Business models to offer customized output in electronic commerce

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172 C. Scheer et al. / Business models to offer customized output in electronic commerceTable 1Data collection in customer model and utilization for predictionHow to get the experience model? How to get the customer’s model?Customer active Customer passivedata collection data collectionModel-based experience Method 1 Method 2Memory-based experience Method 3 Method 4The overall procedure of generating strict associationrules is as follows:– At first candidates are generated by combiningobject combinations with length K − 1, as long asthey differ only in one element.– In a second step all candidates are checked againstthe confidence threshold, as not all candidates necessarilyneed to be strict association rules, thoughall strict association rules can be found among thecandidates in reverse. All candidates, whose confidencevalue transcends the threshold are acceptedas strict association rules in the form A ⇒ B andare included in the model.The result of this procedure is an model of experience,which consists of strict association rules takeninto account all past configuration runs within thedatabase (cf. Fig. 7).7. Consultation process in detailWhenever the customer needs additional help in formof a prediction, a pattern consisting of three steps basedon our previously developed model starts:1) Based on the query of the user, all associationrules within the model are picked, in which exactlyone object is within the range of the optionvalues to be predicted.2) Each relation then needs to satisfy certain requirements:The object in the picked relation, which isnot within the range of the option values a) maynot be zero and b) has to be previously picked bythe customer in the configuration process.3) At this point, there are precisely three possibilities:a) Exactly one relation is found. In this case nofurther evaluation is necessary and the resultcan be used as a basis for prediction.b) Several or even contradicting relations arefound. So it has be decided by a selectioncriterion such as the confidence value, whichrelation is most likely to appeal to the configurationgoals and preferences of the customer.As a result the relation with the highestconfidence value is chosen.c) No relation can be found. All relations havebeen filtered out in the previous steps, whicheither was caused by overly strict definitionsof the thresholds when creating the model ora small-sized database. The foremost mentionedproblem is less a technical than a strategicalproblem: The quality of the predictionsis dependent on the amount of strict associationrules in the model. The system administratortherefore can lower the thresholds to enforceprediction generation, while the qualityanalogously gets reduced. The limit concerningtechnical realization is a certain maximalcapacity of the database, which still allows forprompt response times.Still we face difficulties to make predictions when thecustomer requests support at the very beginning of theconfiguration procedure, when no or little informationabout his goals were previously gathered.As a solution we propose the usage of clusteringtechniques in combination with the previously developedmodel in order to be able to immediately generatepredictions. Precondition for this is the existenceof a registration process in which the customer revealshis top-level preferences,demographic information andvague design ideas about the attributes he wants hisproduct to have. Based on this information the systemis able to classify the user into a pre-defined category,whereas the amount of categories is dependant fromthe desired degree of detailing.For every user group there is not only one optionvalue pre-defined rather than a ranking which optionvalues correspond more closely to the specific user category.This is done in order to combine our previously developedmodel with the clustering technique to furtherimprove our prediction quality.We introduce a correlation coefficient which measuresthe degree of compliance between the value chosenvia the association framework and the in the regis-
C. Scheer et al. / Business models to offer customized output in electronic commerce 173Information provided inregistration processQueryResultsCorrelation* ConfidenceResultOption AOption Value #2Option BOption Value #6ClusteringCustomerClassificationGroup #16Correlationnumbers#136#1450,65 * 1000,45 * 10065,0045,00Information provided inconfiguration runQueryResultsOption AOption Value #12AR ModelOption BOption Value #34Strict Association RulesSupport/ ConfidenceOption COption Value #102#10 => #145#31 => #13650/10050/100Option DPredictionrequestedQuery:? => #130 - #165#75 => #203#136 => #03150/10050/100#145 => #01050/100#203 => #07550/66.6Prediction with higher value is chosenFig. 7. Model displaying the integration of AR und clustering techniques.tration process mentioned configuration goal. The finalselection therefore is no longer dependent exclusivelyon the confidence value of the relation, but on the productof correlation number and confidence value; if nomatching association rule could be found, the predictionis based solely on the advice out of the clusteringroutine. This newly calculated product is a compositethat tells about how likely the via association rule generatedvalue goes along with the user stated configurationgoals. This value therefore unites primary and secondaryinformation retrieval methods. By this it is possible,to intercept wrong predictions which otherwisewill appear especially in unusual configurations. Theexplicitly stated goals of the user featuring top levelinformation gets combined with detailed collaborativebased information about the most likely option values,to form a value, which combines best of both worlds:All possible prediction values derived via the associationrule mechanism get multiplicated with a number,stating how likely this prediction value is consideringthe user stated goals. The higher the total number, themore likely is that the value is an accurate prediction.When a prediction is requested, a query is issued.The model is searched for appropriate association rules,which include one of the previously selected optionvalues on the left side of the association rule as wellas an option value within the specified range on theright side of the rule. If the query was succesful, eachof the results get matched with a correlation numbermanually stored in the system for each customer groupdescribing how likely the results will match customer’sconfiguration goals (cf. Fig. 8).As stated before, customer-driven output does notrequire the existence of any pre-defined options, sothat it might be impossible to state an exact correlationnumber for the values of some options in advance. Inthese cases, a classification based on basic attributescan help to identify cohesions among the option val-
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