U.S. patent application number 16/705610 was filed with the patent office on 2020-08-20 for methods and apparatus to improve reach calculation efficiency.
The applicant listed for this patent is The Nielsen Company (US), LLC. Invention is credited to Peter Campbell Doe, Paul Donato, Peter Lipa, Michael Sheppard, Jonathan Sullivan.
Application Number | 20200265461 16/705610 |
Document ID | 20200265461 / US20200265461 |
Family ID | 1000004798962 |
Filed Date | 2020-08-20 |
Patent Application | download [pdf] |
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United States Patent
Application |
20200265461 |
Kind Code |
A1 |
Sheppard; Michael ; et
al. |
August 20, 2020 |
METHODS AND APPARATUS TO IMPROVE REACH CALCULATION EFFICIENCY
Abstract
Methods, apparatus, systems and articles of manufacture are
disclosed to improve reach calculation efficiency. An example
method includes estimating, with a processor, a sample distribution
of marketing data to generate a maximum entropy distribution,
generating, with the processor, a geometric distribution based on
estimating a minimum cross entropy of (a) the maximum entropy
distribution and (b) the sample distribution of marketing data, and
improving calculation efficiency of the public reach of the sample
distribution of marketing data by generating, with the processor,
conserved quantity expressions of the geometric distribution.
Inventors: |
Sheppard; Michael;
(Brooklyn, NY) ; Sullivan; Jonathan; (Hurricane,
UT) ; Lipa; Peter; (Tucson, AZ) ; Doe; Peter
Campbell; (Ridgewood, NJ) ; Donato; Paul; (New
York, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Nielsen Company (US), LLC |
New York |
NY |
US |
|
|
Family ID: |
1000004798962 |
Appl. No.: |
16/705610 |
Filed: |
December 6, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14984310 |
Dec 30, 2015 |
10504138 |
|
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16705610 |
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62212097 |
Aug 31, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0244 20130101;
G06Q 30/0242 20130101; G06Q 30/0201 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. (canceled)
2. An apparatus to improve an efficiency of determining a published
reach value, the apparatus comprising: a market data evaluator to
identify a negative binomial distribution (NBD) feasibility region
corresponding to a published gross rating point (GRP) value, the
published GRP value corresponding to a quantity change associated
with an advertising campaign; and a conserved quantity engine to,
after the market data evaluator has identified the NBD feasibility
region is associated with a number of samples below a threshold:
generate a model of the published GRP value; and reduce a
computational burden associated with determining the published
reach value of a sample distribution of marketing data by
generating closed-loop conserved quantity expressions based on the
model of the published GRP value.
3. The apparatus as defined in claim 2, further including a maximum
entropy engine to, in response to the market data evaluator
identifying the NBD feasibility region is associated with the
number of samples below the threshold, generate a maximum entropy
distribution based on estimating the sample distribution of the
marketing data.
4. The apparatus as defined in claim 3, wherein the maximum entropy
engine is to constrain the maximum entropy distribution by a first
GRP value and a first reach value, the first GRP value (a)
empirically measured and (b) corresponding to the sample
distribution of the marketing data, the first reach value based on
the first GRP value.
5. The apparatus as defined in claim 4, wherein the maximum entropy
engine is to constrain the first GRP value and the first reach
value for a probability of zero advertising impressions associated
with the sample distribution of the marketing data.
6. The apparatus as defined in claim 2, further including a maximum
entropy constraint manager to generate a geometric distribution
based on estimating a minimum cross entropy corresponding to (a) a
maximum entropy distribution and (b) the sample distribution of the
marketing data, the maximum entropy distribution based on the
sample distribution of the marketing data.
7. The apparatus as defined in claim 6, further including a minimum
cross entropy constraint manager to constrain the minimum cross
entropy with the published GRP value of the sample distribution of
the marketing data.
8. The apparatus as defined in claim 2, wherein the conserved
quantity engine is to associate at least one of a combination of
(a) one or more additional GRP values and one or more additional
reach values, (b) one or more additional GRP values and one or more
frequencies, or (c) one or more additional reach values and one or
more frequencies, via the closed-loop conserved quantity
expressions.
9. The apparatus as defined in claim 2, wherein the quantity change
associated with the advertising campaign includes an increase of
product sales.
10. A tangible computer readable storage medium comprising
instructions to improve an efficiency of determining a published
reach value that, when executed, cause a processor to, at least:
identify a negative binomial distribution (NBD) feasibility region
corresponding to a published gross rating point (GRP) value, the
published GRP value corresponding to a quantity change associated
with an advertising campaign; after having identified that the NBD
feasibility region is associated with a number of samples below a
threshold, generate a model of the published GRP value; and reduce
a computational burden associated with determining the published
reach value of a sample distribution of marketing data by
generating closed-loop conserved quantity expressions based on the
model of the published GRP value.
11. The tangible computer readable storage medium as defined in
claim 10, wherein the instructions, when executed, cause the
processor to, in response to having identified that the NBD
feasibility region is associated with the number of samples below
the threshold, estimate the sample distribution of the marketing
data to generate a maximum entropy distribution.
12. The tangible computer readable storage medium as defined in
claim 11, wherein the instructions, when executed, cause the
processor to constrain the maximum entropy distribution by a first
GRP value and a first reach value, the first GRP value (a)
empirically measured and (b) corresponding to the sample
distribution of the marketing data, the first reach value based on
the first GRP value.
13. The tangible computer readable storage medium as defined in
claim 12, wherein the instructions, when executed, cause the
processor to constrain the first GRP value and the first reach
value for a probability of zero advertising impressions associated
with the sample distribution of the marketing data.
14. The tangible computer readable storage medium as defined in
claim 10, wherein the instructions, when executed, cause the
processor to generate a geometric distribution based on estimating
a minimum cross entropy corresponding to (a) a maximum entropy
distribution and (b) the sample distribution of the marketing data,
the maximum entropy distribution based on the sample distribution
of the marketing data.
15. The tangible computer readable storage medium as defined in
claim 14, wherein the instructions, when executed, cause the
processor to constrain the minimum cross entropy with the published
GRP value of the sample distribution of the marketing data.
16. The tangible computer readable storage medium as defined in
claim 10, wherein the instructions, when executed, cause the
processor to associate at least one of a combination of (a) one or
more additional GRP values and one or more additional reach values,
(b) one or more additional GRP values and one or more frequencies,
or (c) one or more additional reach values and one or more
frequencies, via the closed-loop conserved quantity
expressions.
17. The tangible computer readable storage medium as defined in
claim 10, wherein the quantity change associated with the
advertising campaign includes an increase of product sales.
18. An apparatus to improve an efficiency of determining a
published reach value, the apparatus comprising: market data
evaluation means to identify a negative binomial distribution (NBD)
feasibility region corresponding to a published gross rating point
(GRP) value, the published GRP value corresponding to a quantity
change associated with an advertising campaign; and conserved
quantity generation means to, after the market data evaluation
means has identified the NBD feasibility region is associated with
a number of samples below a threshold: generate a model of the
published GRP value; and reduce a computational burden associated
with determining the published reach value of a sample distribution
of marketing data by generating closed-loop conserved quantity
expressions based on the model of the published GRP value.
19. The apparatus as defined in claim 18, further including maximum
entropy distribution generation means to, in response to the market
data evaluation means identifying the NBD feasibility region is
associated with the number of samples below the threshold, generate
a maximum entropy distribution based on estimating the sample
distribution of the marketing data.
20. The apparatus as defined in claim 19, wherein the maximum
entropy distribution generation means is to constrain the maximum
entropy distribution by a first GRP value and a first reach value,
the first GRP value (a) empirically measured and (b) corresponding
to the sample distribution of the marketing data, the first reach
value based on the first GRP value.
21. The apparatus as defined in claim 20, wherein the maximum
entropy distribution generation means is to constrain the first GRP
value and the first reach value for a probability of zero
advertising impressions associated with the sample distribution of
the marketing data.
Description
RELATED APPLICATION
[0001] This patent arises from a continuation of U.S. patent
application Ser. No. 14/984,310, filed on Dec. 30, 2015, entitled
"METHODS AND APPARATUS TO IMPROVE REACH CALCULATION EFFICIENCY,"
which claims the benefit of, and priority to U.S. Provisional
Patent Application Ser. No. 62/212,097, filed Aug. 31, 2015,
entitled "METHODS AND APPARATUS TO CALCULATE GROSS RATINGS POINTS
AND REACH WITH ENTROPY," the contents of which are hereby
incorporated herein by reference in their entireties.
FIELD OF THE DISCLOSURE
[0002] This disclosure relates generally to market data analysis,
and, more particularly, to methods and apparatus to improve reach
calculation efficiency.
BACKGROUND
[0003] In recent years, market analysts have measured observation
impressions associated with media, in which impressions may include
sales of an advertised product, observations of an advertisement,
observations of a particular broadcast event, etc. To gain an
understanding of an effectiveness of a particular media
distribution technique (e.g., advertisements via television,
advertisements via Internet media, etc.), the analysts typically
calculate a gross rating point (GRP). The GRP is calculated as a
ratio of a number of observed impressions and a defined population
and, to account for unique impressions, the analysts may also
calculate a corresponding reach metric, which is a ratio of a
unique number of exposures and the defined population.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 is a schematic illustration of a negative binomial
distribution model traditionally applied to published reach
calculations.
[0005] FIG. 2 is an example feasibility region plot that identifies
feasibility regions for traditionally applied methods to calculate
published reach values.
[0006] FIG. 3 is a schematic illustration of an example market data
evaluator system to improve reach calculation efficiency.
[0007] FIGS. 4-6 are flowcharts representative of example machine
readable instructions that may be executed to implement the example
market data evaluator system of FIG. 3 to improve reach calculation
efficiency.
[0008] FIG. 7 is a chart of example solutions to determine a
published reach as a function of raw reach.
[0009] FIG. 8 is a block diagram of an example processor platform
structured to execute the example machine readable instructions of
FIGS. 4-6 to implement the example market data evaluator system of
FIG. 3.
DETAILED DESCRIPTION
[0010] In recent years, a gross rating point (GRP) metric, which is
sometimes referred to as an average or a mean, was calculated as a
ratio of a number of observed impressions and a defined population,
but the GRP does not account for whether such impressions were
unique to just one individual from the defined population. As such,
a reach metric is typically used together with the GRP calculation
to consider unique impressions. The reach metric is calculated as a
ratio of a unique number of exposures and the defined
population.
[0011] A market analyst typically measures both the GRP and the
reach empirically for a particular event (e.g., an advertising
campaign), but may wish to understand a corresponding reach value
in the event a newly proposed (e.g., published) GRP value occurs.
For example, a raw (e.g., initial) GRP and raw reach value measured
from panelist observations may reveal values of 100 and 30,
respectively. However, this example GRP value is associated with a
particular event (e.g., advertising campaign) having a particular
amount of advertising revenue applied thereto. The market analyst
may know that one or more changes to the marketing campaign can
boost the GRP value from 100 to 200 (e.g., spending more
advertising dollars to utilize a greater number of media outlets,
such as purchasing more commercial air-time, and/or purchasing
commercial air-time during prime viewership times of day). Using
the originally provided market data, the analyst applies a model to
scale the GRP up from 100 to a value of 200 to determine a
corresponding reach value at the newly published GRP value.
[0012] Traditionally, the market analyst determines a corresponding
published reach value from the proposed published GRP value by
applying a negative binomial distribution (NBD) model. The NBD
models a frequency distribution to reflect a percentage of the
population that participated in the impression(s), and assumes an
infinite domain. When the NBD model is applied to circumstances
having a particular number of samples, accurate results may occur,
however the NBD model has erroneous results for certain feasibility
regions, particularly when the number of samples is relatively low
or below a threshold value. In the event such problematic
feasibility regions occur in view of the provided market data
(frequency distribution), traditional techniques applied by
analysts include application of a Poisson distribution, which has
become an industry standard. Unfortunately, application of the
Poisson distribution produces results that are independent of the
published GRP and/or the original market data (frequency
distribution). Stated differently, application of the NBD model
presupposes a particular distribution in an attempt to fit the data
to that distribution, and predictions are derived therefrom.
[0013] Examples disclosed herein prevent any assumption and/or
presupposition of which distribution to use and, instead, preserve
the integrity of the known marketing data to derive a distribution
that best fits that data. Further, examples disclosed herein apply
a principle of maximum entropy and minimum cross entropy to solve
for the proper distribution that best fits the market data. Entropy
reflects disorder in a distribution, and examples disclosed herein
apply the principle of maximum entropy to derive the most accurate
distribution in an effort to reduce uncertainty. Because a maximum
entropy exposes what is maximally uncertain about what is unknown,
any other distribution, such as an assumed NBD distribution, means
that more information is being forced into that distribution/model
that does not reflect the empirical data (e.g., the provided market
data). Examples disclosed herein determine a maximum entropy of
which observations are at zero (e.g., zero impressions), which is a
portion of the NBD model that produces the most error.
Additionally, examples disclosed herein apply the minimum cross
entropy to modify the maximum entropy distribution to create a new
distribution that still depends on values from the empirical
observations, unlike the application of the Poisson
distribution.
[0014] As described above, GRP is a metric to measure impressions
in relation to the number of people in target data for an
advertising campaign, which is calculated in a manner consistent
with example Equation 1.
GRP = 1 0 0 * Impressions Defined Population . Equation 1
##EQU00001##
Additionally, as described above, reach is the total number of
different people in the defined population exposed, at least once,
to the campaign during a given period, which is calculated in a
manner consistent with example Equation 2.
Reach = 100 * Unique People Exposed Defined Population . Equation 2
##EQU00002##
A mathematical constraint between a GRP calculation and a
corresponding reach calculation is that the GRP value must always
be greater-than or equal to the reach value. For instance, if five
(5) impressions are detected and/or otherwise observed in a
distribution having a population of ten (10) people, then a
corresponding GRP value is fifty (50). Because reach reflects an
indication of how many unique exposures occurred, the number of
unique exposures mathematically cannot exceed the example five (5)
impressions detected.
[0015] In the event the analyst can improve (e.g., increase) the
GRP of the original distribution by applying one or more marketing
drivers (sometimes referred to as "scaling-up"), then the analyst
would also like to justify whether such marketing drivers will be
effective and/or a degree by which such marketing drivers will be
effective by determining a corresponding reach value associated
with the scaled-up GRP value. Traditional application of the NBD
model have been used by market analysts under the qualified
assumption that a scaled-up frequency distribution follows the
curve/distribution of that NBD model, as shown in FIG. 1.
[0016] In the illustrated example of FIG. 1, an NBD curve 100
includes an x-axis of GRP values 102 and a y-axis of probability
104. As described above, because the GRP reflects an average, the
non-zero reach probabilities 106 correspond to an example GRP of
1.33 (or 133 after traditional multiplication of the ratio of
example Equation 1 by 100) 108. The reach corresponding to the
example GRP of FIG. 1 is 64 (or 64%) to reflect unique or different
exposures of the audience. Because the NBD curve 100 includes all
participants of a given defined population (e.g., the denominator
of example Equations 1 and 2), a corresponding probability at zero
is known to be 36% (100-64). The NBD in the context of GRP and
reach is defined in a manner consistent with example Equation
3.
P ( i ) = .GAMMA. ( k + i ) .GAMMA. ( k ) .GAMMA. ( i + 1 ) * ( 1 (
1 + a ) ) k * ( a 1 + a ) i . Equation 3 ##EQU00003##
In the illustrated example of Equation 3, P(i) reflects a
proportion of viewing an i.sup.th spot/index (e.g., advertisement),
.GAMMA. reflects a Gamma function, and a and k are parameters of
the model. As described above, application of the NBD model
presupposes a particular distribution/shape, to which provided
observation data (e.g., market data) is forced. When the market
analyst scales-up a given distribution having a new/published GRP
value, the NBD is solved for a proportion of zero (0) viewing spots
(i.e., P(0)), in which the NBD equation of Equation 3 simplifies to
example Equation 4.
P ( 0 ) = ( 1 1 + a ) k = 1 - Raw Reach 1 0 0 . Equation 4
##EQU00004##
Parameters a and k are isolated in a manner consistent with example
Equation 5.
Expected Value = a * k = Raw GRP 1 0 0 . Equation 5
##EQU00005##
Under the assumption that any scaled-up distribution will
reasonably conform to the NBD, the new/published/target GRP is
scaled up to generate a modified parameter a, which is derived in
example Equation 6 as scaled parameter A.
A = a * Published GRP Raw GRP . Equation 6 ##EQU00006##
In connection with scaled parameter A, a new published reach
corresponding to the published GRP is calculated in a manner
consistent with example Equations 7 and 8.
P ( 0 ) = ( 1 1 + A ) k = 1 - Published Reach 1 0 0 . Equation 7 P
( i ) = .GAMMA. ( k + i ) .GAMMA. ( k ) .GAMMA. ( i + 1 ) * ( 1 1 +
A ) k * ( A 1 + A ) i . Equation 8 ##EQU00007##
[0017] To illustrate limitations of the traditionally applied NBD
model, assume an observed distribution includes a GRP value of 100
with a corresponding reach value of 80. Applying example Equation 5
and solving for parameters a and k results in a value of -0.647 for
parameter a, and a value of -1.546 for parameter k. For varying
values of index (i), corresponding probability values result, as
shown in Table 1 below.
TABLE-US-00001 TABLE 1 i P(i) 0 0.200 1 0.567 2 0.283 3 -0.079 4
0.052 5 -0.047
As shown in the illustrated example of Table 1, application of the
NBD model breaks down for the 3.sup.rd and the 5.sup.th index
values, which produce erroneous negative values for probability.
The qualified assumptions made by analysts using the NBD model
described above include at least two considerations, the first of
which is that the applied raw (e.g., initial) GRP is greater than
or equal to the reach. This consideration is met by the example
above, where GRP is 100 and the corresponding reach is 80. However,
the second consideration includes an inherent limitation of the NBD
model for those combinations of GRP and reach that are not
themselves mathematically impossible, but nonetheless impossible
when employed with the NBD model.
[0018] FIG. 2 illustrates a feasibility region plot 200 associated
with the NBD model. In the illustrated example of FIG. 2, the
feasibility region plot 200 includes an x-axis to reflect reach
values 202 and a y-axis to reflect GRP values 204. The example
feasibility region plot 200 also includes a mathematically
impossible region 206, in which the GRP is not greater than or
equal to the reach. However, the example feasibility region plot
200 also includes an NBD-impossible region 208, which may reflect
valid values for GRP and reach that could be observed in market
data, but cause the NBD model to fail, as shown by the results of
example Table 1 above. Finally, the example feasibility region plot
200 includes an NBD-feasible region 210 for which corresponding
values of GRP and reach will not cause the NBD model to fail.
[0019] When an analyst identifies circumstances where combinations
of GRP and reach reside within the example NBD-impossible region
206, such traditional solutions apply a Poission distribution. In
some examples, the Poission distribution is applied as a
work-around to the NBD-impossible region 206 when the corresponding
a parameter is calculated at a value less than zero. The Poission
distribution replaces the NBD approach and is used with an
alternate parameter lambda (.lamda.) as shown in example Equation
9.
.lamda. = Published GRP 1 0 0 . Equation 9 ##EQU00008##
The published reach is then calculated in a manner consistent with
example Equation 10 for a proportion of viewing zero (0) spots
(advertisements).
Proportion Viewing 0 Spots = e - .lamda. = 1 - Published Reach 1 0
0 . Equation 10 ##EQU00009##
To determine a corresponding frequency distribution for proportions
viewing any number of non-zero spots, example Equation 11 is
applied.
Proportion Viewing i Spots = .lamda. i e - .lamda. i ! . Equation
11 ##EQU00010##
While application of the Poission distribution as shown above has
been adopted as an industry standard, worth noting is that this
approach no longer reflects any influence of the published GRP and
completely ignores the original sample distribution.
[0020] Examples disclosed herein do not presuppose a distribution
and attempt to fit the market data thereto, but rather generate one
or more alternate distributions based on the available data. FIG. 3
illustrates an example market data evaluator (MDE) system 300 that
includes an MDE 302 communicatively connected to one or more
campaign data sources 304 via an example network 306. In the
illustrated example of FIG. 3, the MDE 302 includes a marketing
data interface 308, an example gross rating point (GRP) engine 310,
an example reach engine 312, an example simulation engine 314, an
example conserved quantity engine 316, an example maximum entropy
(ME) engine 318, and an example minimum cross entropy (MCE) engine
320. In the illustrated example of FIG. 3, the ME engine 318
includes an example maximum entropy (ME) constraint manager 322 and
an example maximum entropy (ME) distribution evaluator 324. In the
illustrated example of FIG. 3, the MCE engine 320 includes an
example MCE constraint manager 326 and an example published GRP
manager 328.
[0021] In operation, the example marketing data interface 308
retrieves a dataset having an unknown distribution, such as market
data associated with one or more of the campaign data sources 304.
The dataset may represent market behavior activity such as, but not
limited to, impression count data associated with promotional
activity in which particular participants of a population are
exposed to advertisements. The example GRP engine 310 calculates a
corresponding GRP value associated with the dataset in a manner
consistent with example Equation 1, and the example reach engine
312 calculates a corresponding reach value in a manner consistent
with example Equation 2. In some examples, GRP and reach values
that have been calculated from empirical datasets are referred to
as raw GRP values and raw reach values. On the other hand, in the
event an analyst wishes to use the empirical dataset to scale-up to
a proposed/candidate published GRP value, which is not based on
empirically collected data, then traditional techniques employed
the NBD model, as described above.
[0022] The example simulation engine 314 selects a candidate
published GRP value of interest as a scaling-up value, and the
maximum entropy engine 318 estimates a distribution using the
principal of maximum entropy to generate a maximum entropy
distribution. Generally speaking, entropy quantifies an uncertainty
involved in predicting a value of a random variable. Entropy
reflects an expected value (E) of information (X), as shown by
example Equation 12.
H ( X ) = E [ I ( X ) ] = i P ( x i ) I ( x i ) = - i P ( x i ) l o
g b P ( x i ) . Equation 12 ##EQU00011##
The principle of maximum entropy states that a probability
distribution that best represents the current state of knowledge is
the one having the largest entropy. While an analyst goal may be to
reduce uncertainty in any prediction made, examples disclosed
herein first establish a foundation of being maximally uncertain
about what we do not know. Because entropy is maximum when all
outcomes are equally likely, any instance that deviates from
equally likely outcomes reduces the entropy by introducing
information to a distribution.
[0023] The example ME constraint manager 322 establishes
constraints for the provided distribution to prevent computational
waste, as shown by example Equations 13 and 14.
k = 0 .infin. q k = 1. Equation 13 k = 0 .infin. k q k = u 1 .
Equation 14 ##EQU00012##
[0024] In the illustrated examples of Equations 13 and 14, three
constraints include (a) q.sub.0 is a known constraint (also
reflects a shorthand notation for a probability at zero, which is
1-reach), (b) sums are expected to reach 100%, and .mu..sub.1 is an
expected value mean, which is sometimes referred to as a sample GRP
or the empirically known GRP. Additionally, q.sub.k reflects a
reach value at a k.sup.th frequency. At least one additional
benefit of the example ME constraint manager 322 establishing the
constraints in a manner consistent with example Equations 13 and 14
is that only positive values result for a probability set. This
also ensures that any impossibility region(s) will not affect
prediction accuracy.
[0025] The example ME distribution evaluator 324 applies the
principle of maximum entropy to the distribution in a manner
consistent with example Equation 15.
maximize Q .fwdarw. H = - k = 0 .infin. q k log ( q k ) . Equation
15 ##EQU00013##
In the illustrated example of Equation 15, Q reflects a reach
distribution with a maximized entropy (H). Example Equation 15 is
solved to generate a zero-modified geometric distribution using the
known values for the sample reach for a probability at zero
(q.sub.0) and the sample GRP (.mu..sub.1). In particular, the
example ME constraint manager 322 facilitates (e.g., based on the
constraints of (a) .mu..sub.1, (b) q.sub.0, (c) .mu..sub.2 and (d)
the sum equaling 100%) cross entropy calculations in a manner
consistent with example Equations 16, 17 and 18, in which the cross
entropy calculations consider a published GRP that may be achieved
by the market analyst.
q k = C r k where k = 1 , 2 , , .infin. . Equation 16 C = ( 1 - q 0
) 2 u 1 + q 0 - 1 . Equation 17 r = u 1 + q 0 - 1 u 1 . Equation 18
##EQU00014##
The illustrated example of Equation 16 reflects the prior
distribution and in example Equation 17, C reflects one of two
constants to facilitate cross entropy calculations, and in the
illustrated example of Equations 16, 17 and 18, r reflects the
second of two constants to facilitate cross entropy calculations of
the geometric maximum entropy distribution.
[0026] The example MCE engine 320 modifies the maximum entropy
distribution generated by the ME engine 318 with the principle of
minimum cross entropy to create a new distribution that is
constrained by the candidate published GRP of interest. In
particular, the example MCE constraint manager 326 establishes
constraints for applying the minimum cross entropy as shown by
example Equations 19 and 20.
k = 0 .infin. p k = 1. Equation 19 k = 0 .infin. k p k = u 2 .
Equation 20 ##EQU00015##
Note that example Equations 19 and 20 are similar to example
Equations 13 and 14, but the former reflect constraints of reach
values (p) at different frequencies (k) and the
published/target/candidate GRP (u.sub.2).
[0027] The example MCE engine 320 applies a minimum cross entropy
function based on (a) the maximum entropy distribution (Q) and (b)
the unknown distribution to be solved (P), as shown in example
Equation 21.
minimize P .fwdarw. D ( P : Q ) = p 0 log ( p 0 q 0 ) + k = 1
.infin. p k log ( p k C r k ) . Equation 21 ##EQU00016##
In the illustrated example of Equation 21, q.sub.0 reflects the
sample reach at a probability of zero, p.sub.0 reflects the
published reach at a probability of zero, constraint values C and r
are known from before, and p.sub.k is the unknown reach values at
the k.sup.th frequency to solve for. In some examples, the minimum
cross entropy is calculated as a measure of the difference between
two probability distributions in a manner consistent with
Kullback-Leibler (KL) divergence. The KL divergence is a measure of
information gained between distributions. The example MCE engine
320 integrates assistance values with zero-modified geometric
constraints in a manner consistent with example Equations 22 and
23.
p.sub.0=s.sub.0q.sub.0 Equation 22.
p.sub.k=s.sub.0s.sub.1.sup.kCr.sup.k where k=1,2, . . . ,.infin.
Equation 23.
In the illustrated examples of Equations 22 and 23, s.sub.0 and
s.sub.1 are solved to satisfy the constraints, as described in
further detail below, and explicitly identified values of p.sub.0
will guarantee a unique solution. One or more desired values for
p.sub.k can be solved by way of example Equations 16-18.
[0028] As described above, solving for s.sub.1 to facilitate an
integration of the published GRP constraint employs the example
published GRP manager 328, which solves s.sub.1 in a manner
consistent with example Equation 24.
1 = k = 0 .infin. Pk = p 0 + k = 1 .infin. p 0 q 0 s 1 k C r k = p
0 - C p 0 r s 1 q 0 ( r s 1 - 1 ) s 1 = p 0 q 0 - q 0 r ( - C p 0 +
p 0 q 0 - q 0 ) . Equation 24 ##EQU00017##
[0029] With all of the constraints and assistance values solved
(including s.sub.1), the example published GRP manager 328
integrates the published GRP constraints in a manner consistent
with example Equation 25.
u 2 = k = 0 .infin. k p k = 0 + k = 1 .infin. k p 0 q 0 s 1 k C r k
= k = 1 .infin. k p 0 q 0 ( p 0 q 0 - q 0 r ( - C p 0 + p 0 q 0 - q
0 ) ) k C r k = - ( q 0 - 1 ) ( C q 0 + p 0 ( - q 0 ) + p 0 ) C q 0
. Equation 25 ##EQU00018##
[0030] The example conserved quantity engine 316 further simplifies
to example Equations 26, 27 and 28 to solve for the published reach
value to illustrate that quantities can be conserved for values of
raw GRP, raw reach, published GRP and published reach.
u 2 = ( p 0 - 1 ) ( p 0 ( u 1 q 0 + q 0 - 1 ) - q 0 ( u 1 + q 0 - 1
) ) p 0 ( q 0 - 1 ) 2 . Equation 26 ( u 1 + q 0 - 1 ) q 0 1 - q 0 =
( u 2 + p 0 - 1 ) p 0 1 - p 0 . Equation 27 ( u 1 1 - q 0 - 1 ) ( 1
- q 0 q 0 ) = ( u 2 1 - p 0 - 1 ) 1 - p 0 p 0 . Equation 28
##EQU00019##
[0031] From example Equation 28, because the form of the equation
is similar on both sides, and each individual side only contains
variables of either the prior and unknown, the example conserved
quantity engine 316 generates conserved quantity equivalent
expressions. In particular, u reflects a mean (M), which is also
referred to as a GRP, R reflects a reach value, which can also be
expressed as 1-q.sub.0, and F reflects a frequency, which can also
be expressed as a ratio of the mean and reach (e.g., u/1-q.sub.0).
An example equivalent expression for mean (M) and reach (R)
generated by the example conserved quantity engine 316 is shown in
example Equation 29.
( M - R ) ( 1 - R ) R 2 . Equation 29 ##EQU00020##
An example equivalent expression for mean (M) and frequency (F)
generated by the example conserved quantity engine 316 is shown in
example Equation 30.
( F - 1 ) ( F - M ) M . Equation 30 ##EQU00021##
An example equivalent expression for reach (R) and frequency (F)
generated by the example conserved quantity engine 316 is shown in
example Equation 31.
( F - 1 ) ( 1 - R ) R . Equation 31 ##EQU00022##
[0032] To illustrate a computational improvement of using the
conserved quantities of derived example Equations 29-31 over the
relatively more computationally burdensome NBD approach, in which a
simultaneous system of equations must be solved, assume an initial
raw GRP value of 350 and a corresponding reach of 70. The raw GRP
and reach values are derived from empirically obtained market data.
Also assume that a campaign manager, market researcher, or other
individual/entity determines that a future campaign could be
initiated to result in a published/new GRP value of 400, for which
empirically obtained market data is not available. Assuming that
the market researcher first employed the traditional NBD approach,
a computationally intensive system of nonlinear equations would
need to be solved in a manner consistent with example Equations 4
and 5 described above. Parameters of the NBD model result in
a=5.393285 and k=0.648955. Knowing the value for parameter k,
scaled-up parameter A (or a.sub.2) can be determined in view of the
published GRP value to yield a value of 6.163. Finally, knowing A
and k, the new/published reach value can be solved in a manner
consistent with example Equation 7 to yield a new published reach
of 72.1%.
[0033] However, taking the same initial raw GRP value of 350 and
corresponding initial raw reach value of 70, with a published GRP
of 400 expected as a viable target for a future campaign, examples
disclosed herein facilitate calculation via closed-loop conserved
quantity expressions that yield substantially similar results. In
particular, example Equation 29 is shown below using the GRP and
reach values of interest, expressed as example Equation 32.
( M 1 - R 1 ) ( 1 - R 1 ) R 1 2 = ( M 2 - R 2 ) ( 1 - R 2 ) R 2 2 .
Equation 32 ##EQU00023##
In the illustrated example of Equation 32, M.sub.1 reflects the raw
GRP of 350, R.sub.1 reflects the raw reach of 70, M.sub.2 reflects
the published GRP of 400, and R.sub.2 reflects the published reach,
which is solved below and shown as example Equation 33.
( 3 . 5 0 - 0 . 7 0 ) ( 1 - 0 . 7 0 ) ( 0 . 7 0 ) 2 = ( 4 . 0 0 - R
2 ) ( 1 - R 2 ) R 2 2 . Equation 33 ##EQU00024##
Solving for R2 in example Equation 33 yields a published reach of
72.5%, which is substantially the same value as determined via the
relatively more computationally complex NBD approach. Additionally,
by using the conserved quantity equivalent expression approach
disclosed herein, problems associated with the NBD are avoided,
such as effects of the NBD-impossibility regions, assumptions of a
distribution shape being an appropriate fit for unknown data and/or
resorting to a Poission distribution, which pays no regard to
original distribution inputs.
[0034] Examples disclosed above consider an upper bound that is
infinite, while examples disclosed below assume a maximum number of
"spots" (e.g., television spots, advertising spots, Internet
placement spots, etc.) that can be used in a particular
calculation. As such, any GRP value has a maximum consistent with
example Equation 34.
Reach.ltoreq.GRP.ltoreq.N*Reach Equation 34.
In the illustrated example of Equation 34, N represents a finite
number of spots in which the left-hand side of Equation 34 must be
true while the right-hand side of Equation 34 is assumed. For
example, in the event it is assumed a reach of 100%, then everybody
is watching (e.g., watching television), and the inequality reduces
to example Equation 35.
1.ltoreq.GRP.ltoreq.N Equation 35.
In the illustrated example of Equation 35, the left-hand side of
the inequality states that viewers watch at least one spot, and the
right-hand side of the inequality illustrates that every viewer is
watching every possible spot. Examples described below account for
a bounded GRP value by a numerical solution.
[0035] Similar to examples disclosed above, parameters from the NBD
for the raw reach and GRP are calculated and scaled-up to that the
expected value of the new NBD matches the published reach. However,
for a finite number of spots, the expected value of the first N
spots may not equal the published reach value, thus the
distribution is both truncated and modified so that the domain is
the first N spots and the expected value within those N spots
equals the published reach. To illustrate, an interim GRP is
defined in a manner consistent with example Equation 36.
Interim GRP = k = 1 N kf ( k ) . Equation 36 ##EQU00025##
The interim GRP is employed to define a scale factor in a manner
consistent with example Equation 37.
Factor = Published GRP Interin GRP . Equation 37 ##EQU00026##
For frequencies greater-than or equal to one (k.gtoreq.1), each
proportion is multiplied by the factor in a manner consistent with
example Equation 38.
g(k)=(Factor)f(k) Equation 38.
The zero frequency is then defined in a manner consistent with
example Equation 39.
g ( 0 ) = 1 - k = 1 N g ( k ) . Equation 38 ##EQU00027##
Accordingly, the published reach is represented in a manner
consistent with example Equation 40.
Published Reach=1-g(0) Equation 40.
[0036] The above examples take into account a finite value of N and
results in the expected value equal to the published GRP, but the
raw and published values are treated as two different
distributions. Improved approaches include assuming both
distributions are truncated NBDs and fitting the data to both. As
described above in connection with example Equations 13-15, an
unknown distribution (X) is estimated using maximum entropy given
the constraint that E[X]=.mu..sub.1 and q.sub.0=P(X=0). However,
unlike example Equations 13-15 above, the domain is specified in a
finite manner to be {0, 1, . . . , N}.
[0037] Additionally, finite domain examples include creating a new
distribution with minimum cross entropy in a manner similar to
example Equations 19-21, in which the domain is again specified in
a finite manner to be {0, 1, . . . , N}. Numerical solution of the
published reach (p0) may be solved in a manner consistent with
example Table 2, in which the answer depends only upon (a) a raw
GRP value, (b) a raw reach value and (c) a published GRP value. In
particular, example Table 2 illustrates pseudo code that may be
employed by the example MDE system 300. The example pseudo code of
Table 2 facilitates handling both distributions as a finite domain
to find the best distribution that fits the known data.
TABLE-US-00002 TABLE 2 Raw_GRP=50; % (.mu..sub.1) Raw_Reach=30; %
(q0) Published_GRP=200 % (.mu..sub.2) N=20; % Maximum number of
"spots" % Construct constraints finite domain Maximum Entropy
C=[ones(1,N+1)]; 0:N; 1 zeros (1, N)]; D=[1; (Raw GRP/100);
1-(Raw_Reach/100)]; %Solve for prior distribution Q=MaxEnt(C,D);
%New constraints C=[ones(1,N+1); 0:N]; D=[1, (Published_GRP/100)];
%Solve for prior distribution P=MaxEnt(C,D,Q);
Published_Reach=100*(1-P(1));
In the illustrated example of Table 2, variable C represents a
constraint matrix, not to be confused with variable C in example
Equation 17, which represents a constant. Additionally, the pseudo
code "P=MaxEnt(C,D,Q)" represents solving for the minimum cross
entropy solution. As described in the illustrated example of FIG.
7, solutions to the question of how a published reach depends on a
function of raw reach is shown in view of an (a) NBD approach, (b)
a maximum entropy approach, (c) an NBD approach with finite
correction (e.g., the industry standard), and (d) a maximum entropy
approach with a finite domain (e.g., via numerical analysis
consistent with example pseudo code of Table 2).
[0038] While an example manner of implementing the market data
evaluator (MDE) of FIG. 3 is illustrated in FIG. 3, one or more of
the elements, processes and/or devices illustrated in FIG. 3 may be
combined, divided, re-arranged, omitted, eliminated and/or
implemented in any other way. Further, the example marketing data
interface 308, the example GRP engine 310, the example reach engine
312, the example simulation engine 314, the example conserved
quantity engine 316, the example maximum entropy engine 318, the
example minimum cross entropy engine 320, the example maximum
entropy constraint manager 322, the example maximum entropy
distribution evaluator 324, the example minimum cross entropy
constraint manager 326, the example published GRP manager 328
and/or, more generally, the example market data evaluator (MDE) 302
of FIG. 3 may be implemented by hardware, software, firmware and/or
any combination of hardware, software and/or firmware. Thus, for
example, any of the example marketing data interface 308, the
example GRP engine 310, the example reach engine 312, the example
simulation engine 314, the example conserved quantity engine 316,
the example maximum entropy engine 318, the example minimum cross
entropy engine 320, the example maximum entropy constraint manager
322, the example maximum entropy distribution evaluator 324, the
example minimum cross entropy constraint manager 326, the example
published GRP manager 328 and/or, more generally, the example
market data evaluator (MDE) 302 of FIG. 3 could be implemented by
one or more analog or digital circuit(s), logic circuits,
programmable processor(s), application specific integrated
circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or
field programmable logic device(s) (FPLD(s)). When reading any of
the apparatus or system claims of this patent to cover a purely
software and/or firmware implementation, at least one of the
example marketing data interface 308, the example GRP engine 310,
the example reach engine 312, the example simulation engine 314,
the example conserved quantity engine 316, the example maximum
entropy engine 318, the example minimum cross entropy engine 320,
the example maximum entropy constraint manager 322, the example
maximum entropy distribution evaluator 324, the example minimum
cross entropy constraint manager 326, the example published GRP
manager 328 and/or, more generally, the example market data
evaluator (MDE) 302 of FIG. 3 is/are hereby expressly defined to
include a tangible computer readable storage device or storage disk
such as a memory, a digital versatile disk (DVD), a compact disk
(CD), a Blu-ray disk, etc. storing the software and/or firmware.
Further still, the example MDE 302 of FIG. 3 may include one or
more elements, processes and/or devices in addition to, or instead
of, those illustrated in FIG. 3, and/or may include more than one
of any or all of the illustrated elements, processes and
devices.
[0039] Flowcharts representative of example machine readable
instructions for implementing the MDE system 300 of FIG. 3 are
shown in FIGS. 4-6. In these examples, the machine readable
instructions comprise a program for execution by a processor such
as the processor 812 shown in the example processor platform 800
discussed below in connection with FIG. 8. The program(s) may be
embodied in software stored on a tangible computer readable storage
medium such as a CD-ROM, a floppy disk, a hard drive, a digital
versatile disk (DVD), a Blu-ray disk, or a memory associated with
the processor 812, but the entire program and/or parts thereof
could alternatively be executed by a device other than the
processor 812 and/or embodied in firmware or dedicated hardware.
Further, although the example program(s) is/are described with
reference to the flowcharts illustrated in FIGS. 4-6, many other
methods of implementing the example MDE 302 may alternatively be
used. For example, the order of execution of the blocks may be
changed, and/or some of the blocks described may be changed,
eliminated, or combined.
[0040] As mentioned above, the example processes of FIGS. 4-6 may
be implemented using coded instructions (e.g., computer and/or
machine readable instructions) stored on a tangible computer
readable storage medium such as a hard disk drive, a flash memory,
a read-only memory (ROM), a compact disk (CD), a digital versatile
disk (DVD), a cache, a random-access memory (RAM) and/or any other
storage device or storage disk in which information is stored for
any duration (e.g., for extended time periods, permanently, for
brief instances, for temporarily buffering, and/or for caching of
the information). As used herein, the term tangible computer
readable storage medium is expressly defined to include any type of
computer readable storage device and/or storage disk and to exclude
propagating signals and to exclude transmission media. As used
herein, "tangible computer readable storage medium" and "tangible
machine readable storage medium" are used interchangeably.
Additionally or alternatively, the example processes of FIGS. 4-6
may be implemented using coded instructions (e.g., computer and/or
machine readable instructions) stored on a non-transitory computer
and/or machine readable medium such as a hard disk drive, a flash
memory, a read-only memory, a compact disk, a digital versatile
disk, a cache, a random-access memory and/or any other storage
device or storage disk in which information is stored for any
duration (e.g., for extended time periods, permanently, for brief
instances, for temporarily buffering, and/or for caching of the
information). As used herein, the term non-transitory computer
readable medium is expressly defined to include any type of
computer readable storage device and/or storage disk and to exclude
propagating signals and to exclude transmission media. As used
herein, when the phrase "at least" is used as the transition term
in a preamble of a claim, it is open-ended in the same manner as
the term "comprising" is open ended.
[0041] The program 400 of FIG. 4 begins at block 402 where the
example marketing data interface 308 retrieves and/or otherwise
receives a data set having an unknown distribution of marketing
data. Such data set(s) may be retrieved from one or more campaign
data sources 304 via the example network 306, in which the data
set(s) include campaign results associated with one or more
promotions. In some examples, the data set(s) include information
related to a campaign population size and corresponding information
to indicate which ones of the campaign population were exposed to
promotional media (e.g., billboard advertisements, television
advertisements, Internet advertisements, etc.). The example GRP
engine 310 calculates a GRP value associated with the empirically
derived data set, and the example reach engine 312 calculates a
corresponding reach value (block 404).
[0042] While the empirically determined GRP and reach values
provide the market analyst with a measurement to indicate a number
of impressions the population experienced, and an indication of how
many of those impressions were unique, the market analyst also
seeks to know how the reach will be affected in the event a new GRP
value (published GRP) is achieved for that defined population. As
described above, the market analyst may identify and/or otherwise
select a new/published GRP value that can be targeted to the
defined population. In some examples, the market analyst can apply
marketing resources to the defined population (e.g., increased
advertising budget, additional media advertisements, etc.) with an
expectation that a resulting published GRP value is achieved.
However, because the data set associated with the defined
population reflects empirical data associated with the original
campaign, any determination of a new/published reach value in view
of the published GRP value must be predicted and/or otherwise
estimated. As described above, conventional techniques to predict
the published reach utilize computationally intensive nonlinear
techniques to achieve convergence when applying the NBD. Further,
particular circumstances of the NBD cause computational failures,
despite real-world inputs that are practically observed in market
behavior.
[0043] The example simulation engine 314 selects and/or otherwise
retrieves a published GRP of interest (block 406), and the example
maximum entropy engine 318 estimates an unknown distribution (e.g.,
the empirical market data set) to determine a maximum entropy
distribution (block 408).
[0044] FIG. 5 includes additional detail associated with
determining the maximum entropy distribution of block 408. In the
illustrated example of FIG. 5, the example maximum entropy
constraint manager 322 establishes constraints for the unknown
distribution to prevent computational waste (block 502), which may
be achieved in a manner consistent with example Equations 13 and 14
described above. In particular, the maximum entropy constraint
manager 322 prevents computational waste by ensuring that only
positive values are produced for a probability set, thereby
avoiding potential impossibility region(s), such as those that
produce errors in an NBD model (see region 208 of FIG. 2). The
example maximum entropy distribution evaluator 324 calculates a
maximum entropy to the unknown distribution (block 504) in a manner
consistent with example Equation 15, and the example maximum
entropy constraint manager 322 calculates zero-modified geometric
distribution constraints to facilitate cross entropy calculations
(block 506), as described in further detail below.
[0045] Returning to FIG. 4, the example minimum cross entropy
engine 320 modifies the calculated zero-modified geometric
distribution now representing a maximum entropy condition with
minimum cross entropy to create a new distribution that is
constrained by the published GRP of interest (block 410). FIG. 6
includes additional detail associated with applying the minimum
cross entropy of block 410. In the illustrated example of FIG. 6,
the example minimum cross entropy constraint manager 326
establishes constraints for calculating the minimum cross entropy
(block 602) in a manner consistent with example Equations 19 and
20. Unlike conventional techniques, the example minimum cross
entropy engine 320 applies the minimum cross entropy based on
available data under consideration rather than forcing such data to
fit within a predetermined distribution. In particular, the example
minimum cross entropy engine 320 applies the minimum cross entropy
based on (a) the previously calculated maximum entropy distribution
(Q) and (b) the unknown distribution to be solved (P) (block 604)
in a manner consistent with example Equation 21. Additionally, the
example minimum cross entropy engine 320 integrates assistance
values with zero-modified geometric distribution constraints (block
606) in a manner consistent with example Equations 22 and 23. The
example published GRP manager 328 solves for the assistance values
(e.g., s1) to facilitate the integration of the published GRP
constraint of interest (block 608) in a manner consistent with
example Equation 24. Additionally, the example published GRP
manager 328 integrates the published GRP constraint (block 610) in
a manner consistent with example Equation 25.
[0046] Returning to FIG. 4, the example conserved quantity engine
316 solves for the published reach (block 412) in a manner
consistent with example Equations 26-28. In particular, because the
conserved quantities are similar on both sides of example Equation
28, desired quantities of interest can be solved for via
closed-form conserved quantity equivalent expressions (block 414),
which are less computationally rigorous than conventional methods
that employ the NBD.
[0047] FIG. 7 is a chart 700 to illustrate example solutions to
determine a published reach as a function of raw reach. In the
illustrated example of FIG. 7, a given raw GRP value is 50 having a
corresponding published GRP of 200. The example chart 700 includes
an x-axis of raw reach 702 and a y-axis of published reach 704. The
example chart 700 illustrates (a) an NBD solution 706, (b) a
maximum entropy solution 708, (c) an NBD solution using finite
correction 710, and (d) a maximum entropy solution using a finite
domain 712. As described above, the NBD solution 706 and the NBD
solution using finite correction 710 break down at particular
values of raw reach, as shown in the chart 700 as a breakdown
region 714. Such breakdowns, when encountered by traditional
industry standard approaches sometimes revert to a Poisson
analysis, which are completely independent of any actual
observations available. In fact, such solutions remain constant and
independent of any changes to the raw reach value(s). Stated
differently, the Poisson analysis discards any panel data that may
have been available during the analysis. On the other hand,
examples disclosed herein that employ the maximum entropy solution
708 and the maximum entropy finite domain solution 712 do not break
down and, instead, converge. Additionally, the solution for the NBD
using finite correction (710) breaks down at a low raw reach region
716. However, example solutions disclosed herein (e.g., the maximum
entropy solution 708 and the maximum entropy solution using a
finite domain 712) corrects for break downs at both extremes of raw
reach. As the raw reach approaches a lower (toward zero) boundary,
examples disclosed herein converge toward the origin, while
traditional techniques converge to erroneous non-zero values (e.g.,
predicting that reach is approximately 40% when an accurate value
is closer to 1%). In other words, if zero people were watching
television (raw value), then a corresponding reach value is also
expected to be at or near zero, rather than the erroneous
traditional technique that illustrates approximately 40%.
[0048] FIG. 8 is a block diagram of an example processor platform
800 capable of executing the instructions of FIGS. 4-6 and/or the
example pseudo code of Table 2 to implement the market data
evaluator system 300 and, in particular, the MDE 302 of FIG. 3. The
processor platform 800 can be, for example, a server, a personal
computer, a mobile device (e.g., a cell phone, a smart phone, a
tablet such as an iPad.TM.), a personal digital assistant (PDA), an
Internet appliance, a set top box, or any other type of computing
device.
[0049] The processor platform 800 of the illustrated example
includes a processor 812. The processor 812 of the illustrated
example is hardware. For example, the processor 812 can be
implemented by one or more integrated circuits, logic circuits,
microprocessors or controllers from any desired family or
manufacturer. In the illustrated example of FIG. 8, the processor
812 includes one or more example processing cores 815 configured
via example instructions 832, which include the example
instructions of FIGS. 4-6 and pseudo code of Table 2 to implement
the example MDE 302 of FIG. 3.
[0050] The processor 812 of the illustrated example includes a
local memory 813 (e.g., a cache). The processor 812 of the
illustrated example is in communication with a main memory
including a volatile memory 814 and a non-volatile memory 816 via a
bus 818. The volatile memory 814 may be implemented by Synchronous
Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory
(DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any
other type of random access memory device. The non-volatile memory
816 may be implemented by flash memory and/or any other desired
type of memory device. Access to the main memory 814, 816 is
controlled by a memory controller.
[0051] The processor platform 800 of the illustrated example also
includes an interface circuit 820. The interface circuit 820 may be
implemented by any type of interface standard, such as an Ethernet
interface, a universal serial bus (USB), and/or a PCI express
interface.
[0052] In the illustrated example, one or more input devices 822
are connected to the interface circuit 820. The input device(s) 822
permit(s) a user to enter data and commands into the processor 812.
The input device(s) can be implemented by, for example, an audio
sensor, a microphone, a camera (still or video), a keyboard, a
button, a mouse, a touchscreen, a track-pad, a trackball, isopoint,
a voice recognition system and/or any other human-machine
interface.
[0053] One or more output devices 824 are also connected to the
interface circuit 820 of the illustrated example. The output
devices 824 can be implemented, for example, by display devices
(e.g., a light emitting diode (LED), an organic light emitting
diode (OLED), a liquid crystal display, a cathode ray tube display
(CRT), a touchscreen, a tactile output device, a printer and/or
speakers). The interface circuit 820 of the illustrated example,
thus, typically includes a graphics driver card, a graphics driver
chip or a graphics driver processor.
[0054] The interface circuit 820 of the illustrated example also
includes a communication device such as a transmitter, a receiver,
a transceiver, a modem and/or network interface card to facilitate
exchange of data with external machines (e.g., computing devices of
any kind) via a network 826 (e.g., an Ethernet connection, a
digital subscriber line (DSL), a telephone line, coaxial cable, a
cellular telephone system, etc.).
[0055] The processor platform 800 of the illustrated example also
includes one or more mass storage devices 828 for storing software
and/or data. Examples of such mass storage devices 828 include
floppy disk drives, hard drive disks, compact disk drives, Blu-ray
disk drives, RAID systems, and digital versatile disk (DVD)
drives.
[0056] The coded instructions 832 of FIGS. 4-6 and/or pseudo code
of Table 2 may be stored in the mass storage device 828, in the
volatile memory 814, in the non-volatile memory 816, and/or on a
removable tangible computer readable storage medium such as a CD or
DVD.
[0057] From the foregoing, it will be appreciated that the above
disclosed methods, apparatus and articles of manufacture overcome
computationally intensive processing of systems that calculate
reach values for market data. Additionally, examples disclosed
herein eliminate inherent limitations of conventional industry
standard techniques when calculating published reach values in
connection with published GRP values, particularly in regard to
applications of the NBD. Examples disclosed herein eliminate a need
to perform iterative nonlinear techniques to produce convergence in
market data distributions and, instead, facilitate closed-form
expressions to calculate published GRP, published reach and/or
published frequency estimates.
[0058] Although certain example methods, apparatus and articles of
manufacture have been disclosed herein, the scope of coverage of
this patent is not limited thereto. On the contrary, this patent
covers all methods, apparatus and articles of manufacture fairly
falling within the scope of the claims of this patent.
* * * * *