CA2112377A1 - Reach and frequency estimation for media - Google Patents
Reach and frequency estimation for mediaInfo
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- CA2112377A1 CA2112377A1 CA002112377A CA2112377A CA2112377A1 CA 2112377 A1 CA2112377 A1 CA 2112377A1 CA 002112377 A CA002112377 A CA 002112377A CA 2112377 A CA2112377 A CA 2112377A CA 2112377 A1 CA2112377 A1 CA 2112377A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- 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
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- 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
- H04H60/29—Arrangements for monitoring broadcast services or broadcast-related services
- H04H60/33—Arrangements for monitoring the users' behaviour or opinions
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- 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
- H04H60/64—Arrangements for services using the result of monitoring, identification or recognition covered by groups H04H60/29-H04H60/54 for providing detail information
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Abstract
The reach and readership contact frequency are determined for a set of advertising media vehicles (m) with a specific number of advertisements (nm) per medium. Firstly a subset of survey respondents for each vehicle are selected. The resulting response database is then filtered. Beta distributions are used to build an array containing the probabilities of being exposed to i out of nm advertisements; the beta distribution parameters being a function of the regularity of exposure of a survey respondent to a media vehicle. The array of probabilities is then combined with survey data on media usage by the respondents to determine a joint frequency distribution of opportunities to see an advertisement, distribution parameters, reach and average contact frequency.
Description
WO93/01554 PCT/AU92~00286 211~377 REAC~ AND F~EQ~ENCY ~-TIMArIoN FOR MEDIA
TECHNICA~ FIE~D
The present invention rela~es to media research, and particularly to an advextising schedule evaluation technique known as `reach a~d ~requency' estimation.
BA~gGROUND OF ~HE INVEN~ION
The extent of exposure to mass media is a matter of interest and concern to the owners or publishers of media, to those who use the media to convey a message, normally in the form of paid advertising, and to ~he agencies they employ to assist them.
These groups are interested in both the size and the nature of the audience. However, absolute information on these qu~stions is not available from any source. In one instance, newspaper and magazine circulation may be audited by an independent body, but this gives only the number of copies sold or distributed and does not indicate how many people or what ~ind of people read these publications. , - -~0 To estimate the sizes of the audiences of individual media, it is therefore customary to conduct sample surveys in which a cross ~ection of the population (or sometimes of a specific subset of the population) is investigated in detail.
There are a number of ways in which the information may be collected and the choice will depend on the objectives of the particular study and on practical and financial considerations. Among other methods, members of a sample may be questioned about their exposure to media within a spPcific time period, record their media exposure over a period of time, or have their exposurP
~112377 2 -automatically monitored.
A particular reason for the collection of this information is the assessment (prospective or retrospective~ of the per~ormance of alternative media as vehicles for advertising. Advertisers wish to know what they are likely to get, or did get, for their expenditure in terms of the quantity and quality o~ audience~ Media owners, apart from assessing their strengths and weaknesses, wish to impress upon advertisers the benefits of their products.
The average level of exposure to a medium can be readily obtained ~rom these studies. This level is typically taken ~s the proportion of those surveyed claiming or being observed to see, hear or read the medium at any given point in time or within a given period. For some media it may be necessary to collect information from survey respondents at specific points in time; for others, it may be preferable to spread the data collection over a long period so that short-term fluctuations are evened out ~0 in the final aggregated results.
~ n many cases, media owners and advertisers wish to estimate the potential of two or more media, or o~ two or more advertisements in one medium, or (most frequently) of combinations o~ different numbers of advertisements in mor~ than;one medium. This requires knowledge both of the cross-usage of distinct media and of the turnover o~ users of individual media over time. FPW individuals are exposed to only one medium and few, if any, are consistent in their usage of anyimedium.
~or instance, peopl~ tend to be fairly regular in their reading o~ a daily newspaper, but even regular readers miss the occasional issue, and there are those who may only read an issue from time to time, or may regularly read it on one particular day only. General interest magazines tend to have a higher proportion of occasional or casual reader. Similarly, many people read one daily WOg3/01554 PCT/AU92/00286 paper, but some habitually read more than one, some sometimes read more than one and some generally read none at all.
The regularlty of the exposure of individuals to a medium can be quantified in a number of ways. `Turnover r and its complement `loyalty' are two. In Australia, the most common measure is `casualness'. The casualness (~) of a medium is defined as the ratio of the additional reach of a second issue (over the first) to the additional reach which could be expected if the persons reading, seeing or hearing the first and second issues were chosen independently of one another. Thus, 0~ casualness implies that people always do the same thing, whilst 100%
casualness constitutes completely random behavior.
Media surveys must in one way or another assess the degree of consistency of usage of îndividual media and the extent of cross-u~age. Where the behavior of respondents cannot be studied over a prolonged period, the~e must be estimated. Two methods of doing this are commonly used.
Respondents are asked to generalise their behavior in respect of individual media to giYe some indication of the regularity of their exposure to them, or hey are re~
con~acted a* a later time and asked about the same media, thus providing an indication of audience turnover. 5uch methods are used to provide indi~-ations of the con~istency of use o~ individual media within the overall population and within designated subsets of it.
The information provided by these studi~s is u~ed to simulate or assess th~ ability of alternative advertising schedules (a schedule being a list of media vehicles and numbers and types of advertisements inscrted in them) to reach designated segments of the population. The principal measures ~ommonly used are: the number or proportion of people exposed to at 1 ast one of the advertising insertions, the total number of impacts (where an impact is defined as one person being exposed to one WO g3/01554 PCr/~V92/00286 211~377 insertion) and the average impacts amongst those reached (which equals total impacts divided by reach). These are generally referred to as `reach', `total impacts' and `average frequency' respectively. In addition, it may be desirable to estimate the numbers exposed once, twice, three times, etc.; this is commonly known as the OTS
(opportunities-to-see) distribution or the `number seen' distribution. These two expressions are equivalent and may be used interchangeably.
DES~RIPTION OF T~E PRIOR ART
There are two types of media reach and frequency system in general use. These utilize either ~i) global formula methods or (ii) personal probability methods.
1~ Global formula methods, as the name implies, are based on the application of a formula or an algorithm to aggregated survey da*a such as average readership~ and pairwise cross-readerships, in the case of print media.
Personal probability models, on the other hand, treat each individual in the database separately, with each respondent a signed a prob~bility of seeing each medium.
The probabilities of 0, 1, 2,..., n OTS are then evaluated using the binomial distribution and these are then aggregated over all respondents to give the OTS
distribution for the total population.
Both techniques have their drawbacks. Critics of global formula me*hods cite: ~
(a) The problem of declining reach, whereby an extra insertion can cause the estimate of the overall reach of a schedule to decline;
(b) Non-additivity, whereby the distributions for complementary subsets of a population (e.g. men and women) do not n~cessarily reconcile with the distribution for the total (e.g. all people);
WO93/01554 ~11.23~ ~
(c) The inability to model correctly non-overlapping combinations of media (e.g. in schedules across geographical regions that are not covered by all media);
(d) The failure to model the actu~l shape of the OTS
distribution (e.g. in cases where a high-audience medium is included in a schedule with a low~audience medium); and (e) Order dependency, whereby the answer given may be dependent on the order in which the media are specified~
To a very large extent the success or failure of personal probability methods depends on the adequacy of the determination of the personal probabilities. Critics of these methods draw attention to the following drawbacks:
(a) Failure to reconcile with 'head-count data among all subsets;
(b) The dilution of ~ross-media relationships; and (c) The und~restimation o~ reach.
Presently available systems are also liable to encounter problems in dealing with incomplete data. In some cases~ where a survey covers an extended period~
information may,have been collected on particular media vPhicles fsr only part of the time, either because of a change in the scope of the survey or because those media were only avail~ble for part of the period~ In some cases; it is possible to process a schedule including one such incomplete medium by applying a weight to the aggregate results relating to that medium~ but wh re two or more such incomplete media are included, this is not possible.
Where a survey covers an extended period, it may be desirable to restrict the evaluation of a schedule ~o respondents interviewed during only a specific portion of the overall time period. In such cases, the resulting 3 ~ 7 estimates of numb~rs exposed are depressed unles~ a scaling method or an alternative set of respondent weights is employed which grosses the reduced sample up to the required population.
S~MMARY OF ~HE INVEMTION
The present invention is directed to overcoming some or all of ~he drawbacks listed above.
Therefore, as a non-limiting statement to indicate the scope and intention of the invention, it can be ~aid, in one instance, that the invention pro~ides an estimation method for an information processing system including a processor means, a memory means coupled to the processor means and operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles, input means coupled to the processor means and operable for obtaining user input, and output means coupled to the processor means. The estimation method . comprises the steps of:
(A) selecting, by the input means, at least one sch~dule of medi~ vehicles Sm~ and, for each sel~cted schedule, specifying a number of insertions (nm) in each media vehicle;
(B) defining, by the input means, at least one filter specifying a set of ~he survey respondents for which each schedule is to be evaluated;
(C) applying, by the processor means, the filter to the database; -(D) for each media vehicle, calculating, by the processor means, an array of the probabilities of being WO93/015~ PCT/AU92/00286 211~3 ~ ,~
- 7 - :
exposed to i out of nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle;
(E) combining, by the processor mear6s, the data relating to the usage of each selected media vehicle with said array of probabilities to give a probability of being exposed to i insertions in each selected media vehicle, combining the probabilities of being exposed to i insertions across the media vehicles and accumulating over all respondents passing the filter to yield a composite distribution ~Xj);
(F) by the processor means, within tbe set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usag for each selected media vehicle, and summing the ~edia vehicle usage of each pair of media vehicles (ml, m2) to give total estimated ~ross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities-to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, : the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle;
(G) operati~g, by the processor means, on each said composite distribution (Xj) to modify it so that it matches W093/0l554 PCT/AU92/00286 ~ .23~ 8 -with the said mean and the said variance of the true OTS
di~tribution to form a final composite OTS di~tribution;
and (H) outputting, by the output means, the final composite OTS diætribution for each selected schedule.
The method can preferably calculate the reach for each selected schedule by forming the sum ~f the ~inal composite OTS distribution frequencies ~or the cases of insertions being one and more. ~lternately, the reach c~n be calculated by taking the complement o~ the ~inal composite OTS dis-tri~ution frequency for the case of the number of insertions being zero. The average ~requency for each selected schedule can then be determined as the total impacts divided by the reach.
The method can also determine a partial final composite O~S distribution of one or more terms. In this case, the reach is determined as the complement of the.
distribution frequen~y CXo) of z~ro exposures.
The invention also provides fvr an estimation method for an information processing system as described above, which method comprises the steps o~:
(AA) determining, by the processor means, whether any adjustment is to be made to allow for ~a) media vehicles not included in the survey ~5 throughout the whole survey period;
(b) sample subsets defined wholly or partly in - terms of time periods;
(c) sample subsets defined in terms of informatiDn not collected throughout the whole survey period;
W~93/0155q PCT~AU92/00286 211~ ~ 17 g (d~ any sample subset used at any stage in the calculation which is formed as the inter~ection of any two or more sample subsets of the types (a)-(c);
(BB) calculating, by the processor means, the adjustment factors for (a) the total estimated usage of each media vehicle;
(b) the total estimated cross-usage of each pair of media vehicl~s;
(c) the frequencieS (X0, X~ , XN) f the composite fre~uency distribution; and (CC) applying, by the processor means, these adjustment fa~tors to produce revised estimates of the composite distribution frequencies, total impacts and the mean and variance of the true OTS distribution.
The invention also contemplates an information processing system aomprising a memory means, inpu~ ~eans, a proce 50r means a~d output means operable in accordance with the method as described in the Poregoing paragraphs.
Such a system may pr~ferably also include a printer ~or printing the ~inal composite OTS distribution(s).
BRI~3F DE:SCRIP~I0~ OF T~133 I~WINGS
In order that the invention can be more clearly explained, an embodiment will be described with reference to the accompanying drawings in which:
Figure 1 shows a flow diagram of the method of $he invention;
Figure 2 shows a hardware realization of a system em~odying the invention suitable for a personal omputer 2112~7 environment; and Figure 3 shows a hardware environment similar to Figure 2, but sui~ed to a mini-computer or mainframe.
DESCRIPTION OF PRE~BRR~D BMBDDIM~N~8 An illustrative embodiment of the present invention relates to print media; however, it is to be appreciated that the invention is equally applicable to other media or to cross-media implementations, e.g. print in combination with television or radio. In this regard, the expression `media vehicle' is to ~e taken as relating to any appropriate forum in which advertising could be placed.
Therefore, when `media vehicle' is used in the case of print media, it refers to any individual `publication'.
In the case of television and radio, it typically designates individual programs or defined time periods, etc.
The term `insertions' is used to encompass a unit or instance of an advertisement in any one media vehicle.
Therefore, in print media an insertion may represent one advertisement placed in one issue of a particular publication, whereas for television, an insertion may represent one advert~isement during ~ particular program or time slot.~ Similar con~iderations apply for radio and cinema.
The embodiment will be described as a system that is conveniently realized by use of a suite of computer files designed to run on a personal comput~r (operating independently or attached to a network) or on a larger mini~computer or mainframe computer. The suite of programmes is commercialy available from the applicant company and sold under the name ASTEROID.
Figure 2 shows, in block diagram form, personal computer-based elements which can be utilized to implement the system. Memory means is provided variously by the WO93~01554 PCT/AU92/00286 21123~7 internal memory block ~such as RAM), the hard disk (external memory) block, the floppy disk block and the virtual disk block. The virtual disk is extended internal memory which may assist in ~peeding up calculations. The floppy disk is optional, but it does provide a use~ul facility for inputting the survey database or the host software. Processor means is shown by the processor block. Input means is represented by the keyboard block.
This allows a user to input the user specified criteria as will be presently discussed. The output means can be such as the VDU block for a visual representatiQn, the printer block to which haxd copy can be directed or a mass storage medium such as thç hard disk on which output can be stored. The elements identified by an asterisk are considered optional to a minimal system, but would most often be provided~
It has been found that the following specification operates satisfactorily: an IBM or compatible PC (type XT
or upwards), having at least 640 kb of memory (~AM). The environment/operating system should be MS DOS/PC-DOS (or equivalent) version 3.0 or later, with the screen driver ANSI.SYS installed. A numeric (math) co-processor is also advantageous in speeding up calculation times, as is extended memory. ~ -Figure 3 is similar to Figure 2, but is a block diagram representation of a mini computer or mainframe system. The internal memory block, disk ~external memory) block and tape block are each examples of memory means.
Input means is provided by t~he ANSI standard terminal.
The terminal has a screen, which together with t~e printer block form the output m~ans. Again, processor mea~s is realized by the processor block. Typically, a VAX or .. Microvax system running VMS 5.0 or later is acceptable.
The hardware described for supporting the system is exemplary only. The system has been designed to be easily transportable; hence many other hardware platforms could ..
211~377 ~ : - 12 -equally be utilized.
The system incorporates database files pertaining to survey data from some number of respondents, as well as executable programs, and when run, the system outputs a tangible written or printed presentatîon of the OTS
distribution together with the reach and average frequency estimation.
There are five basic types of file stored on hard disk:
1. The main program file.
TECHNICA~ FIE~D
The present invention rela~es to media research, and particularly to an advextising schedule evaluation technique known as `reach a~d ~requency' estimation.
BA~gGROUND OF ~HE INVEN~ION
The extent of exposure to mass media is a matter of interest and concern to the owners or publishers of media, to those who use the media to convey a message, normally in the form of paid advertising, and to ~he agencies they employ to assist them.
These groups are interested in both the size and the nature of the audience. However, absolute information on these qu~stions is not available from any source. In one instance, newspaper and magazine circulation may be audited by an independent body, but this gives only the number of copies sold or distributed and does not indicate how many people or what ~ind of people read these publications. , - -~0 To estimate the sizes of the audiences of individual media, it is therefore customary to conduct sample surveys in which a cross ~ection of the population (or sometimes of a specific subset of the population) is investigated in detail.
There are a number of ways in which the information may be collected and the choice will depend on the objectives of the particular study and on practical and financial considerations. Among other methods, members of a sample may be questioned about their exposure to media within a spPcific time period, record their media exposure over a period of time, or have their exposurP
~112377 2 -automatically monitored.
A particular reason for the collection of this information is the assessment (prospective or retrospective~ of the per~ormance of alternative media as vehicles for advertising. Advertisers wish to know what they are likely to get, or did get, for their expenditure in terms of the quantity and quality o~ audience~ Media owners, apart from assessing their strengths and weaknesses, wish to impress upon advertisers the benefits of their products.
The average level of exposure to a medium can be readily obtained ~rom these studies. This level is typically taken ~s the proportion of those surveyed claiming or being observed to see, hear or read the medium at any given point in time or within a given period. For some media it may be necessary to collect information from survey respondents at specific points in time; for others, it may be preferable to spread the data collection over a long period so that short-term fluctuations are evened out ~0 in the final aggregated results.
~ n many cases, media owners and advertisers wish to estimate the potential of two or more media, or o~ two or more advertisements in one medium, or (most frequently) of combinations o~ different numbers of advertisements in mor~ than;one medium. This requires knowledge both of the cross-usage of distinct media and of the turnover o~ users of individual media over time. FPW individuals are exposed to only one medium and few, if any, are consistent in their usage of anyimedium.
~or instance, peopl~ tend to be fairly regular in their reading o~ a daily newspaper, but even regular readers miss the occasional issue, and there are those who may only read an issue from time to time, or may regularly read it on one particular day only. General interest magazines tend to have a higher proportion of occasional or casual reader. Similarly, many people read one daily WOg3/01554 PCT/AU92/00286 paper, but some habitually read more than one, some sometimes read more than one and some generally read none at all.
The regularlty of the exposure of individuals to a medium can be quantified in a number of ways. `Turnover r and its complement `loyalty' are two. In Australia, the most common measure is `casualness'. The casualness (~) of a medium is defined as the ratio of the additional reach of a second issue (over the first) to the additional reach which could be expected if the persons reading, seeing or hearing the first and second issues were chosen independently of one another. Thus, 0~ casualness implies that people always do the same thing, whilst 100%
casualness constitutes completely random behavior.
Media surveys must in one way or another assess the degree of consistency of usage of îndividual media and the extent of cross-u~age. Where the behavior of respondents cannot be studied over a prolonged period, the~e must be estimated. Two methods of doing this are commonly used.
Respondents are asked to generalise their behavior in respect of individual media to giYe some indication of the regularity of their exposure to them, or hey are re~
con~acted a* a later time and asked about the same media, thus providing an indication of audience turnover. 5uch methods are used to provide indi~-ations of the con~istency of use o~ individual media within the overall population and within designated subsets of it.
The information provided by these studi~s is u~ed to simulate or assess th~ ability of alternative advertising schedules (a schedule being a list of media vehicles and numbers and types of advertisements inscrted in them) to reach designated segments of the population. The principal measures ~ommonly used are: the number or proportion of people exposed to at 1 ast one of the advertising insertions, the total number of impacts (where an impact is defined as one person being exposed to one WO g3/01554 PCr/~V92/00286 211~377 insertion) and the average impacts amongst those reached (which equals total impacts divided by reach). These are generally referred to as `reach', `total impacts' and `average frequency' respectively. In addition, it may be desirable to estimate the numbers exposed once, twice, three times, etc.; this is commonly known as the OTS
(opportunities-to-see) distribution or the `number seen' distribution. These two expressions are equivalent and may be used interchangeably.
DES~RIPTION OF T~E PRIOR ART
There are two types of media reach and frequency system in general use. These utilize either ~i) global formula methods or (ii) personal probability methods.
1~ Global formula methods, as the name implies, are based on the application of a formula or an algorithm to aggregated survey da*a such as average readership~ and pairwise cross-readerships, in the case of print media.
Personal probability models, on the other hand, treat each individual in the database separately, with each respondent a signed a prob~bility of seeing each medium.
The probabilities of 0, 1, 2,..., n OTS are then evaluated using the binomial distribution and these are then aggregated over all respondents to give the OTS
distribution for the total population.
Both techniques have their drawbacks. Critics of global formula me*hods cite: ~
(a) The problem of declining reach, whereby an extra insertion can cause the estimate of the overall reach of a schedule to decline;
(b) Non-additivity, whereby the distributions for complementary subsets of a population (e.g. men and women) do not n~cessarily reconcile with the distribution for the total (e.g. all people);
WO93/01554 ~11.23~ ~
(c) The inability to model correctly non-overlapping combinations of media (e.g. in schedules across geographical regions that are not covered by all media);
(d) The failure to model the actu~l shape of the OTS
distribution (e.g. in cases where a high-audience medium is included in a schedule with a low~audience medium); and (e) Order dependency, whereby the answer given may be dependent on the order in which the media are specified~
To a very large extent the success or failure of personal probability methods depends on the adequacy of the determination of the personal probabilities. Critics of these methods draw attention to the following drawbacks:
(a) Failure to reconcile with 'head-count data among all subsets;
(b) The dilution of ~ross-media relationships; and (c) The und~restimation o~ reach.
Presently available systems are also liable to encounter problems in dealing with incomplete data. In some cases~ where a survey covers an extended period~
information may,have been collected on particular media vPhicles fsr only part of the time, either because of a change in the scope of the survey or because those media were only avail~ble for part of the period~ In some cases; it is possible to process a schedule including one such incomplete medium by applying a weight to the aggregate results relating to that medium~ but wh re two or more such incomplete media are included, this is not possible.
Where a survey covers an extended period, it may be desirable to restrict the evaluation of a schedule ~o respondents interviewed during only a specific portion of the overall time period. In such cases, the resulting 3 ~ 7 estimates of numb~rs exposed are depressed unles~ a scaling method or an alternative set of respondent weights is employed which grosses the reduced sample up to the required population.
S~MMARY OF ~HE INVEMTION
The present invention is directed to overcoming some or all of ~he drawbacks listed above.
Therefore, as a non-limiting statement to indicate the scope and intention of the invention, it can be ~aid, in one instance, that the invention pro~ides an estimation method for an information processing system including a processor means, a memory means coupled to the processor means and operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles, input means coupled to the processor means and operable for obtaining user input, and output means coupled to the processor means. The estimation method . comprises the steps of:
(A) selecting, by the input means, at least one sch~dule of medi~ vehicles Sm~ and, for each sel~cted schedule, specifying a number of insertions (nm) in each media vehicle;
(B) defining, by the input means, at least one filter specifying a set of ~he survey respondents for which each schedule is to be evaluated;
(C) applying, by the processor means, the filter to the database; -(D) for each media vehicle, calculating, by the processor means, an array of the probabilities of being WO93/015~ PCT/AU92/00286 211~3 ~ ,~
- 7 - :
exposed to i out of nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle;
(E) combining, by the processor mear6s, the data relating to the usage of each selected media vehicle with said array of probabilities to give a probability of being exposed to i insertions in each selected media vehicle, combining the probabilities of being exposed to i insertions across the media vehicles and accumulating over all respondents passing the filter to yield a composite distribution ~Xj);
(F) by the processor means, within tbe set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usag for each selected media vehicle, and summing the ~edia vehicle usage of each pair of media vehicles (ml, m2) to give total estimated ~ross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities-to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, : the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle;
(G) operati~g, by the processor means, on each said composite distribution (Xj) to modify it so that it matches W093/0l554 PCT/AU92/00286 ~ .23~ 8 -with the said mean and the said variance of the true OTS
di~tribution to form a final composite OTS di~tribution;
and (H) outputting, by the output means, the final composite OTS diætribution for each selected schedule.
The method can preferably calculate the reach for each selected schedule by forming the sum ~f the ~inal composite OTS distribution frequencies ~or the cases of insertions being one and more. ~lternately, the reach c~n be calculated by taking the complement o~ the ~inal composite OTS dis-tri~ution frequency for the case of the number of insertions being zero. The average ~requency for each selected schedule can then be determined as the total impacts divided by the reach.
The method can also determine a partial final composite O~S distribution of one or more terms. In this case, the reach is determined as the complement of the.
distribution frequen~y CXo) of z~ro exposures.
The invention also provides fvr an estimation method for an information processing system as described above, which method comprises the steps o~:
(AA) determining, by the processor means, whether any adjustment is to be made to allow for ~a) media vehicles not included in the survey ~5 throughout the whole survey period;
(b) sample subsets defined wholly or partly in - terms of time periods;
(c) sample subsets defined in terms of informatiDn not collected throughout the whole survey period;
W~93/0155q PCT~AU92/00286 211~ ~ 17 g (d~ any sample subset used at any stage in the calculation which is formed as the inter~ection of any two or more sample subsets of the types (a)-(c);
(BB) calculating, by the processor means, the adjustment factors for (a) the total estimated usage of each media vehicle;
(b) the total estimated cross-usage of each pair of media vehicl~s;
(c) the frequencieS (X0, X~ , XN) f the composite fre~uency distribution; and (CC) applying, by the processor means, these adjustment fa~tors to produce revised estimates of the composite distribution frequencies, total impacts and the mean and variance of the true OTS distribution.
The invention also contemplates an information processing system aomprising a memory means, inpu~ ~eans, a proce 50r means a~d output means operable in accordance with the method as described in the Poregoing paragraphs.
Such a system may pr~ferably also include a printer ~or printing the ~inal composite OTS distribution(s).
BRI~3F DE:SCRIP~I0~ OF T~133 I~WINGS
In order that the invention can be more clearly explained, an embodiment will be described with reference to the accompanying drawings in which:
Figure 1 shows a flow diagram of the method of $he invention;
Figure 2 shows a hardware realization of a system em~odying the invention suitable for a personal omputer 2112~7 environment; and Figure 3 shows a hardware environment similar to Figure 2, but sui~ed to a mini-computer or mainframe.
DESCRIPTION OF PRE~BRR~D BMBDDIM~N~8 An illustrative embodiment of the present invention relates to print media; however, it is to be appreciated that the invention is equally applicable to other media or to cross-media implementations, e.g. print in combination with television or radio. In this regard, the expression `media vehicle' is to ~e taken as relating to any appropriate forum in which advertising could be placed.
Therefore, when `media vehicle' is used in the case of print media, it refers to any individual `publication'.
In the case of television and radio, it typically designates individual programs or defined time periods, etc.
The term `insertions' is used to encompass a unit or instance of an advertisement in any one media vehicle.
Therefore, in print media an insertion may represent one advertisement placed in one issue of a particular publication, whereas for television, an insertion may represent one advert~isement during ~ particular program or time slot.~ Similar con~iderations apply for radio and cinema.
The embodiment will be described as a system that is conveniently realized by use of a suite of computer files designed to run on a personal comput~r (operating independently or attached to a network) or on a larger mini~computer or mainframe computer. The suite of programmes is commercialy available from the applicant company and sold under the name ASTEROID.
Figure 2 shows, in block diagram form, personal computer-based elements which can be utilized to implement the system. Memory means is provided variously by the WO93~01554 PCT/AU92/00286 21123~7 internal memory block ~such as RAM), the hard disk (external memory) block, the floppy disk block and the virtual disk block. The virtual disk is extended internal memory which may assist in ~peeding up calculations. The floppy disk is optional, but it does provide a use~ul facility for inputting the survey database or the host software. Processor means is shown by the processor block. Input means is represented by the keyboard block.
This allows a user to input the user specified criteria as will be presently discussed. The output means can be such as the VDU block for a visual representatiQn, the printer block to which haxd copy can be directed or a mass storage medium such as thç hard disk on which output can be stored. The elements identified by an asterisk are considered optional to a minimal system, but would most often be provided~
It has been found that the following specification operates satisfactorily: an IBM or compatible PC (type XT
or upwards), having at least 640 kb of memory (~AM). The environment/operating system should be MS DOS/PC-DOS (or equivalent) version 3.0 or later, with the screen driver ANSI.SYS installed. A numeric (math) co-processor is also advantageous in speeding up calculation times, as is extended memory. ~ -Figure 3 is similar to Figure 2, but is a block diagram representation of a mini computer or mainframe system. The internal memory block, disk ~external memory) block and tape block are each examples of memory means.
Input means is provided by t~he ANSI standard terminal.
The terminal has a screen, which together with t~e printer block form the output m~ans. Again, processor mea~s is realized by the processor block. Typically, a VAX or .. Microvax system running VMS 5.0 or later is acceptable.
The hardware described for supporting the system is exemplary only. The system has been designed to be easily transportable; hence many other hardware platforms could ..
211~377 ~ : - 12 -equally be utilized.
The system incorporates database files pertaining to survey data from some number of respondents, as well as executable programs, and when run, the system outputs a tangible written or printed presentatîon of the OTS
distribution together with the reach and average frequency estimation.
There are five basic types of file stored on hard disk:
1. The main program file.
2. Local files (files specific to the user and not available to other users). These include files describing the configuration of the user's preferred output format, private dictionary files, input and output ~iles generated by the user, etc.
3. Data files. These include the main database file, the files governing acce~s to it and the file of weighting factors. In the case of media surveys there is an additional `respondent loyalty' file.
4. Usex utilities. These assist in customising reference files and in the creation of priv~te dictionaries.
5. Reference file~. These are accessible to all users and include the standa~d (or `pub~ic') ; 25 dictionary files, files containing the menus, error and information messages and prompts. -The main database file contains, in compressed form, the respondent survey information (demographic, media exposure, product usage, attitudes, etc.~. Its generation is described below.
The data from which the system compiles its results may be taken from conventional market researrh questionnaires, from active or passive metering or recording devices, or by other means. Because of the nature of many of the questions, which allow (indeed encourage) multiple responses (e.g. "Which of these ...
W093/015s4 PCT/~U92/002~6 . . .
- 13 - ~ ~
have you ever used?") it may be expedient to record the in~ormation as though by physically punching holes in a punch card, although this is not an essential strategy.
The data from a punch card is essentially binary, that is, one bit can represent each punch position.
Normally a physical record in a file represents the contents of one nominal card. There are various conventions which can be used.
The punch card `used' in the present system is an IBM
standard card on which the data area is divided into 80 `columns' and 12 `rows'~ Thus there are 960 possible punch positions on a card. Data from one respondent may require more than one card and therefore, any piece of information is identified in terms of its card number, column number and row (or code) number. The system uses a notation with, for example, card 2, column 33, code 6 being written as:
#2 /33 6 Each column covers twel~e rows or codes. These are designated V, X, 0, 1...9. 'V' is the position nearest the top edge of the card and 9 is the bottom position.
The system assumes the logical sequence stated above~
The software takes binary files in which the record represents s~me or all of the information about one survey respondent and ` inv rts ' this into files in which ea~h logical record represents one punch position. The result is a string of ~it`s which indicate wh`ether each respondent has or has not a particular characteristic ~maleness, reading the Washington Post, owning a dishwasher, etc.).
Because only cert~in facts, forming generally a very small propor~ion of the total data available, are required to produce each table, extracting only the records containing the relevant information minimises or eliminates redundant - input/output.
.. . , . . . ~ ., ...... , .. . , ., , ~ ., . .~ , 211~377 14 -The inverted file is compressed by the omission of records relating to unused punch positions. Because the logical records may be too long to handle comfortably, they are broken up into segments. The file is scrambled to make it difficu~t to read by any other program. This file is always assigned the `.CPR' file-type. A pointer file contains values which allow the system to calculate the physical location of any segment required.
The respondent loyalty file is a direct acce~s file which aontains loyalty estimates Pm for each respondent for each medium m covered by the survey. For convenience in the construction of lookup tables, these take on a finite set of discrete ~alues (typically 256) and are encoded as characters.
Weighting factors may be assigned to each respondent.
These factors are calculated to redre~s any imbalances in the composition of the sample and in addition are normally scaled so that the sum of the weights represents the estimated size of a parent population (e.g. all households, all persons)O In the present embodiment, these weights may be disabled ~replaced with dummy weights of l.0~ by the user if desired.
The .CPR file:is accessed by specifying the card :
locations relating to the data to be extracted. The-us r, however, normally specifies these as a data name, which is associated with a definition in primitive ~card locatisn) terms. The data name specified may in fact be a name associated with a series of related data names. Thuæ a user may ask for `men'~ ~one subset o~ the sample) or ~sexJ
(which will yield `men' and `women').
Names like `sex' are called `variables'. A ~ariable is compo~ed of two or more `groups', each representing a - specific subset of the survey sample. These groups need no~ be mutually exclusive and need not collectively represent the entire population.
Subsets of the sample may also be defined without 21123~7 - 15 - ;
being a group in a variable. These are referred to as `entities'.
A ~ilter is a definition of a subset of the data to which an analysis is to be restricted. This definition may be expre~sed in terms of card locations or data names or a mixture of the two, data names being converted into their existing card-location definitions during processing. Elements of the definition are linked by logical operators to indicate whether the union or intersection of two elements or the complement of an element is required. The term `filter' is also applied to the process of applying this definition to exclude all data other than that defined from subsequent processing.
Each variable, group and entity is defined in a dictionary file. Each variable has a data name or tag of up to twelve characters and this tag is aæsociated with a text string used to provide a fuller description in the final table. Each group within the variable has an optional tag, an optional verbal description, and a (mandatory) definition, in terms of punch card locations, of the data comprising that group.
This definition may encompass, at its simplest, a single card location. Alternatively, the definition may consist of a se~ies of ~ard locations, logically linked.
For instan~ , `old men' may be defined as `any code 7-~ on column 23 of card 1 but also code 2 on column 65 of card 1'. The actual definition would look lîke this:
#1 /23 7-9 & /65 ~
This definition is read and interpreted by the software into a series of steps of reading and logically combining the contents of specific records to form the rows and columns of a table and logically combining each row and column to form the individual cells of the table.
A subset of the sample normally corresponds to a WO 93~01554 PCI/AU92/00286 2il2377 - 16 -subset of the population. However, in a survey co~ducted over a substantial period, a subset defin~d purely in terms o~ the date of interview may need to represent the entire population. To gross up from such a subset requires either an alternative set of weights specific to each definable time period or, as in the present case, a mechanism for making the appropriate adjustment.
The program checks the definition to ascertain whether any elements of it relate to time periods. It recognises this throuyh card locations identified when the database was created. If any such time elements are found, a further series of logical steps is generated ~o extract separately, and where necessary combine, the contents of records relating to the referenced time periods. This information is used to calculate adjustment factors:
~a) for media vehicles not included in the survey throughout the whole survey period;
(b) for sample subsets defined wholly or partly in terms of time periods;
: (c) for sample subsets defined in terms of informatisn not collected throughout the whole survey period;
(d) for any sample subset used at any stage in.the calculation which is formed as the intersection of : : any two or:more sample subsets of the typss (a)-(c) above.
The data relating to each time element in a definition are extracted as bit-strings. Multiple time bit-strings within a definition are combined by logically `OR'ing (bits in a resultant string are set `on' if the corresponding bit in any input string is `on', otherwise ; set 'off') to yield a single time bit-string representing the time elements. The weighted total of all respondents is divided by the weighted sum of the `on' bits in the resultant time bit-string to give an adjustment factor for WO93/01~54 PCT/AU92/00286 21123~7 l7 i! .~., that subset and for any intersections with other subsets whose de~inition do not contain time elements.
Alternatively, the adjustment factors may be calculated as the weighted sum of respondents in a ~iltered subset divided by the weighted sum of respondents in the subset who are also identified (`on~ bit~) in the time bit string.
Where two subsets with definitions containing time elements intersect (for instance, the definitions of a row and column of a table, or a media vehicle with a filter) their time bit-strings are also logically `AND'ed (bits in the resultant string are set `o~' if the corresponding bits in all input strings are `on', otherwise set `off'.) An adjustment factor is then calculated as above.
When the complete definitions are evaluated, applied, combined and aggregated, these adjustment factors are applied to the resulting aggregates so that the final o~tput figures are comparable estimat~s of the required populatio~. Of cour~e, îf there is ef~ectively no intersection (~or instance, two media v~hicles about which no data were ever gathered at the same time) there can be no estimate of joint audience, except by projective or data fusion techniques, but this is a problem of media surveys generally and not confined to the processing system described here.
This feature may be enabled or disabled by the user.
When it is enabled, the tim~ corrections are made automatically requiring no intervention by the user.
The main executable program ASTEROID.EXE performs the main analysis-function required with survey data, that being two-way tabulations of selected variables or subsets, restricted (filtered) where required to desired subsets. It provides the user with a high degree of flexibility in the selection of data and the presentation 35 of output. For most functions, users can choose courses of action or data either by selecting items from menus or ~ 1 1 2 3 7 1 by entering semi-natural language commands.
In relation to the present invention, the program also can be used to generate an OTS distribution for any number of impacts (i~e. either the complete OTS
distribution or a partial or truncated OTS distribution can be output containing any number or combination of impacts from zero up to the total number of insertions), the reach and average fr~quency estimates for each defined filtered population. These require the user selection of the respondent survey data and one or more media schedules and the definition of one or more input filters or subsets of the data over which the schedule is to be evaluated.
All this information is input to the system via a keyboard and is viewable by the user on the video screen of a personal or mainframe computer. The input da-ta will also be stored in a user-named local file of the type .SCH.
In specifying the schedules, the media vehicles in which ~he advertisement insertions are to be considered must f irst be selected. Each schedule is then completed by specifying the number of insertions in each selected media vehicle. The final step is to specify one or more filters, which typically designate demographic target groups.
In the following example particular to Australia:
NAT-GEOGRAP~
NEW-IDEA
ADV-M-F
are tags for National Geographic, ~ew Idea and the Monday-Friday average issue of the Adelaide Advertiser newspaper7 These are respectively a monthly magazine, a weekly magazine and a daily newspaper published in the State of South ~ustralia~ Four completed schedules for the three filters of Gentlemen, Ladies and All Adults are shown:
WO 93/0155q PCI'/AU92/00286 2 1 1 S~ 7 -- 19 -- 1 "., ;
Schedule File: SCHO1 Filter: Gentlemen Publications, m Schedules (Insertions, n) Schedule File: SCHO1 Filter: Ladies Publlcations, m Schedules (InsertionsJ nL
Schedule File: SCHO1 Filter: All Adults 15 Publications. m Schedules fInsertions._n2 NEW IDEA ~ 2 3 2 2 ADV-M-F 1 ~ 2 O
.;
Once all hP input data have been specified, the progra~ ASTEROID.EXE is able to estimate the OTS
dis~xibution, rea¢h and average frequency for each schedule and each~filter as a basis for comparison of advertising strategies. The following steps are involved.
The adjustment factors to correct for the presence of time-dependent definitions have been omitted from the formulae for clarity~
The system accepts as input the 5pecif ication of the schedules including the number of insertions, nm, for WO 93/01554 PCr/AU92/00286 , ~112~7 - 20 -each medium, m and the filters required.
tep 2 For each possible value o~ respondent loyalty, Pm I the system generates an nm long array o~ the probability of the respondent being exposed to 0, 1, 2, ..., (nm-1) insertio~s, viz., Pmo ~ P~l ~ Pmz ~ Pm ~n~ l ~
using an indi~idual beta distribution with parameters:
PmGm (l~pm) Gm l-Gm l-Gm where Gm is the internal casualness of the publication.
~Wh~lst the beta binomial distribution is preferred, it is also possible to utilize other discrete frequency distributions of appropriate order in performing this step). The system stores each of the arrays for sub~equent use.
In this way, the survey respondent may be viewed not as an individual in the population but as a demographic `cell' t i.e. the representation o~ a number of individuals with t~e same demographic characteri~tics. The internal casualness G~ is the within-demographic component of casualness. The between-demographic component is: -~pOp Pm ( 1 Pm) / [p ( 1 ~~p) ] , ., .
wh re the sum ~p may be weighted to a parent population if this is required, and the overall casualness is the Pcr/Au ¦ 9 2 / O 0 2 ~ 6 211~ ~ 7 7 RE(~El.\lED ~ 2 DEC 199 ~roduct of ~he ~wo.
Ste~ 3.1 The ~y~tem then read~ the ~urvey databa~e file and ~imultaneously the re3~0ndent loyalty file. For each re~ondent ~as~in~ the filter, the ~y~tem take~ the actual reader~hi~ o~ each ~ublication ~m~ ~hich for exam~le could have been obtained by asking the re~ondent whether he or qhe had read a ~ecific issue of the ~ublication, and combines it with the ~robability di~tribution array ~e~er~ted in ~te~ 2 to ~ive the ~robability o~ ~eein~ 0, 1, ..., nm i~ue~, viz., Probability (seeing i) = Pmi if ~m =
Pm(i-l) if ~im In other word~, ~he i~ue about which the res~onden~
re~orted readershi~ can be con~idere~ a~ one of the is~ue~
in ~he ~chedule.
The 3ystem combinea these ~robabilitie3 acros~ ~ublication~
and accumulate~ a compo~ite aistributlon over the filtered ~o~ulatio~:
Xi = ~ f il ~ 9r Prob ( seeing i ) where the 8 ~ tion ~flltQr may be wei~htea to a parent ~o~ulation if thi~ i~ required.
Ste~ 3.~ The ~y~te~ al~o e~timate~ the actual (head~ount) reader~hi~ as:
Rm ~filte~ ~m and the cross-reader~hi~3 a~:
3 0 Rm~ f i 1 cer ml m2 an~ u~e~ the~e to e~ima~e the mean, ~, and variance, V, ~i WO93/01554 ~ PCT/AU92/a0286 r i ~ ~ ~ 2 2 o~ ke ~rue OTS distribution for the schedule, viz., M = R/P, where R = total impacts = ~mnmRm~ and P 3 size of the ~iltered population;
V = S/P - M
whe~e S = R (2~mnm-1) ~ml ~ 2 nml nm2 ( Rml ~Rm;?~ Rm -~ Rmnm(nm-l) ~1 +Ym(P-Rm) /P) ~m = the casualness of publication m over the filtered population.
Again, the summations ~f~r may be weighted to a parent population if this is required.
t~p ~ The system then modifies the c~mposite distribution formed in step 3.1 ~0 that its mean and variance match those of the true OTS distribution estimated in step 3.2, thus generating the final composite OTS dis~ribution ~or each schedule. In this step, the reach ~number of persons seeing at lea~t one insertion) and average frequèn~y (average impacts am~ngst those reached~ can also be determined.
There are many ways of performing this step, and the 20 system can utilizè either of two options: ~.
Alter~ati~e 4.1 Determining and then using multiplicative adjustment factors f; ~i = 0,.. , N~ which ::
maximise the function: ~
WO 93/01554 P~r/AU92/00286 211~377 Xrlog (frXr) ~o subj ect to the constraints:
r-Xr = P
~ r~Xr = R
r~o ~2.~rXir=s The solution to this problem is of the form:
r 1 ~ ~ (r-R/P) + v (r2-slP) where ,u and v ar~ (Lagrange multiplier) constants.
~lt~æ~ ti~ ~ . 2 Determining and then using absolute 5 adjustments Dj ( i -- 0 , . . ., N) which minimise the weighted sum of squares N
Wr Dr r=o subject to the constraints:
WO 93/015~4 PCI'/AU92/00286 ( S
N
~ Dr =
r~o IV .
~, r J~r = ErrOl: in ~
r=o = ~R
N
~2.Dr = Error in S
r=o = ~S
The solution to this problem is given explicitly as: :
i w~ 2Wfl Dr = w1r (1 ~ r~) ~ i2w~ 3w-l (~5) ~ i2W~ 3 when wO > O and, as:
` :
.
Dr = wl (r rZ) ~i3W-l ~ iGWillJ (l~
N
with Do = - ~ Dr ~-1 whell wO ~ -~0~3/01554 PCT/~U92/00286 The formulae set out in steps 1-4 above cover the essential principles underlying the system. Slight variations may be introduced for average is~ue readerships of d~ily newspapers, ~or time dependent cross-readerships and for alternative definitions of regularity.
The weights w; (i - 0,..., N~ are weights used by the system in guiding the adjustment process. Where wi is large the adjus~ment Di to the corresponding X; will be relatively small and vice versa. These weights are not to be confused with the respondent weights previou~ly described.
N is the maximum number of insertions which it is possible for any respondent to see. This may be less than the total number of insertions in the schedule. A
schedule may contain two or more media which are not available to the same respondent~, for instance because they do not overlap geographically, so that no respondent can be exposed to all of them.
The first method of adjustment ~4.1) involves smaller percentage changes in the distribution, but it does not guarantee additivity of the dîstribution o~er subsets of the population.
For a given set of weights w, where N is constant for the subgroups and for the total group, the second method (4.2) does guarantee additivity. The second method also has a further advantage. Because it is possible to determine the errors in R and S, and hence the Dj~ from summary statistics, this method can be used to estimate reach, and hence average frequency, by takin~ the c~mplement of the OTS zero frequency (XO) without having to determin~ the full OTS distribution. In cases where there are many insertions, this can save considerable computer time. For the same reason, this method can also be~used to generate partial distributions (X;) containing any number of kerms n < N. For a given set of weights w; the WO93/01554 PCT/AU92/002~6 2~37~
answers are always consistent.
The disadvantage of the second method is that it can produce negative estimates. However, thes~ are rare, and when they do occur, they are very small. Such ~stimates are analogou~ to the situation of negative counts that can be produced in some automatic counting machlnes through the subtraction of background noise.
The system almost completely overcomes the problem of declining reach and is not order dependent. It can model the actual shape of the OTS distribution. Its estimates of reach and frequency and its OTS distribution are always compatible with the headcount readership estimates. It does not dilute cross-media readerships.
It can also handle non-overlapping readerships.
Indeed regions can be specifically excluded from consideration of coverage hy selected media by ha~in~ the appropriate respondent loyalties set to zero.
Clearly, however, the system is dependent upon the accuracy of th~ estimation/measurement of the respondent loyalties contained in the file (SURVEY).PRB, although not to the same extent as personal probability methods. For consistency and for be~t operation nf the system, not only should:
.
~pOp Pm ~ Rm but also the estimate of casualness determined by aggregating the individual beta distributions, viz., 'Ym = Gm ~p~,p Pm ~ l~Pm) / [ p ( l -p ) ~
should reconcile with the overall loyalty, L, datermined W~93/01~4 PCT/AU92/00286 211~ 3 ~ 7 ~ 27 -by averaging the loyalties of the actual readers, i.e.:
~ z 1-L
~/P
whexe L = ~ pPml;m/Rm-It would be within the capacity of the skilled addressee to write computer code to achieve the methodology o~ the steps given abo~e when guided by the flow diagram of Figure 1. In the alternative, the ASTEROID code may be obtained from the applicant company.
The steps of Figure 1 are well annotated and will not be discussed in detail. Common elements can be identified in the step 1-4 above. Even so, the following comments will be made with reference to Figure 1.
Data are stored a:s binary (bît) strings or, in the ~ase of the `loyalty data' as a character string, with ~ach bit/characte~ representing one respondent. B~cause the`sample size is usua~ly large and there are memory constraints, the sample is usually broken up into ~segments'. Each ~egment is processed completely, and the : results are cu~ulated~befQre moving to the next segment.
Data extracted from the survey database in respect of one possible response (e.g. `read the newspap~r'3 are thus in the form of a bit vector repres nting the ~egment concerned, with `on' bits indicating, in this instanc~, respondents claiming to have read the newspaper and `off~
bits those claiming not to have read it. In some cases : bit vectors are com~ined to produce real number vectors 25 ~e . g. a person claiming to have read three issues of a daily paper out of a possible five issues would be given a value of 0.6). At completion of the calculations, the 2~ 77 - 28 -results are tabulated ~nd output to a device such as a printer, hard disk file or video screen. For the æchedule and filters described earlier, the composit~ OTS
distribution, reach and av~rag~ frequency are as follows.
The abbreviation, `Cume.' represents a cumulative representation of the composite OTS distributîons ~'Dist.').
Schedule File: 5CH01 Filter: Gentlemen Population ~000's~: 551.2 Publications, m Schedules (Insertions, n) AD~-M~F 1 2 2 0 Reach t~00's):307O3 349.1 345~5 105.2 ~ 55.8~ 63.3% 62.7% 19.1%
Ave. ~requency:1.54 2.36 2.04 2.30 Impacts (000's). 472.7 824.4 703.3 242.1 O.T.S. Dist. Cume. Dist. ~ume. Dist. Cume. Dist. Cume.
0 44.2% 36.7% 37.3% ~0.9%
1 39 . 4% 55,. 8% 15 . 5% 63.3% ~6.7% 62.7%6.4~.~9.1%
2 7.2% 16~3% 31.5% 47.8~ 34.7% 45.g%6.4% 12.7%
3 5.6~ 9.1% 5.7% 16.3% 5.1~ 11.2% 1.9% 6.3%
4 2.7% 3.5% 4.2% ~0.7% 5.0% 6.2% 3.5% 4.4 ~5 5 0.6% 0.8% 3.4% 6.5% 0.7% 1.1% 0O5% ~.9%
The data from which the system compiles its results may be taken from conventional market researrh questionnaires, from active or passive metering or recording devices, or by other means. Because of the nature of many of the questions, which allow (indeed encourage) multiple responses (e.g. "Which of these ...
W093/015s4 PCT/~U92/002~6 . . .
- 13 - ~ ~
have you ever used?") it may be expedient to record the in~ormation as though by physically punching holes in a punch card, although this is not an essential strategy.
The data from a punch card is essentially binary, that is, one bit can represent each punch position.
Normally a physical record in a file represents the contents of one nominal card. There are various conventions which can be used.
The punch card `used' in the present system is an IBM
standard card on which the data area is divided into 80 `columns' and 12 `rows'~ Thus there are 960 possible punch positions on a card. Data from one respondent may require more than one card and therefore, any piece of information is identified in terms of its card number, column number and row (or code) number. The system uses a notation with, for example, card 2, column 33, code 6 being written as:
#2 /33 6 Each column covers twel~e rows or codes. These are designated V, X, 0, 1...9. 'V' is the position nearest the top edge of the card and 9 is the bottom position.
The system assumes the logical sequence stated above~
The software takes binary files in which the record represents s~me or all of the information about one survey respondent and ` inv rts ' this into files in which ea~h logical record represents one punch position. The result is a string of ~it`s which indicate wh`ether each respondent has or has not a particular characteristic ~maleness, reading the Washington Post, owning a dishwasher, etc.).
Because only cert~in facts, forming generally a very small propor~ion of the total data available, are required to produce each table, extracting only the records containing the relevant information minimises or eliminates redundant - input/output.
.. . , . . . ~ ., ...... , .. . , ., , ~ ., . .~ , 211~377 14 -The inverted file is compressed by the omission of records relating to unused punch positions. Because the logical records may be too long to handle comfortably, they are broken up into segments. The file is scrambled to make it difficu~t to read by any other program. This file is always assigned the `.CPR' file-type. A pointer file contains values which allow the system to calculate the physical location of any segment required.
The respondent loyalty file is a direct acce~s file which aontains loyalty estimates Pm for each respondent for each medium m covered by the survey. For convenience in the construction of lookup tables, these take on a finite set of discrete ~alues (typically 256) and are encoded as characters.
Weighting factors may be assigned to each respondent.
These factors are calculated to redre~s any imbalances in the composition of the sample and in addition are normally scaled so that the sum of the weights represents the estimated size of a parent population (e.g. all households, all persons)O In the present embodiment, these weights may be disabled ~replaced with dummy weights of l.0~ by the user if desired.
The .CPR file:is accessed by specifying the card :
locations relating to the data to be extracted. The-us r, however, normally specifies these as a data name, which is associated with a definition in primitive ~card locatisn) terms. The data name specified may in fact be a name associated with a series of related data names. Thuæ a user may ask for `men'~ ~one subset o~ the sample) or ~sexJ
(which will yield `men' and `women').
Names like `sex' are called `variables'. A ~ariable is compo~ed of two or more `groups', each representing a - specific subset of the survey sample. These groups need no~ be mutually exclusive and need not collectively represent the entire population.
Subsets of the sample may also be defined without 21123~7 - 15 - ;
being a group in a variable. These are referred to as `entities'.
A ~ilter is a definition of a subset of the data to which an analysis is to be restricted. This definition may be expre~sed in terms of card locations or data names or a mixture of the two, data names being converted into their existing card-location definitions during processing. Elements of the definition are linked by logical operators to indicate whether the union or intersection of two elements or the complement of an element is required. The term `filter' is also applied to the process of applying this definition to exclude all data other than that defined from subsequent processing.
Each variable, group and entity is defined in a dictionary file. Each variable has a data name or tag of up to twelve characters and this tag is aæsociated with a text string used to provide a fuller description in the final table. Each group within the variable has an optional tag, an optional verbal description, and a (mandatory) definition, in terms of punch card locations, of the data comprising that group.
This definition may encompass, at its simplest, a single card location. Alternatively, the definition may consist of a se~ies of ~ard locations, logically linked.
For instan~ , `old men' may be defined as `any code 7-~ on column 23 of card 1 but also code 2 on column 65 of card 1'. The actual definition would look lîke this:
#1 /23 7-9 & /65 ~
This definition is read and interpreted by the software into a series of steps of reading and logically combining the contents of specific records to form the rows and columns of a table and logically combining each row and column to form the individual cells of the table.
A subset of the sample normally corresponds to a WO 93~01554 PCI/AU92/00286 2il2377 - 16 -subset of the population. However, in a survey co~ducted over a substantial period, a subset defin~d purely in terms o~ the date of interview may need to represent the entire population. To gross up from such a subset requires either an alternative set of weights specific to each definable time period or, as in the present case, a mechanism for making the appropriate adjustment.
The program checks the definition to ascertain whether any elements of it relate to time periods. It recognises this throuyh card locations identified when the database was created. If any such time elements are found, a further series of logical steps is generated ~o extract separately, and where necessary combine, the contents of records relating to the referenced time periods. This information is used to calculate adjustment factors:
~a) for media vehicles not included in the survey throughout the whole survey period;
(b) for sample subsets defined wholly or partly in terms of time periods;
: (c) for sample subsets defined in terms of informatisn not collected throughout the whole survey period;
(d) for any sample subset used at any stage in.the calculation which is formed as the intersection of : : any two or:more sample subsets of the typss (a)-(c) above.
The data relating to each time element in a definition are extracted as bit-strings. Multiple time bit-strings within a definition are combined by logically `OR'ing (bits in a resultant string are set `on' if the corresponding bit in any input string is `on', otherwise ; set 'off') to yield a single time bit-string representing the time elements. The weighted total of all respondents is divided by the weighted sum of the `on' bits in the resultant time bit-string to give an adjustment factor for WO93/01~54 PCT/AU92/00286 21123~7 l7 i! .~., that subset and for any intersections with other subsets whose de~inition do not contain time elements.
Alternatively, the adjustment factors may be calculated as the weighted sum of respondents in a ~iltered subset divided by the weighted sum of respondents in the subset who are also identified (`on~ bit~) in the time bit string.
Where two subsets with definitions containing time elements intersect (for instance, the definitions of a row and column of a table, or a media vehicle with a filter) their time bit-strings are also logically `AND'ed (bits in the resultant string are set `o~' if the corresponding bits in all input strings are `on', otherwise set `off'.) An adjustment factor is then calculated as above.
When the complete definitions are evaluated, applied, combined and aggregated, these adjustment factors are applied to the resulting aggregates so that the final o~tput figures are comparable estimat~s of the required populatio~. Of cour~e, îf there is ef~ectively no intersection (~or instance, two media v~hicles about which no data were ever gathered at the same time) there can be no estimate of joint audience, except by projective or data fusion techniques, but this is a problem of media surveys generally and not confined to the processing system described here.
This feature may be enabled or disabled by the user.
When it is enabled, the tim~ corrections are made automatically requiring no intervention by the user.
The main executable program ASTEROID.EXE performs the main analysis-function required with survey data, that being two-way tabulations of selected variables or subsets, restricted (filtered) where required to desired subsets. It provides the user with a high degree of flexibility in the selection of data and the presentation 35 of output. For most functions, users can choose courses of action or data either by selecting items from menus or ~ 1 1 2 3 7 1 by entering semi-natural language commands.
In relation to the present invention, the program also can be used to generate an OTS distribution for any number of impacts (i~e. either the complete OTS
distribution or a partial or truncated OTS distribution can be output containing any number or combination of impacts from zero up to the total number of insertions), the reach and average fr~quency estimates for each defined filtered population. These require the user selection of the respondent survey data and one or more media schedules and the definition of one or more input filters or subsets of the data over which the schedule is to be evaluated.
All this information is input to the system via a keyboard and is viewable by the user on the video screen of a personal or mainframe computer. The input da-ta will also be stored in a user-named local file of the type .SCH.
In specifying the schedules, the media vehicles in which ~he advertisement insertions are to be considered must f irst be selected. Each schedule is then completed by specifying the number of insertions in each selected media vehicle. The final step is to specify one or more filters, which typically designate demographic target groups.
In the following example particular to Australia:
NAT-GEOGRAP~
NEW-IDEA
ADV-M-F
are tags for National Geographic, ~ew Idea and the Monday-Friday average issue of the Adelaide Advertiser newspaper7 These are respectively a monthly magazine, a weekly magazine and a daily newspaper published in the State of South ~ustralia~ Four completed schedules for the three filters of Gentlemen, Ladies and All Adults are shown:
WO 93/0155q PCI'/AU92/00286 2 1 1 S~ 7 -- 19 -- 1 "., ;
Schedule File: SCHO1 Filter: Gentlemen Publications, m Schedules (Insertions, n) Schedule File: SCHO1 Filter: Ladies Publlcations, m Schedules (InsertionsJ nL
Schedule File: SCHO1 Filter: All Adults 15 Publications. m Schedules fInsertions._n2 NEW IDEA ~ 2 3 2 2 ADV-M-F 1 ~ 2 O
.;
Once all hP input data have been specified, the progra~ ASTEROID.EXE is able to estimate the OTS
dis~xibution, rea¢h and average frequency for each schedule and each~filter as a basis for comparison of advertising strategies. The following steps are involved.
The adjustment factors to correct for the presence of time-dependent definitions have been omitted from the formulae for clarity~
The system accepts as input the 5pecif ication of the schedules including the number of insertions, nm, for WO 93/01554 PCr/AU92/00286 , ~112~7 - 20 -each medium, m and the filters required.
tep 2 For each possible value o~ respondent loyalty, Pm I the system generates an nm long array o~ the probability of the respondent being exposed to 0, 1, 2, ..., (nm-1) insertio~s, viz., Pmo ~ P~l ~ Pmz ~ Pm ~n~ l ~
using an indi~idual beta distribution with parameters:
PmGm (l~pm) Gm l-Gm l-Gm where Gm is the internal casualness of the publication.
~Wh~lst the beta binomial distribution is preferred, it is also possible to utilize other discrete frequency distributions of appropriate order in performing this step). The system stores each of the arrays for sub~equent use.
In this way, the survey respondent may be viewed not as an individual in the population but as a demographic `cell' t i.e. the representation o~ a number of individuals with t~e same demographic characteri~tics. The internal casualness G~ is the within-demographic component of casualness. The between-demographic component is: -~pOp Pm ( 1 Pm) / [p ( 1 ~~p) ] , ., .
wh re the sum ~p may be weighted to a parent population if this is required, and the overall casualness is the Pcr/Au ¦ 9 2 / O 0 2 ~ 6 211~ ~ 7 7 RE(~El.\lED ~ 2 DEC 199 ~roduct of ~he ~wo.
Ste~ 3.1 The ~y~tem then read~ the ~urvey databa~e file and ~imultaneously the re3~0ndent loyalty file. For each re~ondent ~as~in~ the filter, the ~y~tem take~ the actual reader~hi~ o~ each ~ublication ~m~ ~hich for exam~le could have been obtained by asking the re~ondent whether he or qhe had read a ~ecific issue of the ~ublication, and combines it with the ~robability di~tribution array ~e~er~ted in ~te~ 2 to ~ive the ~robability o~ ~eein~ 0, 1, ..., nm i~ue~, viz., Probability (seeing i) = Pmi if ~m =
Pm(i-l) if ~im In other word~, ~he i~ue about which the res~onden~
re~orted readershi~ can be con~idere~ a~ one of the is~ue~
in ~he ~chedule.
The 3ystem combinea these ~robabilitie3 acros~ ~ublication~
and accumulate~ a compo~ite aistributlon over the filtered ~o~ulatio~:
Xi = ~ f il ~ 9r Prob ( seeing i ) where the 8 ~ tion ~flltQr may be wei~htea to a parent ~o~ulation if thi~ i~ required.
Ste~ 3.~ The ~y~te~ al~o e~timate~ the actual (head~ount) reader~hi~ as:
Rm ~filte~ ~m and the cross-reader~hi~3 a~:
3 0 Rm~ f i 1 cer ml m2 an~ u~e~ the~e to e~ima~e the mean, ~, and variance, V, ~i WO93/01554 ~ PCT/AU92/a0286 r i ~ ~ ~ 2 2 o~ ke ~rue OTS distribution for the schedule, viz., M = R/P, where R = total impacts = ~mnmRm~ and P 3 size of the ~iltered population;
V = S/P - M
whe~e S = R (2~mnm-1) ~ml ~ 2 nml nm2 ( Rml ~Rm;?~ Rm -~ Rmnm(nm-l) ~1 +Ym(P-Rm) /P) ~m = the casualness of publication m over the filtered population.
Again, the summations ~f~r may be weighted to a parent population if this is required.
t~p ~ The system then modifies the c~mposite distribution formed in step 3.1 ~0 that its mean and variance match those of the true OTS distribution estimated in step 3.2, thus generating the final composite OTS dis~ribution ~or each schedule. In this step, the reach ~number of persons seeing at lea~t one insertion) and average frequèn~y (average impacts am~ngst those reached~ can also be determined.
There are many ways of performing this step, and the 20 system can utilizè either of two options: ~.
Alter~ati~e 4.1 Determining and then using multiplicative adjustment factors f; ~i = 0,.. , N~ which ::
maximise the function: ~
WO 93/01554 P~r/AU92/00286 211~377 Xrlog (frXr) ~o subj ect to the constraints:
r-Xr = P
~ r~Xr = R
r~o ~2.~rXir=s The solution to this problem is of the form:
r 1 ~ ~ (r-R/P) + v (r2-slP) where ,u and v ar~ (Lagrange multiplier) constants.
~lt~æ~ ti~ ~ . 2 Determining and then using absolute 5 adjustments Dj ( i -- 0 , . . ., N) which minimise the weighted sum of squares N
Wr Dr r=o subject to the constraints:
WO 93/015~4 PCI'/AU92/00286 ( S
N
~ Dr =
r~o IV .
~, r J~r = ErrOl: in ~
r=o = ~R
N
~2.Dr = Error in S
r=o = ~S
The solution to this problem is given explicitly as: :
i w~ 2Wfl Dr = w1r (1 ~ r~) ~ i2w~ 3w-l (~5) ~ i2W~ 3 when wO > O and, as:
` :
.
Dr = wl (r rZ) ~i3W-l ~ iGWillJ (l~
N
with Do = - ~ Dr ~-1 whell wO ~ -~0~3/01554 PCT/~U92/00286 The formulae set out in steps 1-4 above cover the essential principles underlying the system. Slight variations may be introduced for average is~ue readerships of d~ily newspapers, ~or time dependent cross-readerships and for alternative definitions of regularity.
The weights w; (i - 0,..., N~ are weights used by the system in guiding the adjustment process. Where wi is large the adjus~ment Di to the corresponding X; will be relatively small and vice versa. These weights are not to be confused with the respondent weights previou~ly described.
N is the maximum number of insertions which it is possible for any respondent to see. This may be less than the total number of insertions in the schedule. A
schedule may contain two or more media which are not available to the same respondent~, for instance because they do not overlap geographically, so that no respondent can be exposed to all of them.
The first method of adjustment ~4.1) involves smaller percentage changes in the distribution, but it does not guarantee additivity of the dîstribution o~er subsets of the population.
For a given set of weights w, where N is constant for the subgroups and for the total group, the second method (4.2) does guarantee additivity. The second method also has a further advantage. Because it is possible to determine the errors in R and S, and hence the Dj~ from summary statistics, this method can be used to estimate reach, and hence average frequency, by takin~ the c~mplement of the OTS zero frequency (XO) without having to determin~ the full OTS distribution. In cases where there are many insertions, this can save considerable computer time. For the same reason, this method can also be~used to generate partial distributions (X;) containing any number of kerms n < N. For a given set of weights w; the WO93/01554 PCT/AU92/002~6 2~37~
answers are always consistent.
The disadvantage of the second method is that it can produce negative estimates. However, thes~ are rare, and when they do occur, they are very small. Such ~stimates are analogou~ to the situation of negative counts that can be produced in some automatic counting machlnes through the subtraction of background noise.
The system almost completely overcomes the problem of declining reach and is not order dependent. It can model the actual shape of the OTS distribution. Its estimates of reach and frequency and its OTS distribution are always compatible with the headcount readership estimates. It does not dilute cross-media readerships.
It can also handle non-overlapping readerships.
Indeed regions can be specifically excluded from consideration of coverage hy selected media by ha~in~ the appropriate respondent loyalties set to zero.
Clearly, however, the system is dependent upon the accuracy of th~ estimation/measurement of the respondent loyalties contained in the file (SURVEY).PRB, although not to the same extent as personal probability methods. For consistency and for be~t operation nf the system, not only should:
.
~pOp Pm ~ Rm but also the estimate of casualness determined by aggregating the individual beta distributions, viz., 'Ym = Gm ~p~,p Pm ~ l~Pm) / [ p ( l -p ) ~
should reconcile with the overall loyalty, L, datermined W~93/01~4 PCT/AU92/00286 211~ 3 ~ 7 ~ 27 -by averaging the loyalties of the actual readers, i.e.:
~ z 1-L
~/P
whexe L = ~ pPml;m/Rm-It would be within the capacity of the skilled addressee to write computer code to achieve the methodology o~ the steps given abo~e when guided by the flow diagram of Figure 1. In the alternative, the ASTEROID code may be obtained from the applicant company.
The steps of Figure 1 are well annotated and will not be discussed in detail. Common elements can be identified in the step 1-4 above. Even so, the following comments will be made with reference to Figure 1.
Data are stored a:s binary (bît) strings or, in the ~ase of the `loyalty data' as a character string, with ~ach bit/characte~ representing one respondent. B~cause the`sample size is usua~ly large and there are memory constraints, the sample is usually broken up into ~segments'. Each ~egment is processed completely, and the : results are cu~ulated~befQre moving to the next segment.
Data extracted from the survey database in respect of one possible response (e.g. `read the newspap~r'3 are thus in the form of a bit vector repres nting the ~egment concerned, with `on' bits indicating, in this instanc~, respondents claiming to have read the newspaper and `off~
bits those claiming not to have read it. In some cases : bit vectors are com~ined to produce real number vectors 25 ~e . g. a person claiming to have read three issues of a daily paper out of a possible five issues would be given a value of 0.6). At completion of the calculations, the 2~ 77 - 28 -results are tabulated ~nd output to a device such as a printer, hard disk file or video screen. For the æchedule and filters described earlier, the composit~ OTS
distribution, reach and av~rag~ frequency are as follows.
The abbreviation, `Cume.' represents a cumulative representation of the composite OTS distributîons ~'Dist.').
Schedule File: 5CH01 Filter: Gentlemen Population ~000's~: 551.2 Publications, m Schedules (Insertions, n) AD~-M~F 1 2 2 0 Reach t~00's):307O3 349.1 345~5 105.2 ~ 55.8~ 63.3% 62.7% 19.1%
Ave. ~requency:1.54 2.36 2.04 2.30 Impacts (000's). 472.7 824.4 703.3 242.1 O.T.S. Dist. Cume. Dist. ~ume. Dist. Cume. Dist. Cume.
0 44.2% 36.7% 37.3% ~0.9%
1 39 . 4% 55,. 8% 15 . 5% 63.3% ~6.7% 62.7%6.4~.~9.1%
2 7.2% 16~3% 31.5% 47.8~ 34.7% 45.g%6.4% 12.7%
3 5.6~ 9.1% 5.7% 16.3% 5.1~ 11.2% 1.9% 6.3%
4 2.7% 3.5% 4.2% ~0.7% 5.0% 6.2% 3.5% 4.4 ~5 5 0.6% 0.8% 3.4% 6.5% 0.7% 1.1% 0O5% ~.9%
6 0.2% 0.2%. ~2.1% 3.1% 0.4% 0.4% 0.~% 07 7 0.5% 1.0%
: 8 0.3% 0.5%
9 0.2% 0.2%
, WO 93J01554 PCr/AU92/00286 ~9 Schedule File: SCHOl Filter: Ladies Population (OOO's): 570.8 Publications, m Schedules Insertions, n) Reach (OOO's): 387.0 424.5 410.5 236.9 ~%): 67.8% 74.4% 71.9% 41.5%
Ave. frequency 1.82 2.76 2.28 1.97 Impacts (OOO's):702.5 1171.1 937.2 467.8 ,, O . T. S . Dist . Cume . Dist . Cume. Dist . Cume. Dist. Cume 0 32.2% 25.6% 28.19~ 58.5%
35.1% 67.8% 15.3% 74.4% 16.5% 71.9% 14.6% 41.5%
2 17.6% 32.7% 26.7% 59.1% 34.6% 55.4% ZO.4% 26.9%
3 10.5% 15.0% 14.0% 32.4% 8.6% 20~7% 2.1% 6.5%
4 2.5% 4.6% ~.6% 18.4% 9.3% 12.1% 2.6% 4.4%
1.2% 2.1% 7.3% 11.8% 1.5% 2.8% 0.9% 1.8%
6 0.9% 0.9% 1.9% 4.S% 1.3% 1.3% 0.9% 0.9%
7 1.0% 2.5%
~ 8 0.7% 1.5%
9 0.8% 0.8%
.
::
W093/01554 PCT/AU92/ffO286 . .
21123~ 30 Schedule File: SCHO1 Filter: All Adults Population (OOO's): 1122.0 PublicationsL_m Schedules ~Insertions, n) Reach (OOO's): 694.3 773.6 756.0 342.1 ~ 61.9% 69.0% 67.4% 30.5%
Ave. frequency:1.69 2.58 2.17 2.08 Impacts (OOO's)1175.2 1995.5 1640.~ 709.9 O.T~S. Dist. Cume. Dist. Cume. ~ist. Cume. Dist. Cume.
O 38.1~ 31.0~ 32.6% 69.5%
1 37.3% 61.9% 15.4~ 69.0% 16.7% 67.4% 10.6% 30.5 2 12~5% 24.6% 2~ 53.6% 34.7% 50.7% 13.5% lg.9 3 8.1~ ~2.1% 9.g% 24.5% 6.9% 16.1% 2.0% 6.4~
4 2.~% 400~ S.4% 14.6% 7.2% 902% 3.0% 4.4%
5 0.9% 1.5% 5.4~ 9.2~ 1.1% 2.0~ 0.7% 1.4%
6 0.6% 0.6% 2.0% 3.8% 0.9% O.g~ 0.7% 0.7 7 0.8% 1.8~
: 8 0.3% 0.5%
9 0.2% 0.2%
, WO 93J01554 PCr/AU92/00286 ~9 Schedule File: SCHOl Filter: Ladies Population (OOO's): 570.8 Publications, m Schedules Insertions, n) Reach (OOO's): 387.0 424.5 410.5 236.9 ~%): 67.8% 74.4% 71.9% 41.5%
Ave. frequency 1.82 2.76 2.28 1.97 Impacts (OOO's):702.5 1171.1 937.2 467.8 ,, O . T. S . Dist . Cume . Dist . Cume. Dist . Cume. Dist. Cume 0 32.2% 25.6% 28.19~ 58.5%
35.1% 67.8% 15.3% 74.4% 16.5% 71.9% 14.6% 41.5%
2 17.6% 32.7% 26.7% 59.1% 34.6% 55.4% ZO.4% 26.9%
3 10.5% 15.0% 14.0% 32.4% 8.6% 20~7% 2.1% 6.5%
4 2.5% 4.6% ~.6% 18.4% 9.3% 12.1% 2.6% 4.4%
1.2% 2.1% 7.3% 11.8% 1.5% 2.8% 0.9% 1.8%
6 0.9% 0.9% 1.9% 4.S% 1.3% 1.3% 0.9% 0.9%
7 1.0% 2.5%
~ 8 0.7% 1.5%
9 0.8% 0.8%
.
::
W093/01554 PCT/AU92/ffO286 . .
21123~ 30 Schedule File: SCHO1 Filter: All Adults Population (OOO's): 1122.0 PublicationsL_m Schedules ~Insertions, n) Reach (OOO's): 694.3 773.6 756.0 342.1 ~ 61.9% 69.0% 67.4% 30.5%
Ave. frequency:1.69 2.58 2.17 2.08 Impacts (OOO's)1175.2 1995.5 1640.~ 709.9 O.T~S. Dist. Cume. Dist. Cume. ~ist. Cume. Dist. Cume.
O 38.1~ 31.0~ 32.6% 69.5%
1 37.3% 61.9% 15.4~ 69.0% 16.7% 67.4% 10.6% 30.5 2 12~5% 24.6% 2~ 53.6% 34.7% 50.7% 13.5% lg.9 3 8.1~ ~2.1% 9.g% 24.5% 6.9% 16.1% 2.0% 6.4~
4 2.~% 400~ S.4% 14.6% 7.2% 902% 3.0% 4.4%
5 0.9% 1.5% 5.4~ 9.2~ 1.1% 2.0~ 0.7% 1.4%
6 0.6% 0.6% 2.0% 3.8% 0.9% O.g~ 0.7% 0.7 7 0.8% 1.8~
8 0.5% 1.0%
9 0~5% 0.5%
From the foregoing tables, users can determine which schedule best meets their objectives, and an assessment of the worth of various advertising strategies can be made.
The invention described above may be embodied in other ~pecific forms from those discussed. The embodiments described are to be considered as illustrative :~
and not limiting on the scope of the invention.
From the foregoing tables, users can determine which schedule best meets their objectives, and an assessment of the worth of various advertising strategies can be made.
The invention described above may be embodied in other ~pecific forms from those discussed. The embodiments described are to be considered as illustrative :~
and not limiting on the scope of the invention.
Claims (27)
1. In an information processing system including a processor means, a memory means coupled to the processor means and operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles, input means coupled to the processor means and operable for obtaining user input, and output means coupled to the processor means, an estimation method comprising the steps of:
(A) selecting, by the input means, at least one schedule of media vehicles (m) and, for each selected schedule, specifying a number of insertions (nm) in each media vehicle;
(B) defining, by the input means, at least one filter specifying a set of the survey respondents for which each schedule is to be evaluated;
(C) applying, by the processor means, the filter to the database;
(D) for each media vehicle, calculating, by the processor means, an array of the probabilities of being exposed to i out of nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle;
(E) combining, by the processor means, the data relating to the usage of each selected media vehicle with said array of probabilities to give a probability of being exposed to i insertions in each selected media vehicle, combining the probabilities of being exposed to i insertions across the media vehicles and accumulating over all respondents passing the filter to yield a composite distribution (Xi);
(F) by the processor means, within the set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usage for each selected media vehicle, and summing the media vehicle usage of each pair of media vehicles (m1, m2) to give total estimated cross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities-to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle;
(G) operating, by the processor means, on each said composite distribution (Xi) to modify it so that it matches with the said mean and the said variance of the true OTS
distribution to form a final composite OTS distribution;
and (H) outputting, by the output means, the final composite OTS distribution for each selected schedule.
(A) selecting, by the input means, at least one schedule of media vehicles (m) and, for each selected schedule, specifying a number of insertions (nm) in each media vehicle;
(B) defining, by the input means, at least one filter specifying a set of the survey respondents for which each schedule is to be evaluated;
(C) applying, by the processor means, the filter to the database;
(D) for each media vehicle, calculating, by the processor means, an array of the probabilities of being exposed to i out of nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle;
(E) combining, by the processor means, the data relating to the usage of each selected media vehicle with said array of probabilities to give a probability of being exposed to i insertions in each selected media vehicle, combining the probabilities of being exposed to i insertions across the media vehicles and accumulating over all respondents passing the filter to yield a composite distribution (Xi);
(F) by the processor means, within the set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usage for each selected media vehicle, and summing the media vehicle usage of each pair of media vehicles (m1, m2) to give total estimated cross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities-to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle;
(G) operating, by the processor means, on each said composite distribution (Xi) to modify it so that it matches with the said mean and the said variance of the true OTS
distribution to form a final composite OTS distribution;
and (H) outputting, by the output means, the final composite OTS distribution for each selected schedule.
2. In an information processing system including a processor means, a memory means coupled to the processor means and operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles, input means coupled to the processor means and operable for obtaining user input, and output means coupled to the processor means, an estimation method comprising the steps of:
(A) inputting, by the input means, at least one schedule of media vehicles (m) and, for each schedule, specifying a number of insertions (nm) in each media vehicle;
(B) generating, by the processor means, a composite OTS distribution (Xi);
(C) operating, by the processor means, on each said composite OTS distribution (Xi) to modify it so that it matches with the mean and the variance of the true OTS
distribution as estimated from media vehicle usage and regularity of exposure to form a final composite OTS
distribution; and (D) outputting, by the output means, the final-composite OTS distributions.
(A) inputting, by the input means, at least one schedule of media vehicles (m) and, for each schedule, specifying a number of insertions (nm) in each media vehicle;
(B) generating, by the processor means, a composite OTS distribution (Xi);
(C) operating, by the processor means, on each said composite OTS distribution (Xi) to modify it so that it matches with the mean and the variance of the true OTS
distribution as estimated from media vehicle usage and regularity of exposure to form a final composite OTS
distribution; and (D) outputting, by the output means, the final-composite OTS distributions.
3. In an information processing system including a processor means, a memory means coupled to the processor means and operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles, input means coupled to the processor means and operable for obtaining user input, and output means coupled to the processor means, an estimation method comprising the steps of:
(A) inputting, by the input means, at least one schedule of media vehicles (m) and, for each schedule, specifying a number of insertions (nm) in each media vehicle;
(B) for each media vehicle calculating, by the processor means, a distribution array of the probabilities of being exposed to i out of nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle;
(C) utilising these distribution arrays in the generation, by the processor means, of a composite OTS
(opportunities-to-see) distribution for each schedule; and (D) outputting, by the output means, the composite OTS distributions.
(A) inputting, by the input means, at least one schedule of media vehicles (m) and, for each schedule, specifying a number of insertions (nm) in each media vehicle;
(B) for each media vehicle calculating, by the processor means, a distribution array of the probabilities of being exposed to i out of nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle;
(C) utilising these distribution arrays in the generation, by the processor means, of a composite OTS
(opportunities-to-see) distribution for each schedule; and (D) outputting, by the output means, the composite OTS distributions.
4. In an information processing system including a processor means, a memory means coupled to the processor means and operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles, input means coupled to the processor means and operable for obtaining user input, and output means coupled to the processor means, an estimation method comprising the steps of:
(A) selecting, by the input means, at least one schedule of media vehicles (m) and, for each selected schedule, specifying a number of insertions (nm) in each media vehicle;
(B) defining, by the input means, at least one filter specifying a set of the survey respondents for which each schedule is to be evaluated;
(C) applying, by the processor means, the filter to the database;
(D) for each media vehicle, calculating, by the processor means, a partial array of the probabilities of being exposed to some numbers i of the possible nm insertions using beta distributions for each respondent, said beta distributions having parameters that axe a function of the regularity of exposure to the media vehicle;
(E) combining, by the processor means, the data relating to the usage of each selected media vehicle with said partial array of probabilities to give a probability of being exposed to i insertions in each selected media vehicle, combining the probabilities of being exposed to i insertions across the media vehicles and accumulating over all respondents passing the filter to yield a partial composite distribution (Xi);
(F) by the processor means, within the set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usage for each selected media vehicle, and summing the media vehicle usage of each pair of media vehicles (m1, m2) to give total estimated cross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities-to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle;
(G) operating, by the processor means, on each said partial composite distribution (Xi) to modify it so that it matches with the said mean and the said variance of the true OTS distribution to form a final partial composite OTS distribution; and (H) outputting, by the output means, the final partial composite OTS distribution for each selected schedule.
(A) selecting, by the input means, at least one schedule of media vehicles (m) and, for each selected schedule, specifying a number of insertions (nm) in each media vehicle;
(B) defining, by the input means, at least one filter specifying a set of the survey respondents for which each schedule is to be evaluated;
(C) applying, by the processor means, the filter to the database;
(D) for each media vehicle, calculating, by the processor means, a partial array of the probabilities of being exposed to some numbers i of the possible nm insertions using beta distributions for each respondent, said beta distributions having parameters that axe a function of the regularity of exposure to the media vehicle;
(E) combining, by the processor means, the data relating to the usage of each selected media vehicle with said partial array of probabilities to give a probability of being exposed to i insertions in each selected media vehicle, combining the probabilities of being exposed to i insertions across the media vehicles and accumulating over all respondents passing the filter to yield a partial composite distribution (Xi);
(F) by the processor means, within the set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usage for each selected media vehicle, and summing the media vehicle usage of each pair of media vehicles (m1, m2) to give total estimated cross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities-to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle;
(G) operating, by the processor means, on each said partial composite distribution (Xi) to modify it so that it matches with the said mean and the said variance of the true OTS distribution to form a final partial composite OTS distribution; and (H) outputting, by the output means, the final partial composite OTS distribution for each selected schedule.
5. In an information processing system including a processor means, a memory means coupled to the processor means and operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles, input means coupled to the processor means and operable for obtaining user input, and output means coupled to the processor means, an estimation method comprising the steps of:
(A) selecting, by the input means, at least one schedule of media vehicles (m) and, for each selected schedule, specifying a number of insertions (nm) in each media vehicle;
(B) defining, by the input means, at least one filter specifying a set of the survey respondents for which each schedule is to be evaluated;
(C) applying, by the processor means, the filter to the database;
(D) for each media vehicle, calculating, by the processor means, a partial array of the probabilities of being exposed to some numbers i of the possible nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle;
(E) combining, by the processor means, the data relating to the usage of each selected media vehicle with said partial array of probabilities to give a probability of being exposed to zero insertions in each selected media vehicle, combining the probabilities of being exposed to zero insertions across the media vehicles and accumulating over all respondents passing the filter to yield a composite distribution frequency (X0) of zero exposures;
(F) by the processor means, within the set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usage for each selected media vehicle, and summing the media vehicle usage of each pair of media vehicles (m1, m2) to give total estimated cross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities-to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle;
(G) operating, by the processor means, on each said composite distribution frequency (X0) of zero exposures to modify it so that it matches with the said mean and the said variance of the true OTS distribution to yield a final composite OTS distribution frequency of zero exposures and therefore the reach; and (H) outputting, by the output means, the reach for each selected schedule.
(A) selecting, by the input means, at least one schedule of media vehicles (m) and, for each selected schedule, specifying a number of insertions (nm) in each media vehicle;
(B) defining, by the input means, at least one filter specifying a set of the survey respondents for which each schedule is to be evaluated;
(C) applying, by the processor means, the filter to the database;
(D) for each media vehicle, calculating, by the processor means, a partial array of the probabilities of being exposed to some numbers i of the possible nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle;
(E) combining, by the processor means, the data relating to the usage of each selected media vehicle with said partial array of probabilities to give a probability of being exposed to zero insertions in each selected media vehicle, combining the probabilities of being exposed to zero insertions across the media vehicles and accumulating over all respondents passing the filter to yield a composite distribution frequency (X0) of zero exposures;
(F) by the processor means, within the set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usage for each selected media vehicle, and summing the media vehicle usage of each pair of media vehicles (m1, m2) to give total estimated cross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities-to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle;
(G) operating, by the processor means, on each said composite distribution frequency (X0) of zero exposures to modify it so that it matches with the said mean and the said variance of the true OTS distribution to yield a final composite OTS distribution frequency of zero exposures and therefore the reach; and (H) outputting, by the output means, the reach for each selected schedule.
6. The method of any one of claims 1-3, comprising the further step of calculating, by the processor means, the reach for each selected schedule by forming the sum of the final composite OTS distribution frequencies for the cases of the number of insertions being one and more.
7. The method of claim 6, comprising the further step of calculating, by the processor means, the average frequency for each selected schedule determined as the total impacts divided by the reach.
8. The method of any one of claims 1-4, comprising the further step of calculating, by the processor means, the reach for each selected schedule by taking the complement of the final composite OTS distribution frequency for the case of the number of insertions being zero.
9. The method of claim 8, comprising the further step of calculating, by the processor means, the average frequency for each selected schedule determined as the total impacts divided by the reach.
10. The method of either one of claims 1 or 2 wherein said step, being step (G) when appended to claim 1 and step (C) when appended to claim 2, comprises use of multiplicative adjustment factors (f0, f1,..., fN) which maximise a summation function involving said composite distribution (X0, X1,..., XN).
11. The method of claim 10, wherein the summation function is:
.
.
12. The method of any one of claims 1, 2, 4 or 5, wherein said step, being step (G) when appended to claim 1, 4 or 5 and step (C) when appended to claim 2, comprises use of absolute adjustments, (D0, D1,..., DN) which minimise a weighted sum of squares, .
13. The method of any one of claims 1-5, comprising the further step of:
(I) printing, by the output means, the final output composite OTS distribution for each schedule.
(I) printing, by the output means, the final output composite OTS distribution for each schedule.
14. The method of any one of claims 1-5, comprising the further steps before step (A) of conducting a survey to obtain the said database of survey responses, and storing the database in the said memory means.
15. The method of claim 14, comprising the further step of:
(I) printing, by the output means, the final output composite OTS distribution for each schedule.
(I) printing, by the output means, the final output composite OTS distribution for each schedule.
16. The method of any one of claims 1-5, comprising the further steps of:
(AA) determining, by the processor means, whether any adjustment is to be made to allow for (a) media vehicles not included in the survey throughout the whole survey period;
(b) sample subsets defined wholly or partly in terms of time periods;
(c) sample subsets defined in terms of information not collected throughout the whole survey period;
(d) any sample subset used at any stage in the calculation which is formed as the intersection of any two or more sample subsets of the types (a)-(c);
(BB) calculating, by the processor means, the adjustment factors for (a) the total estimated usage of each media vehicle;
(b) the total estimated cross-usage of each pair of media vehicles;
(c) the frequencies (X0, X1,..., XN) of the composite frequency distribution; and (CC) applying, by the processor means, these adjustment factors to produce revised estimates of the composite distribution frequencies, total impacts and the mean and variance of the true OTS distribution.
(AA) determining, by the processor means, whether any adjustment is to be made to allow for (a) media vehicles not included in the survey throughout the whole survey period;
(b) sample subsets defined wholly or partly in terms of time periods;
(c) sample subsets defined in terms of information not collected throughout the whole survey period;
(d) any sample subset used at any stage in the calculation which is formed as the intersection of any two or more sample subsets of the types (a)-(c);
(BB) calculating, by the processor means, the adjustment factors for (a) the total estimated usage of each media vehicle;
(b) the total estimated cross-usage of each pair of media vehicles;
(c) the frequencies (X0, X1,..., XN) of the composite frequency distribution; and (CC) applying, by the processor means, these adjustment factors to produce revised estimates of the composite distribution frequencies, total impacts and the mean and variance of the true OTS distribution.
17. The method of claim 16, wherein the said adjustment factors are calculated by dividing a weighted number of respondents in the survey by a weighted number of respondents providing information within the identified time period(s) or intersections of time periods.
18. The method of claim 15, comprising the further steps of:
(AA) determining, by the processor means, whether any adjustment is to be made to allow for (a) media vehicles not included in the survey throughout the whole survey period;
(b) sample subsets defined wholly or partly in terms of time periods;
(c) sample subsets defined in terms of information not collected throughout the whole survey period;
(d) any sample subset used at any stage in the calculation which is formed as the intersection of any two or more sample subsets of the types (a)-(c);
(BB) calculating, by the processor means, the adjustment factors for (a) the total estimated usage of each media vehicle;
(b) the total estimated cross-usage of each pair of media vehicles;
(c) the frequencies (X0, X1,..., XN) of the composite frequency distribution; and (CC) applying, by the processor means, these adjustment factors to produce revised estimates of the composite distribution frequencies, total impacts and the mean and variance of the true OTS distribution.
(AA) determining, by the processor means, whether any adjustment is to be made to allow for (a) media vehicles not included in the survey throughout the whole survey period;
(b) sample subsets defined wholly or partly in terms of time periods;
(c) sample subsets defined in terms of information not collected throughout the whole survey period;
(d) any sample subset used at any stage in the calculation which is formed as the intersection of any two or more sample subsets of the types (a)-(c);
(BB) calculating, by the processor means, the adjustment factors for (a) the total estimated usage of each media vehicle;
(b) the total estimated cross-usage of each pair of media vehicles;
(c) the frequencies (X0, X1,..., XN) of the composite frequency distribution; and (CC) applying, by the processor means, these adjustment factors to produce revised estimates of the composite distribution frequencies, total impacts and the mean and variance of the true OTS distribution.
19. In an information processing system including a processor means, a memory means coupled to the processor means and operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles, input means coupled to the processor means and operable for obtaining user input, and output means coupled to the processor means, an estimation method for determining a composite OTS (opportunities-to-see) distribution for at least one schedule of media vehicles, with a specified number of insertions in each media vehicle and at least one filter specifying a set of survey respondents for which each schedule is to be evaluated, the method comprising the steps of:
(AA) determining, by the processor means, whether any adjustment is to be made to allow for (a) media vehicles not included in the survey throughout the whole survey period;
(b) sample subsets defined wholly or partly in terms of time periods;
(c) sample subsets defined in terms of information not collected throughout the whole survey period;
(d) any sample subset used at any stage in the calculation which is formed as the intersection of any two or more sample subsets of the types (a)-(c);
(BB) calculating, by the processor means, the adjustment factors for (a) the total estimated usage of each media vehicle;
(b) the total estimated cross-usage of each pair of media vehicles;
(c) the frequencies (X0, X1,..., XN) of the composite frequency distribution; and (CC) applying, by the processor means, these adjustment factors to produce revised estimates of the composite distribution frequencies, total impacts and the mean and variance of the true OTS distribution.
(AA) determining, by the processor means, whether any adjustment is to be made to allow for (a) media vehicles not included in the survey throughout the whole survey period;
(b) sample subsets defined wholly or partly in terms of time periods;
(c) sample subsets defined in terms of information not collected throughout the whole survey period;
(d) any sample subset used at any stage in the calculation which is formed as the intersection of any two or more sample subsets of the types (a)-(c);
(BB) calculating, by the processor means, the adjustment factors for (a) the total estimated usage of each media vehicle;
(b) the total estimated cross-usage of each pair of media vehicles;
(c) the frequencies (X0, X1,..., XN) of the composite frequency distribution; and (CC) applying, by the processor means, these adjustment factors to produce revised estimates of the composite distribution frequencies, total impacts and the mean and variance of the true OTS distribution.
20. The method of claim 19, wherein the said adjustment factors are calculated by dividing a weighted number of respondents in the survey by a weighted number of respondents providing information within the identified time period(s) or intersections of time periods.
21. In an information processing system including a processor means, a memory means coupled to the processor means and operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles, input means coupled to the processor means and operable for obtaining user input, and output means coupled to the processor means, an estimation method comprising the steps of:
(A) inputting, by the input means, at least one schedule of media vehicles (m) and, for each schedule, specifying a number of insertions (nm) in each media vehicle;
(B) generating, by the processor means, a distribution array of the probabilities of being exposed to i out of nm insertions using non-binomial distributions for each respondent;
(C) utilising the distribution array in the generation, by the processor means, of a composite OTS
(opportunities-to-see) distribution for each schedule; and (D) outputting, by the output means, the composite OTS distributions.
(A) inputting, by the input means, at least one schedule of media vehicles (m) and, for each schedule, specifying a number of insertions (nm) in each media vehicle;
(B) generating, by the processor means, a distribution array of the probabilities of being exposed to i out of nm insertions using non-binomial distributions for each respondent;
(C) utilising the distribution array in the generation, by the processor means, of a composite OTS
(opportunities-to-see) distribution for each schedule; and (D) outputting, by the output means, the composite OTS distributions.
22. An information processing system for use in media research, the system comprising:
(A) a memory means operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles;
(B) input means operable for obtaining user input and selection of at least one schedule of media vehicles (m) and, for each selected schedule, specification of a number of insertions (nm) in each media vehicle and at least one filter specifying a set of the survey respondents for which each schedule is to be evaluated;
(C) a processor means coupled to the memory means and the input means for applying the filter to the database, and for each media vehicle, calculating an array of the probabilities of being exposed to i out of nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle, for combining the data relating to the usage of each selected media vehicle with said array of probabilities to give a probability of being exposed to i insertions in each selected media vehicle, combining the probabilities of being exposed to i insertions across the media vehicles and accumulating over all respondents passing the filter to yield a composite distribution (Xi), and within the set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usage for each selected media vehicle, and summing the media vehicle usage of each pair of media vehicles (m1, m2) to give total estimated cross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle, operating on each said composite distribution (Xi) to modify it so that it matches with the said mean and the said variance of the true OTS distribution to form a final composite OTS distribution; and (D) output means coupled to the processor means for outputting the final composite OTS distribution for each selected schedule.
(A) a memory means operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles;
(B) input means operable for obtaining user input and selection of at least one schedule of media vehicles (m) and, for each selected schedule, specification of a number of insertions (nm) in each media vehicle and at least one filter specifying a set of the survey respondents for which each schedule is to be evaluated;
(C) a processor means coupled to the memory means and the input means for applying the filter to the database, and for each media vehicle, calculating an array of the probabilities of being exposed to i out of nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle, for combining the data relating to the usage of each selected media vehicle with said array of probabilities to give a probability of being exposed to i insertions in each selected media vehicle, combining the probabilities of being exposed to i insertions across the media vehicles and accumulating over all respondents passing the filter to yield a composite distribution (Xi), and within the set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usage for each selected media vehicle, and summing the media vehicle usage of each pair of media vehicles (m1, m2) to give total estimated cross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle, operating on each said composite distribution (Xi) to modify it so that it matches with the said mean and the said variance of the true OTS distribution to form a final composite OTS distribution; and (D) output means coupled to the processor means for outputting the final composite OTS distribution for each selected schedule.
23. An information processing system for use in media research, the system comprising:
(A) a memory means operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles;
(B) input means operable for obtaining user input and selection of at least one schedule of media vehicles (m) and, for each selected schedule, specification of a number of insertions (nm) in each media vehicle and at least one filter specifying a set of the survey respondents for which each schedule is to be evaluated;
(C) a processor means coupled to the memory means and the input means for applying the filter to the database, and for each media vehicle, calculating a partial array of the probabilities of being exposed to some numbers i of the possible nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle, for combining the data relating to the usage of each selected media vehicle with said partial array of probabilities to give a probability of being exposed to i insertions in each selected media vehicle, combining the probabilities of being exposed to i insertions across the media vehicles and accumulating over all respondents passing the filter to yield a partial composite distribution (Xi), and within the set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usage for each selected media vehicle, and summing the media vehicle usage of each pair of media vehicles (m1, m2) to give total estimated cross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities-to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle, operating on each said partial composite distribution (Xi) to modify it so that it matches with the said mean and the said variance of the true OTS distribution to form a final partial composite OTS distribution; and (D) output means coupled to the processor means for outputting the final partial composite OTS distribution for each selected schedule.
(A) a memory means operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles;
(B) input means operable for obtaining user input and selection of at least one schedule of media vehicles (m) and, for each selected schedule, specification of a number of insertions (nm) in each media vehicle and at least one filter specifying a set of the survey respondents for which each schedule is to be evaluated;
(C) a processor means coupled to the memory means and the input means for applying the filter to the database, and for each media vehicle, calculating a partial array of the probabilities of being exposed to some numbers i of the possible nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle, for combining the data relating to the usage of each selected media vehicle with said partial array of probabilities to give a probability of being exposed to i insertions in each selected media vehicle, combining the probabilities of being exposed to i insertions across the media vehicles and accumulating over all respondents passing the filter to yield a partial composite distribution (Xi), and within the set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usage for each selected media vehicle, and summing the media vehicle usage of each pair of media vehicles (m1, m2) to give total estimated cross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities-to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle, operating on each said partial composite distribution (Xi) to modify it so that it matches with the said mean and the said variance of the true OTS distribution to form a final partial composite OTS distribution; and (D) output means coupled to the processor means for outputting the final partial composite OTS distribution for each selected schedule.
24. An information processing system for use in media research, the system comprising:
(A) a memory means operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles;
(B) input means operable for obtaining user input and selection of at least one schedule of media vehicles (m) and, for each selected schedule, specification of a number of insertions (nm) in each media vehicle and at least one filter specifying a set of the survey respondents for which each schedule is to be evaluated;
(C) a processor means coupled to the memory means and the input means for applying the filter to the database, and for each media vehicle, calculating a partial array of the probabilities of being exposed to some numbers i of the possible nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle, for combining the data relating to the usage of each selected media vehicle with said partial array of probabilities to give a probability of being exposed to zero insertions in each selected media vehicle, combining the probabilities of being exposed to zero insertions across the media vehicles and accumulating over all respondents passing the filter to yield a composite distribution frequency of zero exposures (X0), and within the set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usage for each selected media vehicle, and summing the media vehicle usage of each pair of media vehicles (m1, m2) to give total estimated cross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities-to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle, operating on each said composite distribution frequency of zero exposures (X0) to modify it so that it matches with the said mean and the said variance of the true OTS
distribution to form a final composite OTS distribution frequency of zero exposures and therefore, by subtraction, the reach; and (D) output means coupled to the processor means for outputting the reach for each selected schedule.
(A) a memory means operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles;
(B) input means operable for obtaining user input and selection of at least one schedule of media vehicles (m) and, for each selected schedule, specification of a number of insertions (nm) in each media vehicle and at least one filter specifying a set of the survey respondents for which each schedule is to be evaluated;
(C) a processor means coupled to the memory means and the input means for applying the filter to the database, and for each media vehicle, calculating a partial array of the probabilities of being exposed to some numbers i of the possible nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicle, for combining the data relating to the usage of each selected media vehicle with said partial array of probabilities to give a probability of being exposed to zero insertions in each selected media vehicle, combining the probabilities of being exposed to zero insertions across the media vehicles and accumulating over all respondents passing the filter to yield a composite distribution frequency of zero exposures (X0), and within the set of survey respondents defined by the filter:
(i) summing media vehicle usage to give total estimated usage for each selected media vehicle, and summing the media vehicle usage of each pair of media vehicles (m1, m2) to give total estimated cross-usage of each pair of media vehicles, (ii) estimating the mean of the true OTS
(opportunities-to-see) distribution for each selected schedule from said total estimated usage for each selected media vehicle, (iii) estimating the variance of the true OTS
distribution for each schedule from the said mean, the said total estimated cross-usage and the said estimates of regularity of exposure for each media vehicle, operating on each said composite distribution frequency of zero exposures (X0) to modify it so that it matches with the said mean and the said variance of the true OTS
distribution to form a final composite OTS distribution frequency of zero exposures and therefore, by subtraction, the reach; and (D) output means coupled to the processor means for outputting the reach for each selected schedule.
25. An information processing system for media research, the system comprising:
(A) a memory means operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles;
(B) input means operable for obtaining user input and for selection of at least one schedule of media vehicles (m) and, for each selected schedule, specification of a number of insertions (nm) in each media vehicle;
(C) a processor means coupled to the memory means and the input means for generating distribution arrays of the probabilities of being exposed to i out of nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicles, utilising the distribution arrays in the generation of a composite OTS (opportunities-to-see) distribution for each schedule; and (D) output means coupled to the processor means for outputting the composite OTS distributions.
(A) a memory means operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles;
(B) input means operable for obtaining user input and for selection of at least one schedule of media vehicles (m) and, for each selected schedule, specification of a number of insertions (nm) in each media vehicle;
(C) a processor means coupled to the memory means and the input means for generating distribution arrays of the probabilities of being exposed to i out of nm insertions using beta distributions for each respondent, said beta distributions having parameters that are a function of the regularity of exposure to the media vehicles, utilising the distribution arrays in the generation of a composite OTS (opportunities-to-see) distribution for each schedule; and (D) output means coupled to the processor means for outputting the composite OTS distributions.
26. An information processing system for media research, the system comprising:
(A) a memory means operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles;
(B) input means operable for obtaining user input and for selection of at least one schedule of media vehicles (m) and, for each selected schedule, specification of a number of insertions (nm) in each media vehicle;
(C) a processor means coupled to the memory means and the input means for generating a composite OTS
distribution (Xi), for operating on each said composite OTS
distribution (Xi) to modify it so that it matches with the mean and the variance of the true OTS distribution as estimated from media vehicle usage and regularity of exposure to form a final composite OTS distribution for each schedule; and (D) output means coupled to the processor means for outputting the final composite OTS distributions.
(A) a memory means operable for storing therein a database of survey responses including media vehicle usage and estimates of regularity of exposure of respondents to the media vehicles;
(B) input means operable for obtaining user input and for selection of at least one schedule of media vehicles (m) and, for each selected schedule, specification of a number of insertions (nm) in each media vehicle;
(C) a processor means coupled to the memory means and the input means for generating a composite OTS
distribution (Xi), for operating on each said composite OTS
distribution (Xi) to modify it so that it matches with the mean and the variance of the true OTS distribution as estimated from media vehicle usage and regularity of exposure to form a final composite OTS distribution for each schedule; and (D) output means coupled to the processor means for outputting the final composite OTS distributions.
27. The apparatus of any one of claims 22-26, further comprising:
(E) printer means for printing the final composite OTS distributions.
(E) printer means for printing the final composite OTS distributions.
Applications Claiming Priority (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AUPK702391 | 1991-07-02 | ||
| AUPK7023 | 1991-07-02 | ||
| AUPK917591 | 1991-10-29 | ||
| AUPK9175 | 1991-10-29 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2112377A1 true CA2112377A1 (en) | 1993-01-21 |
Family
ID=25644066
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA002112377A Abandoned CA2112377A1 (en) | 1991-07-02 | 1992-06-16 | Reach and frequency estimation for media |
Country Status (4)
| Country | Link |
|---|---|
| EP (1) | EP0592467A4 (en) |
| CA (1) | CA2112377A1 (en) |
| GB (1) | GB2272316A (en) |
| WO (1) | WO1993001554A1 (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2417750A1 (en) * | 2000-07-31 | 2002-02-07 | Intermedia Advertising Group | Computerized system and method for increasing the effectiveness of advertising |
| WO2002091262A1 (en) * | 2001-05-02 | 2002-11-14 | Dentsu Inc. | System and method for managing distribution of advertisement |
| CN101836227A (en) * | 2007-08-06 | 2010-09-15 | 汤姆森许可贸易公司 | Method and system for product services analysis and optimization |
| CN104992342B (en) * | 2015-05-11 | 2019-08-13 | 腾讯科技（北京）有限公司 | Promotion message delivery effectiveness determines method, monitoring server and terminal |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4659314A (en) * | 1986-02-07 | 1987-04-21 | Weinblatt Lee S | Survey technique for readership of publications |
| US4781596A (en) * | 1986-06-16 | 1988-11-01 | Weinblatt Lee S | Survey technique for readership of publications |
| US4930077A (en) * | 1987-04-06 | 1990-05-29 | Fan David P | Information processing expert system for text analysis and predicting public opinion based information available to the public |
| GB2258065A (en) * | 1991-07-26 | 1993-01-27 | Mcconnells Developments Limite | Television programme and advertising data analysis |
-
1992
- 1992-06-16 CA CA002112377A patent/CA2112377A1/en not_active Abandoned
- 1992-06-16 WO PCT/AU1992/000286 patent/WO1993001554A1/en not_active Application Discontinuation
- 1992-06-16 EP EP92911889A patent/EP0592467A4/en not_active Withdrawn
-
1993
- 1993-12-24 GB GB9326468A patent/GB2272316A/en not_active Withdrawn
Also Published As
| Publication number | Publication date |
|---|---|
| EP0592467A1 (en) | 1994-04-20 |
| WO1993001554A1 (en) | 1993-01-21 |
| EP0592467A4 (en) | 1995-11-02 |
| GB9326468D0 (en) | 1994-03-02 |
| GB2272316A (en) | 1994-05-11 |
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Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FZDE | Discontinued | ||
| FZDE | Discontinued |
Effective date: 19970616 |
|
| FZDE | Discontinued |
Effective date: 19970616 |