WO1999046719A1 - Method and apparatus for analyzing data and advertising optimization - Google Patents

Method and apparatus for analyzing data and advertising optimization Download PDF

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Publication number
WO1999046719A1
WO1999046719A1 PCT/US1999/005363 US9905363W WO9946719A1 WO 1999046719 A1 WO1999046719 A1 WO 1999046719A1 US 9905363 W US9905363 W US 9905363W WO 9946719 A1 WO9946719 A1 WO 9946719A1
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WO
WIPO (PCT)
Prior art keywords
advertising
exposure
data
person
index
Prior art date
Application number
PCT/US1999/005363
Other languages
English (en)
French (fr)
Inventor
Mark E. Cannon
Original Assignee
Cannon Holdings, L.L.C.
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
Application filed by Cannon Holdings, L.L.C. filed Critical Cannon Holdings, L.L.C.
Priority to AU30809/99A priority Critical patent/AU3080999A/en
Publication of WO1999046719A1 publication Critical patent/WO1999046719A1/en

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04H—BROADCAST COMMUNICATION
    • H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
    • H04H60/35—Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users
    • H04H60/45—Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying users
    • 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
    • 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
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • 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
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0269—Targeted advertisements based on user profile or attribute
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04H—BROADCAST COMMUNICATION
    • H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
    • H04H60/35—Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users
    • H04H60/46—Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for recognising users' preferences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04H—BROADCAST COMMUNICATION
    • H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
    • H04H60/61—Arrangements for services using the result of monitoring, identification or recognition covered by groups H04H60/29-H04H60/54
    • H04H60/66—Arrangements for services using the result of monitoring, identification or recognition covered by groups H04H60/29-H04H60/54 for using the result on distributors' side
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
    • H04N21/258—Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data
    • H04N21/25866—Management of end-user data
    • H04N21/25883—Management of end-user data being end-user demographical data, e.g. age, family status or address
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
    • H04N21/44213—Monitoring of end-user related data
    • H04N21/44222—Analytics of user selections, e.g. selection of programs or purchase activity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
    • H04N21/81—Monomedia components thereof
    • H04N21/812—Monomedia components thereof involving advertisement data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/47—End-user applications
    • H04N21/478—Supplemental services, e.g. displaying phone caller identification, shopping application

Definitions

  • the present invention relates to methods and techniques
  • viewers is also a very desirable group of consumers, sought after for their purchasing
  • Broadcast television advertising revenue is estimated to be in excess of
  • optimization tools impose artificial limits on advertising agencies and media planners that
  • the data can be manipulated to evaluate, score and optimize an
  • the most preferred embodiment of the present invention is a computer-based decision support
  • a database mining engine DME
  • an API database mining engine
  • the user interface in conjunction with the DME, provides a unique and
  • media-related access data which describe the access habits and preferences of the media
  • businesses, networks, and advertising agencies can interactively create, score,
  • the user interface for the system provides access to both the DME and the optimization mechanism in a simple and straightforward manner
  • FIG. 1 is a block diagram of a preferred embodiment of the present invention
  • FIG. 2 is a flowchart for converting person-by-person information from one
  • FIG. 3 is a flowchart depicting a process for loading database information
  • FIG. 4 is a block diagram of a data conversion method according to a preferred
  • FIG. 5 is a filter mask according to a preferred embodiment of the present invention.
  • FIG. 6 is a detailed graphical representation of a preferred embodiment of the data
  • FIG. 7 is a graphical representation of the viewing catalog for a three-week
  • FIG. 8 is a flowchart of a method 800 for using a graphical user interface to
  • FIG. 9 is a screen shot of cross tabulation data extracted from a media-related
  • FIG. 10 is a screen shot of an icon for accessing data contained in a database file
  • FIG. 11 is screen shot of a line graph representing data contained in a database file
  • FIG. 12 is a detailed graphical representation of a media-related database structure according to an alternative preferred embodiment of the present invention.
  • FIG. 13 is a flow chart depicting an optimization method according to a preferred
  • FIG. 14 is a block diagram of a scoring calculation method according to a
  • FIG. 15 is a tabular representation of a viewing history for a series of advertising
  • FIG. 16 is tabular representation of frequency values for a given number of
  • FIG. 17 is a tabular representation of age range index values for a group of
  • FIG. 18 is a tabular representation of income range index values for a group of
  • FIG. 19 is a tabular summary of various techniques for valuing exposures to an
  • FIG. 20 is a tabular comparison of comparative average exposure frequencies
  • FIG. 21 is a graphical representation of two different exposure plans or schedule
  • FIG. 22 is a tabular representation of effective frequency for a simple advertising
  • FIG. 23 is a tabular representation of sample index valuation for a given number
  • FIG. 24 is a graphical comparative representation of a number of exposure
  • FIG. 25 is a graphical comparative representation of total exposure valuation
  • FIG. 26 is a tabular representation of exposure valuation for various frequencies
  • FIG. 27 is a tabular representation of scoring exposure values as applied in several
  • FIG. 28 is a graphical representation of change in frequency based on choosing
  • FIG. 29 is a tabular representation of frequency exposure valuation for a series of
  • FIG. 30 is a graphical representation of the decay of influence resulting from the
  • FIG. 31 is a graphical representation of an index for logarithmic influence
  • FIG. 32 is a graphical representation of influence to advertising based on exposure
  • FIG. 33 is a graphical representation of the average influence index values for
  • FIG. 34 is a graphical representation an influence index using two different
  • FIG. 35 is a tabular representation of a scoring example using a time weighted
  • FIG. 36 is a graphical representation of an influence index for a given audience
  • FIG. 37 is a tabular representation of index values based on audience age
  • FIG. 38 is a tabular representation of index values based on income information
  • FIG. 39 is a tabular representation of index values based on the size of the county
  • FIG. 40 is a tabular representation of index values for exposure recency
  • FIG. 41 is a tabular representation of scoring using index values and models
  • FIG. 42 is a tabular representation of indices for three advertising alternatives
  • FIG. 43 is a tabular representation of values for a series of advertising spots for
  • FIG. 44 is a graphical representation of the optimization information presented in
  • person-by-person data for any type of media may be utilized with various preferred
  • size of a data file for one week is about 35 Mbytes.
  • sample population can be judged by Nielsen to be out of tab for a day. This means that
  • each household member is assigned a unique person number
  • sample household are logged and identified in the data, they are not assigned unique
  • this task can be
  • 17 refers to the process of correlating the individual data elements from multiple sets of data.
  • the sample For limited data sets and moderate sample sizes, the sample is included in the sample. For limited data sets and moderate sample sizes, the sample is not included in the sample. For limited data sets and moderate sample sizes, the sample is not included in the sample. For limited data sets and moderate sample sizes, the sample is not included in the sample. For limited data sets and moderate sample sizes, the sample is not included in the sample. For limited data sets and moderate sample sizes, the sample is not included in the sample. For limited data sets and moderate sample sizes, the sample
  • browsing In this mode a user interactively interrogates the data, often with a
  • the SQL database query language for example, supports
  • Calendar record (record type 0). The calendar record identifies the broadcast
  • This calendar record indicates that the week of data included in the data set begins
  • Classification data record (record type 1).
  • the classification data record (record type 1).
  • 20 typically includes 5000 records per week
  • the week includes four household members and one visitor.
  • Each household data item in the week includes four household members and one visitor.
  • age and gender data for each person in the record is also translated to binary form and
  • Program data lead record (record type 2).
  • the program data lead record (record type 2).
  • program data records are used to describe all programming broadcast in a given week
  • the sample program data lead record shown above indicates, among other things,
  • program data item are converted from an ASCII representation as shown above to a
  • Usage data lead record (record type 4).
  • the usage data lead record identifies
  • This usage data continuation record identifies some of the 2,748 households and
  • the specific program being watched is not specified. It may have been a network
  • program may also have been a cable channel or a broadcasting station
  • the program data record indicates all
  • usage record indicates television usage by person and household.
  • Homes Base is defined as the total number of homes in the United
  • This figure on a percentage basis, includes more than 98% of all homes.
  • Prime Time The federal government defines prime time as those evening hours
  • HUTS Households Using Television
  • percent HUTS percent HUTS
  • HUT rating percent HUTS
  • HUT ratings typically peak at over 60% during prime time and can
  • HUTS typically
  • Household Rating is defined as the portion of all homes
  • Demographic rating is similar to household rating, but this figure is calculated
  • 25 used as another indicator used for decision-making based on demographics.
  • Share is defined as the portion of homes with television sets on which
  • Homes Delivered. Advertising effectiveness is sometimes based on homes
  • This value is defined as the rating for a show multiplied by the
  • VPH Viewers Per Viewing Household
  • impressions homes delivered x
  • VPH homes base x rating x VPH
  • 27 embodiment of the present invention includes four main components: a database mining
  • DME DME engine
  • optimization mechanism a user interface
  • a suitable computer system is necessarily a part of the
  • computing apparatus or a single user device such as a personal computer or workstation.
  • Computer system 100 suitably comprises a processor 110, main memory 120, a memory
  • controller 130 an auxiliary storage interface 140, and a terminal interface 150, all of
  • FIG. 1 is presented to simply illustrate some of the salient features of
  • Processor 110 performs computation and control functions of computer system
  • Processor 110 may be any suitable central processing unit (CPU).
  • CPU central processing unit
  • a single integrated circuit such as a microprocessor, or may comprise any combination thereof
  • Processor 110 suitably executes an object-
  • Auxiliary storage interface 140 allows computer system 100 to store and retrieve
  • auxiliary storage devices such as magnetic disk (e.g., hard disks or
  • DASD 170 is a direct access storage device 170. As shown in FIG. 1, DASD 170 may be a
  • floppy disk drive which may read programs and data from a floppy disk 180. It is
  • signal bearing media examples include: recordable type media such as
  • floppy disks e.g., disk 180
  • CD ROMS compact disc ROMS
  • transmission type media such as digital signals
  • Memory controller 130 through use of a processor (not shown) separate from
  • processor 110 is responsible for moving requested information from main memory 120
  • auxiliary storage interface 140 to processor 110. While memory
  • controller 130 is shown as a separate entity for purposes of explanation, those skilled in
  • controller 130 may actually reside in the circuitry associated with processor 110, main
  • Terminal interface 150 allows system administrators and computer programmers
  • system bus 160 of the preferred embodiment is a
  • Main memory 120 suitably contains an operating system 122, a graphical user
  • DME Database Mining Engine
  • Graphical user interface 125 in memory 120 provides access for
  • system 100 a user of system 100, allowing the user to access the various features of system 100.
  • DME database 126 is a customized version of a previously created database that is
  • DME 127 is a
  • DBMS database management system
  • DME 127 uses a
  • Advertising optimization mechanism 129 allows a user of system 100 to
  • DME database 126 uses the data contained in DME database 126. Although shown as separate components
  • main memory 120 does not necessarily contain all
  • portions of operating system 122 may be
  • files may well be stored on magnetic or optical disk storage devices (not shown).
  • Database Mining Engine (DME) 127 is shown to reside in the same
  • main memory 120 may consist of multiple disparate memory locations (e.g. backside
  • sample populations can successfully implement the various techniques and methods
  • a system 100 according to various preferred embodiments of the present
  • portions of existing databases from location to location (such as from a central server to a
  • the data in memory, and analyze the data; the ability to filter the data according to user
  • DME database 126 The design of DME database 126 is performance driven and, for at
  • At least one preferred embodiment is specifically designed to efficiently access the Nielsen
  • DME database 126 and DME 127) can be created to take advantage of the specific
  • a DME database 126 according to a preferred embodiment of the present
  • invention is capable of spanning many weeks and is composed of .tvd discrete files, one
  • tvd. TVD is an acronym for Television Viewing Data and is used to identify
  • viewing data is simply placed into the directory for DME database 126 along with all
  • a user or system administrator can create a copy of all or a selected portion of
  • DME database 126 by copying some or all of the .tvd files to another memory storage
  • interface 125 described below, direct system 100 to access the new database location.
  • the .tvd data files contained in DME database 126 can be considered object-
  • a household object i.e. a household object, a person object, a television
  • each object in the database file is the same as the required length and byte position of
  • a region of memory 120 is allocated for loading a
  • Memory 120 is sized
  • the second byte of data is loaded into the first byte of allocated memory.
  • the second byte of data is
  • system 100 can operate on the data as objects.
  • data items are loaded into memory 120, system 100 can operate on the data as objects.
  • This unique database structure allows for binary data transfer of large blocks of
  • DME 127 requires memory based data objects for processing and can begin operating
  • process 200 generally involves organizing the input data from the standard format (in
  • program objects read the data supplied by Nielsen and assign values to the
  • object data elements in memory such as age, or program name (step 230); and, write the
  • step 240 It is important to note that there is no requirement to locate all blocks of
  • blocks of memory 120 are allocated and the blocks of memory 120 are
  • step 310 the blocks of data are read from the .tvd data files
  • DME 127 can access the television viewing data. This type of data retrieval is not
  • data in a typical database is typically not the same as the data in memory 120.
  • week 1 file are the same size as person objects from the week 10 file.
  • DME database 126 does not provide the data type independence of
  • Each household data item in each classification record is translated to binary form
  • the income indicator "5" in the record above, for example, is translated to the binary
  • classification record is also translated to binary form and assigned to the corresponding
  • Each program data item in each program data lead record is assigned to the
  • Viewing Data the memory location in the viewing data memory for this
  • One is a program data record, and the other is a usage record.
  • the program data record indicates all those persons and households in the sample who
  • the "usage” record indicates television usage by
  • audience counts will be accurate, but the non-network audience counts will be inflated.
  • database 126 information in database 126 is illustrated. Rather than allocate a fixed set of memory
  • memory space can be
  • the data conversion mechanism will use Processor 110
  • the process of creating a .tvd data file for DME database 126 involves reorganizing a
  • Each household data item in each classification record is translated to binary
  • usage viewing event refers to this network viewing event.
  • cable television network data can be added
  • the Nielsen data contains
  • This data can be used to identify and select
  • a user might wish to select all women between the ages of 18 and 49 who live
  • database 126 is organized so that the selection can be made using Boolean logic, which is
  • audience members are not stored as integers, but are stored as a 16 bit field
  • each bit represents one of the available age ranges.
  • the first several age ranges are
  • graphical user interface 125 utilizes a 16 bit age
  • DME 127 performs a logical "and" operation using the
  • this procedure may be represented as "PersonAge & AgeSelectionMask.”
  • 0000000000010000 (age of audience member) + 00000000001 11000 (age selection mask) 0000000000010000
  • the person is in the requested age range.
  • DME database 126 can be quickly and efficiently screened to locate the sample audience
  • the Nielsen person-by-person data provides “viewing data" for any given week.
  • the viewing data indicates the viewing choices made by sample households and members
  • the data may indicate that the viewing
  • DME database 126 the sample viewing data provided by Nielsen for a given
  • the broadcasting week begins at 6:00 AM on Monday
  • the first block of viewing data begins at 6:08 AM on Monday and ends at
  • the second block begins at 12:08 PM and ends at 5:53 PM, etc.
  • Each of these 28 blocks for a given week contains all of the viewing data for a
  • DME database 126 appropriate block of data in DME database 126 will be retrieved from DME database 126
  • This block will be the block that contains all the viewing
  • FIG. 6 a simplified graphical representation of the data
  • the indices in each of the a ⁇ ays indicate a relationship to the data contained in
  • FIG. 6 presents a
PCT/US1999/005363 1998-03-11 1999-03-09 Method and apparatus for analyzing data and advertising optimization WO1999046719A1 (en)

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Application Number Priority Date Filing Date Title
AU30809/99A AU3080999A (en) 1998-03-11 1999-03-09 Method and apparatus for analyzing data and advertising optimization

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US09/038,380 US6286005B1 (en) 1998-03-11 1998-03-11 Method and apparatus for analyzing data and advertising optimization
US09/038,380 1998-03-11

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AU (1) AU3080999A (US06286005-20010904-M00012.png)
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