ESSAYS ON TEXT MINING FOR IMPROVED DECISION MAKING ...

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Sensitivity 100 90 80 70 60 50 40 30 20 10 0 Test Set Test Set A Baseline Analyzing existing Customers’ Websites 0 10 20 30 40 50 1-Specificity 60 70 80 90 100 Figure 6: Sensitivity / Specificity Diagram Precision 100 90 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 Recall 60 70 80 90 100 Figure 7: Precision / Recall Diagram Test Set Test Set A Baseline _______________________________________________________________________ 46
5 Conclusion Analyzing existing Customers’ Websites In this paper, we demonstrate that using information of existing customers’ websites for acquisition purposes helps a B-to-B acquisition manager to identify profitable customers with a higher precision. Consequently, the acquisition process can become more targeted by additionally integrating this textual information. Specific data collection, pre-processing, and dimension reduction steps are required to convert the unstructured textual information into a structured form suitable for profitability prediction. A clustering of websites based on latent semantic concepts leads to the identification of further potential customers that outperforms customers acquired from list brokers by a wide margin. Future work should focus on improving the prediction by adding further unstructured information from existing customers (e.g. e-mails) to the prediction model. Acknowledgments We would like to thank the anonymous German mail-order company for their efforts in providing us their data. This project was realized using SAS v9.1.3, SAS Text Miner v5.2, Fraunhofer Idea Web Miner v1.0, and Matlab v7.0.4. For data collection, a self-developed program for web content mining was used. The second author acknowledges the IAP research network grant nr. P6/03 of the Belgian government (Belgian Science Policy). Bibliography Allison, P. D. (1999). Logistic Regression using the SAS System: Theory and Application. Cary: SAS Institute Inc. Archer, N., & Yuan, Y. (2000). Managing business-to-business relationships throughout the e-commerce procurement life cycle. Internet Research: Electronic Networking Applications and Policy, 10(5), 385-395. Baecke, P. H., & Van den Poel, D. (2010a). Improving purchasing behavior predictions by data augmentation with situational variables. International Journal of Information Technology and Decision Making, Forthcoming. Baecke, P. H., & Van den Poel, D. (2010b). Data Augmentation by Predicting Spending Pleasure Using Commercially Available External Data. Journal of Intelligent Information Systems, Forthcoming. _______________________________________________________________________ 47
Page 1 and 2: ESSAYS ON TEXT MINING FOR IMPROVED
Page 3 and 4: Table of Contents 1. Acknowledgment
Page 5 and 6: 2. Nederlandstalige Samenvatting In
Page 7 and 8: ideeën zijn. Ten einde dit doel te
Page 9 and 10: Ein wesentlicher Erfolgsfaktor für
Page 11 and 12: 4. Summary Data mining is defined a
Page 13 and 14: from customers' and companies' webs
Page 15 and 16: of e-commerce companies divided int
Page 17 and 18: that provides commentary, news, and
Page 19 and 20: Study 3 shows that a semi-automatic
Page 21 and 22: take the granularity of the context
Page 23 and 24: [25] Herring, S.C., Scheidt, L.A.,
Page 25 and 26: [56] Thorleuchter, D., Van den Poel
Page 27 and 28: Table of Contents Analyzing existin
Page 29 and 30: 1 Introduction Analyzing existing C
Page 31 and 32: Analyzing existing Customers’ Web
Page 33 and 34: Fig. 2: Different steps in data col
Page 39 and 40: 3.4 Website clustering Analyzing ex
Page 41 and 42: 4 Empirical verification 4.1 Resear
Page 45: Analyzing existing Customers’ Web
Page 51 and 52: Chapter II Predicting E-Commerce Co
Page 53 and 54: Predicting E-Commerce Success using
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Page 61 and 62: 4.3 Comparing predictive performanc
Page 63 and 64: 4.4 Results Predicting E-Commerce S
Page 69 and 70: Chapter III Finding new technologic
Page 71 and 72: Finding New Technological Ideas Fin
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Page 81 and 82: Chapter IV Mining Ideas from Textua
Page 83 and 84: Mining Ideas from Textual Informati
Page 85 and 86: 1.2. Idea Definition Mining Ideas f
Page 87 and 88: 3. Idea Mining Process Mining Ideas
Page 95 and 96: 6. Results and Discussions Mining I
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Mining Ideas from Textual Informati
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Chapter V Mining Innovative Ideas t
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Mining Innovative Ideas to Support
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Mining Innovative Ideas idea is a n
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Mining Innovative Ideas identifies
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Mining Innovative Ideas categories
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9. Outlook Mining Innovative Ideas
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Chapter VI Extracting Consumers Nee
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Extracting Consumers Needs for New
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2. Rationale Behind this Approach E
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Bibliography Extracting Consumers N
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Table of Contents A Compared Cross
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A Compared Cross Impact Analysis Ke
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A Compared Cross Impact Analysis A
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Table 3: Technology area pairs with
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A Compared Cross Impact Analysis A
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A Compared Cross Impact Analysis [2
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