to assess the
customer loyalty, Journal of Expert Systems with Applications, Vol.37,
No.7, 2010, pp. 5259–5264.
[19] I. C. Yeh, K. J. Yang, T. M. Ting, Knowledge discovery on RFM model
using Bernoulli sequence, Expert Systems with Applications, Vol.36,
No.3, 2009, pp. 5866–5871.
[20] H. C. Chang, H. P. Tsai, Group RFM analysis as a novel framework to
discover better customer consumption behavior, Expert Systems with
Applications, Vol.38, No.12, 2011, pp.14499–14513.
[21] L. H. Li, F. M. Lee, W. J. Liu, The timely product recommendation
based on RFM method, Proceedings of the International Conference on
Business and Information, 2006, Singapore.
[22] S. Chow, R. Holden, Toward an understanding of loyalty: The
moderating role of trust, Journal of Management issues, Vol.9, No.3,
1997, pp. 275–298.
[23] S. Huang, E. C. Chang, H. H. Wu, A case study of applying data mining
techniques in an outfitter’s customer value analysis, Expert Systems with
Applications, Vol.36, No.3, 2009, pp 5909–5915.
[24] J. T. Wei, S. Y. Lin, C. C. Weng, H. H. Wu, Customer relationship
management in the hairdressing industry: An application of data mining
techniques, Expert Systems with Applications, Vol.40, No.18, 2013, pp.
7513–7518.
[25] S. Wang, Cluster analysis using a validated self-organizing method:
Cases of problem identification, Intelligent Systems in Accounting,
Finance and Management, Vol.10, No.2, 2001, pp. 127–138.
[26] K. E. Fish, P. Ruby, An artificial intelligence foreign market screening
method for small businesses, International Journal of Entrepreneurship,
Vol.13, 2009, pp. 65–81.
[27] D. Ordonez, C. Dafonte, M. Manteiga, B. Arcay, Hierarchical Clustering
Analysis with SOM Networks, World Academy of Science, Engineering
and Technology, International Science Index 45, Vol.4, No.9, 2010, pp.
213 - 219.
[28] L. Churilov, A. Bagirov, D. Schwartz, K. Smith, D. Michael, Data
mining with combined use of optimization techniques and selforganizing
maps for improving risk grouping rules: Application to
prostate cancer patients, Journal of Management Information Systems,
Vol.21, No.4, 2005, pp. 85–100.
[29] A. Sindhuja, V. Sadasivam, Automatic Detection of Breast Tumors in
Sonoelastographic Images Using DWT, World Academy of Science,
Engineering and Technology, International Science Index 81,
International Journal of Biological, Biomolecular, Agricultural, Food
and Biotechnological Engineering, Vol.7, No.9, 2013, pp. 590-596.
[30] D. Birant, Data Mining Using RFM Analysis, Knowledge-Oriented
Applications in Data Mining, ISBN: 978-953-307-154-1, 2011, InTech,
DOI: 10.5772/13683, Available from: http://www.intechopen.com/
books/knowledge-oriented-applications-in-data-mining/data-miningusing-
rfm-analysis
[31] G. Punj, D. W. Steward, (1983), Cluster analysis in marketing research:
review and suggestions for applications, Journal of Marketing Research,
Vol.20, No.2, 1983, pp. 134-148.
[32] R. J. Kuo, L. M. Ho, C. M. Hu, Integration of self-organizing feature
map and K-means algorithm for market segmentation, Computers &
Operations Research, Vol.29, No.11, 2002, pp. 1475-1493.
[33] J. Han, M. Kamber, Data Mining: Concepts and Techniques, CA:
Morgan Kaufmann, San Francisco, 2006.
[34] P. N. Tan, M. Steinbach, V. Kumar, Introduction to data mining,
Pearson education, 2005.
[35] O. Krivobokova, 'Evaluating Customer Satisfaction as an Aspect of
Quality Management'. World Academy of Science, Engineering and
Technology, International Science Index 29, Vol.3, No5, 2009, pp. 482 -
485.
[36] R. M. Schindler, T. M. Kibarian, Increased Consumer Sales Response
through Use of 99 Ending Prices, Journal of Retailing, Vol.72, No.2,
1996, pp. 187-199.
[37] N. GUEGUEN, 100 petites expériences en psychologie du
consommateur pour mieux comprendre comment on vous influence,
Paris: Dunod, 2005.
[38] H. Isaac, P. Volle, E-Commerce De la stratégie à la mise en oeuvre
opérationnelle, Pearson Education, 2008.
[39] S. Bellman, G. Lohse, E. Johnson, Predictors of online buying behavior,
Communications of the ACM, Vol.42, Vo.12, 2000, pp. 32-38.
@article{"International Journal of Information, Control and Computer Sciences:71457", author = "Rachid Ait daoud and Abdellah Amine and Belaid Bouikhalene and Rachid Lbibb", title = "Customer Segmentation Model in E-commerce Using Clustering Techniques and LRFM Model: The Case of Online Stores in Morocco", abstract = "Given the increase in the number of e-commerce sites,
the number of competitors has become very important. This means
that companies have to take appropriate decisions in order to meet the
expectations of their customers and satisfy their needs. In this paper,
we present a case study of applying LRFM (length, recency,
frequency and monetary) model and clustering techniques in the
sector of electronic commerce with a view to evaluating customers’
values of the Moroccan e-commerce websites and then developing
effective marketing strategies. To achieve these objectives, we adopt
LRFM model by applying a two-stage clustering method. In the first
stage, the self-organizing maps method is used to determine the best
number of clusters and the initial centroid. In the second stage, kmeans
method is applied to segment 730 customers into nine clusters
according to their L, R, F and M values. The results show that the
cluster 6 is the most important cluster because the average values of
L, R, F and M are higher than the overall average value. In addition,
this study has considered another variable that describes the mode of
payment used by customers to improve and strengthen clusters’
analysis. The clusters’ analysis demonstrates that the payment method is
one of the key indicators of a new index which allows to assess the
level of customers’ confidence in the company's Website.", keywords = "Customer value, LRFM model, Cluster analysis,
Self-Organizing Maps method (SOM), K-means algorithm, loyalty.", volume = "9", number = "8", pages = "2000-11", }
