METHOD OF SOCIAL NETWORK GENERATION

Abstract: A method and system for identifying and creating links that allow the user to more accurately and completely view and measure the relationships it has with its customers and the relationships its customers have with other customers. A method and system has been created for determining the value of the customer or group of customers in the case of a household based on the criteria the user desires to utilize for the analysis being performed. The method for determining these relationships and the system of organizing the data allow for flexible analysis of all the key economic units by which a user might wish to analyze the data. These economic units include: individual at a specific address, household as defined as a specific address, household as defined as an economic buying unit at multiple addresses, or corporation/organization. The data can be further organized and analysis performed using additional criteria the system defines including whether a link to a corporation is Direct or Affinity. It can also include the value of selected types of relationships of customers with other customers any number of links from the target customer. The system is used to make better decisions about how to manage the customer relationship, often referred to as one-to-one marketing. An analogue of this system may also be developed in pharmacology for the tracking of compounds in drugs and their resultant effect on disease.

Description: TITLE METHOD OF SOCIAL NETWORK GENERATION CROSS-REFERENCE TO RELATED APPLICATIONS Under 35 U.S.C. § 119(e), this application claims the benefit of U.S. Provisional Application Serial No. 60/134,018 filed May 12, 1999. A portion of the disclosure of this patent document contains material that is subject to copyright or trademark protection. The copyright or trademark owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright or trademark rights whatsoever. FIELD OF THE INVENTION The present invention relates, in general, to database processing, and, more particularly, to systems and methods for organizing and analyzing customer records. BACKGROUND OF THE INVENTION The current focus within the financial services industry (banking, insurance, brokerage) and other service industries (e.g., telecommunications, hospitality) is the concept of customer relationship management. This focus highlights the customer as the nucleus of the relationship and suggests that all interactions between the firm and the client need to be managed at a customer level, rather than at a product level. Prior to the 1970s the financial services industry took a more account- oriented approach to managing their customers - that is, they viewed the customer as merely an extension of the account (or policy) rather than as the focal point of the business relationship. "Knowing the customer" meant simply that you could identify who was related to a given account as the primary owner in order to be able to address marketing and statement literature. As the ability to group accounts together into "households" (see definitions) became technologically feasible, awareness began to emerge of the need to understand more about the market potential for cross-selling to existing customers while at the same time creating a more comprehensive profile of each customer as an individual entity with which the firm conducts business. This is important for the purposes of risk management, retention management, pricing, channel balancing, sales management, and general customer servicing. While this abstract talks about the financial services industry, the problem has analogies for all types of firms for their business-to-business relationships and their firm-to-retail customer relationships. In addition there is an analog in the pharmaceutical industry and others to which this technology may be applied. Companies and organizations find it critical to their success to understand the value of their customers ("Customers" in this disclosure shall also include prospective customers), inclusive of all opportunities and risks, and wish to manage the relationships they have with customers based upon this understanding. They do this through analyzing a variety of data pertaining to customer accounts, including products or services purchased, payment histories, and sales or performance histories. This information is held in their own data repositories. Further, organizations may link their databases to other outside data sources to enrich their information on the customers. The resultant information will be useful in the course of managing the company or organization's relationship with their customers (and prospects). This data can be used in a variety of ways to value and manage customer relationships. Using this data, companies can customize how the customer is handled based on their assessment of the customer's value (which may include influence over other customers) to the company or organization and any associated credit risk. The determination of the value of the customer is used to make decisions about a number of issues including, but not limited to, the pricing of services provided or to be offered, the types of products and services to be offered, the level of service offered, and the amount of credit granted. To date, identifying a customer's overall relationship and viewing data relating to the customer has been achieved through the development of databases designed to accept extracts of data from the variety of accounting/information systems a company or organization uses to administer accounts (so-called "accounting application" systems). For example, in a multi-division company, central repositories may be used to determine how much business each division does with a customer and what the customer is worth to the holding company as a whole. These databases can also accept data from outside sources. To date the process used to bring this data together to create this unified view has relied primarily on matching names and addresses. By using these methods of creating databases, companies and organizations can "see" all the accounts (and related data) for each customer at a specific location (defined as a Marketing Customer™). However it is also important to understand relationships and value them at the level of the economic buying unit. Consequently current technology uses variations on the name and address matching techniques used to identify and link a customer's relationships to each other and outside data to group customers living at the same address; this process is called "householding." The business-to-business analogue groups companies together that have owners in common. This is called a "corporate household." Householding is important, because for some products if there is more than one individual living at a specific mailing address, a product may be the type that all the individuals may not buy. For example, no more than one first mortgage can be outstanding on a single property, and everyone living in a single home or apartment will use the same washing machine, dishwasher, long distance service on a phone line and so forth. It thus makes no sense to make an offer for such a product or service to multiple people living together at an address or to offer a second person living at the same address another of such a product when someone else living there is already using it. In other cases it does make sense to make offers of additional products or services or other products or services a company or organization might offer to or for the use of others living at the same address. For example if there are two adults living at an address sharing a checking account, but only one ATM card and one credit card not held jointly, the person or persons not possessing these products would be a good candidate to be approached about acquiring these products. Concurrent to this evolving marketing view of customers was the development of customer information files (CIFs) which were constructed in order to identify all of the customers directly linked to an account or policy or product application system purchased by the customer. This would serve to couple the customer to all products they had purchased, allowing the firm to conduct more effective analysis on customer behavior. This identification of single customers (either commercial or retail) also facilitated more intelligent Householding, as it would become possible to group customers (rather than simply accounts) into meaningful economic decision making units. Absent from these new approaches, however, was a means of constructing linkages between customers that were less obvious than the fairly simple association of surname and address (the definition of a marketing household). As the structure of society changed (increased divorces, greater numbers of the presence of children from a previous marriage, higher numbers of women retaining their maiden name, etc.), it became necessary to be able to build relationships between two individuals who may not share the same surname but who may still live at the same address. This kind of "contemporary or extended Householding" required a modification in the manner in which customer linkages could be deciphered, but remained dependent upon an address similarity to actually infer a link. While this was a major step forward, it continues to provide limitations to truly understanding the nature of a clients' overall network of relationships. At present, the values assigned to customers and the relationship management strategies pursued are arrived at using currently available systems whereby the customer and all others residing at a specific address are defined as a "household" for analytical purposes. A customer can only have one address and a unique last name under currently used systems (defined in this application as a Marketing Household™). There are commercially available computer routines that use a series of commands to discern which customer or customers reside at a specific address (even if the data fields are misspelled). All the company or organization's databases, often referred to as "accounting applications" containing information on product or service sales, balances, losses, or any other relevant information, are then sifted through. Information useful for marketing and relationship management on all persons residing at a specific address is extracted from the accounting applications and is used in the creation of a database commonly referred to as a "Customer Information File," or "CIF." The CIF contains information about all the accounts or products held or owned by people residing at the specific address and other data the company or organization deems relevant to managing the relationship from its own records. It may be enriched with data from outside the company or organization's files that is helpful to determining the customer's value or potential value and how the relationship should be managed. The value of the relationship in such a scheme can be looked at from an entire household basis or on a customer-by-customer basis within the household (but also as defined by a specific mailing address for each customer). By virtue of how the data is organized in these schemes and the inherent restrictions of defining a relationship by attaching it to a specific address, this approach has several restrictions. For example, the current state-of-the-art software assigns the value of a specific account to the person whose name first appears on the accounting application from which the data is extracted in cases where more than one name may appear on the account record. This person may or may not be the decision-maker with regard to the account in question or any other accounts that the household in question may consider buying. Additionally this person may be considered to be two or more people. For example, a single customer who uses or has used different names at different times (for example married, maiden, hyphenated, and so forth) would be considered different customers from a person at the same address. Spanish surnames may have one or two surnames and are particularly difficult. More shortcomings are a result of the inherent limitations of tying the organization of the data to a specific address. Most notably, accounts controlled or influenced by a customer residing at one address but listed on accounting applications at another address are excluded. Several examples of account and/or influence relationships that may be missed follow: A. Business accounts. A person residing at one specific address may own or control a business account. The business, with the exception of home-based businesses, will have its own specific address and will be listed in the company name. The company name is almost always different than the individual's name. B. Accounts over which the person has influence, but where that person is listed on one account at an address but is not living at the same physical address. Examples of this sort of situation include, but are not limited to: i. an elderly couple where the wife is living at home and the husband resides at a nursing home; ii. a child of divorced parents with two possible addresses and two possible co-owners; iii. cosignatory or trustee addresses; iv. children at college; v. accounts held jointly with parents or others residing elsewhere where the other party appears first on the accounting application and whose mailing address information is held on file; vi. accounts for which someone is a cosigner or guarantor for others; accounts administered for a person by a bank trust department, lawyer or accountant, and so forth. This problem is magnified where files hold records for owner, administrator and mailing name/addresses (common in Britain and Ireland). C. In other cases influence may be indirect, but can be just as real. For example co-workers at a work place can influence one another, or a business owner can agree to allow his employees to be solicited for a product or service. A great deal of products and services are sold via personal recommendations. Current systems are completely incapable of identifying relationships where a person has the potential to influence another person other than if they physically share an address and there does not exist any measure of the value of the customer in terms of customers to whom he is directly connected. D. Accounts held by the same person, but at another address such as where the customer has multiple addresses due to owning multiple properties. The person may also use a Post Office Box or a work address in addition to a "home" street address in some circumstances. For example, a person or family may own three homes (primary residence, lake getaway cabin, and warm weather home they frequent during the winter months). This person or family may have three mortgages all with the same company. These relationships are missed by current systems. A more expansive and accurate definition of household lowers the customer/household count and raises the average number of products sold per customer. The process and technology of the invention allows for such a definition to be used and by correcting the problem inherent in current systems and provides more accurate counts of customers/households. The results give more accurate measures of product cross sell. Similar dynamics occur where businesses are concerned. The current state of the art uses the same address and name-based matching and grouping techniques for businesses as are used for consumers. The business segment is prone to the same issues as the consumer segment that cause a complete picture of the value of a customer not to be able to be developed using current techniques. Companies and their subsidiaries may have multiple names and addresses that current methodology is unable to recognize and group for analytical purposes. The influence over relationships is thus unable to be tracked. The next step in the evolution of customer relationship management is to understand the full scope of a client's associations to other clients, whether these relationships be of an individual-to-individual, individual-to-business, business-to-business or business to individual nature. The schematic developed from this linking is referred to as a "social network" or "Customer Network™". The primary importance in developing social networks is to be able to comprehend the "Influence Value™" which a client reflects. This measurement is derived by aggregating some value associated with each client (balance, profit contribution, etc.) and assigning that aggregate amount to each customer in the relationship as a measure of the amount of business (or profit, etc.) over which they may exert influence. It is intended to provide insight into the sphere of influence that a customer has over other customers (or prospects) with which a firm may do or wish to do business. This type of information is useful in a variety of relationship management areas that include but are not limited to assessing risk, in determining propensity to buy or in managing customer retention. Building these network links is facilitated by the analysis of a set of characteristics that may be shared by two or more customers. Examples include joint account, phone numbers, tax id numbers, employer or any other distinct element of data captured in a customer record, etc. Thus the challenge is to determine the appropriate metrics with which to monitor customers within their given social network. Further, decisions must be made about the use of the data derived by building not only the network itself, but also by the aggregation and assignment of such metrics described above. In the 1960s, organizations viewed their business through the eyes of their product managers and did not examine customer relationships. It was in this decade that customer files were in their infancy. In the 1970s, enterprises began to attribute metrics to customers, such as sales, deposits, loans, and profits. In the 1980s, there was a move to defining households. Businesses recognized that many purchases were made for the household. A family had one mortgage, one stove, etc. Trying to reach every person in the household with direct marketing was overkill. A family with five members was still going to have only one mortgage. A term was developed called Economic Buying Unit (EBU™). A household was defined as a unique last name occurring at a unique address. Measures were then developed for cross sell to show the number of products purchased by a household and the value of these products. Households and customers as defined above shall be categorized as marketing customers and households. These definitions have several shortcomings. In research on social networks, these shortcomings came to light. There were many people living at the same address with different last names that shared joint accounts. Customers had multiple addresses. An effort was undertaken to determine what was causing customers and households to be categorized incorrectly. For example, married women began to have business relationships under both their married and maiden names. Divorced women remarried, and their children kept the father's name yet they lived in the new husband's domicile. These people are really in the household and are really the same customer. This led to constructing networks of people who lived at the same address and shared an account. This led to the term Real Customer™ and Real Household™. If they shared an account and lived at the same address and did not share a social security number, then they were considered to be in the same household. If a customer is female, has the same social security number and last name - or has a different last name, the woman is assumed to be using her maiden name and is the same person. Carrying the concept further allows one to look over customer data sets for people with multiple addresses who share all the name fields and tax identification numbers with other customers. If so, they are the same customers and in reality there is only one "true customer". (If they are female, the last name is permitted to be different.) Analyzing data this way reduces the number of customers and households and gives a much truer picture. With over counting of customer and households, institutions are underestimating the cross sell ratio of products per customer and household. For the customer and household over counting, this could vary in actual situations by between two and fourteen percent, depending on the geographic area and the data quality. This affects the cross-sell ratios greatly and can lead to incorrect decisions in marketing, de-marketing and pricing. To solve these problems, the invention claimed herein creates customer social networks and the calculation of "influence value" where the above shortcomings are avoided by setting up a database that has a clustering process and indexing methodology that focuses on data available at the account level other than name and address. Further, a value is developed for each customer that measures individual value and the value of related customers. From these techniques large numbers of relationships may be developed and large networks of people and organizations can be developed. These networks can then be used to configure sales and marketing channels. Organizations often attempt to measure the percentage of their customers that account for 80 percent of their profits. One rule of thumb is that 20 percent of the customers accounted for 80 percent of the profits. Through de-marketing, service fees and other techniques, as few as 8 percent of the customers can account for 80 percent of the profits, and it is possible that in reality, half of this 8 percent could influence 80 percent of the profits. The technique of the present invention has the capability for identifying this group of individuals and thus giving companies tremendous understanding of their customers and how sales channels may be configured to give them optimal service. With existing methodologies, customer data is examined at a point in time. This data may be compared then to customer data at an earlier point in time. For example, assume that there are two million customers and there were a hundred thousand less in the previous time period. With time-dated referential history, it is possible to generate databases that have the same customer and household composition across time periods. This allows for changes to be more exactly calibrated and for buying behavior to be compared for the same customer and household composition. The change in customers between time periods is really the net between customers lost and customers gained. The same is true with sales figures across time periods. Sales may be increasing in the aggregate, but this is the net against those customers with declining sales, lost customers, new customers, and customers buying more. This technology allows the customer database composition of customers and households to be consistent. SUMMARY OF THE INVENTION The present invention relates, in general, to a relational database, and, more particularly, to systems and methods for taking accounting application data and organizing it into information by customer, household, and associated customers (networks) and analyzing customer records and associated buying patterns. The method of analysis depends on "influence", which is a defined term developed herein. The analytical technique described herein develops a measure for customers and households which measures the influence an individual customer may have on purchasing within a network of connected customers. The invention uses a new, unique and powerful methodology to identify all the relationships a customer has with a company or organization and the relationships a customer has with other customers or prospective customers of the company or organization. It also allows for links that exist within existing data files to be followed. This means indirect associations can be considered and acted upon. The invention gives companies a more accurate view of their customers and allows them to ascribe more pertinent values to them and to create and pursue better customer and credit management strategies. Furthermore it does so in a manner that is efficient from a data processing perspective, thus minimizing the resources required and costs of producing the results of the invention. The process uses social network theory and non-address-based matching techniques to allow two substantial improvements in defining the value of a customer to be derived. The first is the result of the process employed whereby the "true" value of the customer is arrived at through the inclusion of accounts over which the customer has control at any and all addresses including businesses. The second is called Influence Value™. The same matching techniques used to discern the true value of a customer are used to find links a customer or household has to others (the person's, company or organization's or household's so-called social network). This network is called the Customer Network™ or Client Network™ since it is not necessarily an individual as the word "social" would imply Using this technique the analysis of the value of a customer or a traditional household can be expanded to include the size and value of the person's, company or organization's or household's network one or more links (or levels or direct links) away. For example, an employee of a company would be one link away (defined as influence of the first degree and also indirect or affinity influence). All the people to whom that employee had links, such as his or her spouse and children, would be two links away (influence of the second degree) from the employer, unless the spouse or children also worked for the same employer. The result is that companies can develop more accurate views of the value of customers. A new paradigm of customer or household value is created that allows for different and more effective customer management strategies to be employed. By way of example, consider a comparison of a view of members of a family using the current state of the art and the invention. This family, described briefly previously as an example of the type of important information missed by current systems, has three addresses - a summer weekend getaway home, a principal residence, and a residence in Florida that is the customer's principal residence for a four-month time period. The family also owns a small business for which the wife is the bookkeeper, and the customer's wife has a large trust account administered through a law firm. The wife also has signature authority on one of her wealthy elderly mother's accounts. Currently the wife could be viewed as up to six different "customers" in six different "households." The wife could be a "different" customer at each of the three addresses (principal residence, weekend getaway, and the Florida home) the business address of the family business, her mother's address, and the law firm's address. The number of times she is considered a customer would, using present technology, furthermore be determined to a large degree by chance based on which accounts she happened to be listed as the first name on the accounting application. Standard analytical techniques today attribute all customer value to the person/organization on line one of the account. On their own, none of the current "customer" or "household" or "marketing" systems would identify the wife in this example as deserving of special treatment. In fact, some or all of what appear to be separate customers or households, using currently available tools, might be subject to strategies seeking to make "unprofitable" customers profitable or to encourage them to close their accounts through the imposition of high fees (de-marketing) on the account or accounts identified through the limited view provided by the current state of the art. The right strategy would be to provide this customer special treatment and perhaps even reduce some fees based upon the customer's value. In a test project, a signatory on a privately held business account with two million dollars in certificate of deposits, had his fees increased because his personal checking account did not maintain the proper balance. None of the value calculations that a company could execute using current technology would take note of the large trust account or the wife's influence over other valuable accounts. Imagine the damage that could be done were such a negative action to be taken on just one of the several accounts. The customer would likely be displeased by the negative action and might close the account in question and a series of other accounts. Furthermore it is very possible that the customer will mention their displeasure to those in their network (including the law firm which may have recommended the bank to the customer in the first place). These people or organizations may hold accounts or may be considering opening accounts at the company or organization or, as in the case of the law firm, be in a position to recommend the company to others. (People who are unhappy may tell three people on average, while a happy person on average tells less than one person on average.) The best case is that the customer will take no action, but will have developed a negative perception of the company that leaves them open to considering offers from the company's competitors. On the other hand, imagine the good that would be done if the correct strategy that pampers this customer were employed. The customer would be less likely to accept offers from competitors or to switch their accounts to the competitor and would be more likely to consolidate even more accounts or purchase more products with the company because of the preferential treatment that is being provided. In the same scenario as above, the invention would save money for the company or organization using it by allowing it to treat this customer as one customer instead of as multiple customers. Rather than sending solicitations for a credit card to multiple addresses simultaneously, solicitations would be sent to one address at a time only. In test usage the invention has shown that the current state of the art overestimates the number of customers and households by 7-9%. To obtain the results of the invention the process followed is that starting with accounting application names and addresses, each line that has an individual or company name is assigned a unique identifying number or customer number. A customer record is generated for each customer with the address of the source record. These records are then matched for duplicates, and pointers are set in a customer to accounts cross-reference list. For records that have the same customer last name and address, a Marketing Household™ is created with a list of customers in the household. For each customer, a list is also created that contains customer information for identifying elements that are unique (meaning that only one such valid record can exist even though in some cases multiple people may be associated with the element). These elements include telephone numbers, email addresses, account numbers, employer identification number in the case of companies or organization, and social security number. In addition, customers who share accounts have account numbers in common. Also on the list by customer is the employer identification number, telephone number and email address of the customer. If the customer is a company, the customer is linked to a file such as that available from Dun & Bradstreet. From this, a list of customer to headquarters, establishment, parent, and ultimate Dun & Bradstreet number is built. Whether the customer is an individual or a company or organization the file created is then sorted by data point, then customer number. The file is then traversed by data point. Records that have equal data points produce a new work record consisting of pairs of serial numbers; these serial numbers are those of the customers sharing a common data point such as employer identification number or telephone number. This record with the paired serial numbers is then passed through a process that builds the chain of all of the records that share common serial numbers. The lowest serial number becomes the unique cluster identifier. Since the original serial number pairs were developed based on a data point sort, the same serial number may be present in various points in the file. This requires re-sorting and iterative runs of the process to resolve all of the links of the chain and the assignment of the final cluster number. The iterations continue until no more links are resolved. The output of this step is a record with the serial number and the cluster number (social network). This file is then joined back with the file containing the serial numbers pairs. This establishes the relationships or linkages in the network. Joining back with the database's customer identifier produces the links (called edges in some literature). The database also houses "No Link™" pointers. The purpose of these pointers is to prevent linkages which occur in an automated fashion but which should not come together. For example, a child support account for the ex-wife should not be linked into the same household as the present wife, yet with current technology, it would be. "No links" are hand coded. Further, the approach keeps track of aliases of names. For example, matching technology can find nicknames (Bob versus Robert) and maiden names of customers. The invention auto generates alias records to speed future location and inquiry, regardless of the name used when customer records are combined. From a data processing perspective, once the master database is built, a process whereby the database is updated to reflect changes on an incremental basis is used. Present technology requires extraction of all names and addresses. Lists of fragments of customer records are indexed back to the source records. Fragments are developed from new customer records. The intersection of hits on fragments in common from the new record with records on the database points to a target list of duplicates, which then may be evaluated to determine the most likely duplicate. The invention posts name and address maintenance back to other applications based upon parameters and incorporates a methodology to keep multiple customer and application systems synchronous (for example, a data warehouse and an operational data store). This is defined as the distribution process or Distributor™. The Distributor™ process avoids the need for a total regeneration of data sets when refreshing the system with new data. This approach automatically keeps the warehouse, the application systems, and the central operational database synchronous in terms of customer name and address information. The invention through the lists of links it creates and the way in which it organizes the data provides flexibility to analyze and use the data in a large variety of manners. This is done by including or excluding certain types of linkages and grouping or ungrouping people and households and consumer and corporate or organization accounts. Unlike current systems, the invention's system is not constrained by a reliance on the use of a single customer at a physical address (or multiple addresses) to create and organize associations. Where the old view of the world is apropos and it does make sense to market to a single physical address (for example, in the marketing of a mortgage that must be secured by a specific property), the new invention can identify the most logical person to whom the offer should be addressed based upon household attributes. The current state of the art assigns the recipient based on which person residing at the address' name happens to appear first on the record. The system of the invention would allow the user to use the data to decide which household member is the most likely to respond to an offer. With this invention, the user specifies with which unique names at which unique addresses to correspond. A key with this invention is that the identification and creation of the links and the database organization allows users to target customer management and marketing programs based upon the appropriate definition of the customer or household the activity envisions. The invention can segregate influence on whether the relationship is individual-to- individual, individual-to-organization, organization-to-organization, or organization-to- individual. This allows users of the invention to target promotions or customer management strategies using these attributes and associations that were heretofore not systematically identifiable. For example a user could use the invention to fashion a program to market a business-banking product based on the network affiliations identified by the invention. Another way to manipulate the data for business-to-business applications is by whether the relationship is "direct" (i.e., the person has a direct link to an account; for example, they have signature authority on the account) or "affinity" (for example, a person who works at the business and is potentially influenced as a result of their association with the business). Affinity Relationships™ occur when the organization is on line 2 or greater of the accounting application from which the data is culled. In the case of a direct relationship, the invention's user would be able to target Direct Relationships™ to be sold additional products and services the business might use, for example, a business loan. In the affinity case, a bank might offer the people with an Affinity Relationship no minimum balance free checking with direct deposit of their paycheck into an account with the bank. In summary the invention allows users to: ♦ avoid undervaluing a customer when information such as account balances at a single physical location alone can be misleading or no value is given the customer if their name happens to not be on line one of the account. ♦ establish a customer valuation process that provides compelling evidence based upon all the facts available and allows the user to make business decisions based upon a true picture of customer value that also takes into account the customer's network associations and Influence Value™. ♦ understand the full extent of customer-to-customer relationships and automatically establish customer-to-customer links in order to more accurately represent metrics based on such linkages (i.e., cross-sell ratios, product to customer ratios, product to household ratios, or household credit usage). ♦ link one individual to others to form a network of relationships (and marketing opportunities) that may be otherwise unknown. ♦ identify individuals with influence over other people or businesses and use this information in sales and marketing programs. ♦ apply to large customer bases and in an automated fashion one-to-one customer relationship management. The invention allows users to access and use the type of knowledge a small town bank manager would have of his or her customers - their associations with each other, and their level of influence as measured by the value of business related parties do with the bank through their extended families and/or community associations. This invention may be extended from a business to consumer or business to business sales, marketing and customer management to any problem where networks of relationships might exist. One example of such an extension is in the area of pharmacology. Other advantages of the present invention will become apparent by a perusal of the following detailed description of presently preferred embodiments of the invention taken in connection with the drawings. BRIEF DESCRIPTION OF THE DETAILED DRAWINGS Figure 1 shows a compound that occurs in two drugs Figure 2 shows the family relationship being collapsed to diseases. Figure 3 shows an example of a social network. Figure 4 is a process flow chart showing the steps in the system of the invention. Figures 5 and 6 are examples of a visual display of a Customer Network™ as created by the invention and viewed using the software described in the preferred embodiment. Figure 7 illustrates the Database Creation procedure of the invention Figure 8 illustrates the Identify Relationship Hierarchies procedure of the invention Figure 9 illustrates the Ongoing Record Maintenance and Post and Distribute procedures of the invention Figure 10 illustrates the Attribute Account Information to Marketing Customer and Familial Collapse (Part I) procedures of the invention Figure 11 illustrates the Familial Collapse (Part II) procedure of the invention Figure 12 illustrates the Familial Collapse (Continued) procedure of the invention Figure 13 illustrates the Influence Value calculation procedure of the invention Figure 14 illustrates the Customer Reporting and Analysis procedure of the invention Figures 15 and 16 show a diagram of the communication patterns in a company. Appendix A contains a list of definitions of terms used in the describing the invention as set forth herein. Appendix B provides an example of elements of a programming architecture that can be used to implement an embodiment of the invention DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENTS Figure 3 depicts a network of individuals and organizations that have associations or "relationships" with each other. The invention is comprised of a method for processing data using computer readable code (software) configured to cause a computer to process data such that the lists of relationship links are generated. These lists in the preferred embodiment are then manipulated using a program exhibiting functionality similar to that of MatRes (software developed by Innovative Systems, Inc.) and which can be viewed graphically using software exhibiting functionality similar to that developed by Tom Sawyer known as Graph Layout Toolkit 2.4. Figure 5 is an example of a visual display of a Customer Network™ as created by the invention and as viewed by the software. The data can also be exported to any of a variety of software packages for analysis, use, and viewing of a non-visual nature. The invention consists of the process that is used to generate the lists of the relationships that are used to create a consolidated view of each customer across multiple attributes important to (and selected by) the user and to link customers to other customers and the resultant ability to analyze and measure the Influence Value™ of a customer or household. As previously described, the lists can be used to develop visual diagrams of relationships that can be used for various user activities including but not limited to sales, marketing, customer service, credit or risk evaluation or customer retention. They can also be used to select records for particular handling. The lists and visual diagrams can be organized by a variety of definitions of customer and household that are more or less expansive to create the most appropriate database view against which to perform the aforementioned activities. The lists can also be used to generate a measure of Influence Value™, and this measure can be calculated one or more links away from the target customer or household and again can be based upon whatever definition (narrow or expansive) of customer or household the user chooses. One link away is defined as the first degree of influence. For purposes of this description, a degree is defined as the number of links away from the customer in question. Further, the networks can be collapsed by household or by degree and influence computed, then, by household or degree (Familial Collapse™). Figure 4 is a process flow chart showing the procedures used in creating the system of the invention. These procedures are further broken down into steps that are described below with reference to Figures 7 through 14. Procedure 1 -- Database Creation This procedure defines customer relationships on a Contact record and loads the resultant output to a relational database (see Figure 7). Step 1 A Extract (Account & Contact Data) The process is started by loading all account and prospect information from the account prospect systems that are going to be used in the marketing information system or customer information system. There are then two types of data: Contact Data and Account Data. The Contact Data is shown below in Table 1. This information is associated with an individual (or company) and the associated address. Account Data is information that comes from the accounting system and contains information such as the number of units, the dollar value, interest charged, date of sale, etc. If the information comes from prospects or outside lists, it may contain demographics, marketing campaign, acquisition date, etc. Table 1 Contact Type Data Data Element Name Organization or individual Supplementary Mailing Information Address Line 1 through 3 City State Postal Code Country County Account Number Email Address Work National Identification Number Passport Number Home Telephone Number Work Telephone Number Tax Identification or National Insurance Number Email Address Home Alternative Email Address Link to Accounts (1 through N) Dun & Bradstreet Numbers Establishment Headquarters Parent Ultimate Census Code of Address Geo Demographic Code Account Representative Organization Code Holding Company Organization Number Branch Number Step 1 B Split (into Contact and Account Data) The account data is split away from the above information and contains the account number and information associated with the purchase of goods and services such as the date of purchase, item purchased, quantity, cost per item, and value of this purchase. Step lC Scrub Using conventional techniques such as Innovative Dictionary™ from Innovative Systems contact data is parsed into words, patterns of words are identified, individual data elements are identified, and lines are typed by (where possible): N Name Lines O Organization Name S Address Supplement Line A Care Of Line/ Attention Line S Street Address • R Rural Address/ Box Number/ Mail Stop C City/State/Postal Code P Postal Area L Country K County or Geographic Area In addition to the above-standardized information, the input lines are also carried for each customer. The system is multi-lingual with definitions and patterns and line orders for each unique country. Step 1 D Load Account (Data)* Account data extracted from the legacy systems is staged into a relational database for further processing. This data is organized by account number. Step 1 E. Load Client (Data) * Contact data extracted from the legacy systems is staged into a relational database for further processing. See the following sample tables. * NOTE: The database was designed by developing business process models, logical models, and physical models that organize and locate the data in the desired manner. The content, procedures and attributes used in these models are proprietary to Innovative Systems, Inc. but were developed by conventional modeling and programming techniques utilizing languages such as Cobol and C. Sample Input & Output Records The below table shows the input and output record for the Contact portion of the Account data. Customer records are developed that link to the Account record. Two customer records are created: one for George Jones and one for Sally Smith. A customer record is created for MegaCorp also since this company's name is found on the account record involved. Existing technology assumes that the customer on line 1 makes the purchase of goods and services and subsequent customers are ignored. While this allows for all the customer data to foot to accounting data, it does not give any credit to other customers on the account or their having any influence over the account. In the below example, three customer records would be created. Telephone numbers, tax ID numbers and like data that is not associated with a customer will be associated with only line one. II Plrst J » Last MR GEORGE JONES GEORGE JONES aέst 1 Mf MS SALLY SMITH N MS SALLY SMITH MEGA CORP O MEGA CORP 7890 HIGH STREET S 7890 HIGH ST SLIPPERY ROCK PENSYLVANIA 15201 C SLIPPERY ROCK PA 15201 DATA ELEMENT Account Number 00652189 Email Address Work GJONES@MEGACO RP COM National Identification Number Passport Number 05234683 Home Telephone Number 414/633-4521 Work Telephone Number 415/973-2844 Tax Identification or National Insurance Number 112-46-3952 Email Address Home GJ DAD® AOL COM Alternative Email address x w' Link to Accounts (1 through N) DDA512356 Dun & Bradstreet Numbers Establishment 049652 Headquarters 075233 Parent 026244 Ultimate 752133 Census Code of Address 420561111231 Geo Demographic Code 42 Account Representative John Smith Organization Code Holding 0254 ) ro Company Organization 0001 Number Branch 2560 Number SIC Code 256223 Establishment Headquarters Parent Ultimate Census Code of Address 420561111231 Geo Demographic Code Account Representative Organization Code Holding 0254 Company Organization 0001 Number Branch Number 2560 SIC Code 256223 MS SALLY SMΪTH N ' MS SALLY SMITH MEGA CORP O MEGA CORP 7890 HIGH STREET S 7890 HIGH ST SLIPPERY ROCK C SLIPPERY ROCK PA 15201 PENNSYLVANIA 15201 DATA ELEMENT Account Number 00652189 7890 HIGH STREET S 7890 HIGH ST SLIPPERY ROCK C SLIPPERY ROCK PA 15201 PENNSYLVANIA 15201 DATA ELEMENT Account Number 00652189 Procedure 2 - Set Relationship Hierarchies This procedure creates a table or list like the sample shown below for the identification of related and duplicate records (see Figure 8): List List Type Field One Line # Type First Name Sex Last Name Suffix List Type Field Two Line # Type First Name Sex Last Name Suffix List Type Link Customer 000000001 1 I GEORGE M JONES Customer 000000002 2 I SALLY F SMITH Account 00652189 Customer 000000001 1 I GEORGE M JONES Customer 000000003 3 O MEGA O Account 00652189 CORP Customer 000000002 2 I SALLY F SMITH Customer 000000001 1 I GEORGE M JONES Account 00652189 Customer 000000002 2 I SALLY F SMITH Customer 000000003 3 o MEGA O Account 00652189 CORP Customer 000000003 3 0 MEGA CORP O Customer 000000001 1 I GEORGE M JONES Account 00652189 Customer 000000003 3 0 MEGA CORP O Customer 000000002 2 I SALLY F SMITH Account 00652189 Customer 000000001 1 I GEORGE M JONES Email Address GJONES® Account 00652189 Work MEGACORP .COM Customer 000000001 1 I GEORGE M JONES Passport Number 05234683 Account 00652189 Customer 000000001 1 I GEORGE M JONES Home Telephone 414/633- Account 00652189 Number 4521 Customer 000000001 1 GEORGE M JONES Work Telephone 415/973- Account 00652189 Number 2844 Customer 000000001 1 I GEORGE M JONES Tax Identification 112-46-3952 Account 00652189 or National Insurance Number Customer 000000001 1 GEORGE M JONES Email Address GJDADOAO Account 00652189 Home L.COM Customer 000000001 1 GEORGE M JONES Alternative Email THEMAN® Account 00652189 address HOTMAIL.C OM Customer 000000001 1 I GEORGE M JONES Link to Accounts DDA512356 Account 00652189 (1 through N) Step 2A: Matching Individual Names This process matches First Name, Last Name, Name Suffix, Gender Code, Street Number, Street Name, Postal Code, and Individual identifiers such as Social Security Number if available. There is a variety of software available in the marketplace to perform this task such as Innovative Match™ from Innovative Systems. Step 2B: Matching Organization Names This is the same process as 2A, except for organizational names. There are products available that specialize in corporate name duplicate identification such as Innovative CorpMatch™ from Innovative Systems Inc. Records to be collapsed (combined) to unique organization names at a unique address will be identified. "No Link" cross-references are posted for records that should not be linked. In addition, if two records link, but the names do not match exactly, an alias search key is generated for the search algorithm that is connected to the database. Step 2C: Combine Duplicates This process combines customer records that are duplicates. This entails having one customer survive with a combined account and customer cross-reference list. On the database, records will be combined that are considered to be the same customer. Combine is defined as moving the cross-reference and associated data from one customer to the surviving customer. This process puts the non-surviving record in a delete status and creates alias records for searching where there is a name variation between the two customers being combined. In addition, records, which come together in the matching process which, are not duplicates are stored on the customer to application list for identification by the "No-Match" module of the software that executes this process of the invention. For example if customer A does not link to customer F, but the matching software brings them together, then under customer A's list, there would be a "No-Match" pointing to customer F. Under customer F, there would be a "No-Match" pointing to customer A. Thus in subsequent runs, the matching software available from Innovative Systems would cause the software not to link these two customers. This is a laborsaving device that eliminates future review of link results. In addition, if two records link, but the names are at variance, an alias search key is generated for the search algorithm that is connected to the database. In all cross references, creation date and delete date are kept in this and future steps. Cross references are never created. This allows buying behavior to be analyzed with consistent customer and household definitions by using the composition in one period, but the purchasing behavior in multiple time periods. Step 2D: Extract Customers Separates records into individual and corporate name and address records. Step 2E: Household This process is similar to the process in 2A, except that the first name is not required as matching criteria. For use of the system in Spanish and Portuguese speaking countries, a process known as "Spanish Match and Household" is used to determine duplicates when there are double surnames. Assume there are two last names in each customer record and they are in last name field one and last name field two. If there is customer record A and customer record B, then: - field 1 record A can link to field 2 record B; - field 2 record A can link to field 1 record B; - field 1 record A can link to field 1 record B; or - field 2 record A can link to field 2 record B. Any combinations of these linkages are an indication that they are from the same family if they are at the same address. If the first names are equal, they are more than likely duplicates. Again, if records are placed in a household that do not belong there, a "No-Match" record is generated that prevents the records from being assigned to the same household in future runs. The date of household formation is generated for new households along with the first customer date. Where households split or combine, household numbers that survive are the ones for most remaining members. New household numbers are given to the new formation. The old household numbers are saved a delete code and date of the delete. This allows tracking household composition over time periods and connects families together even after one of the children has moved (or the subsidiary is sold in the corporate household case). Step 2F: Corporate Household The Contact record is linked to a third-party file containing corporate hierarchy data, such as Dun & Bradstreet. A list like the one shown below is constructed. Sample Table Showing Customers Linked with Company Data List with Dun & Bradstreet Link Customer 000000003 3 MEGA CORP Establishment 15643 Duns Customer 000000003 3 MEGA CORP Headquarters 65423 Duns Customer 000000003 3 MEGA CORP Parent 79243 Duns Customer 000000003 3 MEGA CORP Ultimate 88982 Duns Step 2G: Post Households In this process, the household number is posted to the customer list. After the initial run, if there is an existing household, the old household number is kept for referential history. All referential history is time dated with the creation date and reassignment or deletion date (if they occur). This permits a marriage union (or corporate merger) to point back to the families or entities that were the source of the union. This is very important in tracking wealth formation and corporate hierarchy evolution or merger history. Step 2H: Extract Extracts names and addresses and the social security number for records that are in the same Household using the database of Marketing Customers. Step 21: Real Customer™ Linking (Female Matching) Records that have the same female first name at the same address and have the same social security number are considered to be the same customer. The customer record that matches other records in a household last name for a multi- member household is considered to be the true name. If this is a single member household, the name with the most applications is chosen. A record is added to the list, which shows the account type as being the True Customer™ for the record that is considered to be a member of the Real Customer grouping, but both are left on the database. The different first and last name is then posted to the "alias locate" file. For example, a person may use Sue Jones at work (maiden name) and Susan Dewey at home. Both names might be on two accounts at the same address. Step 2J: Post Real Customer Links Where two Marketing Customers are considered to be the same, one customer has a relationship code in its cross-reference list of Real Customer with the other Marketing Customer's identifier. An alias locate record is generated in this case. Step 2K: Extract For each Marketing Customer this step extracts its Marketing Household and Real Customer cross-reference (if one exists). Accounts with more than one customer also have their customer-to-account cross-reference extracted. Step 2L: Real Household™ Linking Individual records that are sourced from the same account are assumed to be members of the same households. For example, if there is a mother-in-law living at the same address on the same account, this person is assumed to be a member of the household. This will handle children of the first marriage where the mother remarries also. The first household is called a Marketing Household. Under the Marketing Household cross-reference, a Real Household cross-reference is created and maintained in the database. This allows the market researcher to get a much better idea of the actual number of households. Whereas not employing this technique will lead to over counting households and undercounting cross-sell ratios, the opposite occurs in this case. There will be mothers-in-law who share an account, but do not live at the same address and are really in separate households. There may be some undercounting of households, but it will be less than the over counting by not using this technique. Step 2M: Post Real Household This program ports the Real Household to the Marketing Customer cross-reference. Step 2N: Extract Social Security and Customer Extracts the name, address, and social security number of each Marketing Customer. Step 2O: True Customer Linking The social security numbers (or email or phone number or passport number, if the user so desires), first, last name, and suffix are compared from the list file generated from Step 2N. If they are equal (or close enough under the user's definition), the customers are assumed to be the same person. Step 2P: Post True Customer This program posts the True Customer cross-reference to the Marketing Customer cross-reference and date of the update (or delete). This program is used repeatedly in the process to post all cross references generated (outputs from 6D and 6F for example). Step 2Q: Extract Existing Household and Customer Cross-References Extracts the Real Household, Real Customer, True Customer and Marketing Customer cross-references generated from Steps 2A through 2P and Procedure 1 above. Step 2R: True Household™ Linking If the True Customer falls into two Real Households or two Marketing Households, then the households are combined to form a True Household. Step 2S: Post True Households True Households are posted to the cross-reference list for associated Marketing Customers and date of the update(or delete). Step 2T: Extract All Cross-References Associated With Marketing Customer Extracts all cross-references described above. Step 2U: SuperHousehold™ or Social Network™ Linking SuperHouseholds™ are considered to be any organization or individual that shares a common link. Constructing a list of the SuperHousehold with all the customer-to- customer linkages allows the presentation of diagrams of the type shown below. An Account Type is maintained for each type of shared link . Sample Connection Types: Another Customer Joint Account Number Account Numbers and Account Types Household Number Telephone Number SuperHousehold™ Number Alias (another name that is used) Link to Account Number Social Security Number "No Link" Customer Email Address "No Link" Household Dun & Bradstreet Number Hard-Link Customer Real Customer™ Hard-Link Household Real Household™ True Customer™ True Household™ Step 2V: Post SuperHousehold or Social Network links The date of the update or delete for the cross reference is kept when the super- households record is posted to the marketing cross reference list. Procedure 3 - Incremental Adds, Changes and Deletes This procedure allows the Account contacts to be compared to the record generated in the last time period (see Figure 9). Alternatively, this maintenance may trapped in real time and sent to the update process as they occur. Three possible record types are identified: Adds (account records that are new), Deletes (account records that are no longer there or are closed), and Changes. Alternatively, if maintenance from disparate systems can be trapped real time, the maintenance data may be sent to the incremental update process real time. The records are scrubbed as discussed in the section on scrubbing. The Deletes are categorized as moved, moved with no forwarding address, or account deleted or closed. Once scrubbed, all records are sent to the database through an incremental update process. Procedure 4 - Post and Distribute New records are incrementally matched to the database by comparing against key fields and evaluating the new Adds against the existing database. Methodologies such as Innovative-Find™ and Innovative-Update™ incrementally update client databases of this type. The content, procedures and attributes used in these systems are proprietary to Innovative Systems, Inc. but have been developed using conventional programming techniques and languages such as Cobol and C. If there is a match, then a cross-reference is posted to the customer record and an Add is made to the account file. If there is a change of address, the address change is posted to the customer record. The Distributor™ function determines if the individual has other accounts. Depending on the rule set, update records are sent back to the legacy systems, including other CIF's and data warehouses. Otherwise, the new address creates a new customer where the old customer record and the new customer share a "True Customer" key. A search is also made of the database for any records that match the customer at the new address. Incremental Householding is also done. If the customer joins an existing household, a reference link is created to the household (to maintain referential history) that the customer came from with a like-link created for all the members of his former household. If the record causes a new household, the referential history is kept showing the old household the person belonged to. Thus, if a child marries into another family on the database, when the two children leave their respective households, time-dated referential history is available so that both are shown in the networks that they came from. This is very useful in tracking wealth formation in the financial services industry. In addition, this permits comparisons of buying patterns between two time periods with the same household composition. Procedure 5 -- Attribute Account Information to Marketing Customer (Existing State of the Art) This procedure sets up all of the links to account information and other customers for each Marketing Customer record generated in Procedures 1 and 2? (see Figure 10). This procedure can be accomplished using conventional techniques as contained in software such as Innovative - MklS™ Marketing System Step 5A: Extract Client Account Cross-Reference As discussed previously, a list is developed for every type of relationship on the database that involves a customer (Marketing Customer). The list contains the customer identifier and a link field. The link fields are: Another Customer Joint Account Number Account Numbers and Account Types Household Number Telephone Number SuperHousehold™ Number Alias (another name that is used) Link to Account Number Social Security Number "No Link" Customer Email Address "No Link" Household Dun & Bradstreet Number Hard-Link Customer Real Customer™ Hard-Link Household Real Household™ True Customer™ True Household™ For the customer, the line number and the customer order number within the line is kept for each customer. A cross-reference file is created of all customer account number records found of the first customer found on line one. For example if line one had Bob & Mary Jones with Account # 524321 , a record would be created for Bob Jones showing a link to the account. The record would show that Bob Jones is linked to Account # 524321. In addition, the Marketing Household number is placed on this cross-reference record which provides for Bob and Mary to be in the same household. By definition, a Marketing Customer may belong to only one Marketing Household. Hard-Links are defined as customer records that should be combined but are not recognized as the same customer with matching software. These are placed in the file by manually reviewing match and household reports, and alias records are automatically generated for the search function. Similarly, records that belong in the same household may be linked together with a Hard-Link function. This forces them to stay in the same household, regardless of what happens in the Householding process. "No Link" functions are also posted. If in the customer and household matching function clients are found to potentially link, but in fact do not link, a "No Link" customer or household cross-reference is generated. This ensures that records previously determined not to link remain unlinked. Step 5B: Marry Account to Customer File with Account Data Account systems typically house data on sales dollars, units sold, current balance, etc. (buying behavior or customer or account attribute). The system may use any one of these values to develop the measure of Influence Value the user desires. The Customer Key to Account Key cross-reference file is sorted by Account Key. There may be multiple Account types, and this process is done for each Account type. Account attributes (account number, dollar sales, units, balance, etc.) are attached to a customer key using the cross-reference table. Only one customer key is attached to each account type/account number record with the user selected buying behavior or customer or account attribute. Step 5C: Aggregation Development of Balance Records for Marketing Customers and Marketing Households The resultant file (from 5B) is then sorted by household key and then by customer key, account type, and account number. A summation is then done for each customer and household by account attribute (buying behavior) that is to be analyzed. Once customer account records are aggregated by marketing and customer attribute, they are then sorted on the summed attribute from the highest to the lowest and ranked from 1 to n, where n are the number of customers (or households if the household balance record is being developed). N is then divided into the rank to compute customer percentile, based upon the relationship value or buying behavior statistic chosen. The process is repeated to determine household ranks and percentile within the Household marketing attribute database Procedures 6 through 8 -- Familial Collapse Since only one account attribute goes to one customer record, there is audit control that allows the sum of customer attributes to equal the enterprise attribute total (units sold, sales footing, deposit balance, etc.). In addition, there are Marketing Customer and Marketing Household counts. Procedures 6 through 8 are designed to arrive at this aggregation of customer attributes for each enterprise attribute to be analyzed. Procedure 6 accomplishes this for the True Customer and Real Customer (see Figure 10) while Procedures 7 and 8 (see Figures 11 and 12, respectively) repeat the steps used in Procedure 6 for the Real Household and True Household, respectively. Procedure 6: Familial Collapse Part I (for True Customers) Step 6A: Develop True Customers The file developed in Procedure 5 of the cross-references of Marketing Customer to True Customer, Real Customer, True Household, and Real Household is sorted by Marketing Customer and True Customer. The True Customer ID replaces the Marketing Customer ID. Step 6B: Sort by True Customer The file is then sorted by True Customer ID. Step 6C: Aggregation The aggregation process is repeated from Procedure 5 resulting in the count of True Customers and True Customer balance records. Step 6D: Reset Edges The customer-to-customer file is sorted by marketing and True Customer. The True Customer identifier then replaces the Marketing Customer in the "from" column and resorted on the "from" customer identifier. The process must be repeated for the "to" cross-reference. The identifiers are then reset to edges for pictorial representation of True Customers network. Steps 6E through 6H: Repeat for Real Customer: Steps 6A through 6D are repeated for each Real Customer. Conclusion: For targeting and analysis, databases now exist for both True and Real Customers. Procedure 7 - Familial Collapse - Part II (Create Real Household Balance Record and Edges) An extract of Marketing Customer to Real Household is extracted. If there is no Real Household belonging to a customer, the Marketing Household is extracted. For customers with Real Households, the Marketing Household key is replaced and the data is aggregated again. Procedure 8 - Familial Collapse - Part III (Repeat Procedure 7 for True Household) Procedure 7 is repeated to develop the edges for the True Household. Procedure 9 -- Influence Value This step describes the process of valuing a customer for his, her or its own worth as well as determining the value of the influence that customer has over other customers, which provides the user with the ability to alter the way sales and marketing channels are configured and balanced. This process is accomplished by examining customer relationships through "Social Networking", a technique developed by sociologists and communications specialists to illustrate the communications between individuals. Figures 15 and 16 show a diagram of the communication patterns in a company. This relates to customer management through the diagram shown in Figure 3, which shows that the CEO of this company has a family and a variety of business interests. This has ramifications on the way a ' company doing business with Mr. Smith will handle his account, and the way that company measures the value of all the additional business that Mr. Smith has influence over bringing to the company. In the example of a financial services company such as a bank, this has a great implication on how sales channels are configured and how pricing is done. Banks have long recognized this phenomenon and put into place departments that are meant to cater to individuals of this nature (private banking). The difficulty is that these departments have had no way to determine which customers belong in this department except on the value of their individual relationship. The table below shows how much business each customer does with the bank. Customer Household Relationship if Balances Total Total John Smith John Smith CEO Checking $1 ,351 $1 ,351 Owner Linda Smith CFO Wife Statement Savings $14,652 Owner IRA $24758 Owner Pension Account $120,600 Owner Trust Account $842,000 Owner Brokerage $586,000 Joint with Sally $1 ,589,361 George Smith Child Trust $35,000 Trustee Savings $2,480 Guardian $37,480 Annie Smith Child Trust $15,000 Trustee Savings $654 Guardian $15,654 Ben Smith Child Trust $8,285 Trustee Savings $120 Guardian $8,405 Mariann Smith Child Trust $6,984 Trustee Savings $475 Guardian $7,459 $1 ,659,710 Sally Jones Mother in Law Checking $8,295 Joint Checking $758 None Brokerage $74,325 None $83,378 $83,378 Mega Manufacturing Owner with Wife Checking $59,640 Signature Pension Plan $1 ,752,986 Trustee Foreign Deposit Pounds $55,982 Signature $1 ,868,608 $1 ,868,608 Total Network Value $3,611 ,696 On the basis of the above from the information available through traditional marketing systems, as a customer John would not be eligible for private banking as he has only $1 ,351 in deposits. Under normal circumstances, John could be charged for check processing instead of having his fees waived, an action that could potentially damage his relationship with the bank, and thus the ability of the bank to reap the benefits of additional business or prospective business John has influence over. Additionally, numerous account managers would be assigned to John; for example, individuals from the following departments: trust (one for investments and one for administration), middle market corporate lending, retail banking, an officer in the London office, and real estate lending. His wife could also have the same number of lenders and also have a manager from the trust department on the pension plan and 401 (k) plan. Potentially, the number of bank personnel that handle this relationship could be reduced. In addition, one department could understand how their actions affect those of another department. Examining the table shows that as a customer John is worth $1 ,351 in deposits. He is a member of a household that has deposits and investments of $1 ,659,710. He is on a joint account with his mother-in-law, and his mother-in-law (even though she lives with him) has investments and deposits of $83,378. The family's privately held company has deposits and investments of $1 ,868,608. The sum total of all the relationships that John can influence is $3,611 ,696, which would not otherwise be identified absent the calculation of an "influence value" as in the present invention. A measure of how organizations fail to identify this customer attribute may be derived by ranking customers on their sole value and then on their influence value. For example, John may be the 1000 th customer in ranking based on his deposits. Based on his influence, he is 10 th . Subtracting his customer ranking from his influence ranking can highlight the degree of under measurement of a customer's value. "Influence value" is defined as all the business (sales, deposits, profits, loans, etc.) that is derived by summing the metrics of the customers one or more nodes away from an entity such as an individual or company, and can be measured using the following procedure (see Figure 13): Step: 9A: Extract (Customer Cross-References) As discussed previously, a list is developed for every type of relationship on the database that involves a customer (Marketing Customer). The list contains the customer identifier and a link field. The link fields are: • Another Customer Joint Account Number • Account Numbers and Account Household Number Types SuperHousehold™ Number • Telephone Number Link to Account Number • Alias (another name that is used) "No Link" Customer • Social Security Number "No Link" Household • Email Address Hard-Link Customer • Dun & Bradstreet Number Hard-Link Household • Real Customer™ True Customer™ • Real Household™ True Household™ For each customer, one record is created for each customer on the database and the customer that they link to. Step 9B: Marry With the Balance Records The customer-to-customer cross-reference file is sorted by customer identification number and account type. In Process 5, a balance record for each Marketing Customer was created. The balance record is attached to all linked customers. Example: Customer Type Link to Customer Type Custorr A A I $1 ,000 A B I $1 ,000 A C O $1 ,000 A D A $1 ,000 A E I $1 ,000 A F A $1 ,000 A G I $1 ,000 A G I $1 ,000 [Note: In the "Type" column, "I" represents an individual; "O" represents an organization; and "A" represents an Affinity relationship to an organization as defined previously] In the above example, customer A has a balance of $1 ,000 in all of his accounts combined. The first record that is created shows customer A linking to itself. The type shown is derived from the definition in Process 5A. Step 9C: Develop Influence Value™ The file created in 6B is then sorted by the "Link to Customer" field, and then by the "Customer" field. The occurrence of a customer number within similar "Link to Customer" groups is unique. Three types of records are developed, one for each record type. Thus, there are summary records for like record types and a maximum of three cross types (Examples: individual to organization, individual to affinity, organization to individual, organization to affinity, organization to organization, individual to individual). Two summary records are created: one for direct influence (not Affinity Relationships) and one for Indirect Relationships™ (Affinity Relationships). Thus, through the development of these records, a measure of influence a customer has directly and indirectly on customers to whom there is a relationship within the database is created. Procedure 10 -- Customer Reporting & Analysis Using the balance files for influence and customer balance, the links may be shown to other customers for a selected customer. In addition, the influence the customer has on other customers may be shown (see Figures 5 and 6). Example: John Jones Customer Balance $10,500 percentile 85.64% Household Balance $32,000 percentile 88.43% Influence Individual to Organization $2,562,000 percentile 15.4% Individual to Individual $120,000 percentile 8.42% Total Influence Direct $3,682,000 percentile 9.42% Influence Affinity Individual to Affinity $1 ,523,422 percentile 10.52% Procedure 11 -- Analyze Data The resultant information is then used to create sales, credit, and marketing intelligence using commercially available software. End of Procedure description. This invention may be extended from a business to consumer or business to business sales, marketing and customer management to any problem where networks of relationships might exist. One example of such an extension is in the area of pharmacology. or example, diseases may be categorized by: • Family • Morphology (the characteristics under a microscope) • What is affected: - Organ - Tissue - Joint - Bone • Symptoms Drugs may be categorized by: • Compound • Disease treated Using the system of the invention it is possible to build lists of all drugs that have common compounds. Compounds can be isolated, then, that are common to the treatment of a symptom or symptoms, family of disease, or a specific disease. Further, the analogue of influence may be extended to this application by giving the success rate of a drug, imputing the success rate to the compound, and then adding up the joint success rates of compounds. Potentially, then, this could lead to drugs developed from combinations of compounds found in existing drugs. At a minimum, cross-references are built of compounds, which are cross-referenced to drugs, disease family, and microscopic characteristic. Figure 1 shows a compound that occurs in two drugs. Figure 2 shows the family relationship being collapsed to diseases. This shows that in Disease 2, both Drugs B and C are effective and they both share a common compound. If Drug B and E shared a compound and were effective on Disease 2, then possibly a new protocol could be developed using the compounds in common in Drugs B and C with the compound in common with B and E. Further, the compound that is common to the most number of drugs in a disease family might be considered to have the most influence. Variants on quantity of compound and disease category may also be examined. While presently preferred embodiments of the invention have been shown and described in particularity, the invention may be otherwise embodied within the scope of the appended claims. APPENDIX A: DEFINITIONS The words "process" and "system" are used interchangeably. Marketing Client (Customer)™: All accounts that share a common last name, first name, address, and tax identification number (if present). Automatic alias generation: When two customer records are combined and first and last names have differences, locate record using the first and last name that does not survive on the database. Direct Relationship™: Two direct customers sharing an account, phone number, tax number, D&B number, or email address where the organization comes from line one only. Edge: A relationship between two customers (or households) which shows both identifiers in a list. Equal: May be defined by the user as a misspelling or mismatch that is acceptable. Indirect (Affinity) Relationships™: Relationships as defined in Direct Relationships where an organization occurs on line 2 or greater of an accounting application. Marketing Household™: All customers with the same address and last name. No Link™: Pairs of customer or household keys that indicate two customers should never be linked or a customer should never be put in a household. No-Match Links™: In the application cross-reference list, customers and household cross-references that are not permitted are kept. Real Customer™: a) A female first name equal, address equal, and social security equal; b) Male with a double surname in one record that has an equal first name; c) Companies at the same address with the same tax number and/or the same D&B establishment number and/or headquarters number. Real Household™: Customers with joint accounts and different last names, and also Real Customers with one or more Marketing Customer records. True Customer™: a) Customers with equal first and last names and equal tax numbers and at least one last name at the second address are equal to one last name at the first address for multi-member households; b) Organizations with same tax number and/or headquarters or establishment D&B number. True Household™: Bringing the True Customer relationships into the Real Household. Familial Collapse™: A system to re-cluster data by household or by combining links that are one or more links away (each link away is called a Degree of Influence). Time-Dated Referential History: All previous links of Marketing Customers are kept. This allows the household composition for the measurement of "buying behavior" to be the same over two time periods and eliminates behavior that is the result of household shifts and not buying behavior. Influence Value™: Categorized as direct and indirect. Direct influence is categorized as individual-to-organization, individual-to-individual, organization-to- individual, and organization-to-organization. All customers are categorized as individual or organization. Individual-to-individual adds the measures of all related individual customers together that are related to one customer. If the individual is related to an organization customer from line one of an account, then the individual is said to have a Direct Relationship. If the individual is related to an organization from line 2 or greater, the influence is considered to be indirect or affinity. Incremental Match™: The process of matching incremental updates to names and addresses by joining cross-reference lists of fragments of data that do not require the off-loading of all names and addresses. Distributor™: The process of determining which applications receive name and address updates if one application name and address is changed for a customer that has many applications. Spanish Match and Household: The process of determining duplicates when there are double surnames. Assume there are two last names in each customer record and they are in last name field one and last name field two. If there is customer record A and customer record B, then: - field 1 record A can link to field 2 record B; - field 2 record A can link to field 1 record B; - field 1 record A can link to field 1 record B; or - field 2 record A can link to field 2 record B. Any combinations of these linkages are an indication that they are from the same family if they are at the same address. If the first names are equal, they are more than likely duplicates. Affinity Organization™: Organization line on line 2 or greater in the original application accounting record. Customers and Households Real and True: (see definitions). Appendix B: Example Programming Architecture The system of the present invention can be implemented in both a batch and API format. The batch version allows for the creation of social network linkages across the whole of a dataset to whatever depth (or distance) as the links will sustain. In other words, there is no means for limiting the distance of relationships it creates. The API version provides the capability to tune the creation of links far more extensively than the batch version and presumes that a relational database management system (RDBMS) is in place. Batch The batch version of the system consists of two parts, a Windows GUI program and a series of eight C programs. Some of the C programs are run multiple times creating 15 steps in the process. All of these steps are executed through a PC DOS BATCH file, script file or procedure depending on the operating system. The C programs can run on any platform supporting the C language. The GUI is currently limited to the Microsoft Windows environment. The C programs receive their operational information from a common INI file. They all read in the INI file as a command line argument. The INI includes things such as where the input file is located, where the output file can be written to, and the lengths of the records and fields within the records. This INI file is a text file that can be made available on any platform where the C programs are to be executed. The purpose of the GUI interface is to build the INI file in a user-friendly fashion. All of the details needed by the C programs are presented by the GUI for the user to enter. The information needed is as follows: Name and location of the input file Name and location of the log file to be produced Name and location of the output file Location of the C executable programs Location of a file (referred to as a VFD file) describing the input file's characteristics The VFD file, which may be built with the GUI, aids in defining the data needed from the input file. The length of a record in the input file must be given. In addition, the length and displacement in the record of two fields must be given. One field is the KEY field, which is typically a customer identifier. This will become a node. The other field is typically a relationship. This will become an edge. These edges are relationships such that two nodes share: telephone number, SS TIN, D&B numbers (establishment, parent, headquarters, and ultimate D&B numbers; joint account; household; and referential history). Sets of links are used to build lists of edges. The lists are then joined to form a network. Once the INI file has been defined the running of the C programs can be done through the GUI or the INI can be saved and used outside of the GUI. Although not available to be changed through the GUI the INI also contains all of the labels, messages, warnings used in the system. These are currently in English but could be changed to another language if needed. After the system has been run the output file contains the KEY field, the KEY field of a 'connected to' record and a clustering number common to all records that have been married together. This file can then be loaded into a database or other repository and viewed through a display mechanism. API Overview: This product will discover direct and indirect relationships between a series of clients (individuals and/or organizations). These relationships are stored in a relational database management system. Optionally, it will compute the influence, as defined here, each of these clients exerts on one another by examining account balance information. The criteria used to build these relationships are user defined; and they may be used individually or in combinations. The user also has a means to control whether or not the results of this product are stored in the RDBMS for future examination and the ability to limit the depth of the discovery. This product may be integrated into a third party graphical visualization tool to better understand the nature of the data. Currently, the product exists as a Windows 95 compatible dynamic link library (DLL) written in ANSI C and embedded ANSI SQL working with an Oracle database. Definition of terminology: Node Any unique individual or organization within a network. Edge The means by which two nodes are related. Network A collection of nodes and edges; all edges are of the same type. Social Network All notes that are connected by edges that form a set. Direct Relationship Object A is related to object B. Object B is related to object C. Indirect Relationship From direct relationship, we infer that object A is related to object C. Public Interface: There are several publicly accessible function calls used to interact with the product. A list and brief description of each follow: int chmlnit (short postResults, short depth, short influence ); This function initializes several flags used during the run of the product. The first parameter is used to determine if the resulting network should be stored in the database. The second parameter is used to limit the depth of the network. The last parameter determines if the influence computations are to be done. int chmDestroy ( void ); This function is reserved for future use. int chmlnitNetwork ( void **networkPtr ); This function's purpose is to initialize a memory location such that it is suitable to store a network. It accepts one parameter and returns success/failure. The sole parameter is a user-specified address in the computer's memory where internal data structures are stored and accessed. int chmBuildNetwork (void *networkPtr, unsigned long id, ISI_SQL_ERROR *se); This function's purpose is to build a network. It accepts three parameters and returns success/failure. The first is the address of the network. The second is a unique numeric identifier that maps to a single individual or organization in the RDBMS. The last parameter is a user-specified address that is used as the communications area between this product and the end user's. If an error occurs this function returns failure and includes detailed information in the third parameter. int chmListNodes ( void *networkPtr, DB_ROW * row Array ); This function's purpose is to list all the nodes in a given network. It accepts two parameters and returns success/failure. The first parameter is the address of the network. The second is an address (user specified once again) of a buffer which contains a list of all nodes in the network. int chmListAdjNodes ( void *networkPtr, long id, DB_ROW * row Array); This function's purpose is to list all the nodes that are adjacent to a given node in the network. It accepts three parameters and returns success/failure. The first parameter is the address of the network. The second is the identifier of a parent node. The third is an address of a buffer, which contains a list of all the nodes in the network that have a direct relationship to the parent node. int chm Destroy Networkf void *networkPtr ); This function's purpose is to clean up the internally allocated data structures. It accepts the address of the network and returns success/failure. int chmGetNode (void *networkPtr, long cid, DB_ROW * row); This function's purpose is to return relevant data about a single node in the network. It accepts three parameters and returns success/failure. The first parameter is the address of the network. The second is the identifier of a single node. The third is an address of a buffer that contains various information about the node. int chmConnect (char *connectStr, ISI_SQL_ERROR *se); This function's purpose is to connect to the database. It accepts two parameters and returns success/failure. The first parameter contains user and database specific information, including a user id, password, and the name of the database to be used. The second will contain error information, if an error occurs. int chmDisconnect (ISI_SQL_ERROR *se); This function's purpose is to disconnect from the database, it returns success/failure If an error occurs the sole parameter will contain detail information. Parameter File Description: The parameter file resides in the same location as the product. It is divided into logical blocks denoted by square brackets. Each block contains six fields: active, linkDataType, NnkType, queryl , query2, and label. The active field may contain a 0 or 1 , which indicates that the product is to skip or process the block. The linkDataType field may contain a 0 or 1. A 0 in this field indicates character based data, while a 1 indicates numeric. The linkType field contains a sequential number, to differentiate types of relationships. The queryl field contains a valid SQL statement that accesses the database and returns one or more rows given a client identification number. The query2 field contains a valid SQL statement that accesses the database and returns one or more rows given the results of queryl . The label field is used to simply describe the type of relationship you are examining. An example of a single block: [ test ] active=1 linkDataType=0 linkType=1 queryl =select ssn_tin_num from clnt_core cc where cc.clnt_id = :b0 ; query2=select cc.clnt_id, cc.nmjin from clnt_core cc where cc.ssn_tin_num = :b0; label= Network on Social Security Number Output Description: As stated earlier, the product optionally stores the network in the database after it has been built. A result table should be created prior to running the product. The table structure is as follows: PARENT NOT NULL NUMBER(10) MEMBER NOT NULL NUMBER(10) KEYDATE NOT NULL DATE KEYTYPE NOT NULL NUMBER(10) KEY NOT NULL NUMBER(10) The parent and member fields contain the unique identification number of a single individual or organization in the database. The keydate field contains the date that the network was built. The keytype field corresponds to the linkType field from the parameter file, and is used to differentiate types of networks. The key field is used to easily group common members of a network together.

Claims: WHAT IS CLAIMED IS: 1. A computerized method for creating at least one relational database used in identifying and linking all relationships a customer has with other customers in a network comprising the following steps: A. extracting account and contact data for each said customer from one or more existing customer records and creating a new customer record in said database containing said extracted data; B. separating said extracted data into contact data and account data; C. parsing said contact data to identify individual data elements thereof; D. staging said account data for further processing; E. staging said contact data for further processing 2. The method of Claim 1 further comprising creation of a table or list for the identification of related and duplicate records, which comprises the following steps: A. matching individuals contained within said new customer records to detect duplicates; B. matching organizations contained within said new customer records to detect duplicates; C. combining said duplicate customer records to create a consolidated marketing customer record having a combined account and customer cross-reference list with the date of creation; D. separating said marketing customer records into individual and corporate records. E. matching said individual marketing customer records to organize said matching records into individual marketing households; F. matching said corporate marketing customer records to organize said matching records into corporate marketing households; G. referentially creating for each individual marketing household and each corporate marketing household an identification number H. extracting contact information for marketing customer records that are in the same household; I. matching said individual marketing customer records to identify a real customer for each female customer having more than one name J for each real customer providing links with its associated marketing customers to identify every other marketing customer associated with that real customer K extracting for each marketing customer its marketing household and real customer cross-reference list and customer-to-account cross- reference list if applicable; L. matching said individual marketing household records to identify a real household for each individual marketing customer record sourced from the same account; M. referentially creating for each real household an identification number and providing a cross-reference to said identification number for each marketing customer in said real household; N. extracting contact information for each marketing customer. O providing a link for each marketing customer record associated with the same true customer by comparing the social security number, email or phone number, passport number, first, last name, suffix, or other identifying information from the list file generated in the preceding step and creating said link if said comparison generates a substantially matching result P for each true customer providing links with its associated marketing customers to identify every other marketing customer associated with that true customer; Q extracting cross-references to the real household, real customer, true customer and marketing customers identified in the preceding steps. R for each true customer providing links with its associated marketing customers to identify the true household associated with that true customer if the true customer falls into two real households or two marketing households; S extracting all cross-references associated with a marketing customer; T constructing a list of super households for any organization or individual that shares a common link and maintaining an account type for each type of shared link; U for each super household providing links with its associated marketing customers to identify every other marketing customer associated with that super household. 3. The method of Claim 2 wherein no link cross-references are posted with each pair of customer records that should not be linked. 4. The method of Claim 2 wherein an alias search algorithm is invoked if two customer records link but do not identically match. 5. The method of Claim 2 wherein a Spanish match and household algorithm is invoked to identify duplicate customer records having double surnames recorded in the Spanish or Portuguese languages. 6. The method of Claim 2 wherein incremental additions, changes and deletions from said customer records are referentially generated by comparing the data contained in each record account to the data contained in said record from the last examined time period. 7. The method of Claim 6 wherein said deletions are categorized as moved, moved with no forwarding address, or account deleted or closed. 8. The method of Claim 6 wherein new customer records are incrementally created by comparing the data contained in said additions against the existing data contained in said customer records. 9. The method of Claim 2 wherein links to account information and other customer records are provided for each marketing customer record generated comprising the following steps: A extracting a client account cross-reference by developing a list for every type of relationship that involves a marketing customer which contains a customer identifier and a link field identifying links to other customers: B marrying an account to a customer record by providing account data and sorting a customer key to account key cross-reference file by account key for each account type and attaching account attributes to the customer key using the cross-reference table C sorting the resultant file from the preceding step by household key and then by customer key, account type, and account number and then performing an aggregation of for each customer record and household record by account attribute to be analyzed. D. sorting said aggregated records on the aggregated attribute from the highest to the lowest value and ranking said records and computing customer percentile based upon the relationship value or buying behavior statistic chosen; E. repeating the preceding step determine household ranks and percentile within the household marketing attribute database. 10 The method of Claim 9 wherein the file developed from the steps added in Claim 9 is sorted by marketing customer and true customer and the aggregation process is repeated to provide the count of true customers and true customer balance records. 11. The method of Claim 10 wherein the steps added in Claim 10 are repeated for real customers to provide the count of real customers and real customer balance records. 12. The method of Claim 9 wherein the file developed from the steps added in Claim 9 is sorted by marketing customer, marketing household and real household and the aggregation process is repeated to provide the count of real households and real household balance records. 13. The method of Claim 12 wherein the steps added in Claim 12 are repeated for real customers to provide the count of true households and true household balance records. 14. The method of Claim 9 wherein an influence value is calculated for each marketing customer defined as all the business that is derived from that marketing customer by summing the metrics of the customers one or more nodes away from that marketing customer whether an individual or company, wherein said calculation comprises the following procedure: A extracting a client account cross-reference by developing a list for every type of relationship that involves a marketing customer which contains a customer identifier and a link field identifying links to other customers: B marrying an account to a customer record by providing account data and sorting a customer-to-customer cross-reference file by customer identification and account key for each account type and attaching account attributes to the customer key using the cross-reference table to create a balance record for each marketing customer that is available to all other customers linked to said marketing customer C: developing an influence value for a selected marketing customer, true customer, real customer, marketing household, real household, true household, or user defined groups of records within the network by sorting the file created in the preceding step by the link-to-customer field and then by the customer field such that the occurrence of a customer identification key within a similar link-to-customer field is unique. D. creating three types of records for each record type such that there are summary records for like record types and a maximum of three cross- reference types such as individual to organization, individual to affinity, organization to individual, organization to affinity, organization to organization and individual to individual. E. creating a summary record for direct influence and a summary record for indirect relationships or affinity relationships to create a measure of influence that a selected customer has directly and indirectly on customers to whom there is a relationship within the database. 15. A method for identifying and expanding the relationships a customer has with other customers comprising the steps in Claims 9 through 12 performed sequentially. 16. A computer database architecture and system that allow for the linking of customer relationships together to construct a customer network and the calculation of customer influence value measures one or more links away from the target customer as desired that is constructed using the steps comprising the method of Claims 1 through 14 performed sequentially. 17. A computer database architecture and system that allows for the organization of linked relationships to reflect whatever definition or definitions of customer or household the user desires with the result that the customer and household information accurately reflect the data as they should be organized for the particular purposes of the user in the circumstances at any given point in time. 18. A computer database architecture and system that allow for the calculation of customer value measures based upon whatever definition or definitions of customer or household the user desires and inclusive or exclusive of influence value measures and further defined by parameters such as individual to organization, individual to individual, organization to individual, or organization to organization, and with regard to relationships involving organizations by whether the relationship is affinity or direct. 19. A computerized system for enabling one-to-one marketing activities in a company or organization to be based upon a method of processing information about the customer that provides users with information that resembles the type of knowledge that a bank manager in a small town knows about his or her customers and their relationships with the bank and within the community. 20. A computerized system for enabling one-to-one marketing activities in a company or organization to be based upon a method of processing information about the customer allowing the user to define more effective customer management strategies including but not limited to pricing, additional products to be offered, service levels to be offered, and risk management. 21. The method of Claim 14 further comprising a step which allows data that is changed within the database to be updated in an application using said database by identifying application cross references based on user designed rules so that some or all of said applications are sent updates to change the information that was updated in said database such that said applications may be automatically corrected at the user's option to contain the most current information. 22. The method of Claim 6 wherein a creation date or a deletion date is identified for each said incremental addition, change and deletion as applicable to allow customer metrics to be tracked over time for the same household composition and such that individuals or organizations leaving a household can be identified with that household.

Inventors: COLONNA ROBERT J, COLONNA, ROBERT, J.
Applicants: COLONNA ROBERT J, INNOVATIVE SYSTEMS INC, COLONNA, ROBERT, J., INNOVATIVE SYSTEMS, INC.
Application year: 2000
Main classification: G06F17
IPC codes: G06Q10, G06Q30, G06Q40, G06Q50