ESSAYS ON TEXT MINING FOR IMPROVED DECISION MAKING ...

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useful insights of existing markets for a new product of a company [3]. However here, it is also necessary to provide an overview and to generate useful insights of the current technological landscape concerning these new research areas of a company [58]. This is helpful to support companies’ research planning. A new approach for generating these insights specifically for large organizations is presented in a further study. 5.2. Study 1: Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing This study investigates the issue of predicting new customers as profitable based on information about existing customers in a business-to-business environment. In particular, it is shown how latent semantic concepts [9] from textual information of existing customers' websites can be used to uncover characteristics of websites of companies that will turn into profitable customers. Hence, the use of predictive analytics will help to identify new potential acquisition targets. Additionally, it is shown that a regression model [4, 10, 19] based on these concepts is successful in the profitability prediction of new customers. The study contains a case study where the acquisition process of a mail order company is supported by creating a prioritized list of new customers generated by this approach. It is shown that the density of profitable customers in this list outperforms the density of profitable customers in traditional generated address lists (e.g. from list brokers). From a managerial point of view, this approach supports the identification of new business customers and helps to estimate the future profitability of these customers in a company. Consequently, the customer acquisition process can be targeted more effectively and efficiently. This leads to a competitive advantage for B2b companies and improves the traditional acquisition process that is time- and cost-consuming with traditionally low conversion rates. 5.3. Study 2: Predicting E-Commerce Company Success by Mining the Text of Its Publicly-Accessible Website The second study analyzes the impact of textual information from e-commerce companies' web sites on their commercial success. The textual information is extracted from web content _______________________________________________________________________ 14
of e-commerce companies divided into the top 100 worldwide most successful companies and into the top 101 to 500 worldwide most successful companies. It is shown that latent semantic concepts [9] extracted from the analysis of textual information can be adopted as success measures for a top 100 e-commerce company classification. This contributes to the existing literature concerning web site success measures for e-commerce [65]. As evaluation, a regression model [4, 10, 19] based on these concepts is build that is successful in predicting commercial success of the top 100 companies. These findings are valuable e.g. for e-commerce web sites creation. 5.4. Study 3: Finding New Technological Ideas and Inventions with Text Mining and Technique Philosophy The main objective of study 3 is to introduce a rationale for finding textual patterns representing new technological ideas in unstructured technological texts. This rationale follows the statements of technique philosophy [48]. Therefore, a technological idea or invention represents not only a new mean, but a new purpose and mean combination [2]. By systematic identification of the purposes, means, and purpose-mean combinations in unstructured technological texts compared to specialized reference collections, a semiautomatic finding of ideas and inventions is realized. For this, collocations [37] are used as known method in corpus linguistics. Collocations are defined as a combination of terms, which co-occur more frequently than it would be expected by chance [30]. Additionally, characteristics (comprehensibility, novelty, and usefulness [28]) that are used to measure the quality of these patterns found in technological texts are examined. 5.5. Study 4: Mining Ideas from Textual Information The purpose of study 4 is to use the rationale of study 3 and extend it to a text mining approach that extracts new and useful ideas from unstructured text automatically. Therefore, the study also uses the idea definition from technique philosophy [48] as described above and it focuses on ideas that can be used to solve technological problems [47]. This approach follows how persons create ideas [44, 60]. To realize the processing, methods from text mining and text classification (tokenization [14], term filtering methods [27, 36], Euclidean distance measure [62], alpha cut method [1] etc.) are used and are combined with a newly created heuristic measure for mining ideas. This “idea mining measure” is contributed to the scientific community. _______________________________________________________________________ 15
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: from customers' and companies' webs
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 47 and 48: 5 Conclusion Analyzing existing Cus
Page 51 and 52: Chapter II Predicting E-Commerce Co
Page 53 and 54: Predicting E-Commerce Success using
Page 59 and 60: with , )} x T = the training set, }
Page 61 and 62: 4.3 Comparing predictive performanc
Page 63 and 64: 4.4 Results Predicting E-Commerce S
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Predicting E-Commerce Success using
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Predicting E-Commerce Success using
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Chapter III Finding new technologic
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Finding New Technological Ideas Fin
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Finding New Technological Ideas to
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Finding New Technological Ideas 3.
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p( Ξ raw δ ,.., δ ) 1 μ ⎧ raw
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Finding New Technological Ideas inv
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Chapter IV Mining Ideas from Textua
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Mining Ideas from Textual Informati
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1.2. Idea Definition Mining Ideas f
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3. Idea Mining Process Mining Ideas
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6. Results and Discussions Mining I
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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 Th
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A Compared Cross Impact Analysis te
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A Compared Cross Impact Analysis In
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A Compared Cross Impact Analysis De
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A Compared Cross Impact Analysis A
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A Compared Cross Impact Analysis Ta
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A Compared Cross Impact Analysis Ea
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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 Th
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A Compared Cross Impact Analysis [2
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