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WO2007014203A2 - Exploitation d'une base de donnees pour un ciblage de clients - Google Patents

Exploitation d'une base de donnees pour un ciblage de clients Download PDF

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Publication number
WO2007014203A2
WO2007014203A2 PCT/US2006/028810 US2006028810W WO2007014203A2 WO 2007014203 A2 WO2007014203 A2 WO 2007014203A2 US 2006028810 W US2006028810 W US 2006028810W WO 2007014203 A2 WO2007014203 A2 WO 2007014203A2
Authority
WO
WIPO (PCT)
Prior art keywords
subset
entries
relevant
database
generating
Prior art date
Application number
PCT/US2006/028810
Other languages
English (en)
Other versions
WO2007014203A3 (fr
Inventor
David Fox
Kelli Fox
Original Assignee
Zdk Interactive, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US11/281,263 external-priority patent/US20070021200A1/en
Priority claimed from US11/487,145 external-priority patent/US20070174250A1/en
Application filed by Zdk Interactive, Inc. filed Critical Zdk Interactive, Inc.
Publication of WO2007014203A2 publication Critical patent/WO2007014203A2/fr
Publication of WO2007014203A3 publication Critical patent/WO2007014203A3/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2465—Query processing support for facilitating data mining operations in structured databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F16/00 and subgroups
    • G06F2216/03—Data mining

Definitions

  • the present invention generally relates to data mining. Specifically, the present invention relates to the use birthdates in conjunction with predetermined formulas to generate a subset of relevant entries from a database.
  • Data mining is a term used in the art to describe the extraction of potentially useful information from data. Data mining is normally used in conjunction with databases to determine correlations among data entries. As such, data mining is normally used to identify trends or habits of the subjects within the database.
  • An example of data mining involves a database of potential customers for a particular business.
  • the business can track and record purchases made by a customer over a period of time. Once sufficient time has passed, a correlation can be made between the customer and the purchases made by the customer. Marketing materials, promotional items, associated products and the like can be generated and presented to the customers based on the correlation. The customer, thereby, purchases more items in accordance with the customer's spending habits.
  • What is needed is a system and method for determining a subset of relevant database entries using criteria to foresee the spending habits of the customer. Further, what is needed, is a system for predicting the reaction of a customer based on the features of a document, such as a marketing brochure.
  • a module for generating a subset of relevant database entries comprises an input, a mapper, an extractor, a comparer, and an output.
  • the input can accept requests from a user.
  • the mapper can map the requests to at least one of a plurality of predetermined formulas. Each of the predetermined formulas can correspond to a range of birthdates.
  • the extractor can extract birthdate information from each of a plurality of database entries.
  • the comparer can compare the extracted birthdate information to the range of birthdates and flag the entries which are in range.
  • the output can generate a subset of relevant entries, based on the flagged entries, and provide the subset to the user.
  • the input can be facilitated using a GUI.
  • at least some of the predetermined formulas can be based on natal and/or transit charts which can be used to foresee the psyche of the customer.
  • the subset of relevant entries can be used to generate a distribution list and/or custom advertising.
  • the requests can also be instructions to generate a list of people in a buying, investigative, and/or research mode, and the subset of relevant entries can be the desired list.
  • the database comprises a plurality of entries. Each of the entries can comprise a name, an address, buying history, and a birthdate of a person.
  • the distribution list can be generated by using the birthdates to determine the emotional state of the people in the database.
  • the distribution list can be generated and sold for marketing purposes and/or to vendors.
  • marketing materials can be generated based, at least in part, on the distribution list and sent to potential customers. Customers can be notified of the criteria used to send the marketing materials in order to dispense of the notion that the advertising is random.
  • a system for generating a subset of relevant entries from a database comprises a database and a module.
  • the database can have a plurality of entries that reference a plurality of people. Each of the entries can comprise a reference to a person and the person's birthdate.
  • the module can analyze the plurality of entries in the database and generate a subset of relevant entries. The module can use the birthdate as one criteria to generate the subset of relevant entries.
  • the module can comprise an input, a mapper, an extractor, a comparer and an output.
  • the input can accept requests from a user.
  • the mapper can map the request to at least one of a plurality of predetermined formulas. Each of the formulas can correspond to a range of birthdates.
  • the extractor can extract the birthdate from each of the entries and the comparer can compare the birthdate to the range of birthdates. If the birthdate is within the range, the entry can be flagged and included in the output as part of the relevant subset of entries.
  • the reference to the person can be the person's name.
  • the plurality of entries can further comprise an address of the person.
  • the request can be an instruction to generate a list of people in a buying, investigative, and/or research mode, and the subset of relevant entries can be the desired list.
  • Each of the entries can further comprise a birthtime which can be used by the module to generate the subset of relevant entries.
  • the subset of relevant entries can be used to compile a mass mailing to customers who are in the appropriate mode.
  • the reaction prediction tool comprises an input, a mapper, an extractor, a comparer, and an output.
  • the input can receive features of an existing document from a user.
  • the mapper can map the features to a range of birthdates using at least one of a plurality of predetermined formulas.
  • the extractor can extract a birthdate from each of a plurality of database entries which reference a plurality of people.
  • the comparer can compare each of the birthdates to the range of birthdates and flag the entries with birthdates within range.
  • the output can generate a subset of the plurality of people, based upon the flagged entries, who will react to the features in a desired manner.
  • the predetermine formulas can be based on natal and/or transit charts and be used to foresee the psyche of the customers.
  • the predetermined formulas can correspond to people in a buying, investigative and/or research mode.
  • the subset of the plurality of people can be those who react favorably to the document features and used to generate a distribution list and/or additional marketing material.
  • a method for generating a subset of relevant database entries is also disclosed. The method includes receiving a request from a user. Once received, the request is mapped to at least one of a plurality of predetermine formulas.
  • Each of the predetermined formulas can correspond to a range of birthdates.
  • birthdate information is extracted from each of a plurality of database entries.
  • the birthdate information is compared to the range of birthdates.
  • the entries containing birthdates within the range of birthdates are flagged.
  • a subset of relevant entries is generated based on the flagged entries. Once generated, the subset is provided to the user.
  • the predetermined formulas are based on natal and/or transit charts to predict the emotional state of the customer.
  • the request can be an instruction to generate a list of people in a buying, investigative, and/or research mode
  • the subset of relevant entries can be the list of people in the appropriate mode.
  • the subset of relevant entries can be used to generate a distribution list and/or used to generate custom advertising.
  • the birthdate information can include both birthdate and birth time.
  • a method for generating a subset of relevant entries from a database comprises receiving a request to generate a subset of relevant entries from a database.
  • the database can have a plurality of entries referencing a plurality of people.
  • Each of the entries can comprise a reference to one of a plurality of people and a birthdate of the person referenced.
  • the subset of relevant entries can be generated using a formula which is based, at least in part, on the birthdate.
  • the request can be an instruction to provide a subset of customers who are in a buying, investigative, and/or research mode in order to generate relevant material and/or products.
  • generating a subset of relevant database entries using birthdate information facilitates the use of additional criteria, including emotional factors and modes, to create appropriate material for existing and/or potential customers.
  • Fig. 1 illustrates a module for generating a subset of relevant database entries.
  • Fig. 2 illustrates a database from which a distribution list is generated.
  • Fig. 3 illustrates a system for generating a subset of relevant entries from a database.
  • Fig. 4 illustrates a reaction prediction tool
  • Fig. 5 illustrates a method of generating a subset of relevant database entries.
  • Fig. 6 illustrates a method of generating a subset of relevant entries.
  • the present invention teaches a variety of devices, methods, and other subject matter described herein or apparent to one skilled in the art in light of the present teaching.
  • the present invention further teaches a variety of embodiments, aspects and the like, all distinctive in their own right.
  • the person of skill in the art suitable for the present invention can have a background from computer science, computer engineering, electrical engineering, or the like.
  • the systems and methods taught by the present invention generate a subset of relevant entries from a database.
  • the generation of the subset is facilitated utilizing birthdate information in order to identify the emotional mode of existing and/or potential customers.
  • the resulting subset of entries can be used to generate promotional, marketing, advertising or similar materials which can be distributed to consumers.
  • Fig. 1 illustrates a module 2 for generating a subset of relevant database entries.
  • the module includes an input 6, a mapper 4, a plurality of predetermined formulas 8, an extractor 10, a comparer 12, and an output 14.
  • the input 6 is capable of receiving a request from a user.
  • the request can be provided to the input in any convenient and/or known manner, external or internal, including external peripheral devices, internal bus, and/or network connection.
  • the request can facilitated in any convenient and/or known manner including a mouse click, text, keyboard entry, speech, touch, data transfer, wireless communication, frequency modulation, electrical signal, or any other manual or automatic process capable producing a command.
  • the user can be anything capable of communication including humans and machines.
  • the mapper 4 maps the request to at least one of the predetermined formulas 8.
  • the extractor 10 extracts birthdate information from a plurality of database entries. The extraction can be facilitated using artificial intelligence, data processing techniques, search engines, statistical algorithms and/or any other process that facilitates the extraction of data.
  • the comparer 12 compares the extracted birthdate information to the range of birthdates generated by the formulas. The entries that contain birthdates within the range are flagged. Entries can be flagged by inclusion into a subset and/or using an indicium. The output generates a subset of relevant entries, which are based on the flagged entries, and provides the subset to the user.
  • a user can provide an instruction to generate a list of customers who are in an emotionally buying mode.
  • the mapper 4 can map the instruction to formulas 8 that are based on natal and transit charts.
  • a range of birthdate and birthtimes for people who are currently in a buying mode is determined.
  • birthdate and birtht ⁇ ne information is then extracted from a database (by the extractor 10) and compared to the range (by the comparer 12).
  • the output 14 is a list of all people having birthdates and birthtimes within the range, and therefore is a list of customers in a buying mode. Using the list, marketing materials can be created and distributed to the appropriate customers.
  • Fig. 2 illustrates a database 20 from which a distribution list is generated.
  • the database 20 includes a plurality of entries 22.
  • Each of the entries includes a name 24 of a person, an address 26 of the person, and a birthdate 28 of the person.
  • a formula utilizing the birthdates generates the desired distribution list from the database 20.
  • the module in Fig. 1 can be used to generate a subset of relevant entries from the database illustrated in Fig. 2.
  • Fig. 3 illustrates a system 40 for generating a subset of relevant entries from a database.
  • the system 40 includes a database 42 and a module 44.
  • the database 40 includes a plurality of entries 46. Each entry includes a reference 48 to a person and a birthdate of the person 50.
  • the module 44 includes an input 52, a mapper 54, a plurality of predetermined formulas 56, an extractor 58, a comparer 60, and an output 62.
  • the input 52 is capable of receiving a request from a user.
  • the request can be provided to the input in any convenient and/or known manner, external or internal, including external peripheral devices, internal bus, and/or network connection.
  • the request can facilitated in any convenient and/or known manner including a mouse click, text, keyboard entry, speech, touch, data transfer, wireless communication, frequency modulation, electrical signal, or any other manual or automatic process capable producing a command. Further, the user can be anything capable of communication including humans and machines.
  • the mapper 54 maps the request to at least one of the predetermined, formulas 56.
  • the extractor 58 extracts birthdate information from the plurality of database entries 46. The extraction can be facilitated using artificial intelligence, data processing techniques, search engines, statistical algorithms and/or any other processes that facilitate the extraction of data.
  • the comparer 60 compares the extracted birthdate information to the range of birthdates generated by the formulas. The entries that contain birthdates within the range are flagged.
  • Entries can be flagged by inclusion into a subset and/or using an indicium.
  • the output 62 generates a subset of relevant entries, which are based on the flagged entries, and provides the subset to the user.
  • a user can provide an instruction to generate a list of customers who will be in an emotionally investigative mode in the next six months.
  • the mapper 54 can map the instruction to formulas 56 that are based on composite charts (a composite chart is a merging of two or more natal charts).
  • a range of birthdates for people who will be in an investigative mode in the next six months is determined.
  • the birthdate information is then extracted from the database 42 and compared to the range using the extractor 58 and comparer 60.
  • the output 62 is a list of all people having birthdates within the range, and therefore are people who will be in an investigative mode in the next six months. Using the list, promotional materials can be generated and distributed to the appropriate people.
  • Fig. 4 illustrates a reaction prediction tool 80.
  • the reaction prediction tool 80 includes an input 82, a mapper 84, a plurality of predetermined formulas 86, an extractor 88, a comparer 90, and an output 92.
  • the input 82 receives features of an existing document from a user.
  • the mapper 84 maps the features to a range of birthdates using the predetermined formulas 86.
  • the extractor 88 extracts birthdate information from a plurality of database entries.
  • the comparer 90 compares the extracted birthdate information to the range of birthdates generated by the formulas 86. The entries that contain birthdates within the range are flagged.
  • the output 92 which is based on the flagged entries, generates a subset of people who will react to the features in a desired manner.
  • the input 82 can receive features of a marketing brochure from a user.
  • the user can be a human who manually input the features of the marketing brochure, such as color, shape, font, graphics, tone, product, word choice, and/or any other descriptive features.
  • the user can be a machine that provides a scan of the marketing brochure to the input.
  • the mapper 84 can map the features to a range of birthdates using predetermined formulas 86.
  • the formulas 86 can be based on natal charts and be used to determine who will be most influenced by the marketing brochure's features.
  • the marketing brochure is blue
  • applying the formulas would yield a range of birthdates that would be most influenced by the color blue at the time the brochure is sent.
  • the birthdate information is then extracted from the database and compared to the range using the extractor 88 and comparer 90.
  • the output 92 is a generated list of all people having birthdates within the range.
  • the output 92 is a list of all people who would be most influenced by the features of the marketing brochure at the time the marketing brochure is sent.
  • Fig. 5 illustrates a method 100 of generating a subset of relevant database entries.
  • a step 102 receives user input.
  • the receiving of user input 102 can be facilitated using text, hyperlinks, windows, menus, radio buttons, checkboxes, icons, CLI, PUI, GUI, or any other device capable of interacting with and/or obtaining data from a user.
  • a user can be any entity that provides information to the system, including a human and/or machine.
  • a step 104 selects at least one of a plurality of formulas. Using the selected predetermined formulas, a step 106 determines an appropriate range of birthdates.
  • a step 108 extracts birthdate information from at least one entry in a database.
  • the extracted birthdate is compared to the range of birthdates in a step 110.
  • Step 112 determines whether the extracted birthdate is within range. If so, a step 114 flags the entry. If not, a step 116 does not flag the entry.
  • a step 118 determines whether all of the birthdates have been extracted and compared to the range of birthdates. If not, steps 108-118 are repeated until birthdates in the database of entries has been compared to the range of birthdates. Once all birthdates have been compared, a subset of flagged entries is generated in step 120.
  • a step 122 provides the subset of flagged or relevant entries to the user.
  • birthdate information can be extracted from the entries in the database before the range of birthdates is determined, the predetermined formula is selected, or the input is received. Further, in other embodiments, all of the birthdate information can be extracted from all of the entries in the database and then compared to the range of birthdates. In another embodiment, the user can select the predetermined formula or can create a formula to be used to determine the range of birthdates. In certain embodiments, the predetermined formulas can be based on natal, transit, or composite charts. Moreover, in additional embodiments, the flagging of entries can be facilitated by inclusion into a subset of entries and/or manipulating the entry using an indicium.
  • Fig. 6 illustrates a method of 140 of generating a subset of relevant entries.
  • a step 142 receives a request to generate a subset of relevant entries from a database.
  • the database can have a plurality of entries referencing a plurality of people.
  • Each of the entries can comprise a reference to a person and a birthdate of the person.
  • a step 144 a subset of relevant entries is generated using a formula. The formula can be based on the birthdate of the person a capable of predicting emotional state.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Databases & Information Systems (AREA)
  • Finance (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Game Theory and Decision Science (AREA)
  • Probability & Statistics with Applications (AREA)
  • Marketing (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • General Business, Economics & Management (AREA)
  • Software Systems (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

L'invention concerne un système et une méthode pour générer un sous-ensemble d'entrées pertinentes. Dans un mode de réalisation de l'invention, un module comprend une entrée, un dispositif de cartographie, un extracteur, un comparateur et une sortie. L'entrée accepte une demande provenant d'un utilisateur et le dispositif de cartographie cartographie la demande selon une formule prédéterminée correspondant à une plage de dates de naissance. L'extracteur extrait des informations de dates de naissance à partir de chaque entrée de base de données et le comparateur compare les informations de dates de naissance extraites à la plage de dates de naissance. Les entrées comprises dans cette plage sont balisées et la sortie génère un sous-ensemble d'entrées pertinentes en fonction des entrées balisées.
PCT/US2006/028810 2005-07-22 2006-07-24 Exploitation d'une base de donnees pour un ciblage de clients WO2007014203A2 (fr)

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
US70202005P 2005-07-22 2005-07-22
US60/702,020 2005-07-22
US11/281,263 2005-11-16
US11/281,263 US20070021200A1 (en) 2005-07-22 2005-11-16 Computer implemented character creation for an interactive user experience
US11/487,145 2006-07-13
US11/487,145 US20070174250A1 (en) 2005-07-22 2006-07-13 Database mining for customer targeting

Publications (2)

Publication Number Publication Date
WO2007014203A2 true WO2007014203A2 (fr) 2007-02-01
WO2007014203A3 WO2007014203A3 (fr) 2009-04-16

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9336192B1 (en) 2012-11-28 2016-05-10 Lexalytics, Inc. Methods for analyzing text

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7076503B2 (en) * 2001-03-09 2006-07-11 Microsoft Corporation Managing media objects in a database

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9336192B1 (en) 2012-11-28 2016-05-10 Lexalytics, Inc. Methods for analyzing text

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