Predicting website audience demographics for web advertising targeting using multi website clickstream data

Several recent studies have explored the virtues of behavioral targeting and personalization for online advertising. In this paper, we add to this literature by proposing a cost-effective methodology for the prediction of demographic web site visitor profiles that can be used for web advertising targeting purposes. The methodology involves the transformation of web site visitors’ clickstream patterns to a set of features and the training of Random Forest classifiers that generate predictions for gender, age, educational level and occupation category. These demographic predictions can support online advertisement targeting (i) as an additional input in personalized advertising or behavioral targeting, in order to restrict ad targeting to demographically defined target groups, or (ii) as an input for aggregated demographic web site visitor profiles that support marketing managers in selecting web sites and achieving an optimal correspondence between target groups and web site audience composition. The proposed methodology is validated using data from a Belgian web metrics company. The results demonstrate that Random Forests demonstrate superior classification performance over a set of benchmark algorithms. Further, the ability of the model set to generate representative demographic web site audience profiles is assessed. The stability of the models over time is demonstrated using out-of-period data.(This abstract was borrowed from another version of this item.)