Method and system for managing customer network value

• US 7,941,339 B2
• Filed: 11/17/2005
• Issued: 05/10/2011
• Est. Priority Date: 12/23/2004
• Status: Active Grant

• Current Assignee: Rakuten Group, Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: Argaiz, Joseluis Iribarren
• Primary Examiner(s): CHAMPAGNE, DONALD
• Application Number:
  US11/282,866
• Publication Number:
  US 20060143081A1
• Time in Patent Office: 2,000 Days
• Field of Search: 705/10, 705/14, 705/26, 705/27, 705/14.1, 705/14.13, 705/14.49, 705/52, 705/14.53
• US Class Current: 705/14.49
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0255 based on user history

Abstract

A method, system and computer program product are disclosed for evaluating a customer network value of a set of customers for a given marketing campaign, said method comprising the steps of storing for each customer static information, collecting for each customer, historical and time cumulative network behavior data, building a customer network map from network behavior data said map describing network relation between nodes, one node representing one customer, computing the social network parameters for the nodes of the network map, selecting the most relevant social network parameters according to the objectives of the given marketing campaign, and, computing for each customer a Customer Network Value Index (NVI) by combining the most relevant social network parameters and static information. When a static information profile is defined for an extended customer population, applying data mining techniques on this population allows estimating a probabilistic NVI for each customer in the extended customer population for which no NVI has been computed.

Claims

1. A method for determining a customer network value for a first set of customers for a given marketing campaign, said method comprising the steps of:

storing for each customer static information;

collecting, for each customer, historical and time cumulative network behavior data, the network behavior data being obtained via email and web forms;

building a customer network map from network behavior data, said map describing network relations between nodes, each node representing one customer;

computing by a computer a plurality of social network parameters for each node of the network map;

selecting a predefined subset of the social network parameters according to predefined objectives of the given marketing campaign;

computing by a computer for each customer a customer network value index (NVI) by combining the predefined subset of social network parameters and static information; and

producing a list of customers and their associated NVI,

wherein the step of computing the social network parameters for each node of the network map comprises the step of computing one or more of the group consisting of;

the node degree, the node degree distribution, the node centrality, the node betweenness, the maximum path length and the clustering coefficient parameters, and

wherein the step of computing for each customer a customer network value index (NVI) consists in computing for each customer the expression;

(Node Degree)×

(Node Centrality)×

(Max. Path Length)×

(Clustering coefficient).

2. The method of claim 1 further comprising the steps of:

storing, for a second set of customers, a customer profile reflecting the customer static information of the second set of customers; and

applying data mining techniques to the second set of customers for which static information are stored but no network behavior data has been collected, said data mining techniques providing a probabilistic NVI for the second set of customers.

3. The method of claim 1 wherein the step of computing the social network parameters consists in running a specialized Social Network Analysis software.

4. The method of claim 1 wherein the step of computing for each customer a customer network value index (NVI) comprises a step of recursively combining Network Value selected parameters of neighbor nodes downstream the information propagation tree of the network map.

5. The method of claim 1 further comprising a step of visualizing the customer network map by displaying graphs representing the nodes and their relations as propagation trees.

6. A non-transitory computer readable storage medium comprising programming code instructions for executing the steps of a method for evaluating a customer network value of a first set of customers for a given marketing campaign, the method steps comprising:

storing for each customer static information;

collecting, for each customer, historical and time cumulative network behavior data, the network behavior data being obtained via email and web forms;

building a customer network map from network behavior data, said map describing network relations between nodes, each node representing one customer;

computing a plurality of social network parameters for each node of the network map;

selecting a predefined subset of the social network parameters according to predefined objectives of the given marketing campaign;

computing for each customer a customer network value index (NVI) by combining the predefined subset of social network parameters and static information; and

producing a list of customers and their associated NVI,

wherein the step of computing the social network parameters for each node of the network map comprises the step of computing one or more of the group consisting of;

the node degree, the node degree distribution, the node centrality, the node betweenness, the maximum path length and the clustering coefficient parameters, and

wherein the step of computing for each customer a customer network value index (NVI) consists in computing for each customer the expression;

(Node Degree)×

(Node Centrality)×

(Max. Path Length)×

(Clustering coefficient).

7. The computer program product of claim 6, wherein the method steps further comprise developing targeting for the marketing campaign based on the list of customers and their associated NVI.

8. The computer program product of claim 6, wherein the method steps further comprise:

storing, for a second set of customers, a customer profile reflecting the customer static information of the second set of customers; and

applying data mining techniques to the second set of customers for which static information are stored but no network behavior data has been collected, said data mining techniques providing a probabilistic NVI for the second set of customers.

9. The computer program product of claim 6, wherein the method steps further comprise developing targeting for the marketing campaign based on the list of customers and their associated NVI.