Web Usage Mining for E-Business Applications

Web Usage Mining for E-Business Applications

1 Last update: 21 May 2009 1 Web Mining An introduction to server-log-based Web usage mining Bettina Berendt Universidad Politcnica de Madrid, Department of Computer Science http://vasarely.wiwi.hu-berlin.de/WebMining09/ ( Recall the 2002 slide: ) 2 Web Usage Mining: Basics and data sources Definition of Web usage mining: discovery of meaningful patterns from data generated by client-server transactions on one or more Web servers Typical Sources of Data automatically generated data stored in server access logs,

referrer logs, agent logs, and client-side cookies e-commerce and product-oriented user events (e.g., shopping cart changes, ad or product click-throughs, purchases) user profiles and/or user ratings meta-data, page attributes, page content, site structure 2 3 Agenda 3 Data Acquisition, Understanding, and Preparation Forms of analysis; mining techniques Case study: A multi-channel retailer method: Association-rule discovery

Free tools for logfile analysis Web Usage Mining 4 Discovery of meaningful patterns from data generated by clientserver transactions on one or more Web servers Typical Sources of Data automatically generated data stored in server access logs, referrer logs, agent logs, and client-side cookies e-commerce and product-oriented user events (e.g., shopping cart changes, ad or product click-throughs, etc.) user profiles and/or user ratings meta-data, page attributes, page content, site structure 4

5 Data collection 5 Web server Client (Browser) Proxy Whats in a typical Web server log (Requests to www.acr-news.org) - -- - 203.30.5.145 - - [01/Jun/1999:03:09:21 -0600] "GET /Calls/OWOM.html HTTP/1.0" 200 3942 "http://www.lycos.com/cgi-bin/pursuit?query=advertising+psychology&maxhits=20&cat=dir" "Mozilla/4.5 [en] (Win98; I)" 203.30.5.145 - - [01/Jun/1999:03:09:23 -0600] "GET /Calls/Images/earthani.gif HTTP/1.0" 200 10689 "http://www.acr-news.org/Calls/OWOM.html" "Mozilla/4.5 [en] (Win98; I)" 203.30.5.145 - - [01/Jun/1999:03:09:24 -0600] "GET /Calls/Images/line.gif HTTP/1.0" 200 190 "http://www.acr-news.org/Calls/OWOM.html" "Mozilla/4.5 [en] (Win98; I)" 203.252.234.33 - - [01/Jun/1999:03:12:31 -0600] "GET / HTTP/1.0" 200 4980 "" "Mozilla/4.06 [en] (Win95; I)" 203.252.234.33 - - [01/Jun/1999:03:12:35 -0600] "GET /Images/line.gif HTTP/1.0" 200 190 "http://www.acr-news.org/" "Mozilla/4.06 [en] (Win95; I)"

203.252.234.33 - - [01/Jun/1999:03:12:35 -0600] "GET /Images/red.gif HTTP/1.0" 200 104 "http://www.acr-news.org/" "Mozilla/4.06 [en] (Win95; I)" 203.252.234.33 - - [01/Jun/1999:03:12:35 -0600] "GET /Images/earthani.gif HTTP/1.0" 200 10689 "http://www.acr-news.org/" "Mozilla/4.06 [en] (Win95; I)" 203.252.234.33 - - [01/Jun/1999:03:13:11 -0600] "GET /CP.html HTTP/1.0" 200 3218 "http://www.acr-news.org/" "Mozilla/4.06 [en] (Win95; I) 203.30.5.145 - - [01/Jun/1999:03:13:25 -0600] "GET /Calls/AWAC.html HTTP/1.0" 200 104 "http://www.acr-news.org/Calls/OWOM.html" "Mozilla/4.5 [en] (Win98; I)" and what does it mean? (Requests to www.acr-news.org) - -- - 203.30.5.145 - - [01/Jun/1999:03:09:21 -0600] "GET /Calls/OWOM.html HTTP/1.0" 200 3942 "http://www.lycos.com/cgi-bin/pursuit?query=advertising+psychology&maxhits=20&cat=dir" "Mozilla/4.5 [en] (Win98; I)" 203.30.5.145 - - [01/Jun/1999:03:09:23 -0600] "GET /Calls/Images/earthani.gif HTTP/1.0" 200 10689 "http://www.acr-news.org/Calls/OWOM.html" "Mozilla/4.5 [en] (Win98; I)" 203.30.5.145 - - [01/Jun/1999:03:09:24 -0600] "GET /Calls/Images/line.gif HTTP/1.0" 200 190 "http://www.acr-news.org/Calls/OWOM.html" "Mozilla/4.5 [en] (Win98; I)" 203.252.234.33 - - [01/Jun/1999:03:12:31 -0600] "GET / HTTP/1.0" 200 4980 "" "Mozilla/4.06 [en] (Win95; I)" 203.252.234.33 - - [01/Jun/1999:03:12:35 -0600] "GET /Images/line.gif HTTP/1.0" 200 190 "http://www.acr-news.org/" "Mozilla/4.06 [en] (Win95; I)" 203.252.234.33 - - [01/Jun/1999:03:12:35 -0600] "GET /Images/red.gif HTTP/1.0" 200 104 "http://www.acr-news.org/" "Mozilla/4.06 [en] (Win95; I)"

203.252.234.33 - - [01/Jun/1999:03:12:35 -0600] "GET /Images/earthani.gif HTTP/1.0" 200 10689 "http://www.acr-news.org/" "Mozilla/4.06 [en] (Win95; I)" 203.252.234.33 - - [01/Jun/1999:03:13:11 -0600] "GET /CP.html HTTP/1.0" 200 3218 "http://www.acr-news.org/" "Mozilla/4.06 [en] (Win95; I) 203.30.5.145 - - [01/Jun/1999:03:13:25 -0600] "GET /Calls/AWAC.html HTTP/1.0" 200 104 "http://www.acr-news.org/Calls/OWOM.html" "Mozilla/4.5 [en] (Win98; I)" Sources and destinations 8 Logs may extend beyond visits to the site and show where a visitor was before (referrer) ... 203.30.5.145 - - [01/Jun/1999:03:09:21 -0600] "GET /Calls/OWOM.html HTTP/1.0" 200 3942 "http://www.lycos.com/cgi-bin/pursuit? query=advertising+psychology-&maxhits=20&cat=dir" "Mozilla/4.5 [en] (Win98; I)" ... and where s/he went next (URL rewriting): 8 9 Preprocessing of Web Usage Data 9 Raw Usage Data

Data Cleaning User/Session Identification Page View Identification Path Completion Server Session File Episode Identification Usage Statistics Site Structure and Content Episode File 10 Preprocessing of Web Usage Data 10

Raw Usage Data Data Cleaning User/Session Identification Page View Identification Path Completion Server Session File Episode Identification Usage Statistics Site Structure and Content Episode File

not always necessary and/or done 1 Data Preprocessing (1) 11 11 Data cleaning remove irrelevant references and fields in server logs remove references due to spider navigation remove erroneous references add missing references due to caching (done after sessionization)

Data integration synchronize data from multiple server logs Integrate semantics, e.g., meta-data (e.g., content labels) e-commerce and application server data integrate demographic / registration data 1 Data Preprocessing (2) 12

12 Data Transformation user identification sessionization / episode identification pageview identification a pageview is a set of page files and associated objects that contribute to a single display in a Web Browser Data Reduction sampling and dimensionality reduction (ignoring certain pageviews / items) Identifying User Transactions (i.e., sets or sequences of pageviews possibly with associated weights)

1 Why sessionize? 13 13 Quality of the patterns discovered in KDD depends on the quality of the data on which mining is applied. In Web usage analysis, these data are the sessions of the site visitors: the activities performed by a user from the moment she enters the site until the moment she leaves it. Difficult to obtain reliable usage data due to proxy servers and anonymizers, dynamic IP addresses, missing references due to caching, and the inability of servers to distinguish among different visits.

Cookies and embedded session IDs produce the most faithful approximation of users and their visits, but are not used in every site, and not accepted by every user. Therefore, heuristics are needed that can sessionize the available access data. 1 Mechanisms for User Identification 14 14 Examples: page tags (use javascript), some browser plugins 1 15 Examples of software agents or: Alternatives to Webserver-log based data collection 15

Page tagging with Javascript: see also http://www.bruceclay.com/analytics/disadvantages.htm 1 16 Sessionization strategies: Sessionization heuristics 16 These heuristics are quite accurate! (see Spiliopoulou et al., 2003) 1 Path Completion 17 17 Refers to the problem of inferring missing user references due to caching. Effective path completion requires extensive knowledge of the link structure within the site Referrer information in server logs can also be used in disambiguating the inferred paths.

Problem gets much more complicated in frame-based sites. 1 18 Why integrate semantics? 18 Basic idea: associate each requested page with one or more domain concepts, to better understand the process of navigation / Web usage Example: a shopping site From ... p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:03:51 +0100] "GET /search.html?l=ostsee%20strand&syn=023785&ord=asc HTTP/1.0" 200 1759 p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:05:06 +0100] "GET /search.html?l=ostsee%20strand&p=low&syn=023785&ord=desc HTTP/1.0" 200 8450 p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:06:41 +0100] "GET /mlesen.html?Item=3456&syn=023785 HTTP/1.0" 200 3478 To ... Refine search Search by category Choose item

Search by Category+title Look at individual product 1 19 From URLs to topics / concepts: Basics of semantic session modelling 1 request 1 concept or n concepts Concepts can concern content or service Concepts can be part of an ontology (simple case: concept hierarchy) Session = set / sequence / tree / graph of requests

19 also possible: n requests 1 concept 1 20 20 Ontology-based behaviour modelling basic ideas (1) The request for a Web page signals interest in the concept(s) and relations dealt with in this page interest in the obtained content as well as in the requested service. Formally: a request as a (multi)set, or as a vector, of concepts/relations. 2 Resulting format: if the request is the instance 21 21 Usually flat file (format like Web server log) or database 2

Resulting format: If a session is the instance 22 22 What features can a session have? Refer again to the example: p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:03:51 +0100] "GET /search.html?l=ostsee%20strand&syn=023785&ord=asc HTTP/1.0" 200 1759 p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:05:06 +0100] "GET /search.html?l=ostsee%20strand&p=low&syn=023785&ord=desc HTTP/1.0" 200 8450 p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:06:41 +0100] "GET /mlesen.html?Item=3456&syn=023785 HTTP/1.0" 200 3478 Refine search Search by category Choose item Search by Category+title

Look at individual product 2 Site Content Basic Framework for E-Commerce Data Analysis Web Usage and E-Business Analytics Content Analysis Module Web/Application Server Logs Data Cleaning / Sessionization Module Data Integration Module Integrated Sessionized

Data E-Commerce Data Mart Session Analysis / Static Aggregation OLAP Tools OLAP Analysis Data Cube Site Map customers orders products Site Dictionary Operational Database Data Mining Engine

Pattern Analysis 24 Agenda 24 Data Acquisition, Understanding, and Preparation Forms of analysis; mining techniques Case study: A multi-channel retailer method: Association-rule discovery Free tools for logfile analysis 2 Web Usage and E-Business Analytics 25 25 Different Different Levels Levels of of Analysis

Analysis Session Analysis Static Aggregation and Statistics OLAP Data Mining 2 Session Analysis 26 26 Simplest form of analysis: examine individual or groups of server sessions and e-commerce data.

Advantages: Gain insight into typical customer behaviors. Trace specific problems with the site. Drawbacks: LOTS of data. Difficult to generalize. 2 27 Static Aggregation (Reports) 27 Most common form of analysis. Data aggregated by predetermined units such as days or sessions. Generally gives most bang for the buck.

Advantages: Gives quick overview of how a site is being used. Minimal disk space or processing power required. Drawbacks: No ability to dig deeper into the data. Page View Home Page Catalog Ordering Shopping Cart Number of Sessions 50,000 500 9000 Average View Count per Session 1.5

1.1 2.3 2 28 Online Analytical Processing (OLAP) 28 Allows changes to aggregation level for multiple dimensions. Generally associated with a Data Warehouse. Advantages & Drawbacks Very flexible Requires significantly more resources than static reporting. Page View Kid's Stuff Products Number of Sessions 2,000

Average View Count per Session 5.9 Page Number of View Sessions Kid's Stuff Products Electronics Educational 63 Radio-Controlled 93 Average View Count per Session 2.3 2.5 2 29 Data Mining: Going deeper 29

Prediction of next event Discovery of associated events or application objects Sequence mining Markov chains Association rules Discovery of visitor groups with common properties and interests Clustering Discovery of visitor groups with common behaviour Session Clustering Characterization of visitors with respect to a set of predefined classes

Classification Card fraud detection 2 30 KDD Techniques for Web Applications: Examples (1) 30 Calibration of a Web server: Prediction of the next page invocation over a group of concurrent Web users under certain constraints Sequence mining, Markov chains Cross-selling of products: Mapping of Web pages/objects to products

Discovery of associated products Association rules, Sequence Mining Placement of associated products on the same page 3 31 KDD Techniques for Web Applications: Examples (2) 31 Sophisticated cross-selling and up-selling of products: Mapping of pages/objects to products of different price groups Identification of Customer Groups

Discovery of associated products of the same/different price categories Clustering, Classification Association rules, Sequence Mining Formulation of recommendations to the end-user Suggestions on associated products Suggestions based on the preferences of similar users 3 32 Agenda

32 Data Acquisition, Understanding, and Preparation Forms of analysis; mining techniques Case study: A multi-channel retailer method: Association-rule discovery Free tools for logfile analysis 3 33 CRM questions example: Why go to a shop ... 33 ... if everything is available on the Internet? 3 34 A multi-channel retailer, its business goals, and analysis questions

34 General goals: Standard e-tailer goals attract users/shoppers and convert them into customers Specific goals: assess the success of the Web site in relation to other distribution channels Background: Internet market shares [BCG 100% Pure Internet 2002] 90% 80% 70% companies Multi-channel 48 businesses 46 33 31 67

69 60% 50% 40% 30% 52 54 10% 0% 2000 What business metrics can be calculated from Web usage data, transaction and demographic data for determining online success? Are there cross-channel 20%

1999 Questions of the evaluation: 2001 2002 (proj.) effects between a companys eshop and its physical stores? 3 The site 35 35 3 Outline of the KDD process 36 36 Business underst.: customer buying process Data:

Web server sessions, transaction info. Data understanding main step: modelling the semantics of the site in terms of a hierarchy of service concepts Data preparation: Session IDs; usual data cleaning steps Linking of sessions & transaction information (anonymized) Modelling / pattern discovery: Web metrics, cluster analysis, association rules, sequence mining + correlation analysis, questionnaire study, qualitative market analysis Evaluation: Interesting patterns

3 Agenda Case Study 37 37 Business Understanding Data understanding and preparation Pattern discovery + evaluation: Success metrics Pattern disc. + eval.: Behavioural patterns Pattern disc. + eval.: User types Pattern disc. + eval.: Behaviour & demographics 3 Agenda Case Study 38 38 Business Understanding Data understanding and preparation Pattern discovery + evaluation: Success metrics Pattern disc. + eval.: Behavioural patterns Pattern disc. + eval.: User types

Pattern disc. + eval.: Behaviour & demographics 3 Description of the site and its services 39 39 The retailer operates an e-shop and more than 5000 retail shops in over 10 European countries It sells a wide range of consumer electronics Online customers can pay, pick-up/deliver and return both online and offline Web pages provide for all tasks in the customer buying process

3 40 Purchase Phases (Page Concepts) at Large MC Retailers 40 Home (Acquisition) 1. Acquisition (home): All Web pages that are semantically related to the initial acquisition of a visitor 4 41 Purchase Phases (Page Concepts) at Large MC Retailers Home (Acquisition)

2. 41 Product Impression Catalogue information: pages providing an overview of product categories. 4 42 Purchase Phases (Page Concepts) at Large MC Retailers Home (Acquisition) 3. Product Impression 42 Product

ClickThrough Information product (infprod): pages displaying information about a specific product 4 43 Purchase Phases (Page Concepts) at Large MC Retailers Home (Acquisition) 4. Product Impression Product ClickThrough 43 Offlineinfo offline information (offinfo): All pages related to any offline information:

store locator (pages for finding physical stores in ones neighbourhood), information about offline services, offline referrers etc. 4 44 Purchase Phases (Page Concepts) at Large MC Retailers Home (Acquisition) 5. Product Impression Product ClickThrough Offlineinfo 44 Transaction transaction (transact): steps before an actual purchase, starting with a

customer entering the order process: check-out, input of customer data, payment and delivery preferences (online or offline), etc. 4 45 45 Purchase Phases (Page Concepts) at Large MC Retailers Home (Acquisition) 6. Product Impression Product ClickThrough Offlineinfo Transaction Purchase

purchase: indicates if a visitor completed the transaction process and bought a product, e.g. invocation of an order confirmation page. 4 Agenda Case Study 46 46 Business Understanding Data understanding and preparation Pattern disc. + eval.: Behavioural patterns 4 Data and data preparation 47 47 Data sources and sample:

92,467 sessions from the companys Web logs from 21 days in 2002 anonymized transaction information of 13,653 customers who bought online over a period of 8 months in 2001/02. 621 transaction records (21 days) were linked to Web-usage records Data preparation: Sessions were determined by session IDs Robot visits eliminated, usual data cleaning steps Each URL request mapped to a service concept from {c1,...,cn} Session representation: s = [w1, ...wn], with wi = weight of ci, indicating whether or not the concept was visited (1/0), or how often

it was visited Customer record: feature vector incl. session and transaction data 4 48 Site semantics: A service concept hierarchy 48 760,535 page requests were mapped onto the concepts from this hierarchy: Any Services Game Registration Acquisition Home Offline Referrer Advertiser Company

Other Infos Other Offline Service and Support Transaction Information Information Catalog Store Locator Shopping Cart Fulfillment/ Service Customer Data Payment

Information Product = Multi-Channel Concept 4 Types of patterns 49 49 Conversion rates (~ confidence of content-specified sequential association rules) for assessing business success Association rule and sequence analysis for understanding online/offline preferences and their temporal development Cluster analysis for customer segmentation

Correlation analysis for investigating the relationship between demographic indicators and online/offline preferences 4 >> Session representation 50 50 Each session represented as a feature vector on the multi-channel concepts Two methods used for definition of new conversion metrics: weighted-concept method (number of visits to a concept) Session home infcat infprod servic transa purch. offinfo e ct A 0 3 7 4 2

1 0 B 1 3 5 0 0 0 2 ... dichotomized concept method (whether or not concept was visited) Session home infcat infprod servic transa purch. offinfo e ct A 0 1 1 1 1 1 0 B 1

1 1 0 0 0 1 ... 5 Agenda Case Study 51 51 Business Understanding Data understanding and preparation Pattern disc. + eval.: Behavioural patterns 5 52 52 Internal consistency of preferences

payment and delivery preferences Online payment Direct delivery (s=0.27, c=0.97) < 1/3 traditional onl.users! Online payment In-store pickup (s=0.02, c=0.03) Cash on delivery Direct delivery (s=0.02, c=0.03) In-store payment In-store pickup (s=0.69, c=0.94) s: s:support, support,c: c: confidence of confidence of the thesequence sequence Site is primarily used to collect information. 5 53 53 Internal consistency of preferences

return preferences Return In-store (s=0.06, c=0.87) Return Mail-in (s=0.04, c=0.13) s: s:support, support,c: c: confidence of confidence of the theassociation association rule rule Customers may wish personal assistance. (a result supported by the service mix analysis of different multichannel retailers and by questionnaire results) 5 Development of preferences over time 54 54

s: s:support, support,c: c: confidence of confidence of the thesequence sequence Direct delivery In-store pickup in 1 following transaction (s=0.001,c=0.15) Direct delivery Direct delivery in all following transactions (s=0.003,c=0.85) In-store pickup Direct delivery in 1 foll. transaction (s=0.001, c=0.10) (*) In-store pickup In-store pickup in all foll. transactions (s=0.004, c=0.90) Results for payment migration are similar. 90% of repeat customers did not change transaction preferences at all. Rule (*) as an indicator of the development of trust?! 5 55 Agenda 55 Data Acquisition, Understanding, and Preparation

Forms of analysis; mining techniques Case study: A multi-channel retailer method: Association-rule discovery Free tools for logfile analysis 5 Association-rule mining 56 56 A great tutorial is available here: S. Parthasarathy (2006). Association rules. http://www.cse.ohio-state.edu/~srini/674/assoc1.ppt pp. 1 17, covering What is an association rule? What are interestingness measures for association rules?

support, confidence (there are many further measures) How is association-rule mining performed? the basic apriori algorithm 5 57 Agenda 57 Data Acquisition, Understanding, and Preparation Forms of analysis; mining techniques Case study: A multi-channel retailer method: Association-rule discovery Free tools for logfile analysis 5 In the preparation of a log file

58 (recommendations for open-source tools are shown in green) 58 1. Use qualitative methods for application understanding (read!) 2. Inspect the site and the URLs for data understanding 3. 4. 1. Generate Analog reports for getting base statistics of usage 2. Build concept system / hierarchy and mapping: URLs concepts (notation: WUMprep regex) Use WUMprep for data preparation

1. Remove unwanted entries (pictures etc.) 2. Sessionize 3. Remove robots 4. Replace URLs by concepts 5. (Build a database) Use WEKA for modelling 1. [ Transform log file into ARFF (WUMprep4WEKA) ] 2. Cluster, classify, find association rules, ...

5. Use WUM for modelling 6. Select patterns based on objective interestingness measures (support, confidence, lift, ...) and on subjective interestingness measures (unexpected? Application-relevant?) 7. Present results in tabular, textual and graphical form (use Excel, ...) 8. Interpret the results 9. Make recommendations for site improvement etc. 5 URLs of the tools 59

59 Analog: http://www.analog.cx/ WUMprep: http://www.hypknowsys.de/ WEKA: http://www.cs.waikato.ac.nz/ml/weka/ WUM: http://www.hypknowsys.de/ 5 Short introductions to WUMprep 60 60 Lderitz, S. (2006). Pre-processing of webserver logs for data mining. http://www.cs.kuleuven.be/~berendt/teaching/2007w/adb/Lecture/OtherSlides/luederitz-presentation1-slides_2006_07_10.pdf (pp. 30-32) Dettmar, G. (2003). Logfile-Preprocessing using WUMprep. http://warhol.wiwi.hu-berlin.de/~berendt/lehre/2003w/wmi/Student_Presentations/Gebhard_WUMprep.pdf 6 References / background reading (1) 61

61 Data preparation Cooley, R., B. Mobasher, J. Srivastava. 1999. Data preparation for mining world wide web browsing patterns. J.of Knowledge and Inform.Systems 1 532. http://citeseer.ist.psu.edu/cooley99data.html Spiliopoulou, M., Mobasher, B., Berendt, B., & Nakagawa, M. (2003). A framework for the evaluation of session reconstruction heuristics in Web-usage analyis. INFORMS Journal on Computing, 15, 171-190. http://warhol.wiwi.hu-berlin.de/~berendt/Papers/spiliopoulou_etal_2003.pdf Web mining Baldi, P., Frasconi, P., & Smyth, P. (2003). Modeling the Internet and the Web. Probabilistic Methods and Algorithms. Chichester, UK: John Wiley & Sons.

http://ibook.ics.uci.edu/ Bing Liu (2006). Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications). Springer. http://www.cs.uic.edu/%7Eliub/WebMiningBook.html A general overview of Web usage mining Srivastava, J., Desikan, P., & Kumar, V. (2004). Web Mining - Concepts, Applications and Research Directions. In H. Kargupta, A. Joshi, K. Sivakumar, & Y. Yesha (Eds.), Data Mining: Next Generation Challenges and Future Directions (pp. 405-423). Menlo Park, CA: AAAI/MIT Press. (earlier, longer version: http://www.ieee.org.ar/downloads/ Srivastava-tut-paper.pdf 6 References / background reading (2) 62 62 Case study

Teltzrow, M., & Berendt, B. (2003). Web-Usage-Based Success Metrics for MultiChannel Businesses. In Proceedings of the WebKDD 2003 Workshop - Webmining as a Premise to Effective and Intelligent Web Applications.. August 27th, 2003, Washington DC, USA. Held in conjunction with The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. http://warhol.wiwi.hu-berlin.de/~teltzrow/teltzrow_berendt_webkdd03.pdf Teltzrow, M., Berendt, B., & Gnther, O. (2003). Consumer behaviour at multi-channel retailers. In Proceedings of the 4th IBM eBusiness Conference, School of Management, University of Surrey, 9th December 2003. http://warhol.wiwi.hu-berlin.de/~berendt/Papers/teltzrow_berendt_guenther_2003.pdf 6

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