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1. 发明申请

US20050159996A1 Predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching 有权
标题翻译：使用监督分割和最近邻匹配的消费者财务行为的预测建模
公开(公告)号：US20050159996A1
公开(公告)日：2005-07-21
申请号：US11012812
申请日：2004-12-14
申请人： Michael Lazarus , Larry Peranich , Frederique Vernhes , Matthias Blume , Kenneth Brown , William Caid , Ted Dunning , Gerald Russell , Kevin Sitze
发明人： Michael Lazarus , Larry Peranich , Frederique Vernhes , Matthias Blume , Kenneth Brown , William Caid , Ted Dunning , Gerald Russell , Kevin Sitze
IPC分类号： G06Q30/02 , G06F17/60
CPC分类号： G06Q30/02 , G06Q30/0202 , G06Q30/0207 , G06Q30/0241 , G06Q30/0255 , G06Q30/0269 , G06Q30/0601
摘要： Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. The consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer. Predictions of consumer behavior are made by applying nearest-neighbor analysis to consumer vectors, thus facilitating the targeting of promotional offers to consumers most likely to respond positively.
摘要翻译：通过将消费者交易数据应用于与商家细分相关的预测模型，提供消费者财务行为的预测建模，包括对特定营销努力的可能响应的确定。商业细分是根据交易序列中商家的共同出现从消费者交易数据中得出的。商家向量表示特定商家，并且在向量空间中对齐，作为商家与预期频繁相同程度的函数。监督分割被应用于商家向量以形成商家分段。商业细分预测模型根据消费者以前的支出，为每个特定消费者的每个商业细分市场提供支出预测。消费者个人资料描述了每个消费者在商家细分中以及跨商家细分的消费的总体统计。消费者资料包括作为由消费者光顾的所选商家的汇总向量导出的消费者向量。消费者行为的预测是通过对消费者向量应用最近邻分析来做出的，从而有助于向最有可能积极响应的消费者定位促销优惠。

2. 发明申请

US20070244741A1 Predictive Modeling of Consumer Financial Behavior Using Supervised Segmentation and Nearest-Neighbor Matching 有权
标题翻译：使用监督分割和最近邻匹配的消费者金融行为的预测建模
公开(公告)号：US20070244741A1
公开(公告)日：2007-10-18
申请号：US11623266
申请日：2007-01-15
申请人： Matthias Blume , Michael Lazarus , Larry Peranich , Frederique Vernhes , Kenneth Brown , William Caid , Ted Dunning , Gerald Russell , Kevin Sitze
发明人： Matthias Blume , Michael Lazarus , Larry Peranich , Frederique Vernhes , Kenneth Brown , William Caid , Ted Dunning , Gerald Russell , Kevin Sitze
IPC分类号： G06F17/30
CPC分类号： G06Q30/02 , G06Q30/0202 , G06Q30/0207 , G06Q30/0241 , G06Q30/0255 , G06Q30/0269 , G06Q30/0601
摘要： Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. The consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer. Predictions of consumer behavior are made by applying nearest-neighbor analysis to consumer vectors, thus facilitating the targeting of promotional offers to consumers most likely to respond positively.
摘要翻译：通过将消费者交易数据应用于与商家细分相关的预测模型，提供消费者财务行为的预测建模，包括对特定营销努力的可能响应的确定。商业细分是根据交易序列中商家的共同出现从消费者交易数据中得出的。商家向量表示特定商家，并且在向量空间中对齐，作为商家与预期频繁相同程度的函数。监督分割被应用于商家向量以形成商家分段。商业细分预测模型根据消费者以前的支出，为每个特定消费者的每个商业细分市场提供支出预测。消费者个人资料描述了每个消费者在商家细分中以及跨商家细分的消费的总体统计。消费者资料包括作为由消费者光顾的所选商家的汇总向量导出的消费者向量。消费者行为的预测是通过对消费者向量应用最近邻分析来做出的，从而有助于向最有可能积极响应的消费者定位促销优惠。

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