Abstract:
A method, and corresponding system, provide automated marketing with protection of customer nominative data in a network having importing and exporting nodes. At least one of the exporting nodes stores customer information desired by an importing node for use in automated marketing of a product or service. The method includes the steps, executed at the importing node, of formulating a discovery action, the discovery action requesting a description of the network, the description identifying exporting nodes in the network from which the importing node imports customer information, receiving the network description of the network, formulating a query directed to identified exporting nodes, the query requesting customer behavioral data, the query generating a campaign descriptor for each identified exporting node, receiving the campaign descriptor, creating a campaign based on the campaign descriptor, and extracting customer nominative data from the identified exporting nodes using coupons. The method further includes the steps, executed at each of the exporting nodes, of receiving the query, based on the query, extracting the customer behavioral data from a customer behavioral data database, and based on the extracted customer behavioral data, generating the campaign descriptor. The method still further includes the steps, executed at each of the identified exporting nodes, of sending the campaign descriptor to the network service, receiving the campaign, validating the campaign, generating the coupons, and sending the coupons to the network service.

Description:
TECHNICAL FIELD  
         [0001]    The technical field generally relates to systems and methods for automating marketing operations and managing usage rights on customer information. More particularly, the technical field relates to the management and monetization of customer information usage rights at the level of a network of business partners.  
         BACKGROUND  
         [0002]    According to 1993 Nobel Prize winner Douglas C. North, between 1870 and 1970 the share of transaction costs (i.e. the cost of economic agents interacting together) in the U.S. gross domestic product increased from 25.1% to more than 54.7%. In developed economies, interactions, the fact of matching offer and demand, be it between a company and its clients or between a government and its citizens, has become the primary economic function. Matching offer and demand is about having the right information at the right time, whereas this information comes by nature from outside of the organization&#39;s perimeter. For organizations, the lack of access to this information results in a dramatic increase in customer acquisition and related costs.  
           [0003]    The overall performance of an organization regarding its interaction costs can be monitored through a global indicator of what may be termed transformation rate. The transformation rate measures the share of customer contacts that effectively translate into a transaction. The impact of the transformation rate on an organization&#39;s productivity can be significant. Current industry averages for marketing campaigns give transformation rate values from 0.5% to 1.5%. Improving the transformation rate from 1.5% to 2%, for example, increases the transaction margin by a factor of more than fifteen times the individual cost of contact.  
           [0004]    The transformation rate can be improved by either reducing the number of overall contacts of the organization, or by increasing the number of successful contacts. Those two aims have been addressed so far mainly through data mining on internal data that, in the end, is weakly correlated to the outcome of the transaction. This hypothesis can be explained through the “firm&#39;s paradox”, which states: “Most of the time, a firm has access to client information only once the transaction has taken place. Hence the firm collects this information at the least critical moment, i.e., when the decision of transacting is already taken and implemented. To anticipate a purchase, the firm has to know the information that triggers the purchase, information that most of the time is located outside of the organization&#39;s perimeter.” 
           [0005]    Poor transformation rates can also be explained by the commercial pressure that is imposed upon prospective customers. The average U.S. consumer, for example, receives roughly one million marketing messages a year across all media, or about 3,000 messages a day. The novel and unfettered dispersal of personal information gives a striking example of the limited ability of organizations to act in a cooperative manner. Sharing information is as much about knowing when to act as about knowing when not to act.  
           [0006]    This analysis is supported by the evidence that the only marketing systems to report above-average transformation rates (approximately 6%) are behavioral networks, which enable to detect behavioral patterns across networks of organizations. However, the deployment of behavioral networks is hindered by three major obstacles: 1) the lack of an information usage enforcement mechanism, 2) a lack of a satisfying integration framework between organizations which requires ad-hoc and hence costly investments, and 3) the protection of privacy. As of today, no satisfying cross-industry implementation has been proposed to address those obstacles in an economically efficient manner.  
         SUMMARY  
         [0007]    What is disclosed is a method for automated direct marketing in a network that includes a network service center including a discovery service, a proxy server, and a log database. The method includes, at a first node in the network, retaining customer information, the customer information comprising nominative data and behavioral data, the nominative data providing identities of specific customers of a company and privacy data related to the specific customers, the behavioral data comprising customer propensity information, providing the behavioral data to a second node in the network, and locating the nominative data associated with the provided behavioral data.  
           [0008]    Also disclosed is a system for automated direct marketing in a network comprising nodes and a service center, the service center including a proxy server, a discovery server, and a log database. The system comprises a first data structure comprising behavioral data of customers, a second data structure comprising nominative data of the customers, wherein the nominative data provides identities of specific customers of a company and privacy data related to the specific customers, the behavioral data comprises customer propensity information, a discovery request generator that analyzes the behavioral data and generates a network description, and coupons used to match the behavioral data to the nominative data.  
           [0009]    Further disclosed is a method for automated marketing and customer information usage rights management on a network having at least one importing node and at least one exporting node. At least one of the exporting nodes stores customer information desired by at least one importing node for use in automated marketing of a product or service. The method includes the steps, executed at the importing node, of formulating a discovery action, the discovery action requesting a description of the network, the description identifying exporting nodes in the network from which the importing node can retrieve customer information, receiving the network description of the network, formulating a campaign and sending a campaign descriptor to the identified exporting nodes, the campaign descriptor being updated based on the behavioral data contained at each identified exporting node, receiving the campaign descriptor, optimizing the campaign descriptor and sending a request for coupons to each appropriate exporting node. The method further includes the steps, executed at each of the exporting nodes of receiving the campaign descriptor, based on the campaign descriptor, extracting the customer behavioral data from a customer behavioral data database, and based on the extracted customer behavioral data, updating the campaign descriptor. The method still further includes the steps, executed at each of the identified exporting nodes, of sending the campaign descriptor to the importing node, receiving the campaign, validating the campaign, generating the coupons, and sending the coupons to the importing node.  
           [0010]    Also, what is disclosed is a system that provides automated marketing with customer information usage rights management. The system includes, at an importing node in a network, a user interface that generates messaging used to extract information from other nodes in the network, a first propensity model that generates probabilities related to purchasing products and services by customers of the importing node, a first secure database that stores first node customer nominative data, and a first behavioral database that stores first node customer behavioral data. The system further includes a network service comprising one or more additional nodes that receive imports and exports node generated messages. The network service includes a proxy services that process selected received messages, generate events, reroute messages to appropriate exporting nodes, and for the purpose of non-repudiation logs all events and messages.  
           [0011]    Still further, what is disclosed is a computer readable medium comprising a data structure for storing customer information related to automated marketing in a network having importing nodes and exporting nodes. The data structure includes a relationship object (as in “Customer Relationship Management”) that stores the behavioral information of a customer in the context of its relationship with the company owning the node, and includes authorizations to access this customer behavioral information. The data structure also includes a communication object that defines communication channels available to contact the customer of the exporting nodes and the importing nodes, a propensity object that stores the specific behavioral information required to infer the probability of purchasing a product or service by the customer as well as the personalization criteria valuable for convincing the customer to purchase a product or service, a coupon object that identifies the specific customer information usage right agreed on between the exporting and importing node, a campaign object that defines a specific marketing plan and optionally relating to a group of coupons, and a customer object storing customer nominative information. 
       
    
    
     DESCRIPTION OF THE DRAWINGS  
       [0012]    The detailed description will refer to the following drawings, in which like numerals refer to like items, and in which:  
         [0013]    [0013]FIG. 1 is a diagram of a network that provides customer privacy and automated marketing;  
         [0014]    [0014]FIG. 2 is a conceptual diagram showing relationships between entities of the network of FIG. 1;  
         [0015]    [0015]FIG. 3 is a diagram of a computer system linked into the network of FIG. 1;  
         [0016]    [0016]FIGS. 4A-4H illustrate relationships between data objects used in the network of FIG. 1;  
         [0017]    [0017]FIGS. 5-13 illustrate processes and data flows associated with automated marketing using the network of FIG. 1; and  
         [0018]    [0018]FIG. 14 is a flowchart showing an embodiment of a method for automated marketing with customer privacy. 
     
    
     DETAILED DESCRIPTION  
       [0019]    Customer behavioral information has value. Knowing that a baby has just been born, that a child is going to college, that a family has just moved, that someone has just sold his or her car—these are all events that are predictive of near-term needs. When the appropriate company knows about these needs, the company can offer services using direct marketing, and can expect a much higher transformation rate on its offer than the company could achieve without the information.  
         [0020]    Companies that make use of direct marketing know that certain events provide higher return opportunities for their products, and specialized companies and services exist that give access to this type of information. However, the processes and systems used to provide the information are highly dependent on human intervention. The companies that have information must provide database administrators to extract and transform of relevant data, and to package this data in a standard form. Intermediaries such as data brokers provide points of aggregation for data-selling companies. Data-selling companies also must provide data administrators to compile and publish their collections of data, and sales people to negotiate commercial exchange. Marketing agencies work with data purchasers to identify and find customer profiles that will have the highest probability of successful transformation, and these too must employ data knowledgeable sales people to help formulate and manage requests to data brokers.  
         [0021]    Thus, a system and method that could automate the delivery of useful direct marketing data would be of great economic value. It would significantly diminish the cost of the data, and make it more timely. However, the problem with automation is one of allowing potential purchasers of data to identify useful customer profiles without providing personal (nominative) information about the customer. This is important for two reasons. First, nominative data allows a company to contact an individual (name, address, telephone number, etc.). Typically data-selling companies only want to rent this information, not sell it outright. Second, in many circumstances the exchange of nominative data is prohibited by law.  
         [0022]    Systems and methods for automating marketing operations while managing usage rights on customer information are disclosed. The systems and methods provide management and monetization of customer information usage rights at the level of a network of business partners that are interested in marketing their goods and services to the customers of other partners in the network, therefore using the other partners as a marketing interface. To provide the required management of customer information usage rights, each customer can be defined by data sets: behavioral data and nominative data. Behavioral data describes general characteristics of the customer, such as demographic data, or propensity of making a specific purchase of a product or service. Nominative data are data that are unique to the customer, such as the customers&#39; name and telephone number. The behavioral data are used to command the marketing operations of the business partners through the brokerage of customer information usage rights, which determine the course of action and the compensation of each partner. Once the customer information usage rights are established for a requesting partner, these customer information rights are brokered to the requesting partner in the form of a digital coupon, that stores digitally signed customer behavioral data and a description of the usage rights corresponding to this data, but no nominative data to which the requesting partner never has access through the said system. Each usage of the nominative data is in the end of the sole legal responsibility of the exporting node that should manipulate it himself or through a trusted tier which never has access to the customer behavioral data. In this way, nominative and behavioral data remain at all time strictly insulated from each other at any point on the network but at the exporting node, therefore providing the guarantee to the owner of the exporting node to keep control at all time over its customer information usage rights. Furthermore, as the system conveys only anonymous data, the synchronization of customer information from at least two databases requires a de-duplication key that can be only be generated at the customer touch point through a specific acquisition mechanism, which we refer to as the customer subscribing to an affiliation. That is to say by either the customer giving his or her explicit consent that his or her nominative information be compared between the two nodes, or by providing him or herself a de-duplication key known by one node to the other nodes (e.g. giving the air miles reference number to the phone company, the telephone number to the airline company, etc.). Hence each de-duplication key can be traced back to the explicit consent of the customer, which makes the method and system flexible yet compliant with the strictest privacy standards. At all time, each business partner keeps control over its customer data, and termination of a partnership leaves no remnant nominative data accessible by the terminated partner(s). Furthermore, the partners do not have to make their data models converge, as the network provides an abstraction layer in the form of propensity models. Thus, the methods and systems provide tools for efficiently managing a network of business alliances. Using these methods and tools, the business partners can significantly enhance their own transformation rate by incorporating many possible forms of cooperation into the formal structure of networks of organizations.  
         [0023]    The gains resulting from building a collaborative network for optimizing interactions between organizations and customers are numerous, but such a network requires a utility function to automate the allocation of customer information usage rights and hence command the marketing operations of the partners. The total amount of customer information usage rights capitalized on a node can also be termed customer equity. The expected goal of a node owner might be, for example, to maximize the value of this equity, which is calculated by estimating the net present value of future gains from these usage rights. Therefore, in this case, the utility function might be the value of the client equity given the customer information usage rights at hand, or in a more simple manner, the expected outcome from the next customer contact. Each node might have one or many different utility functions, the allocation of customer information usage rights being in the end determined by the highest bidder. Overall, customer information usage rights are allocated on the network according to a utility function, which can be for example the expected gain from the outcome of the contact, and a set of rules, the partnerships.  
         [0024]    By using the systems and methods for automating marketing operations while managing usage rights on customer information, companies can build, maintain or terminate partnerships based on the potential of their customer information databases. Configuring partnerships, targeting the best customers, managing contact elasticity on transformation rate and overall commercial pressure can all be addressed through the unifying problem of customer information usage rights management, monetization and allocation.  
         [0025]    [0025]FIG. 1 illustrates a network  10  in which companies may share client information to promote sales and other activities between customers and companies. The network  10  solves the problem of customer information usage rights allocation through the use of a digital rights management tool applied to customer equity. The network  10  includes two or more companies  12  that communicate with each other using communication links  13 . The companies  12  also communicate with data brokers  14  and marketing agencies  16 . A router  18  is used to deliver direct marketing material to target customers. Also shown is a network service center  20  that provides discovery services  21  and security services  23 . The links  13  in the network  10  may be of any currently known or future physical configuration including unshielded twisted pair (UTP) wire, coaxial cable, shielded twisted pair wire, fiber optic cable, for example. Alternatively, the links  13  may be wireless links.  
         [0026]    [0026]FIG. 2 is a conceptual diagram showing details of the network  10  of FIG. 1. In FIG. 2, company  12   a  (node A) and company  12   b  (node B) exist in the network  10  and are coupled to the service center  20 . Companies  12   a  and  12   b  each include a firewall  50 , which serves to isolate the company from entities outside the company. Each of the companies  12   a  and  12   b  also incorporates a computer system  22  and a certificate system  24 . The computer system  22  will be described in more detail with reference to FIG. 3.  
         [0027]    The service center  20  communicates with nodes in the network  10 , such as node A (company  12   a ) and node B (company  12   b ) using HTTP over the Internet. The service center  20  includes a firewall  60 , HTTP server  70 , services server  80 , certificate system  24 , database  62 , and private key/public key system  64 . The HTTP server  70  is used to establish communications over the Internet, and the structure and operation of the HTTP server  70  is well known in the art. Similarly, the certificate system  24  and the public key/private key system  64  are also well know in the art.  
         [0028]    In the discussions that follow, the use of the computer system  22 , and other systems and services at the service center  20  will be explained by way of an example wherein company  12   a  (node A) is a credit card company, and company  12   b  (node B) is an automobile manufacturer. The automobile manufacturer (company  12   b ) has a customer list that includes many individuals whom the credit card company (company  12   a ) may want to contact in order to offer its credit services. Conversely, the automobile manufacturer  12   b  may want to access existing customers of the credit card company  12   a  in order to market its automobiles.  
         [0029]    [0029]FIG. 3 is a diagram of the computer system  22  as implemented in the nodes of the network  10  (e.g. at companies  12   a  and  12   b ). The computer system  22  includes three general sections, a client information system (IS), a node, and the Internet. More specifically, the computer system  22  includes a processing system  120 , customer computer system  30 , information system  150 , and the firewall  50 . The firewall  50  was previously described with respect to FIG. 2. The customer computer system  30  includes the graphical user interface  31  that communicates with the processing system  120  using HTTP, for example. The information system  150  includes one or more computer devices  151  that store and process information related to the company  12 .  
         [0030]    The processing system  120  includes application server  100 . The application server  100  includes a view/controller module  101 , a dynamic page service module  103 , administrative services module  105 , interface  130 , business services and calculus module  127 , and communications services module  131 . Coupled to the interface  130  are certificate and key storage  125 , nominative database  129 , and behavioral database  128 . As will be described later, the behavioral database  128  may be used to store customer behavioral data. The nominative database  129  may be used to store customer nominative data. Coupled to the business services and calculus module  127  are the company&#39;s business components  111 . The business components  111  include an interface from the company&#39;s unique business resources into the processing system  120 . Each company  12  in the network  10  may have unique resources that the company  12  uses to gather information related to its customers, its industry, and operational aspects of its business. These resources may include company- or industry-specific models that are used to generate marketing campaigns, predict sales, and gather customer purchasing information, for example.  
         [0031]    The communication services module  131  couples the node to other nodes in the network  10 , using the firewall  50  to provide security for the node. Communications to and from the communication services module  131  through the firewall  50  may be by one or more of HTTP, SSH and SMTP protocols, or other existing or future communication protocols. A transfer services module  123  may communicate with the information system  150  using standard file transfer procedures.  
         [0032]    Each customer is linked to each company in the network  10  of FIG. 1 by a relationship, which may be represented as a data object. Each company in the network  10  can query the relationships of its business partners to identify prospective customers. Taking a specific example, the automobile manufacturer  12   b  and the credit card company  12   a  are each interested in marketing their products and services to the customers of the other company. A number of data objects are defined that allow execution of this cross-marketing opportunity. FIGS. 4A-4H illustrate relationships between data objects used in the network  10  of FIG. 1. FIG. 4A is a customer entity relationship (E/R) diagram. In FIG. 4A, a customer relationship object  200  is identified by a primary key (PK) relationship ID. The object  200  includes certain characteristics, such as date of last contact between the entity and the customer, date of creation, which refers to the date the relationship object was created for the customer, and authorization, which refers to the degree of access the customer has authorized from the company  12  and its business partners. The customer relationship object  200  is defined by other data objects that depend on the object  200 .  
         [0033]    An affiliation object  202  relates queries sent to one or many distributed databases to customers whose information is stored at one or more nodes on the network  10 . The primary key (PK), or characteristic, of the affiliation object  202  is an affiliation ID. The affiliation ID identifies the list of nodes, or ring, on the network  10  from which the customer behavioral information can be retrieved and aggregated to form a dynamic and more exhaustive image of the customer. Other characteristics of the affiliation object  202  include the relationship ID from the customer relationship object  200 , a short label, a long label, a start date, and end date, and remanence. The short label and the long label are descriptive titles for the affiliation. The short label is a shortened or truncated version of the long label. The start date and the end date indicate the time over which the affiliation may be valid with respect to the customer relationship.  
         [0034]    The remanence characteristic refers to the right of the importing nodes to store indefinitely customer behavioral information related to the customer relationship identified by the relationship ID on the exporting node. Taking a specific example, if the automobile manufacturer  12   b  imports customer behavioral information from the credit card company  12   a  regarding the customer&#39;s attitude towards credit, the credit card company may not allow this information to be remanent in the manufacturer&#39;s database. This will not be done by forcing the automobile manufacturer  12   b  to erase the record, which the credit card company  12   a  has almost no way to enforce. Instead, each time the automobile manufacturer  12   b  queries the relationships of the credit card company  12   a,  and retrieves behavioral information, the exporting node of the credit card company  12   a  generates new identifiers for each individual customer, which will bar the automobile manufacturer  12   b  from synchronizing its previously acquired data. Hence, the previously acquired data will no longer be of use to the automobile manufacturer  12   b,  and will likely be discarded. In this sense, the information that is shared with the automobile manufacturer  12   b  is said to be non-remanent. This feature offers to the credit card company  12   a  the guarantee that the termination of the partnership with the automobile manufacturer  12   b  will leave in the automobile manufacturer&#39;s database no remanent nominative or behavioral information. Therefore, under the system for automated marketing and management of customer information usage rights, the circulated information is reputed to be non-remanent.  
         [0035]    Dependent on the affiliation object  202  is ring object  203 . The ring object  203  defines which companies  12  in the network  10  are able to synchronize relationships of customers that have subscribed to this affiliation. The ring object  203  is characterized by the affiliation ID and by a partner ID. The partner ID identifies companies  12  that share the same affiliation ID. The relationship between the affiliation ID and partner ID identifies companies  12  that may synchronize the customer information. Deleting a partner ID terminates the identified partner&#39;s capability to synchronize the customer information.  
         [0036]    Many of the companies  12  in the network  10  may establish rewards programs for their loyal customers. Such rewards programs may provide reduced rate credit, reduced prices for products, cash back awards, special gifts, and other rewards. Reward object  204  defines rewards programs that the customer is eligible to participate in.  
         [0037]    Communication object  205  defines communications channels through which the companies  12  may contact customers. The communication object  205  is characterized by channel ID, which specifies the type of communication channel through which the customer may be contacted. The channel ID may specify e-mail, telephone, regular mail, and any other means of communication with the customer. However, the channel ID does not specify an actual address of the customer. That is, if the channel ID specifies e-mail, the customer&#39;s e-mail address is not given. Similarly, if telephone is listed under channel ID, the customer&#39;s telephone number is not specified.  
         [0038]    Communication object  205  is further characterized by frequency and resilience. frequency refers to the number of times the customer may be contacted per communication channel, and resilience specifies the time over which these contacts may occur through this communication channel. A frequency of three and a resilience of thirty days for the e-mail channel means the customer may be contacted a maximum of three time in thirty days by e-mail.  
         [0039]    Socio-Demographic object  206  defines demographic and other related data that a company  12  may use when targeting products and services to customers. Such demographic data may include age, income, geographic location, marital status, and similar data.  
         [0040]    Propensities object  207  specifies a rating for the customer in terms of the likelihood that the customer will purchase particular goods or services. The propensities object  207  is characterized by the relationship ID and a model ID. The model ID specifies a type of product or service. For example, the model ID may specify a credit model or a new automobile model.  
         [0041]    Typologies object  208  characterizes customers according to specific customer segments, that are aggregated due to their homogeneous behaviors and hence their higher profitability. Different networks of partners may use different typologies. Such a typology may be, for example, “repetitive buyers.” 
         [0042]    [0042]FIG. 4B shows the communication object  205  in more detail. The communication object  205  is defined by channel type object  210 . The channel type object  210  is characterized by type code, short label and long label. The type code refers to the type of communication channel, such as e-mail, telephone, and mail, for example. The short label and the long label are short and long descriptive titles, respectively, for type of communication channel.  
         [0043]    Channel ID  211  indicates a specific communication channel. For example, the automobile manufacturer&#39;s customer may be contacted by multiple communications means. One such communication channel may be e-mail and another may be regular mail. The channel ID  211  provides a specific identification of each of these two communication channels, for example.  
         [0044]    Channel object  212  refers to a specific channel that is used for contacting the customer. The channel object  212  is characterized by a descriptor that describes the parameters of the channel, short and long labels that provide short and long descriptive titles, and template ID. The template ID refers to a company-specific format that is used for contacting the customer. Using the example of the automobile manufacturer  12   b  and the credit card company  12   a,  the customer may be an original customer of the automobile manufacturer  12   b  and the credit card company  12   a  may desire to contact the customer by e-mail to provide credit services. The template ID specifies that the format of the e-mail to be sent by the credit card company  12   a  to the customer must follow a specific format established by the automobile manufacturer  12   b.    
         [0045]    [0045]FIG. 4C provides further details of the propensities object  207 . The propensities object  207  is defined by propensity model object  215 . Propensity model object  215  indicates that the customer may have more than one propensity model, and provides an identification of a specific propensity model, for example the propensity model for cars. The propensity model object  215  is characterized by rating and personalization criteria. The rating refers to a likelihood that the customer will purchase a particular good or service. The rating could be subjective, and state a simple likelihood (e.g., low, medium, high). Alternatively, the rating could be more objectively determined based on individual habits, demographic features, and past purchasing events of the customer. For example, the customer&#39;s marital status may have changed from single to married, income may have increased, and last automobile purchased may have been five years ago, or, alternatively, the customer&#39;s automobile may be leased, with the lease set to expire in the near future. Given these facts, the customer&#39;s rating could be set at a high level (e.g., greater than 80 percent) indicating that the customer may be considering acquiring a new automobile.  
         [0046]    Personalization criteria refers to data that the company  12  may use to personalize an offer for a product or a service to the customer, in order, for example to improve his or her satisfaction or to influence him or her into buying the product or service. For example personalization criteria may indicate the customer prefers red minivans bought on credit with no money down. The automobile manufacturer  12   b  could then structure an offer to the customer that includes one or more of these features. As indicated, the propensity model object  215  for a specific customer may include many personalization criteria.  
         [0047]    Once a company, such as the credit card company  12   a,  has identified potential customers from another company&#39;s customer database, such as the customer database of the automobile manufacturer  12   b,  a mechanism is used to allow the credit card company  12   a  to contact the identified potential customers. For example, the credit card company  12   a  may query the customer database of the automobile manufacturer  12   b,  and may identify 1,000 individuals whose propensity data indicates they each would be interested in obtaining credit services that the credit card company  12   a  offers. The identified potential customers are referenced by the relationship object  200 , meaning that all the data concerning the identified potential customers is anonymous at this stage. To actually contact specific individuals, the credit card company  12   a  will need to acquire usage rights on these customers&#39; information, but will at no time gain access to their nominative information. The customer information usage rights will be brokered by the node owned by the automobile manufacturer  12   b  to the node owned by the credit card company  12   a.  These usage rights are brokered in the form of anonymous, digitally signed coupons. FIG. 4D shows a coupon object  216  used in the network  10  to allow partner companies, such as the automobile manufacturer  12   b  and the credit card company  12   a  to contact the identified potential customers.  
         [0048]    A coupon object  216  basically defines the usage rights for a customer&#39;s information and by doing so, defines the modalities of the contact with that customer. For example, the credit card company  12   a  will have the right to contact each customer once, by e-mail, over a period ranging from the first of September to the sixteenth of September. The coupon object  216  is characterized by client campaign and campaign ID. The client campaign and campaign ID identify a specific marketing plan from a company. Importing node ID identifies the company that receives customer data from another company&#39;s database. Exporting node ID identifies the company that provides the customer data. The channel ID was previously defined with respect to FIG. 4B. The start date and end date refer to the start and end of a specific campaign as defined by the campaign ID. Product ID identifies a product or service that is being marketed using the campaign. Probability is an expected transformation rate, that is, the expected percentage of customer contacts that effectively translate into a transaction. Probability certainty is the statistical likelihood that the expected transformation rate is correct. A probability of 50 percent and a probability certainty of 95 percent means that the expected transformation rate is 50 percent, and there is a 95 percent probability that the 50 percent transformation rate will correctly predict the actual number of customer contacts that result in purchases. Propensity, as discussed previously with respect to FIG. 4C relates to the likelihood, in the form of a rating, that a specific customer will purchase goods or services. Propensity certainty is the likelihood that the propensity is accurate. Personalization vector relates to personal information stored by the exporting node for the customer. The personalization vector may include behavioral data, and a location at which these data are stored. Price is the cost to the importing node for using the customer&#39;s information. Status refers to whether the importing node has used the coupon. For example, the importing node may be authorized to contact the customer three times between the start date and the end date. The status field indicates the number of authorized contacts, and whether any of these contacts was previously used, thereby reducing the remaining allowed contacts. Date of issue is the date on which the coupon issues.  
         [0049]    [0049]FIG. 4E illustrates a campaign object  217 . The campaign object  217  is characterized by the short and long labels, importing node ID, exporting node IDs, channel ID, contacts, query, start and end dates and status. The short label and the long label are short and long descriptive titles, respectively, for the campaign object  217 . The campaign object  217  has the specificity of being a distributed object. Which means that he references resources that belong to other nodes on the network. This also means that the resources that are referenced by the campaign object  217  must be kept track of on the importing node as well on the various exporting nodes, and that this tracking data should be synchronized on all nodes before modifying the object. Accordingly, the campaign object  217  can exist under three states, which are a “standalone” state, a “descriptive” state and a “loaded” state. In the standalone state, the campaign object  217  does not point to any group of campaign descriptors (such as the campaign descriptor  220  shown in FIG. 4F) nor to any group of coupons  216  as shown in FIG. 4D. The campaign object  217  is characterized by importing node ID which is identifies the node on which the object has been instantiated. Exporting nodes, under the standalone state, identifies the nodes that are planned to be queried. The channel ID specifies the type of communication channel through which the customer will be contacted for this campaign. The contacts identifies the number of contacts that will occur during the campaign. The query stores the full extent of the query developed to target the prospective customers. The start date and the end date specify the beginning and the end of the campaign. When the fields of the campaign object  217  are sufficiently defined under the standalone state, the campaign object  217  will be upgraded to the descriptive state, which purpose is to describe precisely the available relationships on the various exporting nodes, no storing however the full extent of the possible coupons.  
         [0050]    The descriptive state is illustrated in FIG. 4F, which is another view of the campaign object  217 , showing a campaign descriptor object  220 , which further defines the campaign object  217 . The campaign descriptor  220  is identified by the descriptor ID. To upgrade the campaign object  217  to the descriptive state, a copy of the campaign parameters is instantiated and sent to each exporting node in the form of a campaign descriptor object  220 . The campaign descriptor object  220  is characterized by campaign ID, importing node ID, exporting node ID, channel ID, contacts, query, start date and end date, status, contract ID and mapping matrix. The campaign descriptor  220  is characterized by the campaign ID, which correlates the campaign descriptor  220  to a corresponding campaign  217 . The importing node ID identifies the node on which the originating campaign object  217  has been initially instantiated. Channel ID, contacts, query, start date and end date are simply a copy of the parameters of the originating campaign object  217 . The exporting node ID identifies the node to which the campaign descriptor object  220  has been sent. The status reflects the status of the campaign descriptor object  220  with respect to the exporting node. The contract ID refers to a contractual document that regulates rules between the partners (i.e., companies  12   a  and  12   b ) regarding sharing of customer information, confidentiality agreements, pricing, and other matters. As noted above, the campaign refers to a certain number of contacts, or customers, that the importing node may contact. This number of contacts ultimately will match a number of coupons issued by the exporting node. However, the potential number of contacts may be very large, on the order of tens to hundreds of thousands. Conventional mechanisms for handling data are not efficient when the data are of this volume. Furthermore, transmitting this volume of data over the network  10  may be very time consuming, and is prone to interruption and error. To compensate, an n-dimensional mapping matrix determines how the available relationships that have been selected in the exporting nodes (in the example used herein, in Node A) are distributed with respect to n qualifying criteria. Once the query has been executed on the exporting node, the mapping matrix is populated with the content of the exporting node behavioral database. The campaign descriptor object  220  is subsequently sent to the importing node for synchronization of the campaign object  217 . Once all the campaign descriptor objects  220  have been received on the importing node, the mapping matrices will be compared, and the best subsets of contacts will be selected from the total number of contacts identified by the campaign and updated to reflect the subsets of the exporting nodes databases that will be subject to the extraction of customer information usage rights in the form of coupons. Once the campaign object  217  is sufficiently defined under the descriptive state, it can then be upgraded to the loaded state.  
         [0051]    Referring to FIG. 4G, to upgrade the campaign object  217  to the loaded state, the updated campaign descriptor objects  220  with the updated mapping matrices are sent back to the exporting nodes. The exporting nodes use the campaign descriptor objects to generate the corresponding coupon object  216 . A first instance of the coupons objects  216  is stored on the exporting node for tracking purposes and a second instance is sent to the importing node, where the coupons object  216  are stored and further define the campaign object  217  through a population association  218 .  
         [0052]    [0052]FIG. 4H illustrates the relationship object  200  in more detail. As shown in FIG. 4H, the relationship object  200  is further defined by customer object  222 . The customer object  222  includes the nominative data for a specific customer, as indicated.  
         [0053]    [0053]FIGS. 5-13 illustrate processes and data flows associated with automated marketing and customer information usage rights management. FIG. 5 illustrates company  12   b  (the automobile manufacturer, at Node B) and company  12   b  (the credit card company, at Node A). Also shown are the network service center  20  and the router  18 . Nodes A and B each include a transfer services module  223 , propensity models  226 , calculus library  227 , database  228 , secure database  229 , graphical user interface (GUI)  230 , communication interface  231 , and transformation and loading module  232 . Also shown for Node B is public key B  225   b.    
         [0054]    As the companies  12   a  and  12   b  operate, each company will receive data related to its own customers. At Node B, the transfer services module  223  receives customer data from the company&#39;s information systems  150  (see FIG. 3). The data are then processed through transformation and loading module  232   b,  which segregates nominative data from behavioral data and packages the behavioral data. The nominative data are encrypted with node B&#39;s public key B  225   b,  and are stored in the secure database  229   b.  The behavioral data are either directly stored into the database  228   b  or, if needed, are processed through the propensity model  226   b  and/or the calculus library  227   b,  and are stored in the database  228   b.  Once the storage process is complete, the description  232  of the content of the databases is sent through the communication interface  231   b  to the discovery server  84 .  
         [0055]    At some point during its operation, company  12   a  (the credit card company, Node A) develops a marketing campaign to sell its credit services. Referring to FIG. 6, a user  224   a  at company  12   a  desires to market its credit services to as wide a customer base as possible, and so initiates a discovery action  233  with its partners in the network  10 . At Node A, the discovery action  233  is sent from the GUI  230   a  through the communications interface  231   a.  From the communications interface  231   a,  the discovery request  233  is sent to discovery server  84  at the network services center  20 . The discovery server  84  sends back a description  234  of the network  10  according to the authorizations of Node A. The GUI  230   a  then retrieves the network description  234  through the communication interface  231   a  and presents the network description to the user  224   a  at Node A.  
         [0056]    Once the user  224   a  at Node A has received the network description  234 , the user  224   a  will attempt to extract behavioral information from its partners&#39; databases according to the network description  234 . In FIG. 7, the user  224   a  at Node A, using the GUI  230   a,  prepares a campaign  217 . Campaign descriptors  220  are then sent  235  through the communications interface  231   a  to proxy server  86  at the network service center  20 . The proxy server  86  logs the content  240  of the campaign descriptors  220  into a log database  82 . The proxy server  86  then dispatches  235  the campaign descriptors  220  to corresponding nodes of the network  10 , such as Node B (company  12   b ). The nodes receiving the campaign descriptors  220  are the exporting nodes. At Node B (company  12   b,  and at all other authorized nodes as defined in the network description  234 ), the query contained in the campaign descriptors  220  is executed on the node&#39;s database  228   b.    
         [0057]    Once the query contained in the campaign descriptors  220  is received at the exporting nodes of the network  10 , the query is executed on the node&#39;s behavioral data, and the result of the query is used to update the campaign descriptors  220 , notably the mapping matrices of the campaign descriptors  220 , to reflect the distribution of the relationships available from the node&#39;s behavioral database  228   b.  In FIG. 8, the database  228   b  at Node B executes the query contained in the campaign descriptors  220  from Node A. The execution examines available relationships  200  based on communications channels  212 , and the propensities  207  of the relationships  200  to purchase Node A&#39;s services. Execution of the query contained in the campaign descriptor  220  results in an update of the campaign descriptor  220 , which is routed through the communication interface  231   b  to the proxy server  86 . The proxy server  86  generates a descriptor log  249 , which is then logged in the database  82 . The proxy server  86  routes the descriptor  220  to Node A (the querying node) through Node A&#39;s communication interface  231   a.  Finally, the descriptor  220  is accessed via the GUI  230   a.    
         [0058]    With the descriptors returned from the exporting nodes by way of the network services center  20 , the user at the importing node can update the campaign  217  for its service. Specifically, and referring to FIG. 9, using the network description  234  and the returned campaign descriptors  220  from the exporting nodes in the network  10 , the user  224   a  at Node A updates the campaign  217  through the GUI  230   a  or through an automated process by selecting the best subsets in the mapping matrices. Consecutively, the campaign descriptors  220  are updated. The updated campaign descriptors  220  are then sent  258  to the proxy server  86  through the communications interface  231   a.  The proxy server  86  creates a new descriptor log  260 , and logs the descriptor log  260  in the log database  82 . The proxy server  86  then sends  258  the updated campaign descriptors  220  to the corresponding node(s) (in his example, Node B) through the communications interface  231   b.    
         [0059]    Once the updated campaign descriptors are received by the exporting node&#39;s databases, the exporting node campaign validation manager  224   b  may validate, modify, or revoke the campaign  217 . The campaign validation manager  224   b  may also revoke the campaign  217  at any time during its run time. More specifically, and referring to FIG. 10, at Node B, the campaign validation manager  224   b  views the campaign descriptor  220  using the GUI  230   b,  and issues a status modification message  266  to the database  228   b.  The status modification message  266 , in this example, may simply authorize importing node A to acquire Node B&#39;s customer information usage rights. The status modification message  266  is sent to the proxy server  86 . The proxy server  86  creates a status log  271  and logs the status log  271  in the log database  82 . The proxy server  86  also sends the status modification message  266  to the requesting node (Node A) through the communications interface  231   a.  The status modification message  266  is then presented to the user  224   a  through the GUI  230   a.    
         [0060]    In FIG. 11, at the exporting node (Node B), the coupons  216  corresponding to the campaign descriptor  258  are then generated from Node B&#39;s database  228   b.  The coupons  216  comprise the customers&#39; behavioral information as well the usage rights corresponding to the campaign descriptor  220 . The behavioral information and the usage rights are then digitally signed using the Node B&#39;s private key B  277 . The digitally signed coupons  216  are then sent  280  to the proxy server  86  through the communications interface  231   b.  The proxy server  86  creates a coupon log  282 , and stores the coupon log  282  in the log database  82 . The proxy server  86  then sends  280  the coupons  216  to the requesting node A. The signed coupons  280  may then be made available to Node A&#39;s information services  150  through the transfer services module  223   a.    
         [0061]    Once Node A has received the signed coupons  216 , the information system  150  of Node A may store the coupons  216 , and may send the coupons  216  to the network service center  20  to enable delivery by the router of the direct marketing message to the customer. Referring to FIG. 12, the information system  150  of Node A retrieves  280  the signed coupons from the transfer services module  223   a.  The information system  150  stores the coupons  216  in any company database suitable for storing customer information, for example the company&#39;s data warehouse or the company&#39;s campaign management tool. Once the company  12   a  wants to use the coupons  216 , the company  12   a  sends them together with the direct marketing message to be delivered to the appropriate router, which receives it through the communication interface  32 . The rights check out module  34  verifies the signatures of the signed coupons  216  using public key B  225 .  
         [0062]    Referring to FIG. 13, once the rights check out module  34  has verified the signature and the usage rights on the signed coupons  216 , a request  301  for retrieval of the nominative information on the coupons  216  is sent to the proxy server  86 . The proxy server  86  sends the request  301  for retrieval of the nominative information to the Node B main database  228   b.  The validity of the coupons  216  is verified against the instance of the same coupons that has been stored in the behavioral and journalization database  228   b.  If the coupons  216  are valid, the coupons  216  are matched  300  to nominative data in the secure database  229   b.  Such nominative data are stored in the customer profile  222 , which is encrypted with the router public key  302 . Once matched, the use of the coupons  216  is recorded in the database  228   b  so as to prevent their reuse. Consecutively, the nominative information is extracted from the secure database and encrypted with the router public key  302 . Consecutively the encrypted nominative information is sent to the proxy server  86 . The proxy server creates a receipt log  294 , and stores the receipt log  294  in the log database  82 . The matched nominative data are then sent to the rights check out module  34  through the communication interface  32 . The rights check out module  34  then decrypts the signed coupons  280  using the router private key  303 . Consecutively the router  18  delivers the direct marketing message to the customer.  
         [0063]    [0063]FIG. 14 is a flowchart illustrating a method  400  for automated marketing and customer information usage rights management. The method  400  begins with block  401 . In block  405 , the user  224   a,  using the GUI  230   a,  formulates a discovery action  233 , and sends the discovery action  233  to the network service center  20 , where the discovery action is distributed to nodes in the network  10 . Next, in block  410 , the network services center  20  receives network descriptions from specific suitable nodes. The network description is then forwarded to Node A. In block  415 , the user  224   a  formulates a campaign  217 , based on the network description, and sends the campaign descriptors  220  to the network services center  20 . The network services center  20  then forwards the campaign descriptors  220  to Node B. In block  420 , Node B executes the query  235  on Node B&#39;s database  228   b,  and the proxy server  86  creates and stores a log of the query  235  in the log database  82 .  
         [0064]    In block  425 , the database  228   b  updates the campaign descriptor, and Node B provides the campaign descriptor to the network services center  20 . In block  430 , the proxy server  86  generates a log descriptor, stores the descriptor in the log database  82 , and send the descriptor to Node A. In block  435 , the user  224   a  at Node A selects the appropriate subsets from the campaign descriptors, and sends the updated campaign descriptor to Node B.  
         [0065]    In block  440 , Node B validates the campaign, and sends a status message to the networks services center  20 . The network services center  20  sends the status message to Node A. In block  445 , Node B generates coupons  216  for the campaign, and provides the coupons  216  to the network services center  20 . From the network services center  20 , the coupons may be provided to Node A. Finally, in block  450 , Node A, using the coupons  216 , access customer nominative information from the secure database  229   a  at Node A.  
         [0066]    The foregoing description of the embodiments is for purposes of illustration and description. The description is not intended to be exhaustive or to be limiting to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. The scope of the invention is not to be limited by the detailed description. Since many embodiments of the invention can be made without departing from the spirit and scope of the invention, the invention resides in the claims hereinafter appended.