Combining visual customer segmentation and response modeling

Neural Computing and Applications

Volumn 25, Issue 1, 2014, Pages 123-134

  Yao, Zhiyuan ^a   Sarlin, Peter ^a   Eklund, Tomas ^a   Back, Barbro ^a  

^a ÅBO AKADEMI UNIVERSITY   (Finland)

Author keywords

Business Intelligence; Campaign response modeling; Customer relationship management (CRM); Customer segmentation; Visual analytics

Indexed keywords

COMPETITIVE INTELLIGENCE; CONFORMAL MAPPING; PUBLIC RELATIONS;

CUSTOMER BEHAVIOR; CUSTOMER RELATIONSHIP MANAGEMENT; CUSTOMER RELATIONSHIPS; CUSTOMER SEGMENTATION; PURCHASING BEHAVIORS; SEGMENTATION MODELS; VISUAL ANALYTICS; VISUAL MONITORING;

SALES;

EID: 84902435820     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-013-1454-3     Document Type: Article

References (57)

1. Shah D, Rust RT, Parasuraman A, Staelin R, Day GS (2006) The path to customer centricity. J Serv Res 9(2): 113-124.
2. Berry LL (2002) Relationship marketing of services perspectives from 1983 and 2000. J Relatsh Mark 1(1): 59-77.
3. Day GS (1997) Aligning the organization to the market. In: Lehmann DR, Jocz KE (eds) Reflections on the futures of marketing. MA: Marketing Science Institute, Cambridge, pp 67-93.
4. Jayachandran S, Sharma S, Kaufman P, Raman P (2005) The role of relational information processes and technology use in customer relationship management. J Mark 69(4): 177-192.
5. Kumar V, Venkatesan R, Reinartz W (2008) Performance implications of adopting a customer-focused sales campaign. J Mark 72(5): 50-68.
6. Rygielski C, Wang JC, Yen DC (2002) Data mining techniques for customer relationship management. Technol Soc 24(4): 483-502.
7. Armbrust M, Fox A, Griffith R, Joseph A, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4): 50-58.
8. Hwang H, Jung T, Suh E (2004) An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert Syst Appl 26(2): 181-188.
9. Holmbom AH, Eklund T, Back B (2011) Customer portfolio analysis using the SOM. Int J Bus Inf Syst 8(4): 396-412.
10. Mo J, Kiang MY, Zou P, Li Y (2010) A two-stage clustering approach for multi-region segmentation. Expert Syst Appl 37(10): 7120-7131.
11. Yao Z, Holmbom A, Eklund T, Back B (2010) Combining unsupervised and supervised data mining techniques for conducting customer portfolio analysis. In: Proceedings of the 10th industrial conference on data mining, Springer, Germany, Berlin.
12. Dennis C, Marsland D, Cockett T (2001) Data mining for shopping centres-customer knowledge-management framework. J Knowl Manag 5(4): 368-374.
13. Hosseini SMS, Maleki A, Gholamian MR (2010) Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Syst Appl 37(7): 5259-5264.
14. Kuo R, Ho L, Hu C (2002) Integration of self-organizing feature map and K-means algorithm for market segmentation. Comput Oper Res 29(11): 1475-1493.
15. McCarty JA, Hastak M (2007) Segmentation approaches in data-mining: a comparison of RFM, CHAID, and logistic regression. J Bus Res 60(6): 656-662.
16. Grieser L, Wilde KD (2010) Adaptive right-time technologies in customer relationship management. Bus Inf Syst Eng 2(1): 41-44.
17. Ganti V, Gehrke J, Ramakrishnan R (1999) A framework for measuring changes in data characteristics. In: Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, ACM, Philadelphia, United States.
18. Chakrabarti S, Sarawagi S, Dom B (1998) Mining surprising patterns using temporal description length. In: Proceeding of the 24th international conference on very large data bases (VLDB '98), Morgan Kaufmann Publishers Inc., San Francisco, USA.
19. Yairi T, Kato Y, Hori K (2001) Fault detection by mining association rules from house-keeping data. In: Proceeding of the international symposium on artificial intelligence, robotics and automation in space, Montreal, Canada.
20. Keogh E, Lonardi S, Chiu BY (2002) Finding surprising patterns in a time series database in linear time and space. In: Proceeding of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, Edmonton, Canada.
21. Liao TW (2005) Clustering of time series data-a survey. Pattern Recogn 38(11): 1857-1874.
22. D'Urso P, De Giovanni L (2008) Temporal self-organizing maps for telecommunications market segmentation. Neurocomputing 71(13): 2880-2892.
23. Denny, Squire DM (2005) Visualization of cluster changes by comparing self-organizing maps. In: Proceedings of the 9th Pacific-Asia conference on knowledge discovery and data mining (PAKDD), Springer, Hanoi, Vietnam.
24. Denny, Williams GJ, Christen P (2008) ReDSOM: relative density visualization of temporal changes in cluster structures using self-organizing maps. In: Proceedings of the eighth IEEE international conference on data mining (ICDM), IEEE, Pisa, Italy.
25. Denny, Williams GJ, Christen P (2010) Visualizing temporal cluster changes using relative density self-organizing maps. Knowl Inf Syst 25(2): 281-302.
26. Yao Z, Sarlin P, Eklund T, Back B (2012) Temporal customer segmentation using the self-organizing time map. In: Proceeding of the 16th international conference on information visualisation (IV), IEEE, Montpellier, France.
27. Sarlin P (2013) Self-organizing time map: an abstraction of temporal multivariate patterns. Neurocomputing 99(1): 496-508.
28. Lingras P, Hogo M, Snorek M, West C (2005) Temporal analysis of clusters of supermarket customers: conventional versus interval set approach. Inf Sci 172(1-2): 215-240.
29. Ha SH, Bae SM, Park SC (2002) Customer's time-variant purchase behavior and corresponding marketing strategies: an online retailer's case. Comput Ind Eng 43(4): 801-820.
30. Denny, Christen P, Williams GJ (2012) Analysis of cluster migrations using self-organizing maps. In: Cao L, Huang J, Bailey J, Koh Y, Luo J (eds) New frontiers in applied data mining, vol 7104. Springer, Berlin, pp 171-182.
31. Basu AK, Basu A, Batra R (1995) Modeling the response pattern to direct marketing campaigns. J Mark Res 32(2): 204-212.
32. Piatetsky-Shapiro G, Masand B (1999) Estimating campaign benefits and modeling lift. In: Proceeding of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, San Diego, United States.
33. Lessmann S, Voß S (2010) Customer-centric decision support. Bus Inf Syst Eng 2(2): 79-93.
34. Sarlin P, Yao Z, Eklund T (2012) A framework for state transitions on the self-organizing map: some temporal financial applications. Intell Syst Account Financ Manag 19(3): 189-203.
35. Ferreira de Oliveira MC, Levkowitz H (2003) From visual data exploration to visual data mining: a survey. IEEE Trans Visual Comput Graphics 9(3): 378-394.
36. Vesanto J (1999) SOM-based data visualization methods. Intell Data Anal 3(2): 111-126.
37. Sarlin P (1999) Data and dimension reduction for visual financial performance analysis. TUCS Technical Reports. TUCS.
38. Lee SC, Suh YH, Kim JK, Lee KJ (2004) A cross-national market segmentation of online game industry using SOM. Expert Syst Appl 27(4): 559-570.
39. Vellido A, Lisboa P, Meehan K (1999) Segmentation of the on-line shopping market using neural networks. Expert Syst Appl 17(4): 303-314.
40. Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Networks 11(3): 586-600.
41. Li H (2005) Data visualization of asymmetric data using Sammon mapping and applications of self-organizing maps. University of Maryland, (College Park, Md.).
42. Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301): 236-244.
43. Theodoridis S, Koutroumbas K (2008) Pattern recognition. Elsevier Science, Amsterdam.
44. Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336): 846-850.
45. Hubert L, Schultz J (1976) Quadratic assignment as a general data-analysis strategy. Br J Math Stat Psychol 29(2): 190-241.
46. Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Ser B (Stat Methodol) 63(2): 411-423.
47. Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3(3): 32-57.
48. Rousseeuw PJ, Kaufman L (1990) Finding groups in data: an introduction to cluster analysis. Wiley-Interscience, Hoboken.
49. Kohonen T (1988) The 'neural' phonetic typewriter. Computer 21(3): 11-22. doi: 10. 1109/2. 28.
50. Eklund T, Back B, Vanharanta H, Visa A (2003) Using the self-organizing map as a visualization tool in financial benchmarking. Inf Vis 2(3): 171-181.
51. Sarlin P, Marghescu D (2011) Visual predictions of currency crises using self-organizing maps. Intell Syst Account Financ Manag 18(1): 15-38.
52. Afolabi MO, Olude O (2007) Predicting stock prices using a hybrid Kohonen self organizing map (SOM). In: Proceedings of the 40th annual Hawaii international conference on system sciences, IEEE, Big Island, Hawaii, USA.
53. Hsu S-H, Hsieh JP-A, Chih T-C, Hsu K-C (2009) A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression. Expert Syst Appl 36(4): 7947-7951.
54. Sulkava M, Hollmén J (2003) Finding profiles of forest nutrition by clustering of the self-organizing map. In: Proceedings of the workshop on self-organizing maps, Springer, Kitakyushu, Japan.
55. Luyssaert S, Sulkava M, Raitio H, Hollmén J (2004) Evaluation of forest nutrition based on large-scale foliar surveys: are nutrition profiles the way of the future? J Environ Monit 6(2): 160-167.
56. SOMine (2009), Viscovery SOMine user manual, Austria, Vienna.
57. Reinartz WJ, Kumar V (2003) The impact of customer relationship characteristics on profitable lifetime duration. J Mark 67(1): 77-99.