CN101128817A - 基于购买的细分方法及系统 - Google Patents

基于购买的细分方法及系统 Download PDF

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CN101128817A
CN101128817A CNA2004800310746A CN200480031074A CN101128817A CN 101128817 A CN101128817 A CN 101128817A CN A2004800310746 A CNA2004800310746 A CN A2004800310746A CN 200480031074 A CN200480031074 A CN 200480031074A CN 101128817 A CN101128817 A CN 101128817A
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马克·E.·泰马雷斯
艾伦·B.·纽曼
努尔·A.·梅奈
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Citicorp Credit Services Inc USA
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Abstract

一种基于购买的潜在顾客细分方法(参见图2)和系统(参见图1)，其利用观测到的实际购买行为而不是假定和相关性来提高细分的准确度，包括：为客户端收集关于一组顾客实际购买行为的经验数据(10)；对购买行为经验数据(10)应用统计模型技术，以识别表现相似购买倾向特征的顾客细分或聚类(12、14和16)。然后，根据具有直接影响所述细分或聚类中顾客的实际购买行为倾向的其它因素，进一步彼此区分所述细分或聚类，从而根据与所述细分或聚类的相关性识别出定制营销的潜在顾客。

Description

基于购买的细分方法及系统
优先权申请
本申请要求2003年9月22日递交的名称为“用于基于购买的目标市场选择的方法和系统”(“METHOD AND SYSTEM FOR PURCHASED-BASEDTARGETING”)的美国临时申请No.60/504,432的权益，在此通过参考援引该申请。
技术领域
本发明涉及与商业交易中所用的基于购买的细分和聚类(purchase-basedsegmentation and clustering)相关的方法及系统，更具体地涉及用于实现基于购买的细分和聚类程序的方法和系统，这种方法和系统可以提高各种商业努力的成功率。这些商业努力包括：直复营销(例如直接面向顾客的广告、直邮、优惠券促销)；基于被识别顾客和相似顾客的实际购买行为，为零售商、产品/服务提供商等产生营销相关服务；提供了解零售商或产品/服务提供商顾客的实际购买行为与竞争者相同顾客的行为之间的比较情况的方法，以及了解零售商或产品/服务提供商顾客的实际购买行为与竞争者其它顾客的行为之间的比较情况的方法；销售规划；房地产规划；以及其它应用。
背景技术
细分和聚类的本质和目标为：通过将营销努力指向更可能产生良好回报的群体，并减少对不能产生良好回报的群体的营销努力，以使营销投资回报最大化。
为使细分和聚类方法的有效性最大化，营销人员必须掌握区分那些更可能产生良好回报的群体的方法。存在许多区分技术，包括与地理因素(例如，确定生活在已有顾客附近的群体)、人口统计因素(例如，确定高收入群体或家庭成员中有孩子的群体)和心理/生活方式因素(例如，确定具有积极生活方式的群体、从事工艺制作的群体或经常做礼拜的群体)有关的技术。
一旦识别了差异因素，营销人员面临的下一挑战将是在给定了所选传播媒体的条件下确定如何以最小的耗费赢得预期顾客。这可以包括购买预期顾客较多地观看的某电视台或某节目期间的广告时间、购买迎合预期顾客兴趣的杂质的订阅者邮件列表、在所选择地域的地方报纸上集中投放广告以及许多其它方法。也可以包括调整所营销产品或服务的定位/信息传递以更好地迎合预期顾客表现的行为和态度、选择对预期顾客更有吸引力的销售和店面位置以及其它方法。
在营销人员可用的方法中，用于识别可能购买者的那些方法称为“聚类”。所述方法基于考虑了上述大量因素的统计“最佳拟合(best fit)”方法，将家庭或个人分配至许多分立的细分或聚类之一。
在所有上述方法中，营销人员获得的任何成功都是由于假定和相关性发挥了作用。例如，运动商品营销人员通过向刊物Sports Illustrated的订阅者邮寄广告可能会比向刊物Time的订阅者邮寄广告获得更好的效果，这是因为Sports Illustrated的读者更有可能参与运动(换句话说，阅读SportsIllustrated与参与运动之间的相关性大于阅读Time与参与运动之间的相关性)。但是，结果是相对的，实际的回报可能较小。许多Sports Illustrated的读者是观众，而不是运动的参与者。许多其它人是参与者，但不一定是运动商品营销人员所销售的产品种类的使用者。
发明内容
本发明的特征和优点是提供一种基于购买的潜在顾客细分方法和系统，其利用观测到的实际交易，而不是假定和相关性，来提高细分的准确度并赢得预期顾客。
本发明的另一特征和优点是提供一种基于购买的潜在顾客细分方法和系统，其利用观测到的实际购买行为来提高当提供商已知购买者时的细分准确度，并作为促进对产品或产品类别的实际购买者的营销的方法。
本发明的再一特征和优点是提供一种基于购买的潜在顾客细分方法和系统，其利用观测到的实际购买行为，提高当提供商不知道具体购买者或其交易时的细分准确度，或者当提供商不知道具体购买者或其交易的关键数据时的细分准确度，并作为基于与其它行为的相关性和改进的现有方法而进行更准确有效的营销的方法。
本发明的又一特征和优点是提供一种基于购买的潜在顾客细分方法和系统，其提供对竞争者的细分顾客的所观测实际行为的分析，以及对竞争者的顾客的所观测实际行为的分析。
为了实现上述及其它特征、优点和目的，本发明的实施例利用例如计算机硬件、操作系统、程序语言、应用软件及其它技术，提供基于购买的顾客细分方法及系统，其中由服务提供商为客户端收集关于一组顾客的实际购买行为的经验数据。所述数据例如可以作为使用由客户端或第三方向顾客提供的支付工具(例如信用卡、借记卡、储值卡和/或射频识别装置)的副产品而被直接或间接收集。可选地，所述数据也可以作为使用由客户端或第三方向顾客提供的利益凭证(例如保单、折扣单、条码扫描和购买凭据数据)的副产品而被直接或间接收集。
在本发明的实施例中，对购买行为经验数据应用统计模型技术，以识别例如在将来从客户端和/或从第三方处进行购买的可能性方面表现出相似购买倾向特征的顾客细分或聚类。根据具有直接影响细分或聚类中顾客的实际购买行为倾向的其它因素，例如地理人口统计和心理/生活方式因素，进一步彼此区分细分或聚类。根据与细分或聚类的相关性，例如通过指数化(indexing)，可以识别定制营销的潜在顾客，其可以是客户端的顾客和/或第三方的顾客。
本发明的附加目的、优点和新特征一部分将在后面的说明中描述，并且一部分对于研究下文的本领域技术人员变得显而易见，或者可以从本发明的实践中了解。
附图说明
图1为示出根据本发明实施例的、对潜在顾客进行基于购买的细分处理的关键构件以及关键构件之间关系的实例示意图，该细分处理利用了关于观测到的实际购买的数据、统计模型以及聚类技术；
图2为示出根据本发明实施例的、对潜在顾客进行基于购买的细分处理实例的流程示意图，该细分处理利用了关于观测到的实际购买的数据、统计模型以及聚类技术。
具体实施方式
以下详细说明本发明的实施例，本实施例的实例在附图中示出，提供的每个实例用于解释本发明，而并非用于限制本发明。本领域的技术人员应该清楚，在不脱离本发明的范围或精神的前提下可以对本发明进行各种修改和变化。例如，作为一个实施例的一部分而示出或描述的特征可以用于其他实施例以获得另一实施例。因此，这意味着本发明将覆盖落入本发明范围内的这种修改和变化。
在优选实施例中，本发明使用多种方法收集关于个人、家庭和/或企业(“预期顾客”)的实际购买的数据；使用统计模型技术基于实际购买而产生细分或聚类，当本发明的使用者已知实际预期顾客时，提供关于如何赢得这些预期顾客的信息或实际方法(例如，使用者的顾客，或通过第三方获得的姓名和地址)；以及利用其他人的实际购买行为，改进对预期顾客的细分/聚类成员或特定行为(“指数值”和/或分数)提供预测的方法，所述预期顾客的实际购买信息是未知的。
在优选实施例中，本发明可以使用相似的数据收集和细分方法，然后提供关于如下内容的信息和分析，即如何比较特定客户端(例如零售商或产品/服务提供商)的顾客的实际购买行为与竞争者或非竞争者的相同顾客的购买行为，如何区分客户端的顾客的行为与非顾客的行为，和/或如何区分顾客自身。
获取数据的方法包括直接通过使用者获取数据的方法和从其它数据占有者收集数据的方法。前一方法包括：发行支付工具(例如信用卡、借记卡、射频识别(RFID)装置等)，其中购买数据可以是实现购买交易的副产品；发行“利益凭证”(例如忠诚度表或“常客”标识)，其中数据收集同样是实现其它客户端利益的副产品；或者通过各种方法(例如保单、需要购买凭据的折扣单、经营场所接受的条码扫描、需要购买凭据或多个凭据集合的保险费(premium)和价格等)从实际顾客自身直接收集信息。
后一方法包括：从支付工具或利益凭证的其他发行者那里直接或间接收集顾客和/或购买信息；从一个或多个销售商那里直接或间接收集顾客和/或购买信息；以及从使用各种方法获取信息的其它第三方信息源那里收集顾客和/或购买信息。
在本发明的实施例中，对于有意获取更好信息的每个零售商或产品/服务提供商，或者对于零售商的产品类别或产品/服务，可使用统计模型和/或聚类方法产生相对更可能成为购买者和相对更不可能成为购买者的细分或聚类，这些细分或聚类之间的可能性程度不同。上述细分或聚类的关键的统计驱动因素为：在特定零售商处的实际购买或对产品/服务提供商的产品的实际购买，以及在相关店面的相似购买或对相关产品类别的相似购买。也可以将可能驱动实际购买的其他因素用于产生细分或聚类之间的进一步区分以及细分或聚类内最大的同质性(homogeneity)，这些因素包括：在其它店面的购买或对其它产品类别的购买；购买方法；以及地理人口统计和心理/生活方式因素(例如，对于未居住在零售商的任何店面附近的、产品类别的大顾客，即使他们会对该产品类别进行实际购买，但是这些大顾客在该零售商处购物的可能性也很小)。
然后，零售商或产品/服务提供商可以各种方式使用以名称、序号、指数或基于统计的分数(score)表示的细分或聚类，在本发明的实施例中这些方式包括：选择更可能赢得最具潜力的细分或聚类中的个人/家庭的大众媒体；观察和了解不同细分或聚类之间的自身顾客行为与竞争者的顾客行为的差异，并调整报价、产品、产品组合/销售规划、店面模式和位置；以及其它吸引并迎合顾客的商业方法。
在本发明的实施例中，使用者或其零售商及产品/服务提供商客户端也可以将由细分或聚类产生的信息与其自身的和其它的数据相结合，以向已知最具潜力的细分或聚类中的个人/家庭营销，并识别可能属于最具潜力的细分或聚类的、使用者未知其具体购买信息的其它个人。这可以通过指数化和/或评分技术实施。本发明的实施例包括对特定个人因素或因素集合进行指数化(例如，特定的高潜力细分或聚类中顾客的住宅拥有指数为250，换句话说，他们拥有住宅的可能性是平均值的2.5倍。这可以表明以住宅拥有者作为目标将是赢得未知潜在顾客的有效手段)，或者利用其它非购买驱动的聚类技术对已有聚类方法进行指数化(例如，特定的高潜力细分或聚类中的顾客在另一聚类方法的“十二聚类”(“cluster twelve”)中的指数为600)。
对于营销人员而言，对其它因素或聚类进行指数化的优点在于：对于特定的营销方法，营销渠道与其它因素或聚类之间的关系已经建立且已知。例如，目前没有关于本发明所述实施例中产生的细分或聚类的电视节目观众指数，并且由于将为特定客户端定制许多产生的细分或聚类，则也可能没有该指数。但是，电视节目观众指数广泛适用于许多特定地理人口统计和心理/生活方式变量，至少对商业公司(Personicx和PRIZM)提供的两种聚类方法而言是这样。通过为已有因素或聚类提供指数或类似的叠加算法(overlaymeasure)，营销人员可以利用这些已知因素和聚类选择媒体。利用上述实例中的数字，营销人员可以选择在对住宅拥有者或对其它聚类方法的十二聚类中的群体吸引力较大的电视节目(例如家居装修节目)上打广告。
在涉及了解竞争者或非竞争者的顾客与自身顾客的比较情况的本发明实施例中，零售商和产品/服务提供商可以几种方式使用细分或聚类。当对所有产品类别产生细分或聚类时，零售商和产品/服务提供商可以比较他们的顾客与其它顾客在每个细分或聚类中的相关存在，并利用该细分或聚类的特征产生对其自身客户端和竞争者或非竞争者的客户端的特性的分析。当分别产生零售商或产品/服务提供商的顾客的细分或聚类以及竞争者或非竞争者的顾客的细分或聚类时，可以识别并比较重叠的和非重叠的细分或聚类的特征。在这两种情况下，如上所述，在本发明实施例中基于实际购买行为或实际购买交易的细分或聚类的能力可以提供以下显著优点：改善营销和研究努力的结果，引起营销、销售及其它商业功能的效率的提高。
图1为示出根据本发明实施例的、对潜在顾客进行基于购买的细分处理的关键构件以及关键构件之间关系的实例示意图，该细分处理利用了关于观测到的实际购买的数据、统计模型以及聚类技术。参照图1，提供大型购买数据仓库(10)。利用这些购买数据，例如如果有意寻找很可能在休闲式餐馆就餐的顾客，就可以产生具有休闲式餐馆行为的购买者聚类。例如，这些聚类中的某些聚类可能为：在工作日而不是在周末去休闲式餐馆的顾客；在周末而不是在工作日去休闲式餐馆的顾客；非常频繁地去休闲式餐馆的顾客；不频繁地去休闲式餐馆的人；非顾客(从来不去休闲式餐馆的人)；既去休闲式餐馆又去正式餐馆(white-tablecloth restaurant)的顾客；去休闲式餐馆和快餐馆但不去高档餐馆的顾客等。识别在各种购买行为(餐馆12、汽车租赁14、百货商店16、衣服等)的细分之间差异较大的餐馆购买行为和非餐馆购买行为的特征。应该理解，上述差异化的行为可以在产品类别内或产品类别外。
通过细分或聚类可以进行一些应用。可以有助于对处于这些特定细分或聚类中的人进行营销，以及可以按照特定餐馆、产品类别或更宽泛的范围定制上述营销。因此，可以有助于对顾客群的营销。在本实施例中，顾客群指已知的人群。
此外，可以确定“同类的人”(birds of a feather)，即潜在顾客，这些人与期望细分或聚类中的顾客非常类似，但在行为上不表现出来。这些人可能是处于顾客群中的、通过在数据中无法观测到的方法进行购买的潜在顾客，或者可能是未处于顾客群中的潜在顾客。此外，可以将所产生的细分或聚类与现有已知细分或聚类方案比较，尤其是可以确定与其它细分或聚类方案的高度重叠的发生。
在图1所示实施例中，在餐馆聚类12和Personicx聚类“1”(18)中，餐馆聚类“8”(20)中的人是Personicx聚类“1”(18)中可能出现的人的3.3倍，而仅是Personicx聚类“6”(22)中可能出现的人的60％。知道上述事实的优点在于某些聚类方案已经映射至外部的源。例如，对几乎每一种杂志而言，读者都已被指数化为Personicx聚类。因此，媒体购买者就已经知道应在哪些杂志做广告以赢得期望Personicx聚类中的预期顾客。此外，参照餐馆聚类“8”(20)，可以在所有媒体与该聚类之间直接进行指数化，或者如图所示，在所示实施例中确定的餐馆聚类可以映射至其它现有顾客方案，例如Personicx。同样地，该聚类也可以与地理或人口统计信息相关联以识别预期顾客。
图2为示出根据本发明实施例的、对潜在顾客进行基于购买的细分处理实例的流程示意图，该细分处理利用了关于观测到的实际购买的数据、统计模型以及聚类技术。参照图2，在S1，服务提供商为客户端收集关于一组顾客的实际购买行为的经验数据。在S2，对服务提供商收集的购买行为经验数据应用统计模型技术，以识别表现相似购买倾向特征的顾客聚类。在S3，根据可能直接影响所述聚类中顾客的实际购买行为的其它因素进一步区分所述聚类。在S4，根据与所述聚类的相关性识别定制营销的潜在顾客。
尽管在某些实施例中使用信用卡购买数据，然而也可使用许多其它数据源来产生细分或聚类。例如，借记卡数据或使用商家的利益凭证的数据；或者从拥有数据的其他来源购买，或者以合作伙伴关系与拥有数据的其他来源联盟以获取其拥有的数据。例如，信用卡发行商拥有数据；AC尼尔森拥有从店面直接获取的数据；ID装置和其它凭证的发行商拥有数据；借记卡的发行商拥有数据；以及店面自身拥有数据。
上面已经采用非限制方式一般性地说明了本发明的各种实施例。应该理解，这些实例仅作为本发明的示例。各种变化和修改对于本领域的普通技术人员来讲将是显而易见的。

Claims (17)

1.一种基于购买的顾客细分方法，包括如下步骤：
由服务提供商为客户端收集关于一组顾客实际购买行为的经验数据；
对所述购买行为经验数据应用统计模型技术，以识别表现相似购买倾向特征的顾客细分或聚类；
根据其它因素区分所述细分或聚类，所述其它因素具有直接影响所述细分或聚类中顾客的实际购买行为的倾向；
根据与所述细分或聚类的相关性识别定制营销的潜在顾客。
2.根据权利要求1所述的方法，其中收集所述经验数据的步骤还包括：收集关于所述顾客实际购买行为的如下经验数据，即，其作为使用向所述顾客提供的支付工具的副产品。
3.根据权利要求2所述的方法，其中收集所述经验数据的步骤还包括：收集作为使用向所述顾客提供的如下支付工具的副产品的经验数据，即，所述支付工具选自至少部分地由信用卡、借记卡、储值卡和射频识别装置构成的集合。
4.根据权利要求2所述的方法，其中收集所述经验数据的步骤还包括：直接收集如下经验数据，即，其作为使用由所述客户端向所述顾客提供的支付工具的副产品。
5.根据权利要求2所述的方法，其中收集所述经验数据的步骤还包括：间接收集如下经验数据，即，其作为使用由第三方向所述顾客提供的支付工具的副产品。
6.根据权利要求1所述的方法，其中收集所述经验数据的步骤还包括：收集关于顾客实际购买行为的如下经验数据，即，其作为使用向顾客提供的利益凭证的副产品。
7.根据权利要求6所述的方法，其中收集所述经验数据的步骤还包括：收集作为使用如下利益凭证的副产品的经验数据，即，所述利益凭证选自至少部分地由保单、折扣单、条码扫描和购买凭据数据构成的集合。
8.根据权利要求6所述的方法，其中收集所述经验数据的步骤还包括：直接收集如下经验数据，即，其作为使用由所述客户端向所述顾客提供的利益凭证的副产品。
9.根据权利要求6所述的方法，其中收集所述经验数据的步骤还包括：间接收集如下经验数据，即，其作为使用由第三方向所述顾客提供的利益凭证的副产品。
10.根据权利要求1所述的方法，其中应用所述统计模型技术来识别表现相似购买倾向特征的细分或聚类的步骤还包括：应用所述统计模型技术，以识别在将来进行购买的可能性方面表现出相似购买倾向特征的顾客细分或聚类。
11.根据权利要求10所述的方法，其中应用所述统计模型技术来识别表现相似购买倾向特征的细分或聚类的步骤还包括：应用所述统计模型技术，以识别在将来从客户端处进行购买的可能性方面表现出相似购买倾向特征的顾客细分或聚类。
12.根据权利要求10所述的方法，其中应用所述统计模型技术来识别表现相似购买倾向特征的细分或聚类的步骤还包括：应用所述统计模型技术，以识别在将来从第三方处进行购买的可能性方面表现出相似购买倾向特征的顾客细分或聚类。
13.根据权利要求1所述的方法，其中根据其它因素区分所述细分或聚类的步骤还包括：根据地理人口统计和心理/生活方式因素区分所述细分或聚类，所述地理人口统计和心理/生活方式因素具有直接影响所述细分或聚类中顾客的实际购买行为的倾向。
14.根据权利要求1所述的方法，其中识别定制营销的潜在顾客的步骤还包括：根据与所述细分或聚类的相关性，识别所述细分或聚类中的、由客户端定制的营销的客户端顾客。
15.根据权利要求1所述的方法，其中识别定制营销的潜在顾客的步骤还包括：根据与所述细分或聚类的相关性，识别所述细分或聚类中的、由客户端定制的营销的第三方顾客。
16.根据权利要求1所述的方法，其中识别定制营销的潜在顾客的步骤还包括：根据指数化的与所述细分或聚类的相关性，识别定制营销的潜在顾客。
17.一种基于购买的顾客细分系统，包括：
用于由服务提供商为客户端收集关于一组顾客实际购买行为的经验数据的装置；
用于对所述购买行为经验数据应用统计模型技术以识别表现相似购买倾向特征的顾客细分或聚类的装置；
用于根据具有直接影响所述聚类中顾客的实际购买行为倾向的其它因素区分所述细分或聚类的装置；以及
用于根据与所述细分或聚类的相关性识别定制营销的潜在顾客的装置。
CNA2004800310746A 2003-09-22 2004-09-22 基于购买的细分方法及系统 Pending CN101128817A (zh)

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