Business models to offer customized output in electronic commerce

C. Scheer et al. / Business models to offer customized output in electronic commerce 1695. Common approaches for consultation interfacein configuration tasksIn order to realize individual user support within theextended configuration process, we will present a consultationinterface, which creates a proposition for anydesired option value based on the individual configurationgoals and past configurations. The objective hereis to complete the configuration process in terms of thecustomer and to keep the customer in the process.At first it is fundamental to understand customer’sgoals of configuration. Therefore a measure needs to bedefined characterizing the preferences of the user. Onthe base of the measure, preferences can be analysedand used for user individual support. Basically fourapproaches are to be considered within this context (cf.Fig. 6).The horizontal dimension in Fig. 6 describes how tocollect information about the customer. A customermodel concerning configuration goals, preferences andresources can either be built by gathering informationvia direct inquiries – the user provides information directlyby himself – or via passive observance basedon behavior analysis. The knowledge about the customerthen needs to be matched with know-how frompast configurations (so called experience model) andthe product model in order to be able to propose likelyoption values. Know-how about past configurationscan either be in the form of the specific past configurationruns themselves (memory-based experience) orin the form of an abstract model (model-based experience)based on the collected data. As a result, thereexist a variety of approaches, each with different attributesand therefore also different suitability for usewith configurations for customer-driven output.Customer active data collection: A registration processis used to query the customer about his preferencesand goals, so that the collected information canafterwards be used as basis for individual support.– Model-based experience (Method 1): The predictionis based on the assignment of the customer to apre-defined user class. The classification dependson information provided by the customer [27]. Theproblem of initially not having any information toapply is avoided, as the predictions are already setbefore the actual configuration starts.– Memory-based experience (Method 2): The providedinformation is compared to informationabout other users which were collected in the past.The profile matching the configuration goal of thecurrent customer best is searched. In a secondstep, the corresponding configuration run containingall the option values is used for predictions.Therefore it is necessary to associate completedconfigurations with the information provided atthe registration process.Customer passive data collection: User’s behaviorsis assessed based on information provided by the userat the time he or she requests additional help. Henceit causes problems if the user asks for support at thevery beginning of the configuration process, since fewinformation about his or her configuration goals areavailable.– Model-based experience (Method 3): A modelis created based on past configuration runs andis compared to the behavior of the user, who requesteda prediction. Different approaches basedon different data structures can be applied: aweighted tree structure could be used [12] as wellas a model consisting of strict association rules [5].– Memory-based experience (Method 4): The behaviorof the customer is compared to individualpast configuration runs of other users. Predictionsare based on those option values of the past configurationrun matching best the information providedby the user so far [7].None of the presented approaches can solve the problemsimposed by configurations for customer-drivenoutput by it one: If active information retrieval is chosen,the option values are already specified before theactual configuration even starts. Thereby the predictionengine works completely independent from decisionswithin the configuration process. With passiveinformation gathering it is not possible to create anylikely predictions in the first steps of the process, sinceno information about the user is known yet. The fourpossible methods can be estimated as follows:– Method 1: The probability of creating a likely predictionwhich matches user’s configuration goalsis very little if clustering methods are used becauseof the large number of possible option valuesin cases of customer-driven Output. As allinformation was supplied by the customer himselfin advance, there is apparently the need to drawconclusions for every single option value based onthat higher level information provided within theregistration. This again cannot be possible, as theconcept of customer driven output asks for a highdegree of user involvement. The gathered infor-