Strategic Online Advertising: Modeling Internet User Behavior with

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3 Website Cluster Ad Campaigns i Users that visit one of the Top 500 Websites Websites Top 500 Websites by Highest Frequency of Visits 1 2 User Cluster Users i Matrix Values based on: Binary Frequency Relative Frequency (Percentage) Fig. 3. Clustering Input Matrix Fig Dimensional Cube Model The methodology is as follows: 1. Classification of users To identify subgroups of users, we employed clustering analysis tools within SAS, a statistical analysis software package. Clustering is a method of organizing data into groups called clusters. Clusters are collections of data objects that are similar or close to each other and dissimilar or far from objects in other clusters. We used clustering to categorize users on the basis of their behaviors so that appropriate advertisements could be served to them. We then extracted patterns in user behavior from each cluster. There are many types of clusters including, hierarchical and centroid clusters. Each clustering method uses different criteria to group data objects. We used centroid clustering because it can manage large data sets. The inputs to clustering are user impression data. A small sample is shown below (Fig. 2): In binary inputs, a value of one corresponds to a particular user encountering an advertisement on a given website. A zero value corresponds to a nonevent. Similarly, frequency inputs record the number of impressions a given user encounters on a particular website. Relative frequency is an extension of the frequency input that takes into account the total number of impressions an individual user encounters. This input is calculated as a percentage of the number of impressions a given user encounters on an individual website versus the total number of impressions the user encounters across all 500 websites. Analysis of the user cluster distribution revealed that binary clustering differentiated clusters best. Next, we picked k 1 number of user clusters using centroid clustering with k 1 means. User clustering groups Internet customers on the basis of similar website visitation behavior (Fig. 4). This clustering approach minimizes the sum of squared errors from an individual observation and the centroid, or multidimensional mean. The centroid can be thought of as the center of a sphere with radius r, where r is minimum distance that encompasses all the observations in the cluster. Websites Fig. 2. Sample Impression Data User Clusters Matrix Values based on: Binary The top 500 websites were extracted on the basis of popularity, indicated by the highest frequency of user visits. These websites represented the sites with the vast majority of the user activity. Next, we extracted users corresponding to the users who have visited the top 500 websites. The intersection of these users and the websites formed the basis of the matrix (Fig. 3). Each cell represents a pairing of an individual online user and a single website. The matrix values can be based on three criteria: (1) binary, (2) frequency, (3) relative frequency. k 1 Fig. 4. User Clustering 2. Classification of websites In website classification, a similar method of clustering is used. First, we picked k 2 number of website clusters using centroid clustering with k 2 means. Clustering on websites will subgroup websites on the basis of similar user clusters. This will

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