Buchner, Alex G

Mulvenna, Maurice

Norwood, Marian

English

Ulster University's Research Portal

downloaded from www.netacademy.orgÓ of this article is held by the authorCourtesy of thehttp://www.netacademy.org.This document has been downloaded from the NetAcademy at http://www.netacademy.org.The NetAcademy is a research platform for the world-wide Scientific Community, hosted atthe Institute for Media and Communications Management, University of St. Gallen,Switzerland.For any use of this document, which is not strictly private, scholarly work,please contact the NetAcademy Editors at NA.editors@netacademy.org.The mcminstituteUniversity of St. GallenMüller-Friedberg-Str. 8CH–9000 St. GallenSwitzerlandTel: +41 / 71 22 97Fax: +41 / 71 27 71emails:NA.Editors@netacademy.orgwebmaster@netacademy.orgEM — Electronic Markets 32Data-Driven Marketingby Maurice Mulvenna, Marian Norwood, and Alex Bchner, University of Ulster, United Kingdom*1. Behavioural – Consumers exhibit awide range of differing behaviouralpatterns;2. Marketing – Consumer and productdescriptive information provides a richsource of useful data;3. Descriptive – Products and services inon-line retail sites are varied.Figure 1 illustrates the main processes thatoccur at an Internet retailing web site,along with the associated data stores. Theprocesses in Figure 1 provide data, whichis at the core of successful Internet mar-keting and suitable for data mining. Thedata may be described along the follow-ing dimensions (which correspond with thereasons given above):1. Server data – data generated by theinteractions between the personsbrowsing an individual site, and theweb server. For example, the ip addressof a computer browsing a retailing site.2. Marketing data – the data stored by theInternet retailer on products, custom-ers, suppliers, etc. For example, theconsumer responses to discounting.IntroductionElectronic commerce has been describedas ”one of those rare cases where chang-ing needs and new technologies come to-gether to revolutionise the way in whichbusiness is conducted“ (ESPRIT 1996).Currently, relatively few consumers andbusinesses are connected to the Internet.Projections estimate over 50 million on-line by the year 2000, but is electroniccommerce ready for the rigours of inter-national trade, both retail and businessto business?Much of the excitement of electroniccommerce is focused on the delivery ofproducts and tradable services to consum-ers in their own homes. This is usuallyknown as on-line retailing and is seen asthe area of electronic commerce wheremany of the following issues will be de-bated first: standards; international elec-tronic trade agreements; security; andtrust. This on-line retailing model is thepermutation selected in the MIMIC project(described later) to research the efficacyof data mining. The primary reasons forselecting the on-line retailing model are:Figure 1Internet Retailer Web Site Processes3. Site ‘meta’ data – the data about thesite, usually generated dynamically andautomatically after a site update. Forexample, location of product pages ona site, which are ‘leaf ‘ pages, naviga-tional pages, etc.There are now hundreds of thousands ofon-line Internet shopping providers world-wide, with thousands more appearing eachmonth. Data generated by clients of theon-line retailers by their interaction withthe site in browsing and buying. Clearlysuch large data sets, which may be dis-tributed and heterogeneous, will containuseful information helpful to businessmarketing strategies, both for retrospec-tive analyses as well as data-driven fore-casting.The potential for this new form of data-driven marketing is enormous – brows-ers and buyers of products can be identi-fied and targeted with attractive offersand sales promotions.Data-Driven Marketing(Dibb; Simkin; Pride; Ferrell 1997) definemarketing as consisting of ”individualsand organisational activities that facili-tate and expedite satisfying exchange re-lationships in a dynamic environmentResearch IssueMarketing DataMeta DataServer DataMarketing/SellingProcessesMeta DataServer DataInternet RetailerWeb SiteBack-EndData Storage33 Vol. 8 — No.  3 — 1998through the creation, distribution, promo-tion and pricing of goods, services andideas“. This definition highlights the needfor the development of the ‘right’ market-ing mix (product, price, promotion, dis-tribution, etc.). According to (Kotler 1997)this is ”a set of marketing tools that thefirm uses to pursue its marketing objec-tives in the target market“. The develop-ment of the ‘right’ mix is governed by anunderstanding of customers’ needs andwants.Sophisticated successful marketing takesthis a step further. An organisation mustunderstand its customers’ needs and wants,and it must satisfy them with a product orservice that displays some form of com-petitive advantage. With an understand-ing of not only the customer but also themarketplace (the business environmentand competition) as well as internal capa-bilities, a marketing strategy can bedeveloped. In developing the strategy, anorganisation attempts to identify groupsof customers where each separate group,or market segment, has ‘similar’ needs.Each group of customers can then be of-fered a specifically tailored programme,also known as target marketing.However, it is a well-known fact that suc-cess in marketing is never guaranteed. Intoday’s dynamic business environment,marketing decisions are extremely com-plex. Such decisions as ‘what customersto target’ or ‘what products or services tooffer targeted customers’ demand reliableand accurate knowledge. As (Druker 1983)states, ”The aim of marketing is to makemarketing superfluous. The aim is to knowand to understand the customer so wellthat the product or service fits him/her andsells itself“.It is interesting to note that the Interneteffectively blurs the distinction betweenthe traditional marketing processes of a)informing customers, b) selling to custom-ers (via advertising and personal sellingstrategies) and c) order taking, processingand even delivery. Increasingly, custom-ers can react immediately to a marketingcommunication aid, and purchase directly.An example is the purchase of softwarethat is downloadable upon payment. TheInternet therefore facilitates instantaneousadvertising, selling and distribution.Normally, all marketing communicationsare subject to measurement and controlin order to monitor the success or other-wise of campaigns. Traditionally effective-ness is measured by tracking awarenessand image, by measuring response rate toall direct communications and of courseby looking at sales volume. It involves acomplex series of tasks. This is not neces-sarily so for Internet marketing. Organi-sations that market on the Internet cannow measure directly the response gener-ated by the campaign, advertisement,discounting, etc. In the case of online ‘ban-ner ads’, the success is measured by the‘click through’ rate. This is the number oftimes that, when someone is presentedwith an ad, they then choose to click onthat ad to obtain further information. Acompany hosting its own merchant servercan measure the number of ‘hits’, andbreak down this information by domain(e.g., .com for commercial).The above examples illustrate that eachinteraction between consumer and selleris recorded in digital form. This is the im-portant difference between conventionalselling and selling on the Internet. At thisstage in the Information Age, each actorin the information cyberspace, for exam-ple, consumers and retailers in Internetretailing, generates digital trails and datathat may be stored and analysed by theorganisation operating the web server.Data mining algorithms are the new ac-tors in this information cyberspace.Data MiningData mining has been defined as ”theautomated discovery of non-obvious, po-tentially useful and previously unknowninformation from large data sources“(Frawley; Piatetsky-Shapiro; Mattheus1991). It applies various artificial intelli-gence techniques to discover patterns thatmay be both useful and actionable.*Maurice Mulvenna(MD.Mulvenna@ulst.ac.uk) is a lecturerin informatics at the School of Comput-ing & Mathematics in the Faculty ofInformatics, University of Ulster. He haspublished over thirty papers on hisresearch specialities – knowledge basedsystems and electronic commerce. He isinterested in research and technologytransfer with small companies inNorthern Ireland, and has obtainedalong with colleagues almost 3 millionpounds in research funds. He is theuniversity’s project leader of the MIMICelectronic commerce project.Marian Norwood(MT.Norwood@ulst.ac.uk) is a lecturerin marketing and marketing research atthe School of Commerce and Interna-tional Business Studies in the Facultyof Business & Management,University of Ulster. She publishes inthe areas of Internet marketing andrelationship marketing. She is currentlyundertaking several market researchprojects in North-west Ireland.Alex Büchner(AG.Buchner@ulst.ac.uk) is a researchfellow in the Northern IrelandKnowledge Engineering Laboratory inthe Faculty of Informatics at theUniversity of Ulster. He works in thearea of data mining and is involved inseveral research projects in Europe andthe Far East. His main researchinterests are in the fields of artificialintelligence, object-oriented technologiesand multi-databases. He has writtenmore than 15 publications in thesefields and has co-written a book on datamining. He is the university’s technicallead in the MIMIC electroniccommerce project.Research IssueEM — Electronic Markets 34Data mining programs mainly comprisealgorithms that can discover associationsbetween data, help to classify items, anddiscover sequences hidden in the data(Anand 1998). An example of a discov-ered association could be the types ofproducts that consumers usually buy atthe one time. An example of classifica-tion would be the grouping of a particu-lar type of buyer, such as ‘high frequencybut low value’ purchaser. An example ofdiscovered sequences could be a buyerperiodically returning to purchase othernovels by a particular author.Within the context of data analysis ofInternet retailing sites, data mining candiscover knowledge about the uniquecharacteristics of identified customer seg-ments, so that business decisions may bemade in relation to predictions about cus-tomer value and appropriate loyaltyincentives can be developed (Mulvenna1997a).Data mining allows marketers to reveallayers of information about markets (orsubsets of markets) in ever increasingdetail, enabling customer or prospect pro-files to be built and the identification ofthe segments upon which marketing ac-tivities can focus.So what are the benefits of using datamining techniques to gather intelligenceabout a market? It allows on-line retail-ers to ‘fine tune’ their selling strategy. Thisgives them a greatly enhanced insight intothe number and types of lines in stock,the best electronic shop front format, andwhich offers should be directed at whichcustomers and how should they be com-municated (Humbry 1996). Using direct,data driven marketing, heavy buyers of aproduct can be identified and targetedwith attractive offers and sales promo-tions. Targeting does not have to involvediscounted offers alone. It could meanmaking certain categories of customeraware of products or services that mightbe of interest to them, or inviting themto special on-line events.The application of data mining techniquesto the data of on-line retailers provideshigh-level knowledge – for example, inthe form of rules – that describes con-sumer navigational and purchasing be-haviour. These rules capture trends andbehaviour patterns that may be appliedwithin a marketing strategy. The authorspropose that the high-level, descriptive,behavioural rules supply the marketingspecialists with the means to feedbackdirectly the aggregated behavioural re-sponses of the clients of an online serv-ice. The high-level mined rules may beincorporated into the architecture of anon-line retailing system. When each per-son interacts with the on-line service tonavigate and purchase goods or services,their on-line behavioural patterns areidentified, and the on-line retailing sys-tem may react to change dynamically theinformation presented to that consumer.This is the goal of MIMIC.The MIMIC ProjectMIMIC (“Mining the Internet for Market-ing IntelligenCe”) is a project funded byESPRIT IV Framework Thematic Pro-gramme for Electronic Commerce (MIMIC1997). The MIMIC Consortium consists offour partners, represented by the Univer-sity of Ulster, and three small-to-mediumenterprises (SME).The technical objective of the MIMICproject is to develop a data mining toolkitthat will make possible the mining of thedata generated by online retailers(Mulvenna 1997b). The business objec-tive is to provide European on-line shop-ping retailers (of which many are SMEcompanies) with advanced technologythat will enable them to maintain theircompetitiveness in the global Internetmarketplace. The approach of the MIMICproject is to capture the requirementsfrom SME on-line shopping providers,and develop specialised Internet-capabledata mining algorithms. The three areasof data mining outlined in the previoussection – classification, association andsequences – will require additions andReferencesAnand, S.S., Büchner, A.G.,Decision Support Using Data Mining,Financial Times Pitman Publishers,ISBN 0-273-63269-8, 1998Dibb, L.  Simkin, W.M.  Pride, O.C.Ferrell: Marketing Concepts and Strate-gies, 3rd European Edition,Houghton Mifflin, Boston,New York, 1997.Drucker, F., Managing for Results,Heinemann, London, 1983.“Electronic Commerce –An Introduction“,http://www.cordis.lu/esprit/src/ecomint.htmFrawley, W.J., Piatetsky-Shapiro G. andMattheus, C.J., ”An Overview:Knowledge Discovery in Databases“,in Knowledge Discovery in Databases,AAAI/MIT Press, 1991, pp.  1-27“Knowing When to be of Service“,Financial Times, London,12 February, 1998Hagel III, J., Rayport, J.F.,“The Coming Battle for CustomerInformation“, Harvard Business Review,January-February 1997Humbry, C., Digging for Information,Marketing, November 21,pp.41-42, 1996Kotler: Marketing Management:Analysis, Planning, Implementation andControl, 9th Edition, Prentice HallInternational Inc, pp. 466-467, 1997.Research Issue35 Vol. 8 — No.  3 — 1998specialised extensions to cope with thespecific nature of Internet data. These willbe validated by the users and incorpo-rated ultimately into a special version ofexisting data mining software.The target market segment is on-lineshopping retailers, and the goal of theMIMIC project is to give these retailersthe ability to market their products andservices effectively. Such customised anddirected marketing communication, how-ever, relies on accurate information aboutthe customer. MIMIC will provide, for thefirst time, the capability for retailers toconsider a cost-effective means of cus-tomised data-driven marketing commu-nication.Although it is widely believed that massmarketing techniques such as advertis-ing are a major proportion of a market-ing budget, many companies now spendmore on sales promotion methods, whichinclude direct marketing techniques. Thebenefits of data-driven marketing are theability to measure directly the responseto the marketing effort, and the resultantcost-effectiveness of this technique. Data-driven marketing also enables on-line re-tailers to monitor their customers’ behav-iour patterns and to detect and entrappotential ‘switchers’ – those people whoshow no allegiance to a particular retailer.Electronic commerce dispenses with geo-graphic boundaries, and empowers cus-tomers to shop where they obtain thecheapest/best bargain. The marketing in-telligence information from the MIMICdata mining toolkit may be used to pro-vide a competitive edge for on-line shop-ping retailers, allowing them to capture,retain and satisfy customers.ConclusionsThis paper has outlined how data miningmay be applied to assist the marketingcommunication process for Internet re-tailers. The algorithms used in data min-ing are capable (with modification) ofprocessing of large, distributed and oftenheterogeneous databases that store thetransactional, behavioural and other datagathered by Internet retailers.The use of data mining in data-drivenmarketing for on-line retailing is clear,but its market application may be tem-pered by customers and consumer organi-sations who may react negatively to thecollection and ‘mining’ of aggregated orpersonal information (Toronto Star 1998).It is impossible to predict the future land-scape of the Internet and the roles of theactors in this information cyberspace.However, it is becoming apparent thatconsumers value their rights and will onlyparticipate in marketing relations thatbenefit them as much as the retailer(Norwood 1997; Financial Times 1998,Easton; Parker 1998). The onus is there-fore on the retailers to provide assistanceand engender trust. This may be donepositively, as a value adding action(Thirkell 1997).Internet retailers who do not attempt toaddress the trust issue directly face lowturnover at best and vigorous collectivereactions from consumers at worst. Ulti-mately, consumers and other actors incyberspace will seek out lateral solutionsto what they view as privacy intrusions(Hagel III; Rayport 1997). These may in-clude a desire for anonymous transac-tions; for example, using digital cashequivalents.Data mining algorithms and techniquesmay be applied to more than Internet re-tailing data. Other areas that involveInternet transactions include business-to-business electronic commerce. If the elec-tronic commerce market grows as somecommentators predict, there will be anexplosion in the growth of digital trans-actional and behavioural data, and anincreased demand for data mining toolsand techniques. The results of the MIMICproject should demonstrate the usefulnessof data mining techniques and explore –to retailers and consumers alike – theimplications of the application of thesetechniques.Mining the Internet for MarketingIntelligenCe – MIMIC, Esprit ProposalN°:26749, 1997Mulvenna, M.D., Büchner, A.G.,“Data Mining and Electronic Commerce,in Overcoming Barriers to ElectronicCommerce“, (OBEC ’97),Malaga, Spain,1997Mulvenna, M.D., Büchner, A.G.,Norwood, M.T., Grant, C.,“The ‘Soft-Push’: Mining Internet Datafor Marketing Intelligence“,In: Working Conference: ElectronicCommerce in the Framework ofMediterranean Countries Development,Ioannina, Greece, October 1997Norwood, M.T., Mulvenna, M.D.,Büchner, A.G., Grant, C.,“Competitive Advantage on the InternetThrough Relationship Marketing“,1st Berlin Internet Economics,October 1997Thirkell, P.C., ”Caught by the Web:Implications of Internet Technologiesfor the Evolving Relationship MarketingParadigm“, Conference of AmericanMarketing Association,Dublin, Ireland, June 1997“Data Mining’ Privacy Threat“,Toronto Star, Toronto, Canada,14 January 1998Easton, J., Parker, J., ”Seeking theThere There:  Building Relationshipsis the Key to Building Profits“,ZD Internet Magazine, February 1998Research Issue

Data Driven Marketing

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Data Driven Marketing

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