ESSAYS ON TEXT MINING FOR IMPROVED DECISION MAKING ...

More documents

Recommendations

Info

Analyzing existing Customers’ Websites Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing Dirk Thorleuchter a , Dirk Van den Poel b , and Anita Prinzie b a Fraunhofer INT, D-53879 Euskirchen, Appelsgarten 2, Germany, & PhD Candidate, Ghent University, Belgium, dirk.thorleuchter@int.fraunhofer.de b Ghent University, Faculty of Economics and Business Administration, B-9000 Gent, Tweekerkenstraat 2, Belgium, dirk.vandenpoel@ugent.be, anita.prinzie@ugent.be Abstract We investigate the issue of predicting new customers as profitable based on information about existing customers in a business-to-business environment. In particular, we show how latent semantic concepts 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, we show that a regression model based on these concepts is successful in the profitability prediction of new customers. In a case study, 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 acquisition process that is time- and cost-consuming with traditionally low conversion rates. Keywords B-to-B marketing, Text Mining, Web Mining, Acquisition, SVD _______________________________________________________________________ 28
1 Introduction Analyzing existing Customers’ Websites While products and services are sold by companies with little knowledge or strategy concerning the customers who bought the products in the past a change from this productcentered to a customer-centered environment can be seen today (Coussement & Van den Poel, 2008). This is because for companies it is important to capture and enhance market share while reducing costs. Therefore, they must reconsider the business relationships with their existing customers (Pan & Lee, 2003). One important aspect is to improve the acquisition of new customers. Normally, this is timeand cost-consuming because it is easier to keep and satisfy existing customers than to attract new ones with a high attrition rate (Reinartz & Kumar, 2003). Therefore, new customers have to be identified who are interested in companies’ products and services. This probably can be done in several different ways (e.g. by presenting products and services on fairs, buying addresses from list brokers etc.). However, only a small percentage of potential customers become profitable customers in the future. In this paper, we propose a new approach to identify new business customers and to predict them as profitable using information of existing customers. For this, existing customers’ information is collected from customer relationship management (CRM) systems where customers probably can be divided into different classes e.g. concerning their sales volume. Then existing customers can be classified as profitable customers, if their sales volume over a specific period of time is greater than a specific threshold (Menon, Homburg, & Beutin, 2005). If we specifically consider on existing customers in a business-to-business environment then we also find information in the CRM system about customers’ companies. Nowadays, companies normally present information on internet websites because of the rapid development of IT and the Internet. Many firms rely on Internet websites to provide product information for their customers. Information on existing customers’ websites can be crawled and analyzed by use of web structure and content mining approaches. However, the received information consists of masses of unstructured textual information (Coussement & Van den Poel, 2009) and decision makers normally do not use it for acquisition purposes. This is because the information is not directly usable in a traditional acquisition context and there is often a lack of in-house knowledge on how to analyze this unstructured information _______________________________________________________________________ 29
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: Table of Contents Analyzing existin
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
Page 69 and 70: Chapter III Finding new technologic
Page 71 and 72: Finding New Technological Ideas Fin
Page 73 and 74: Finding New Technological Ideas to
Page 75 and 76: Finding New Technological Ideas 3.
Page 77 and 78: p( Ξ raw δ ,.., δ ) 1 μ ⎧ raw
Page 79 and 80:
Finding New Technological Ideas inv
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 89 and 90:
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
6. Results and Discussions Mining I
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Chapter V Mining Innovative Ideas t
Page 105 and 106:
Mining Innovative Ideas to Support
Page 107 and 108:
Mining Innovative Ideas idea is a n
Page 109 and 110:
Mining Innovative Ideas identifies
Page 111 and 112:
Mining Innovative Ideas categories
Page 113 and 114:
9. Outlook Mining Innovative Ideas
Page 115 and 116:
Chapter VI Extracting Consumers Nee
Page 117 and 118:
Extracting Consumers Needs for New
Page 119 and 120:
2. Rationale Behind this Approach E
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Bibliography Extracting Consumers N
Page 129 and 130:
Table of Contents A Compared Cross
Page 131 and 132:
A Compared Cross Impact Analysis Ke
Page 133 and 134:
A Compared Cross Impact Analysis Th
Page 135 and 136:
A Compared Cross Impact Analysis te
Page 137 and 138:
A Compared Cross Impact Analysis In
Page 139 and 140:
A Compared Cross Impact Analysis De
Page 141 and 142:
A Compared Cross Impact Analysis A
Page 143 and 144:
A Compared Cross Impact Analysis Ta
Page 145 and 146:
A Compared Cross Impact Analysis Ea
Page 147 and 148:
Table 3: Technology area pairs with
Page 149 and 150:
A Compared Cross Impact Analysis A
Page 151 and 152:
A Compared Cross Impact Analysis al
Page 153 and 154:
A Compared Cross Impact Analysis As
Page 155 and 156:
A Compared Cross Impact Analysis Ta
Page 157 and 158:
A Compared Cross Impact Analysis Th
Page 159:
A Compared Cross Impact Analysis [2
show all

ESSAYS ON TEXT MINING FOR IMPROVED DECISION MAKING ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?