U.S. patent application number 16/654071 was filed with the patent office on 2020-02-13 for translation of data consolidated from multiple sources.
This patent application is currently assigned to iHeartMedia Management Services, Inc.. The applicant listed for this patent is iHeartMedia Management Services, Inc.. Invention is credited to Marwan Shaban, Arthur Weinberger.
Application Number | 20200051096 16/654071 |
Document ID | / |
Family ID | 44259240 |
Filed Date | 2020-02-13 |
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United States Patent
Application |
20200051096 |
Kind Code |
A1 |
Weinberger; Arthur ; et
al. |
February 13, 2020 |
TRANSLATION OF DATA CONSOLIDATED FROM MULTIPLE SOURCES
Abstract
A server device obtains sets of input data including input CUME
values associated with radio stations from different
ratings-data-vendor devices. The input CUME values from a first
ratings-data-vendor device are associated with a first limited set
of input dayparts, and the input CUME values from a second
ratings-data-vendor device are associated with a second limited set
of input dayparts. The server obtains a list of media stations and
schedule information associated with the media stations, the
scheduling information including arbitrary client-specified
dayparts. The input CUME values are consolidated, and translated
consolidated data is generated by translating the input CUME values
for the individual radio stations from the first and second limited
set of input dayparts to determine an output CUME value associated
with the arbitrary client-specified dayparts different from the
input dayparts. The translated consolidated data is presented in a
single interface that provides the ability to switch between the
sets of input data obtained from different ratings-vendor
devices.
Inventors: |
Weinberger; Arthur;
(Sunnyvale, CA) ; Shaban; Marwan; (St. Cloud,
FL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
iHeartMedia Management Services, Inc. |
San Antonio |
TX |
US |
|
|
Assignee: |
iHeartMedia Management Services,
Inc.
San Antonio
TX
|
Family ID: |
44259240 |
Appl. No.: |
16/654071 |
Filed: |
October 16, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
14100689 |
Dec 9, 2013 |
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16654071 |
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12687355 |
Jan 14, 2010 |
8606617 |
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14100689 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0201 20130101;
G06Q 30/0204 20130101; G06Q 30/02 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A method of presenting, to a user, data from a plurality of
different sources, the method comprising: obtaining by a server
device, sets of input data including input CUME values associated
with a plurality of individual radio stations from a plurality of
different ratings-data-vendor devices connected via a
communications network, the input CUME values from a first
ratings-data-vendor device being associated with a first limited
set of input dayparts, and the input CUME values from a second
ratings-data-vendor device being associated with a second limited
set of input dayparts different from the first limited set of input
dayparts; obtaining by the server device a list of media stations
and schedule information associated with the media stations, the
scheduling information including arbitrary client-specified
dayparts, by executing program instructions in a computer system;
consolidating by the server device the input CUME values obtained
from the first and second ratings-data-vendor devices; generating
translated consolidated data by translating the input CUME values
for the individual radio stations from the first and second limited
set of input dayparts to determine an output CUME value associated
with the arbitrary client-specified dayparts different from the
input dayparts, by executing program instructions in a computer
system; and presenting the translated consolidated data in a single
interface that provides the ability to switch between the sets of
input data obtained from different ratings-vendor devices.
2. The method of claim 1, wherein the translating comprises:
identifying a minimal parent daypart, wherein the minimal parent
daypart corresponds to a smallest input daypart that encompasses a
particular arbitrary client-specified daypart, by executing program
instructions in a computer system; determining at least a first
CUME value associated with a maximal input daypart encompassed by
the particular arbitrary client-specified daypart, by executing
program instructions in a computer system; and determining at least
a second CUME value associated with an intersection of the
particular arbitrary client-specified daypart and selected input
dayparts, by executing program instructions in a computer
system.
3. The method of claim 2, further comprising: computing, by
executing program instructions in a computer system, an output CUME
value for the particular arbitrary client specified daypart
according to the following formula: C = [ 1 - .PI. i = 1 , n ( 1 -
C i P cumesubstitute ) ] * P cumesubstitute ##EQU00006## where n is
the number of media stations included in a set of media stations
comprising the plurality of individual radio stations,
P.sub.cumesubstitute is a CUME value of the minimal parent daypart,
and C.sub.i is each CUME value for an individual media station
i.
4. The method of claim 3, further comprising: calculating, by
executing program instructions in a computer system, a CUME value
for the set of media stations based on the following formula: C = [
1 - .PI. i = 1 , n ( 1 - C i P ) ] * P ##EQU00007## where n is the
number of media stations included in the set of media stations, P
is the population value, and C.sub.i is each CUME value for an
individual media station i.
5. The method of claim 2, further comprising: adding the CUME value
of the particular arbitrary client specified daypart to a list, by
executing program instructions in a computer system; removing the
maximal input daypart from the particular arbitrary client
specified daypart, by executing program instructions in a computer
system; adding the CUME value of the intersection to the list, by
executing program instructions in a computer system; and removing
the intersection from the particular arbitrary client specified
daypart, by executing program instructions in a computer
system.
6. The method of claim 1, wherein the arbitrary client-specified
dayparts represent sums of component dayparts in a proposed
advertising schedule.
7. The method of claim 6, further comprising: computing, by
executing program instructions in a computer system, a reach of the
proposed advertising schedule based on an inverse exponential
function of spot count.
8. A computer system comprising: one or more processors to process
program instructions; a memory device coupled to said one or more
processors; and program instructions residing in said memory
device, said program instructions configured to implement a method
including: obtaining sets of input data including input CUME values
associated with a plurality of individual radio stations from a
plurality of different ratings-data-vendor devices connected via a
communications network, the input CUME values from a first
ratings-data-vendor device being associated with a first limited
set of input dayparts, and the input CUME values from a second
ratings-data-vendor device being associated with a second limited
set of input dayparts different from the first limited set of input
dayparts; obtaining a list of media stations and schedule
information associated with the media stations, the scheduling
information including arbitrary client-specified dayparts, by
executing program instructions in a computer system; consolidating
the input CUME values obtained from the first and second
ratings-data-vendor devices; generating translated consolidated
data by translating the input CUME values for the individual radio
stations from the first and second limited set of input dayparts to
determine an output CUME value associated with the arbitrary
client-specified dayparts different from the input dayparts; and
presenting the translated consolidated data in a single interface
that provides the ability to switch between the sets of input data
obtained from different ratings-vendor devices.
9. The computer system of claim 8, wherein the translating
comprises: identifying a minimal parent daypart, wherein the
minimal parent daypart corresponds to a smallest input daypart that
encompasses a particular arbitrary client-specified daypart, by
executing program instructions in a computer system; determining at
least a first CUME value associated with a maximal input daypart
encompassed by the particular arbitrary client-specified daypart,
by executing program instructions in a computer system; and
determining at least a second CUME value associated with an
intersection of the particular arbitrary client-specified daypart
and selected input dayparts, by executing program instructions in a
computer system.
10. The computer system of claim 9, wherein the method includes:
computing an output CUME value for a particular arbitrary client
specified daypart according to the following formula: C = [ 1 -
.PI. i = 1 , n ( 1 - C i P cumesubstitute ) ] * P cumesubstitute
##EQU00008## where n is the number of media stations included in a
set of media stations comprising the plurality of individual radio
stations, P.sub.cumesubstitute is a CUME value of the minimal
parent daypart, and C.sub.i is each CUME value for an individual
media station i.
11. The computer system of claim 10, wherein the method includes:
computing a CUME value for the set of media stations based on the
following formula: C = [ 1 - .PI. i = 1 , n ( 1 - C i P ) ] * P
##EQU00009## where n is the number of media stations included in
the set of media stations, P is the population value, and C.sub.i
is each CUME value for an individual media station i.
12. The computer system of claim 9, wherein the method includes:
adding the CUME value of the particular arbitrary client specified
daypart to a list; removing the maximal input daypart from the
particular arbitrary client specified daypart; adding the Cume
value of the intersection to the list; and removing the
intersection from the particular arbitrary client specified
daypart.
13. The computer system of claim 8, wherein the arbitrary
client-specified dayparts represent sums of component dayparts in a
proposed advertising schedule.
14. The computer system of claim 13, further comprising: computing
a reach of the proposed advertising schedule based on an inverse
exponential function of spot count.
15. A computer program product comprising: a non-transitory
computer-readable storage medium; and program instructions residing
in said non-transitory computer-readable storage medium, said
program instructions including: at least one instruction to obtain
sets of input data including input CUME values associated with a
plurality of individual radio stations from a plurality of
different ratings-data-vendor devices connected via a
communications network, the input CUME values from a first
ratings-data-vendor device being associated with a first limited
set of input dayparts, and the input CUME values from a second
ratings-data-vendor device being associated with a second limited
set of input dayparts different from the first limited set of input
dayparts; at least one instruction to obtain a list of media
stations and schedule information associated with the media
stations, the scheduling information including arbitrary
client-specified dayparts, by executing program instructions in a
computer system; at least one instruction to consolidate the input
CUME values obtained from the first and second ratings-data-vendor
devices; at least one instruction to generate translated
consolidated data by translating the input CUME values for the
individual radio stations from the first and second limited set of
input dayparts to determine an output CUME value associated with
the arbitrary client-specified dayparts different from the input
dayparts; and at least one instruction to present the translated
consolidated data in a single interface that provides the ability
to switch between the sets of input data obtained from different
ratings-vendor devices.
16. The computer program product of claim 15, wherein the at least
one instruction to translate comprises: at least one instruction to
identify a minimal parent daypart, wherein the minimal parent
daypart corresponds to a smallest input daypart that encompasses a
particular arbitrary client-specified daypart; at least one
instruction to determine at least a first CUME value associated
with a maximal input daypart encompassed by the particular
arbitrary client-specified daypart; and at least one instruction to
determine at least a second CUME value associated with an
intersection of the particular arbitrary client-specified daypart
and selected input dayparts.
17. The computer program product of claim 16, wherein the program
instructions include: at least one instruction to compute an output
CUME value for the particular arbitrary client specified daypart
according to the following formula: C = [ 1 - .PI. i = 1 , n ( 1 -
C i P cumesubstitute ) ] * P cumesubstitute ##EQU00010## where n is
the number of media stations included in a set of media stations
comprising the plurality of individual radio stations,
P.sub.cumesubstitute is a CUME value of the minimal parent daypart,
and C.sub.i is each CUME value for an individual media station
i.
18. The computer program product of claim 17, wherein the program
instructions include: at least one instruction to compute a CUME
value for the set of media stations based on the following formula:
C = [ 1 - .PI. i = 1 , n ( 1 - C i P ) ] * P ##EQU00011## where n
is the number of media stations included in the set of media
stations, P is the population value, and C.sub.i is each CUME value
for an individual media station i.
19. The computer program product of claim 16, wherein the program
instructions include: at least one instruction to add the CUME
value of the particular arbitrary client specified daypart to a
list; at least one instruction to remove the maximal input daypart
from the particular arbitrary client specified daypart; at least
one instruction to add the Cume value of the intersection to the
list; and at least one instruction to remove the intersection from
the particular arbitrary client specified daypart.
20. The computer program product of claim 15, wherein the arbitrary
client-specified dayparts represent sums of component dayparts in a
proposed advertising schedule.
Description
CROSS REFERENCE TO RELATED PATENTS
[0001] The present U.S. Utility patent application claims priority
pursuant to 35 U.S.C. .sctn. 120 as a continuation of U.S. Utility
application Ser. No. 14/100,689 entitled "RADIO STATION MARKET
ANALYSIS," filed Dec. 9, 2013, which is a continuation of U.S.
Utility application Ser. No. 12/687,355 entitled "METHOD FOR
COMPUTING REACH OF AN ARBITRARY RADIO ADVERTISING SCHEDULE," filed
Jan. 14, 2010, now U.S. Pat. No. 8,606,617 issued on Dec. 10, 2013,
which are hereby incorporated herein by reference in their entirety
and made part of the present U.S. Utility patent application for
all purposes.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] Not Applicable
INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT
DISC
[0003] Not Applicable
BACKGROUND OF THE INVENTION
Technical Field of the Invention
[0004] The present invention generally relates to consolidation of
data from multiple sources, and more particularly to translating
the consolidated data.
Description of Related Art
[0005] Radio ratings are very important to many different divisions
of a radio station company, including radio station executives,
advertising and marketing departments, and program directors. Radio
station executives use ratings statistics to help them evaluate the
health of the company's radio stations, as well as monitoring
competitors and industry-wide trends. Advertisers and marketers
depend on ratings to measure the effectiveness of their
advertising/marketing strategies and adapt to changing market
environments and fads. It is a program director's responsibility to
not only have an intimate understanding of how ratings are compiled
and calculated, but also how to utilize these ratings in an effort
to adapt and innovate software solutions for varying market
circumstances and business needs.
[0006] There are several standard types of statistics (ratings
data) for researching radio stations, including AQH (or AQHP),
Cume, and primary demographic. AQH stands for Average Quarter Hour
(AQHP is Average Quarter Hour Persons), and refers to the average
number of people listening to a radio station for at least five
minutes in any quarter hour of a radio station's schedule. The
number of people listening to an entire hour is not necessarily the
sum of four quarter hours because of duplication. However, some
people may listen for more than a single quarter hour. Cume is the
total number of different (unique) persons that listen to a radio
station within a given daypart. A daypart is a set of times
throughout a given week. For example, a daypart could be every
weekday (Monday through Friday) from 6:00 am until 10:00 am. If the
daypart is 15 minutes there is no difference between AQH and Cume.
Primary demographic refers to various categories of consumers
(listeners of a given radio station) such as gender or age.
[0007] Arbitron, Inc., is an organization which collects raw radio
listener data and generates statistical information similar to the
standard statistics mentioned above. It is a media and marketing
research firm which primarily serves media companies and
advertisers/advertising agencies who carry out ratings analysis
based on the statistics. Arbitron selects random samples of the
population throughout various metro areas in the United States, and
participants keep a diary of their actual listening times.
Respondent-level data (RLD) is the raw data collected by Arbitron,
while the summary data set (SDS) is the various statistics
calculated by Arbitron, which is derived from the respondent-level
data and has only specifically-selected dayparts (40 dayparts
total).
[0008] Tapscan is a local market radio ratings software suite
developed by Arbitron, which is used by media planners (e.g., ad
agencies) to decide where to place their clients' radio
commercials. Some of the specific features of Tapscan include
ranking radio stations based on their broadcast hours, day,
audiences, etc., using audience composition data (consumer
demographics) to determine which radio stations are listened to by
what people, presenting cost and radio station data in different
ways, providing access to customized demographics, geographies,
dayparts and multibook averages, and determining a radio station's
reach and frequency by specific demographic, daypart, and spot
level. Tapscan uses RLD and SDS, and other data sets such as
Arbitron's Black Radio Data, Hispanic Radio Data, and Eastlan Radio
Data.
[0009] Although Tapscan and other radio station ratings programs
can provide a reach value for a radio station, the reach provided
is calculated based on interpretation of listener statistics. Those
interested in radio station research might find a different source
of reach useful, as well as other statistics which are related to
reach. The values of such statistics as AQH and Cume provided by
Arbitron are calculated using a limited set of dayparts, which
means that these values would be different if an alternative set of
dayparts was defined.
[0010] It would, therefore, be desirable to devise an improved
method of calculating ratings data for radio stations. It would be
further advantageous if the method could effectively approximate
different ratings statistics from previously collected data for
arbitrary user-specified schedules.
SUMMARY OF THE INVENTION
[0011] The foregoing objects are achieved in a method of extending
Cume values for individual media stations to multiple media
stations, by receiving a population value and Cume values for each
of the individual media stations based on given ratings parameters,
identifying a set of multiple media stations including two or more
of the individual media stations, and computing a Cume value C for
the set of multiple media stations according to the formula
C = [ 1 - .PI. i = 1 , n ( 1 - C i P ) ] * P ##EQU00001##
where n is the number of media stations in the set, P is the
population value, and C.sub.i is each Cume value for an individual
media station i in the set, by executing program instructions in a
computer system. The ratings parameters may include a particular
geographic market, a particular demographic, and a particular
daypart, and the population value may be the population of the
particular demographic over the particular geographic market. The
Cume values for each of the individual media stations may be
provided for a limited set of input dayparts (for example from
Arbitron or Nielson), and the Cume values can be translated to an
arbitrary daypart different from the input dayparts by identifying
a smallest one of the input dayparts that encompasses the arbitrary
daypart (the minimal parent), creating a list of Cume values which
include first Cume values for maximal input dayparts encompassed by
the arbitrary daypart and second Cume values for intersections of
the arbitrary daypart and selected input dayparts, and computing a
desired Cume value for the arbitrary daypart according to a similar
formula but substituting the Cume value of the minimal parent for
the population P. The method can operate on first Cume values from
a first vendor for a first set of dayparts to provide a first
output, and operate on second Cume values from a second vendor for
a second set of dayparts different from the first set to provide a
second output. The arbitrary daypart can represent a sum of
component dayparts in a proposed advertising schedule. The reach of
the proposed advertising schedule can be further computed based on
an inverse exponential function of spot count.
[0012] The above as well as additional objectives, features, and
advantages of the present invention will become apparent in the
following detailed written description.
[0013] The present invention may be better understood, and its
numerous objects, features, and advantages made apparent to those
skilled in the art by referencing the accompanying drawings.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)
[0014] FIG. 1 is a block diagram of a computer system programmed to
carry out radio station ratings analysis in accordance with one
implementation of the present invention;
[0015] FIG. 2 is a pictorial representation of a system used in
accordance with one embodiment of the present invention for
computing radio station marketing statistics;
[0016] FIG. 3 is a chart illustrating the logical flow for a
process of translating and rolling up average quarter hour (AQH)
and Cume values to calculate various outputs for radio station
ratings analysis in accordance with one implementation of the
present invention;
[0017] FIG. 4 is a chart illustrating the logical flow for a
process of computing Cume values for arbitrary dayparts in
accordance with one implementation of the present invention;
[0018] FIG. 5 is a chart illustrating the logical flow for a
process of rolling up Cume values over multiple radio stations in
accordance with one implementation of the present invention;
and
[0019] FIG. 6 is a graph illustrating an inverse exponential model
for computing reach of an arbitrary radio advertising schedule in
accordance with one implementation of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0020] The use of the same reference symbols in different drawings
indicates similar or identical items.
[0021] With reference now to the figures, and in particular with
reference to FIG. 1, there is depicted one embodiment 10 of a
computer system in which the present invention may be implemented
to carry out radio station ratings analysis. Computer system 10 is
a symmetric multiprocessor (SMP) system having a plurality of
processors 12a, 12b connected to a system bus 14. System bus 14 is
further connected to a combined memory controller/host bridge
(MC/HB) 16 which provides an interface to system memory 18. System
memory 18 may be a local memory device or alternatively may include
a plurality of distributed memory devices, preferably dynamic
random-access memory (DRAM). There may be additional structures in
the memory hierarchy which are not depicted, such as on-board (L1)
and second-level (L2) or third-level (L3) caches.
[0022] MC/HB 16 also has an interface to peripheral component
interconnect (PCI) Express links 20a, 20b, 20c. Each PCI Express
(PCIe) link 20a, 20b is connected to a respective PCIe adaptor 22a,
22b, and each PCIe adaptor 22a, 22b is connected to a respective
input/output (I/O) device 24a, 24b. MC/HB 16 may additionally have
an interface to an I/O bus 26 which is connected to a switch (I/O
fabric) 28. Switch 28 provides a fan-out for the I/O bus to a
plurality of PCI links 20d, 20e, 20f These PCI links are connected
to more PCIe adaptors 22c, 22d, 22e which in turn support more I/O
devices 24c, 24d, 24e. The I/O devices may include, without
limitation, a keyboard, a graphical pointing device (mouse), a
microphone, a display device, speakers, a permanent storage device
(hard disk drive) or an array of such storage devices, an optical
disk drive, and a network card. Each PCIe adaptor provides an
interface between the PCI link and the respective I/O device. MC/HB
16 provides a low latency path through which processors 12a, 12b
may access PCI devices mapped anywhere within bus memory or I/O
address spaces. MC/HB 16 further provides a high bandwidth path to
allow the PCI devices to access memory 18. Switch 28 may provide
peer-to-peer communications between different endpoints and this
data traffic does not need to be forwarded to MC/HB 16 if it does
not involve cache-coherent memory transfers. Switch 28 is shown as
a separate logical component but it could be integrated into MC/HB
16.
[0023] In this embodiment, PCI link 20c connects MC/HB 16 to a
service processor interface 30 to allow communications between I/O
device 24a and a service processor 32. Service processor 32 is
connected to processors 12a, 12b via a JTAG interface 34, and uses
an attention line 36 which interrupts the operation of processors
12a, 12b. Service processor 32 may have its own local memory 38,
and is connected to read-only memory (ROM) 40 which stores various
program instructions for system startup. Service processor 32 may
also have access to a hardware operator panel 42 to provide system
status and diagnostic information.
[0024] In alternative embodiments computer system 10 may include
modifications of these hardware components or their
interconnections, or additional components, so the depicted example
should not be construed as implying any architectural limitations
with respect to the present invention.
[0025] When computer system 10 is initially powered up, service
processor 32 uses JTAG interface 34 to interrogate the system
(host) processors 12a, 12b and MC/HB 16. After completing the
interrogation, service processor 32 acquires an inventory and
topology for computer system 10. Service processor 32 then executes
various tests such as built-in-self-tests (BISTs), basic assurance
tests (BATs), and memory tests on the components of computer system
10. Any error information for failures detected during the testing
is reported by service processor 32 to operator panel 42. If a
valid configuration of system resources is still possible after
taking out any components found to be faulty during the testing
then computer system 10 is allowed to proceed. Executable code is
loaded into memory 18 and service processor 32 releases host
processors 12a, 12b for execution of the program code, e.g., an
operating system (OS) which is used to launch applications and in
particular the radio station statistical application of the present
invention, results of which may be stored in a hard disk drive of
the system (an I/O device 24). While host processors 12a, 12b are
executing program code, service processor 32 may enter a mode of
monitoring and reporting any operating parameters or errors, such
as the cooling fan speed and operation, thermal sensors, power
supply regulators, and recoverable and non-recoverable errors
reported by any of processors 12a, 12b, memory 18, and MC/HB 16.
Service processor 32 may take further action based on the type of
errors or defined thresholds.
[0026] While the illustrative implementation provides program
instructions embodying the present invention on a disk drive of
computer system 10, those skilled in the art will appreciate that
the invention can be embodied in a program product utilizing other
computer-readable storage media. The program instructions may be
written in the C++ programming language for a Windows 7 environment
or in other programming languages suitable for other operating
system platforms. Computer system 10 carries out program
instructions for a radio station ratings analysis process that uses
novel computational techniques to manage statistical data.
Accordingly, a program embodying the invention may include
conventional aspects of various statistical tools, and these
details will become apparent to those skilled in the art upon
reference to this disclosure.
[0027] FIG. 2 is a pictorial representation of a station marketing
analysis system 44 used in accordance with one embodiment of the
present invention for computing radio station marketing statistics.
Station marketing analysis system 44 employs an engine 45 denoted
"AudiencePro" which may be implemented through execution of program
instructions on computer system 10. AudiencePro engine 45 can
collect various types of information, including (but not limited
to) raw Arbitron data 46, raw Nielson data 47, station list and
schedules 48, and spot prices 49. Raw Arbitron data 46 includes
data such as AQH, Cume, and market populations, for a limited set
of summary dayparts (input/Arbitron dayparts). For example,
Arbitron provides AQH values for the dayparts Monday-Friday 5-6
a.m., Monday-Friday 6-7 a.m., . . . , Monday-Friday midnight-1
a.m., and larger granularities such as Monday-Friday 1-5 a.m. The
same dayparts are provided for Saturday and Sunday, individually.
One daypart is even Monday-Sunday 6 a.m.-6 a.m. (the entire week).
Raw Nielson data 47 is similar to raw Arbitron data 46 but is
provided for a different set of dayparts. Station list and
schedules 48 provides AudiencePro engine 45 with a list of stations
and various advertising schedules including arbitrary
(user-defined) sets of dayparts as desired by the client for a
particular marketing campaign. Radio stations sell their airtime
according to dayparts. A simple daypart lineup might be: 6 am-10
am, 10 am-3 pm, 3 pm-7 pm, and 7 pm-midnight. AudiencePro engine 45
can also receive information regarding spot prices 49 from the
client or other sources such as a yield management system which
automates pricing. A spot price is the current price at which a
particular commodity (in this example, radio advertising time) can
be bought or sold. These information sources can be in operative
communication with AudiencePro engine 45 by a variety of means,
such as public or private wired or wireless (radio or cellular)
networks including the Internet, satellite, public switched
telephone network (PSTN), or any combination of the foregoing,
including some form of direct wiring. When AudiencePro engine 45 is
deployed in a client environment, it can download all Arbitron or
Nielson data needed for an extended period of time and cache it on
the client machine (computer system 10).
[0028] After processing inputs from the information sources,
AudiencePro engine 45 produces delivery information 50 which can
include a wide variety of ratings-related statistics. Delivery
information can be provided in a variety of forms to any output
device (i.e., I/O device 24) of computer system 10, such as a
display device or printer. In a preferred embodiment delivery
information 50 includes: [0029] Gross Impressions ("GI," the sum of
individual AQH numbers, rolled up across stations and dayparts);
[0030] CPM (Cost Per Thousand=price/GI*1000); [0031] GRP (Gross
Rating Points=GI/population*100); [0032] CPP (Cost Per
Point=price/GRP); [0033] Reach (can be derived in conjunction with
FIG. 6 below); [0034] % Market Reach (Reach*100/population); [0035]
Frequency (GI/Reach); [0035] CPMNR (CPM Net
Reach=price/Reach*1000); [0036] AQH (for the daypart in question,
does not depend on spot count); [0037] AQH Rating
(AQH/population*100); [0038] Cume (for the daypart in question,
does not depend on spot count); [0039] Cume Rating
(Cume/population*100).
[0040] AudiencePro engine 45 can maintain this output data and more
(including input data and intermediate data) in multi-dimensional
arrays or matrices with different variables indexed as appropriate,
such as by book, market, demographic, station, Arbitron daypart, or
client daypart.
[0041] As explained further below, in order to produce these
outputs AudiencePro engine 45 executes several calculations
including computing the Cume of an arbitrary daypart from the Cume
of a limited set of dayparts, and computing a Cume for multiple
stations from Cume values of individual stations. The outputs of
AudiencePro engine 45 are accordingly dependent on various user
inputs, such as the list of stations, demographic, flight dates and
schedule for each desired station (including specific dayparts,
spot counts, and any weighting adjustments).
[0042] FIG. 3 is a flow chart for a process of translating and
rolling up AQH and Cume values to calculate the various outputs of
AudiencePro engine 45 and particularly Reach, in accordance with
one implementation of the present invention. In FIG. 3 and the
figures that follow, AQH is represented by the letter "S", and Cume
is represented by the letter "C". The radio station ratings
analysis process begins when computer system 10 receives
information from the various sources, particularly S and C of all
available stations for limited (Arbitron or Nielson) dayparts, a
station list, and a proposed schedule including client
(AudiencePro) dayparts (51). The radio station ratings application
of the present invention can consolidate the data streams from
multiple vendors such as Arbitron and Nielson to present a single
interface to the user. In this manner the system can switch between
sets of input data to provide output based on a selected vendor.
The input S values for each client station are then translated from
the limited dayparts to the client dayparts (52). If the client
daypart is contained in an input daypart, then the S value from
that input daypart will simply be used for the translation. If
instead the client daypart is not contained in an input daypart,
then the smallest set of input dayparts that contain the client
daypart will be used for the translation. In this case, the S value
for the client daypart is the weighted average of S values for the
input dayparts in this set. The average is weighted by the portion
of the client daypart contained in each input daypart used. After S
values are translated, they can be extended (rolled up) across
multiple (client) dayparts and multiple stations (54). To roll up
the S values, gross impressions (GI) are added up across order
lines which can span multiple dayparts and multiple stations. Then,
the rolled up S for the schedule (which can span multiple dayparts
and stations) is the total GI divided by the number of spots.
[0043] Once these intermediate AQH computations are complete,
similar computations are performed for Cume. The input C values for
each client station are translated from the limited dayparts to the
client dayparts (56). A preferred computation for this C
translation is discussed further below in conjunction with FIG. 4.
After C values are translated, they can be rolled up across
multiple (client) dayparts and multiple stations (58). Rolling up C
values across dayparts is accomplished used the same algorithm for
C value translation; the input daypart for the algorithm is given
as the sum of dayparts in the proposed schedule. A preferred
computation for rolling up C values across stations is discussed
further below in conjunction with FIG. 5. Delivery information 50
is then computed according the formulas given above (60), and the
process is complete. The translation and rolling up of S and C
values can introduce inaccuracies to varying degrees but the
resulting outputs are still considered excellent approximations for
marketing and research purposes.
[0044] In some embodiments the translation of C values from a
limited set of dayparts to an arbitrary daypart (56) is
accomplished by the process illustrated in the flow chart of FIG.
4. This process begins when computer system 10 identifies the
smallest input daypart which still covers an entire
(user-specified) daypart, referred to herein as the minimal parent.
A list of C values is created and opened at first without any
entries (64). The process loops through all of the input dayparts,
identifying in each iteration the maximal (longest) input daypart
that is still within the arbitrary daypart. The C value of the
maximal input daypart is added to the list, and the maximal input
daypart for this iteration is subtracted from the arbitrary daypart
(66). The process then loops through selected input dayparts to
find any intersections (overlaps) with the remaining arbitrary
daypart. In the implementation for Arbitron input data, the
selected input dayparts are M-F AM, Sat AM, Sun AM, M-F MD, Sat MD,
Sun MD, M-F PM, Sat PM, Sun PM, M-F EV, Sat EV, Sun EV, and M-Su
ON. If an intersection is found a C value is computed for it and
added to the list. The intersection is removed from the arbitrary
daypart, so any intersections are mutually exclusive (68). The C
values for the intersections may be computed in various manners,
preferably using a hyperbolic fit. The fit uses a hyperbola of the
form y=Ax/(Bx+C) that passes through the points (x.sub.1,y.sub.1)
and (x.sub.2,y.sub.2), where x.sub.1 is 1, y.sub.1 is the AQH of
the input daypart which is being subtracted from the arbitrary
daypart, x.sub.2 is the number of hours in the input daypart which
is being subtracted from the arbitrary daypart, y.sub.2 is the Cume
of the input daypart which is being subtracted from the arbitrary
daypart, A is y.sub.1*y.sub.2, B is
(x.sub.2*y.sub.1-x.sub.1*y.sub.2)/(x.sub.2-x.sub.1), and C is
x.sub.2*y.sub.2-Bx.sub.1.
[0045] A binomial method can be used to calculate C for the
arbitrary daypart based on the compiled C values in the list (70).
This binomial method is the same as that described below in
conjunction with FIG. 5, but the C value of the minimal parent is
substituted for the population, i.e., the Cume value
C.sub.arbitrary for the arbitrary daypart is
C arbitrary = 1 - .PI. j - 1 , m ( 1 - C j C small ) ] * C small
##EQU00002##
[0046] where m is the number of Cume values in the list,
C.sub.small is the Cume value of the smallest input daypart, and
C.sub.j is each Cume value j in the list. The arbitrary daypart can
represent a sum of component dayparts in a proposed advertising
schedule.
[0047] In a simplified example, consider input dayparts which
include a four-hour daypart of Monday-Sunday 6-10 a.m., and 1-hour
dayparts of Monday-Sunday 6-7 a.m., Monday-Sunday 7-8 a.m.,
Monday-Sunday 8-9 a.m., and Monday-Sunday 9-10 a.m. The client is
considering a schedule which includes the 3-hour daypart of
Monday-Sunday 6-9 a.m. In this case, the minimal parent would be
the four-hour daypart, and the accumulated input dayparts would be
Monday-Sunday 6-7 a.m., Monday-Sunday 7-8 a.m., and Monday-Sunday
8-9 a.m. The binomial calculation would then operate on the three C
values for these three input dayparts, using the C value of the
four-hour daypart as the population.
[0048] This binomial calculation is shown in further detail in the
flow chart of FIG. 5, but for rolling up C values across multiple
stations rather than multiple dayparts. The C values for individual
stations are based on given ratings parameters, e.g., a particular
geographic market, a particular demographic, and a particular
daypart. The process begins when computer system 10 receives those
C values and associated populations (P) for a given market over a
specific demographic (72). The process then identifies a set of the
multiple media stations based on user input (74). These stations
have C values denoted C.sub.i. The probability that a given person
in the demographic is listening to the given station at a given
time can be expressed as C.sub.i/P. Accordingly, the probability
that the person is not listening to the station at the given time
is 1-(C.sub.i/P). It also follows that the probability that the
person is not listening to any of the stations in the market at the
given time is
.PI. i - 1 , n ( 1 - C i P ) ##EQU00003##
where n is the number of stations in the set, and that the
probability that the person is listening to at least one of the
identified stations in the market is
1 - .PI. i - 1 , n ( 1 - C i P ) . ##EQU00004##
The final Cume for the multiple media stations as a group can thus
be computed as
[ 1 - .PI. i - 1 , n ( 1 - C i P ) ] * P . ( 76 ) ##EQU00005##
[0049] As noted above, the various translated and rolled up S and C
values can be used to generate a variety of outputs. One audience
statistic that is very important to marketers/advertisers is reach.
In some embodiments the present invention uses an inverse
exponential model to compute the reach of an arbitrary radio
advertising schedule, i.e., the estimated number of different
people actually hearing an ad. FIG. 6 illustrates this model which
can be constructed according to the formula
Reach=n*S*C/[(n*S)+C-S],
[0050] where n is the number of spots in a given schedule, and S
and C are the aggregate (translated and rolled up) values for the
schedule. When using this formula, for just one radio advertising
spot the reach is S, while for a very large number of spots the
reach is C.
[0051] Although the invention has been described with reference to
specific embodiments, this description is not meant to be construed
in a limiting sense. Various modifications of the disclosed
embodiments, as well as alternative embodiments of the invention,
will become apparent to persons skilled in the art upon reference
to the description of the invention. For example, the invention is
applicable to other media stations besides terrestrial radio, such
as internet radio, cable or broadcast television, or satellite. It
is therefore contemplated that such modifications can be made
without departing from the spirit or scope of the present invention
as defined in the appended claims.
* * * * *