Behavior Pattern Extraction by Self-Organizing Maps of Personal Usage Histories : Predicting when credit-card users will switch to credit-card cashing based on personal credit histories

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  • 自己組織化マップによる行動履歴の類型化 : クレジットカード利用履歴を利用したキャッシング移行予測
  • ジコ ソシキカ マップ ニ ヨル コウドウ リレキ ノ ルイケイカ クレジット カード リヨウ リレキ オ リヨウ シタ キャッシング イコウ ヨソク

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Abstract

In this paper, we propose a method to extract a customer segmentation from the voluminous data produced by personal usage histories. First, in order to categorize customer behavior types at specified points in time, we apply SOM (self-organizing maps) to usage records. Second, we define new distances between distributions of behavior types on the SOM map using distribution functions. We then map the distributions of behavior types in order to obtain customer types over the long-term. Finally, in order to obtain a customer segmentation whose segment has a homogeneous functional relation between variates, we propose a model merge method. This method merges neighboring customer types in an SOM map, in cases where the MDL (minimum description length) criterion of the generalized linear model on the merged customer type is smaller than the sum of MDLs of the models on customer types to be merged. In this way, we are able to analyze historical personal usage data exhaustively gathered from multiple sources over the long-term. In order to validate the proposed method, we predict which credit-card users are likely to switch to credit-card cashing based on their credit histories. First, we classify card users into monthly types by applying SOM to monthly usage records of card shopping and cashing. Next, using the distances between distributions over the map, we apply SOM to the distributions of monthly types, thus obtaining yearly customer types. Finally, to predict the switch, we estimate logistic models on yearly customer types, and combine these models using the model merge method. The proposed method reveals useful features of card users who switch to credit-card cashing.

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