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WO2009077655A1 - Procédé et agencement pour la segmentation de clients dans un système de gestion de la clientèle - Google Patents

Procédé et agencement pour la segmentation de clients dans un système de gestion de la clientèle Download PDF

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Publication number
WO2009077655A1
WO2009077655A1 PCT/FI2008/050740 FI2008050740W WO2009077655A1 WO 2009077655 A1 WO2009077655 A1 WO 2009077655A1 FI 2008050740 W FI2008050740 W FI 2008050740W WO 2009077655 A1 WO2009077655 A1 WO 2009077655A1
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Prior art keywords
customer
customers
segmentation
interests
retrieved
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Janne Aukia
Janne Sinkkonen
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the present invention relates in general to customer relationship managements systems (CRM) and more specifically to a method and an arrangement for segmentation of custom- ers in a customer management system that can be further utilized in prediction of customer behavior and interest.
  • CRM customer relationship managements systems
  • the background of the invention is discussed briefly in the following.
  • the invention relates to a problem on how can the marketers of a certain product or service target networked individuals with relevant information on products, without bothering them with irrelevant advertising. Solving this problem requires finding the customers or customer groups who are most interested in the product.
  • the target group to which a marketing message is sent is defined usually by the user' s demographics and/or previous purchase patterns.
  • One of the typical ways to define the target group of users is to se- lect the most potential age and education level for a product. This way of selecting the target group of users to which marketing messages are sent is however inefficient, in a way that large group of messages is sent to different users without any response from the potential buyers. Therefore large group of unnecessary messages are sent through a network (e.g. Internet) .
  • the marketing message covers traditional mail, commercials (on TV or radio), e-mails, mobile messages, etc.
  • Another prior art solution that is used is to send e-mail messages to all possible e-mail addresses.
  • the recent studies have revealed that about half of the e-mails sent in communications networks are already e-marketing messages. This method causes a lot of unnecessary traffic in the communications networks.
  • a social network management system is any system which deals with analyzing, managing or visualizing social networks.
  • social networks are considered in a wide sense.
  • the network represents direct interactions between individuals (e.g., telephone calls, e-mails and online messaging) or indirect relationships between individuals with similar behaviour, interests or demographic features.
  • Nodes represent the individuals and connections the similarity or amount of communication between the individuals. The connections may be either directed or indirected.
  • the network may be either weighted or unweighted.
  • the social network may represent the relationships between a set of items (such as books, videos or stores) which are connected through individuals who buy, use or share interests in the items.
  • the nodes of the social network correspond to the items and the connections correspond to the strength of the similarity between the items.
  • US 6,266,649 there is disclosed an service for recom- mending items to individual users based on a set of items that are known to be of interest to the user.
  • the present invention realizes a method and an arrangement for segmentation of customers in a customer management system that can be further utilized in prediction of customer behavior and interest, and that better solves the presented problems in comparison to solutions according to prior art.
  • a challenge with using network information in segmentation is that network data is noisy and incomplete.
  • the present invention solves these problems by using Bayesian methods, which can flexibly deal with uncertainty and randomness.
  • a method for segmentation of customers in a customer management system comprises the step of - segmenting customers to retrieve one or more customer segments by using social network topology and/or the combination of social network topology and other properties of customers, such as demography.
  • the method further comprises the step of using said retrieved one or more customer segments in prediction of customer interests and preferences.
  • the method further comprises the steps of - constructing a predictive model for predicting customer behavior, demography or interests by using said retrieved one or more customer segments, and using said retrieved one or more customer segments together with said retrieved predictive model in predic- tion of customer interests and preferences.
  • the method further comprises the steps of predicting customer behavior, demography or interests to retrieve a predictive model by using said retrieved one or more customer segments and social network topology, and using said retrieved one or more customer segments together with said retrieved predictive model in prediction of customer interests and preferences .
  • the method further comprises the steps of predicting customer behavior, demography or interests to retrieve a predictive model by using said retrieved one or more customer segments and the combination of social network topology and other properties of customers, such as demography, and using said retrieved one or more customer segments together with said retrieved predictive model in predic- tion of customer interests and preferences .
  • the segmentation of customers is performed by using hierarchical clustering.
  • the segmentation of customers is performed by using Bayesian clus- tering.
  • the segmentation is optimized with collapsed Gibbs sampling.
  • the segmentation is optimized with Expectation Maximization algorithm.
  • the segmentation is optimized with Markov
  • the predicting customer interests and preferences is carried out by averaging over segments.
  • the predicting customer interests and preferences is carried out by using network segments as inputs to a predictive model, such as a regression model.
  • an arrangement for segmentation of customers in a customer management system which arrangement has : - means for segmenting customers to retrieve one or more customer segments by using social network topology and/or the combination of social network topology and other properties of customers, such as demography.
  • the arrangement further has: means for predicting customer interests and preferences by using said retrieved one or more customer segments .
  • the arrangement further has: means for constructing a predictive model for predicting customer behavior, demography or interests by using said retrieved one or more customer segments, and means for predicting customer interests and prefer- ences by using said retrieved one or more customer segments together with said retrieved predictive model.
  • the arrangement further has: means for retrieving a predictive model by using said retrieved one or more customer segments and social network topology in predicting customer behavior, demography or interests, and means for predicting customer interests and preferences by using said retrieved one or more customer seg- ments together with said retrieved predictive model.
  • the arrangement further has: means for retrieving a predictive model by using said retrieved one or more customer segments and the combina- tion of social network topology and other properties of customers, such as demography in predicting customer behavior, demography or interests, and means for predicting customer interests and preferences by using said retrieved one or more customer seg- ments together with said retrieved predictive model.
  • the means for segmenting are suited for performing the segmentation of customers by using hierarchical clustering.
  • the means for segmenting are suited for performing the segmentation of customers by using Bayesian clustering.
  • the means for segmenting are suited for optimizing the segmentation of customers with collapsed Gibbs sampling.
  • the means for segmenting are suited for optimizing the segmentation of customers with Expectation Maximization algorithm.
  • the means for segmenting are suited for optimizing the segmentation of customers with Markov Chain Monte Carlo method.
  • the means for segmenting are suited for optimizing the segmentation of customers with approximate Bayesian inference method.
  • the means for predicting customer interests and preferences are suited for carrying out the predicting by averaging over segments.
  • the means for predicting customer interests and preferences are suited for carrying out the predicting by using network segments as inputs to a predictive model, such as a regression model .
  • Figure 1 illustrates a flowchart presentation of a method for segmentation of customers in a customer management system according to the present invention.
  • Figure 2 illustrates a flowchart presentation of a method for segmentation of customers in a customer management system according to the present invention.
  • Figure 3 illustrates a simplified network topology of a social network according to the present invention. Detailed description of certain embodiments
  • the solution according to the present invention presents a new method and a new arrangement for segmentation of cus- tomers in a customer management system that can be further utilized in prediction of customer behavior and interest.
  • Figure 1 illustrates a flowchart presentation of a method for segmentation of customers in a customer management system according to the present invention.
  • a social network is created 1 based on the relations between customers or potential customers. These relations may be formed based on interactions, communication or similarity of behaviour of individuals. Alternatively, one may also combine interaction, relation and behaviour data in the construction of the relations between individuals.
  • the network is segmented either based on the social network topology alone 2a or on the combination of social network topology and demography 2b.
  • the segmenting of customers for marketing/CRM by using social network topology is carried out by: combining topology and demographics, or using topology alone.
  • This segmentation can be performed by for example: using a Bayesian method, or using hierarchical clustering.
  • Figure 2 illustrates a flowchart presentation of a method for segmentation of customers in a customer management system according to an alternative embodiment of the pre- sent invention.
  • a social network is created 1 based on the relations between customers or potential customers. These relations may be formed based on interactions, communication or similarity of behaviour of individuals. Alternatively, one may also combine interaction, relation and behaviour data in the construction of the relations between individuals.
  • the network is segmented either based on the social network topology alone 2a or on the combination of social network topology and demography 2b.
  • said retrieved one or more customer segments can be used directly 3a in prediction of customer interests and preferences.
  • said retrieved one or more customer segments can be used as an input in predictive modeling 3b of customer behavior, demography or in- terests to retrieve a predictive model.
  • This said predictive model can then be used together with said retrieved one or more customer segments in prediction of customer interests and preferences.
  • social network topology 3c can be used as input in the predictive model in addition to said retrieved one or more customer segments.
  • combination of social network topology and demography 3d can be used as input in the predictive model in addition to said retrieved one or more customer segments.
  • the segmentation and predictive model is used 4 for example in CRM, Customer Insight, Marketing and Targeting.
  • the solution according to an alternative embodiment of the present invention involves: segmenting customers for marketing/CRM by using social network topology, predicting customer behavior, demography or interests on the basis of social network topology combined with be- havior, demography and/or interests of other customers, and using topology based segments in prediction of customer interests and preferences.
  • the segmenting of customers for marketing/CRM by using social network topology is carried out by: combining topology and demographics, or - using topology alone.
  • This segmentation can be performed by for example: using a Bayesian method, or using hierarchical clustering.
  • topology based segments in prediction of customer interests and preferences is carried out by: - by trivial methods, such as averaging over segments, or by using network segments as inputs to a predictive model, such as a regression model (or other statistical predictive tools) .
  • Figure 3 illustrates a simplified network topology of a social network according to the present invention.
  • the social network segmentation is either based on the topology alone or by combining the topology with other information.
  • the segmentation in itself is a form of network clustering problem.
  • Network clustering according to the present invention can be performed using many methods, such as hierarchical clustering or Bayesian approaches.
  • Bayesian methods make it possible to use both the social network topology and demographics in the segmentation. Alternatively, only the social network topology can also be used.
  • Social networks according to the present invention may contain tens of millions of nodes and hundreds of millions edges. Therefore, the clustering method according to the present invention is selected to scale to very large net- works .
  • the segmentation based on topography of social networks is important especially for analyzing online customers, for example visitors of web sites or web forums, or mobile pre-paid customers whose demographical details are not known or incomplete .
  • the segmentation based on topography of social networks can be used in gaining customer insight in order to find out what kinds of customers are interconnected or what are the "natural" social groups of our customers.
  • the segmentation based on topography of social networks is useful in initial targeting of products based on global network segmentation (grouping) from topology; a suitable target segment can be selected for a new product.
  • the segmentation according to the present invention can be carried out based on Bayesian clustering approach.
  • the segmentation based on Bayesian clustering approach uses a generative component model for constructing the edges of a network and optionally the network node attrib- utes.
  • Bayesian clustering approach uses a prior art model for segmenting a network based on topology alone.
  • this prior art model is extended to use node attributes in addition to social network topology.
  • Bayesian methods have been slow and unusable on large data sets.
  • the segmentation based on Bayesian clustering approach of the present invention is usable on net- works with tens of millions of nodes and hundreds of millions of edges.
  • the segmentation model of the present invention can learn the number of clusters (i.e. groups or segments) in the data automatically.
  • the Bayesian segmentation of the present invention can be used in segmenting a network by topology alone or by com- bining the topology and node (person) attributes, such as gender, geographical location and interests.
  • the prior art model is a probabilistic component model which models the way edges of a network have been constructed.
  • components have a Dirichlet process or a Dirichlet distribution as the prior art.
  • the model is assumed to generate edges by drawing edge end points (nodes) from distributions associated with each of the components.
  • this prior art model is extended for modeling the node (customer) properties (such as per- son age, gender or location) in addition to the network structure.
  • node customer
  • properties such as per- son age, gender or location
  • the generative process out of which the network arises is the following (where i and j are the edge endpoints and h is an node attribute) :
  • the model of the present invention is able to combine data from both network and demographical domains in a probabilistic way in which the uncertainty and randomness in any data items is dealt with in a flexible manner. Also, for data sets with no node attributes, the model behaves in a similar way to the prior art model. In this way, it can be used as an effective segmentation tool for a wide range of purposes without making any modifications to the model.
  • each node may belong to multiple clusters. This provides a "smooth" or soft segmentation of the nodes which is richer in structure than that obtained with segmentation methods based on hard clustering.
  • the segments can be estimated using Bayesian inference.
  • a property of generative models with conjugate priors of this type is that when the data generated with the model (i.e., social network and node attributes) is known, we may infer or estimate, what type of clustering (segmentation) is the most likely to have created the network structure. This provides a probabilistic estimation of most likely segments for each of the network edges and nodes .
  • model parameters can be simply and effectively estimated using collapsed Gibbs sampling, which is a form of MCMC (Markov Chain Monte Carlo) method.
  • collapsed Gibbs sampling is a form of MCMC (Markov Chain Monte Carlo) method.
  • the segmentation of customers can also be performed by using some other network clustering method, such as non- probabilistic agglomerative clustering or non- agglomerative clustering.
  • network clustering method such as non- probabilistic agglomerative clustering or non- agglomerative clustering.
  • the predicting customer behavior, demography or interests according to the present invention is carried out by using predictive modeling on the basis of social network topology combined with behavior, demography and/or interests of other customers.
  • the social network features are used in predicting the properties of customers whose information is missing.
  • a standard approach to prediction of missing data is to use linear predictive model, such as a regression (or other statistical prediction tools) to build a model of the parameters.
  • This model can be then used to estimate missing data.
  • the model may incorporate features, such as node demographics, customer behavior, social network properties and segmentation of the customers .
  • Information on customers who belong to the same segment can be used to estimate the missing data. Based on attributes known for even only a few nodes in a segment, the attributes of all the other nodes can be predicted, and
  • Node attributes can be predicted directly based on the attributes of its neighbors. In this case an implicit segmentation of the network is unnecessary.
  • the social network of customers can be used in marketing, advertising and customer relationship manage- ment (CRM) purposes, such as: predicting customer preferences and interests, grouping similar customers (segmentation) , marketing products to existing customers, and/or marketing products to new customers.
  • CRM customer relationship manage- ment
  • the solution according to the present invention can be used in clustering the customers into segments (groups) that are similar to each other and marketing products to them.
  • the solution according to the present invention can also be used in using network attributes as explanatory variables in predictive models, and using the models in finding customers that are likely to match certain properties.
  • the solution according to the present invention can also be used in combining the clustering and the using of network attributes as parameters. This can be done by, in addition to other parameters, using also the segments of customers in a customer management system as a parameter in a predictive model.
  • topology When the topology of a social network is known but not all of the attributes of the nodes and edges in a network, topology can provide information that makes it possible to group similar persons (nodes) or connections (edges) together and to predict properties of persons and connections .
  • the social networks can be used to segment more effectively; e.g. in Internet communities and mobile networks, a social network can be built based on which people interact with each other or have listed each other as ' friends' .
  • a method and an arrangement for segmentation of customers in a customer management system that can be further utilized in prediction of customer be- havior and interest, and that better solves the presented problems in comparison to solutions according to prior art .

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Abstract

La présente invention porte d'une manière générale sur des systèmes de gestion de relation client (CRM) et plus spécifiquement sur un procédé et sur un agencement pour la segmentation de clients dans un système de gestion de la clientèle qui peut être en outre utilisé dans la prédiction d'un comportement et d'un intérêt de clients. Avec l'aide de la solution selon la présente invention, il est proposé un procédé et un agencement pour la segmentation de clients dans un système de gestion de la clientèle qui comprend des moyens pour segmenter des clients pour extraire un ou plusieurs segments de clients à l'aide d'une topologie de réseau social et/ou de la combinaison d'une topologie de réseau social et d'autres propriétés de clients, telles que la démographie.
PCT/FI2008/050740 2007-12-14 2008-12-15 Procédé et agencement pour la segmentation de clients dans un système de gestion de la clientèle Ceased WO2009077655A1 (fr)

Applications Claiming Priority (4)

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FI20075914A FI20075914A0 (fi) 2007-12-14 2007-12-14 Menetelmä ja järjestely asiakkaiden segmentoimiseksi
FI20075914 2007-12-14
FI20085399 2008-04-30
FI20085399A FI20085399A0 (fi) 2007-12-14 2008-04-30 Menetelmä ja järjestely asiakkaiden segmentoimiseksi asiakkaiden hallintajärjestelmässä

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US9763048B2 (en) 2009-07-21 2017-09-12 Waldeck Technology, Llc Secondary indications of user locations and use thereof by a location-based service
WO2011019731A3 (fr) * 2009-08-10 2011-04-28 Mintigo Ltd. Systèmes et procédés pour générer des indications dans un réseau en prédisant des propriétés de nœuds externes
US9300704B2 (en) 2009-11-06 2016-03-29 Waldeck Technology, Llc Crowd formation based on physical boundaries and other rules
US8560608B2 (en) 2009-11-06 2013-10-15 Waldeck Technology, Llc Crowd formation based on physical boundaries and other rules
US9046987B2 (en) 2009-12-22 2015-06-02 Waldeck Technology, Llc Crowd formation based on wireless context information
US8711737B2 (en) 2009-12-22 2014-04-29 Waldeck Technology, Llc Crowd formation based on wireless context information
US8898288B2 (en) 2010-03-03 2014-11-25 Waldeck Technology, Llc Status update propagation based on crowd or POI similarity
US9886727B2 (en) 2010-11-11 2018-02-06 Ikorongo Technology, LLC Automatic check-ins and status updates
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