Note: Descriptions are shown in the official language in which they were submitted.
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METHOD AND APPARATUS FOR CONFIGURABLE MODEL-INDEPENDENT
DECOMPOSITION OF A BUSINESS METRIC
FIELD OF THE INVENTION
[0001] This invention relates generally to analysis of multi-dimensional data
and
more particularly to determining the effect of marketing activities on a
business
metric.
BACKGROUND OF THE INVENTION
[0002] A goal of business is to explain sales volumes results so as to
understand
how marketing activities affect the sales volumes, e.g., how much volume these
marketing activities contributed to overall sales volume. These volume
contributions
are useful to calculate effectiveness measures for the activities such as
volume per
dollar spend or return on investment (ROI). Examples of such activities are
activities
directly controlled by the business (e.g., our TV advertising, a display for
our
products, a price increase for our products), activities controlled by another
businesses
in the market (e.g., a competitor's display, competitor's TV advertising,
etc.), and/or
the environment itself (e.g., a cold spell, a gas-price increase, etc.). For
example, a
company may want to know the approximate change in future sales, growth, and
profit
of the product or service based on these activities or changes in these
activities. In
addition, many companies want to know the effects of changes in these
activities (e.g.,
marketing, advertising, pricing changes, etc.) on forecasted data (e.g., sales
volume,
growth, profit, etc.) that are dependent on these activities.
[0003] In addition to being applied to a sales volume, these same techniques
are
used to interpret the effect of activities on other measurable business
metrics, e.g.,
revenue, profit or market share, etc.
[0004] Typically, an analyst uses a mathematical model to estimate how these
activities affect sales volumes in the past, or in the future. An example is a
regression-based model. A regression-based model will typically relate the
sales
volumes to each of the activities via a coefficient. The analyst determines
the
coefficients based on the regression model (or another multivariate
technique). The
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analyst then interprets the coefficient to assign rates of volume changes for
each
activity. For example, an analyst would determine that for one unit of an
activity,
such as promotion, would equal X percent or Y units change in sales volume.
The
analyst then multiplies this coefficient to the change in the amount of the
activity, and
a corresponding amount of volume is calculated. By doing this analysis for
each of
the activity/coefficient pairs, the analyst predicts and explains the effect
of the activity
on volume (or another relevant measurable business metric, like profit, etc.).
In addition, in order to explain volume changes across time periods, the
analyst would
calculate volume contributions by activity for each time period and report the
difference as explanation of volume change.
[0005] A problem with this approach is that the interpretation of the derived
coefficients is dependent on the model that is used. For example, a price
coefficient in
a linear model and a multiplicative model for the same volume and activities
will
differ significantly from each other. This makes aggregation across products
or
channels for which different types of models were used difficult and requires
volume
interpreting algorithms specific to the model form used. Furthermore, the
rates of
volume change are dependent on the set of activities chosen. In addition, the
results
are inconsistent when using different sets of activities for the same time
period.
Moreover, some activities do not have natural reference values upon which to
base the
volume contribution calculations (e.g., price, distribution), and
consequently, make it
difficult to determine the effect of these activities on the volume and volume
change
across time periods.
SUMMARY OF THE DESCRIPTION
[0006] Methods and apparatuses for decomposing a business metric based on a
plurality of activities and a response model are described. In one embodiment,
the
method accesses the response model and the plurality of activities, the
plurality of
activities each having a reference and executed value. The method computes a
contribution to the business metric based on setting one of the plurality of
activities to
one of the corresponding reference and executed value and setting the other
activities
to the value state opposite of that activity. Furthermore, the method computes
each of
the contributions independent of the response model type.
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BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The present invention is illustrated by way of example and not
limitation in
the figures of the accompanying drawings in which like references indicate
similar
elements.
[0008] Figure 1 is a processing block diagram illustrating one embodiment of a
volume cube.
[0009] Figure 2 is a processing block diagram illustrating one embodiment of a
model structure.
[0010] Figure 3 is a table illustrating one embodiment of a volume
decomposition.
[0011] Figure 4 is a flow diagram of one embodiment of a process for
calculating
a volume decomposition including synergy allocation.
[0012] Figure 5 is a table illustrating one embodiment of a volume
decomposition
calculation.
[0013] Figure 6 is a flow diagram of one embodiment of a process for
calculating
synergy allocations.
[0014] Figure 7 is a table illustrating one embodiment of a synergy
calculation.
[0015] Figures 8AB are block diagrams illustrating synergy allocation by raw
value scaling and absolute value scaling.
[0016] Figure 9 is a processing block diagram illustrating one embodiment of a
volume decomposition hierarchy.
[0017] Figure 10 is a flow diagram of one embodiment of a process for
calculating
a volume decomposition report for the atomic decomposition level.
[0018] Figure 11 is a flow diagram of one embodiment of a process for
determining an atomic decomposition level.
[0019] Figure 12 is a flow diagram of one embodiment of a process for
calculating
a volume decomposition report for aggregate scopes of the atomic decomposition
level.
[0020] Figure 13 is a flow diagram of one embodiment of a process for
calculating
a volume decomposition report for aggregate scopes of decomposition levels
higher in
the decomposition hierarchy.
[0021] Figure 14 is a chart illustrating one embodiment of a due-to report.
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[0022] Figure 15 is a block diagram illustrating one embodiment of the
different
predicted volumes for different time periods.
[0023] Figure 16 is a flow diagram of one embodiment of a process for
calculating
a hybrid due-to and allocating synergy.
[0024] Figure 17 is a flow diagram of one embodiment of a process for
calculating
a compound due-to.
[0025] Figure 18 is a diagram of one embodiment of a data processing system
that
calculates volume decomposition reports, atomic decompositions, volume
decomposition hierarchies, hybrid due-to reports, and/or compound due-to
reports.
[0026] Figure 19 is a diagram of one embodiment of an operating environment
suitable for practicing the present invention.
[0027] Figure 20 a diagram of one embodiment of a data processing system, such
as a general purpose computer system, suitable for use in the operating
environment of
Figures 4, 6, 10-13, 16, and 17.
DETAILED DESCRIPTION
[0028] In the following detailed description of embodiments of the invention,
reference is made to the accompanying drawings in which like references
indicate
similar elements, and in which is shown by way of illustration specific
embodiments
in which the invention may be practiced. These embodiments are described in
sufficient detail to enable those skilled in the art to practice the
invention, and it is to
be understood that other embodiments may be utilized and that logical,
mechanical,
electrical, functional, and other changes may be made without departing from
the
scope of the present invention. The following detailed description is,
therefore, not to
be taken in a limiting sense, and the scope of the present invention is
defined only by
the appended claims.
[0029] Method and apparatus to interpret a measurable business metric using a
response model is described herein. In one embodiment, a response mode is used
to
calculate a measurable business metric (sales volume, revenue, profit or
market share,
etc.) In the response model, marketing activities are represented through a
set of
measurements. These measurements are called "drivers" of the activity. A set
of
drivers for all activities in the model is called a scenario. A scenario can
represent an
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actual state of the world, i.e. a marketing plan that was actually executed in
a real
business environment or a hypothetical state of the world that is reflecting
assumptions on marketing activities and the business environment for planning
or
analysis purposes.
[0030] In one embodiment, interpreting the business metric is performed by
calculating a volume decomposition and/or a volume variance. Volume
decompositions and volume variance reports (also called "due-to reports") are
calculated for any scenario or pair of scenarios, real or hypothetical. The
term
"executed" value for a driver as the value the driver takes in the scenario,
including
scenarios that are hypothetical.
[0031] In one embodiment, a volume decomposition is calculated that is
independent of the type of response model used to model a sales volume. A
volume
decomposition gives an indication of the contribution to the sales volume
resulting
from a set of marketing activities. In one embodiment, the volume
decomposition for
a set of activities is calculated by toggling drivers for each of those
activities between
an off and an on state. The off state for an activity corresponds to the
scenario in
which this activity is not executed (e.g. a promotion is not run or a price is
not
discounted.). In this case, the activity does not add to the volume sold, and
it is
represented by its drivers taking a reference value. The activity's on state
adds a
contribution to the volume and is represented by the activity's drivers'
executed value.
The difference between the volume in the on and off states of an activity is
called the
raw volume contribution of the activity. In addition, in one embodiment,
synergy is
allocated to each of the volume contributions based on the absolute value of
the
volume contributions.
[0032] In another embodiment, a sequence of volume contribution reports are
calculated at different levels of detail using a volume decomposition
hierarchy that is
based on an atomic decomposition level. The atomic decomposition level is a
fundamental set of "indivisible" or "atomic" activities. Furthermore, a set of
tree
hierarchies is defined describing how to roll up the volume contributions from
these
atomic activities into aggregate activities. In one embodiment, the aggregate
activities
are formed into a hierarchy of volume decomposition levels that are internally
consistent with the atomic decomposition level.
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[0033] In a further embodiment, a hybrid due-to report is calculated that
indicates,
for a set of activities, the volume variance between two different sales
volumes. In this
embodiment, the volume variance is calculated by toggling each of these
activities
between a start and end value, where the start and end value are associated
with one of
the two different sale volumes. Furthermore, the change in base volume between
the
two different sales volumes is calculated using the first or second set of
activities with
a response model for the opposite set of activities. In one embodiment, the
set of
activities do not require a reference value for each of the associated
drivers. In
addition, in one embodiment, synergy is allocated to each of the volume
variances
based on the absolute value of the volume variances.
[0034] In a still further embodiment, a compound due-to report is calculated
that
determines the volume variance for a set of activities between two different
sales
volumes. In this embodiment, a difference decomposition is used to calculate
the
volume variance for a set of non-distribution activities that each has drivers
with
corresponding reference values. A reference value for drivers of an activity
represents
the activity in the off state and does not add a contribution to the volume.
The phrase
an "activity is off' is to mean hereinafter the drivers associated with the
activity are in
their reference value. Hybrid due-to is used to calculate the volume variance
for a set
of non-distribution activities that do not have drivers with reference values.
Volume
variance for distribution activities is calculated by subtracting the volume
variance
calculated for the set of non-distribution activities from one of the two
difference sales
volumes. In addition, in one embodiment, synergy is allocated to each of the
volume
variances based on the absolute value of the volume variances.
Volume Decomposition
[0035] In one embodiment, the state of a business' sales volume measured in a
suitable unit (unit count, ounces, dollars, etc.) (hereinafter referred to as
"volume") at
time t is described by a set of measurements {dp,0t
=1,...,k indexed by product p, time t,
1
and location 1. The set of measurements for a fixed i is called a driver. A
driver is an
action that can affect the volume. A market response model maps a history,
e.g., the
set of all the measurements for which t < T, to a volume V,,1 at time T for
product p
and location 1. The market response model can map a set of measurements that
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occurred in the past to a historical volume result or map a set of predicted
measurements to a predicted volume.
[0036] In one embodiment, volume is represented as a volume data cube, with
the
dimensions being time, product and location. Figure 1 is a block diagram
illustrating
one embodiment of volume cubes for historical and predicted sales volumes. In
Figure
1, a cube of historical volume 102 represents a time series of data formed
into a multi-
dimensional cube, such as VT P above. Although in one embodiment, the
dimensions
of historical volume cube 102 are time, products and locations, alternate
embodiments
may have more, less and/or different dimensions. Historical volume 102 ends at
a
specific time 112. The portion of the cube to the left of actual historical
volume 102
represents the very earliest volume available. Furthermore, in Figure 1,
response
model 11OA maps the historical activity 104 to historical volume 102. An
activity is
an action that can have an affect on the volume and can comprise one or more
drivers,
as described further below.
[0037] However, historical volume 102 and historical activity 104 do not
always
end at a specified time 108. In other embodiments, historical volume 102 and
historical activity 104 are for any past time period and of varying length,
such as a
days, weeks, months, years, etc. Furthermore, historical volume information
102 and
historical activity 104 can have different time lengths or represent
overlapping periods
of time.
[0038] In addition, response model 110B maps a predicted activity 106 to a
predicted volume 108. In one embodiment, predicted volume 108 has the same
dimensions as historical volume: time, product, and location. The predicted
activity
106 is copied from the historical activity 104, derived from the historical
activity 104,
derived from some other product activity, generated from user input or a
combination
thereof. This embodiment is meant to be an illustration of predicted activity
106 and
does not imply that predicted activity 106 always starts at present time 108.
Other
embodiments of predicted activity 106 can be for any future time period and of
varying length, such as a days, weeks, months, years, etc. Furthermore, actual
activity
104 and predicted activity 106 can have different time lengths. In one
embodiment,
response model 110B is the same as or different than response model 110A.
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[0039] An analyst uses the response model to estimate the effect of activities
on
volume. In one embodiment, an activity is described by drivers that each can
have a
reference value and an executed value. The reference value for an activity's
drivers
represents the activity in the "off' state, meaning the activity adds no
contribution to
the volume. Some activities' drivers do not have a meaningful off state and,
therefore,
no reference value (e.g. number of stores the product is distributed in,
price, etc.). An
executed value for an activity's drivers is a value that adds a positive or
negative
contribution to the volume. This represents the activity in the "on" state. An
activity is
characterized by a subset of drivers and by a scope of those drivers that is
affected by
the activity.
[0040] In one embodiment, the business' sales volume is represented as the
superposition of the base volume (e.g., no promotions, a reference price for
all
products, average temperature, no advertising, etc.) and an additional volume
due to
an execution of the set of activities. These activities are activities of the
business (e.g.,
TV advertising, a display for products, a price increase for products), other
businesses
in the market (e.g., a competitors display, competitors TV advertising, etc.)
and/or the
environment itself (e.g., a cold spell, a gas-price increase, etc.). An
activity is
characterized by a deviation of some of the drivers from their reference
values for
some combinations of products, locations and time periods. In this embodiment,
an
activity is therefore described by a set of drivers and a scope.
[0041] In one embodiment, the response model is expressed as a mathematical
function of a base volume and the volume due to set of activities, as
illustrated in Eq.
(1):
N
Volume = VolumeBase + Y,lf = f (Activity,) (1)
where Volume is the historical or predicted volume, VolumeBRSe is the base
volume, /3,
is the coefficient for Activity, f is the function applied to the Activity,
and Activity is
the activity affecting Volume such as, for example, TV advertising, product
display,
price increase, etc. While in one embodiment, function f is a linear function,
in
alternative embodiments, function f is another function known in the art
(e.g.,
logarithmic functions, exponential functions, algebraic functions, etc.)
and/or
combinations thereof. For example, in one embodiment, the natural logarithm is
used
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in multiplicative models to represent a constant elasticity model,
normalizations are
used to model pooling and shrinking across products and locations, adstock is
used to
model delayed impact of a marketing action on a behavior (e.g., TV), and
saturation is
used to model diminishing returns (or fatigue) of a marketing action on a
behavior). In
addition, in one embodiment, each activity is modeled as a function of one or
more
drivers
Activityi = g1 (d1,d2, ... , dn) (2)
where Activity is the activity affecting Volume, gi is the function
transforming
drivers (d,, d2, ... , do ) to Activity , and (d,, d2, ... , dn) are the
drivers affecting
Volume. For example, in one embodiment, the activity price comprises drivers
NoPromoPrice, the price charged when there is no promotion in a given week and
AvgNoPromoPrice, the average price in a given year for product sold without
promotion. As another example, in one embodiment, the activity marketing
comprises
television advertising (TV). Furthermore, in one embodiment, a driver is used
for one
or more activities as described below.
[0042] While in one embodiment Eqs. (1) and (2) are used to calculate a
volume,
in alternate embodiments, Eqs. (1) and (2) are used for other purposes
(scenario
analysis, forecasts, insight generation, financial predictions, etc.). Eqs.
(1) and (2)
comprises one embodiment of the response model. Furthermore, the response
model
can have different embodiment than Eqs. (1) and (2). For example, an
alternative
embodiment of the response model is a generalized parameterized form (Eq.
(3)):
Volume = f (A~,A2,...Anõ 8) (3)
where Volume is the historical or predicted volume, f is the model form, Al,
A2, ...,
A, is the set of activities, and ,8 is the vector of coefficients. This
includes any
calculation that takes measures of activities as inputs and returns a number
describing
a measure of sales volume.
In addition, the response model is expressed as a velocity model, Eq. (4):
Volume
ACV .f (Al, A2,..., An) (4)
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where Volume is the historical or predicted volume, f is the model form, Al,
A2, ...,
An is the set of activities, and ACV is all-commodity volume, a measure of
size of a
given location. The response model can also be modeled as a promotion
condition
model, as in Eqs. (5) and (6):
Volume = I Volume PromoCond
PromoCond
PromoCond E {Feature, Display, FeatureDisplay,TPR, NoPromo
(5)
VolumePromocond = ACVPromoCond ' t
/ PromoCond (Al, A2,"., An)
(6)
where Volume is the historical or predicted volume, VolumePromoCond is the
volume for
that promotion condition, f is the model form, Al, A2, ..., An is the set of
activities,
ACVPromoCond is the all-commodity volume of those locations that had the
specified
promotion condition, and PromoCond is type of promotional condition consisting
of
Feature, Display, Feature+Display, temporary price reduction (TPR), and/or no
promotion.
[0043] Figure 2 is a processing block diagram illustrating one embodiment of a
response model structure. While in one embodiment, Figure 2 is a general
structure of
the response model, in alternate embodiment, the response model structure is a
model
structure known in the art (e.g. parameterized form, velocity model, promotion
condition model, neural network model, agent based model, etc.). In Figure 2,
model
202 comprises coefficients 204A-F, variables 206A-F, functions 208A-B, and
drivers
210A-H. In one embodiment, a variable represents one of the set of activities
affecting
the modeled volume. In another embodiment, a variable represents multiple
activities
or an activity might be represented by multiple variables. In one embodiment,
model
202 is a sequence of calculations in which variables 206A-F are combined with
coefficients 204A-F to calculate a volume. Variables 206A-F are composed of
drivers
210A-H and functions 208A-B using those drivers 210A-H. For example, variable
206A comprises a function 208A of drivers 210A and driver 210B. Furthermore,
variable 208B comprises driver 210D and a function 208B of driver 210C. In
addition,
variable 208C comprises driver 210C and 210E. Variable 208D comprises driver
210F and a function 208B of driver 210C. Variable 206F comprises drivers 210G
and
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210H. In addition, a variable does not need to be comprised of drivers. For
example,
variable 206E is not dependent on any drivers. In one embodiment, a variable
of this
type is a constant.
[0044] In one embodiment, each driver has a scope that is the set of products
and
locations for which the specific driver (measurement) enters the model. In one
embodiment, the default scope of a driver entering a model is the scope of the
model
itself, e. g., the price for ProductA in Locationl is part of a model for
ProductA in
Location I. In another embodiment, the same driver can also be used for
ProductB in
Locationl in a model for ProductA in Locationl to describe the effect of
ProductB's
price on ProductA. Furthermore, the scope of a variable is based on the scopes
of the
drivers that are part of that variable.
[0045] The structure of variables and drivers in the models reflects the
activities
that the model is designed to take into account for modeling historical and
predicted
volumes. In one embodiment, the following assumptions on how marketing
activities
are reflected in each model:
1. Each model has a given set of activities of interest.
2. Activities are described by a set of drivers with a scope associated with
each
driver.
3. Each driver has a single "off' state in each Product/Week/Location. The
driver's off state is also referred to as the driver's reference value.
4. An activity can be on or off. If an activity is off, all drivers used to
describe
this activity are in their off state. If the activity is on, at least one
driver is not
at its reference value.
5. The state of the model in which all activities are off is the base state.
The
volume associated with the base state is the base volume.
In one embodiment, the same driver is used for different variables (and/or
activities)
and has different scopes. As an example, driver 210C is used for variables
206B and
206D. In this example, driver 210C would have one scope for variable 206B and
another scope for 206D.
[0046] An example of the relationship between an activity (and/or variable)
and
individual drivers is the activity end cap display. End cap display represents
the
marketing activity of placing a product at the end of a supermarket isle in
high traffic
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areas of the store. In one embodiment, the end cap activity is represented as
a function
of the percentage of stores that have an end cap display for a product and the
price
(usually a decrease in price) associated with that product on display. The
drivers for
this activity are the store percentage having the display and the promotional
price. The
corresponding off state for this activity is zero store percentage and a price
corresponding to the base price (e.g., no price decrease).
[0047] With these defined set of activities and associated drivers, the
response
model described above allows an analyst to compute a base volume and the
volume
resulting from the set of activities used in the model. In addition, this
model allows an
analyst to compute a contribution to the volume from each of the set of
activities.
Each activity can have a positive, negative, or negligent effect on the
volume. For
example, discounting the price of a product could have a positive effect on
volume.
Conversely, raising the price on that same product could have a negative
effect on
volume. Computing volume contributions for each of the set of activities is
called a
volume decomposition. A volume decomposition allows the analyst to determine
which of the set of activities gave the greatest or least amount of volume
contribution.
[0048] Figure 3 is a table illustrating one embodiment of a volume
decomposition.
In Figure 3, the base volume 304 is 89.07% of the total volume. In this
embodiment,
the activities 306A-M make up 11.42% of the total volume with the remaining -
0.35%
attributed to model error. Each of activities 306A-M gives different
contributions to
the total volume. For example, feature activity 306A gives the most
contribution at
2.99%, with TV advertising 306E and temporary price reduction (TPR) 306D also
giving contributions to the total volume above 2%. Conversely, some activities
give
no or little contribution to the total volume, such as feature and display
306B,
competDistrib8thCont (the distribution of a specific competitor) 306L, and
competDistribPL (the distribution of Private Label products) 306M.
[0049] In one embodiment, model error 302 is the difference between the
calculated total volume (including all activities and allocated synergy) and
the actual
volume. Model error 302 is reported as a separate category or is included into
the base
volume, set of activities, and/or a combination thereof.
[0050] Figure 4 is a flow diagram of one embodiment of a process 400 for
calculating a volume decomposition that includes synergy allocation. The
process may
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be performed by processing logic that may comprise hardware (e.g., circuitry,
dedicated logic, programmable logic, microcode, etc.), software (such as run
on a
general purpose computer system or a dedicated machine), or a combination of
both.
In one embodiment, process 400 is performed by data processing system 1800 of
Figure 18.
[0051] Referring to Figure 4, at processing block 402, the process begins by
processing logic accessing inputs for the volume decomposition calculation. In
one
embodiment, these inputs comprise the response model, the parameters of the
model,
the set of activities that contributed to the calculated volume, and a set of
drivers
associated with each of the activities. In one embodiment, the input
corresponds to
the response model, coefficients, functions, and set of activities and drivers
as
described above with respect to Figure 2.
[0052] At processing block 404, processing logic determines the driver
reference
values for each of the set of activities. This is done through user input,
rules based on
historical driver values or any other logic based on historical data or
default values. As
per above, the driver reference values represent the off state of the driver.
In addition,
the off state for an activity means that this activity will yield no
additional
contribution to the volume. An activity in the off state is defined as having
all the
drivers comprising that activity will be in the off state.
[0053] At processing blocks 406 and 408, processing logic calculates the base
and
predicted volume using the response model and the activities with the
reference and
executed values of these activities, respectively. In one embodiment,
processing logic
calculates the base volume by setting each of the activities to the off state
and using
the driver reference values for the base volume calculation. As per above, the
base
volume represents the sales volume if the business did not do any of the
activities for
the product(s), location(s) and/or time period(s) represented in the volume
cube.
Furthermore, processing logic calculates the predicted volumes using the
executed
driver values. In one embodiment, the executed driver values are the driver
values that
were actually used or planned to be used.
[0054] In one embodiment, with the accessed response model, processing logic
calculates different scenarios with certain activities on and certain
activities off. The
difference in predicted volume that results from switching an activity on/off
is the raw
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volume attributed to those activities that are switched. Volume attributed to
a set of
activities may not be the sum of the volumes attributed to the individual
activities.
This is due to non-linearities of the volume response existing in real life
and captured
in the model. Different activities might "help" or "hurt" other activities and
generate
more or less volume if executed together than if executed separately. The
difference
between the predicted volumes is called synergy and is either reported
separately or
allocated to the individual activities. Calculating the synergy is further
described at
processing block 420 below.
[0055] Processing logic executes a processing loop (processing blocks 410-418)
to
calculate a raw volume contribution for each activity. At processing block
412,
processing logic sets one of the activities to the opposite state of all the
other
activities. In one embodiment, processing logic sets the drivers for one of
the activities
to their off state and sets all the other activities' drivers to the on state.
In this
embodiment, processing logic calculates the raw volume contribution for the
activity
using a subtractive scheme, described below. In another embodiment, processing
logic
sets the drivers for one of the activities to the on state and sets all the
other activities
to the off state. In this embodiment, processing logic calculates a raw volume
contribution for the activity using an additive scheme, described below.
[0056] At processing block 414, processing logic calculates the raw volume
contribution of an activity using one of the additive and subtractive schemes.
Using
the inputted set of activities a,,a2,...,an , let a' indicate that activity i
is on such that
the drivers associated with the activity take their executed values.
Respectively,
denote by a that activity i is off such that the drivers associated with the
activity take
their reference values for the associated scopes.
In one embodiment, processing logic calculates raw volume contributions using
an
additive scheme. In the additive scheme, processing logic calculates the
volume
difference for the scenarios in which only a single activity is on and the
scenario in
which all activities are off. The difference is the raw volume contribution
attributed to
that activity, as shown in Eq. (7):
add 0 0 1 0 0 0 0 0 0 0
Volume (a~~=Volume aI ,...,ai-i,ai ,ai+1,...,an -Volume aI ,...,ai ,,ai
,aj+1,...,an
(7).
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In another embodiment, processing logic calculates the raw volume contribution
using
a subtractive scheme. In the subtractive scheme, processing logic calculates
the
volume difference with all activities on with the volume predicted in the case
that a
single activity is off as shown in Eq. (8):
Volume Sub, (ai)=Volume aI.....ai_,,ai,ai+,,...an -Volume a.....aai
,ai+1,...,a,
(8).
The processing loop ends at processing block 418.
[0057] At processing block 420, processing logic determines the synergy
contribution and allocates a portion of that synergy to each individual
activity raw
volume contribution. Synergy results from the non-linearities in the model. To
determine the synergy, processing logic calculates the sum of the activity raw
volumes
from processing block 410-418 and calculates an incremental volume. The
incremental volume from all activities combined is the difference of the
predicted
volume when all activities are active and the predicted volume when all of
them are
inactive. It is independent of the method by which individual activities' raw
volume
contributions are calculated using Eq. (9):
IncVolume = Volume{a~ , az,..., an })-Volume{a , az ,..., a })
(9).
In one embodiment, due to the non-additivity of the world and the model
representation of it, it can that the incremental volume does not equal the
sum of the
raw volume contributions, Eqs. (10a) and (10b):
IncVolume # Volume aaa (ai
ti
(10a)
IncVolume # YVolume sub, (ai
ti
(10b)
The difference of incremental volume and total raw volume contribution is
defined as
synergy, Eqs. (I I a) and (I lb):
Synergy aaa = IncVolume - Volume aaa (ai
ti
(lla)
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Synergy s`~b"r = IncVolume - YVolume `,b" (ai
ti
(llb).
[0058] Synergy means that some or all marketing activities result in "the sum
being grater than the parts." This means that the execution of multiple
activities in
concert (at least as long as these activities make positive contributions to
volume) can
generate a volume that is higher (or lower) than the sum of the volumes
generated by
individual execution of all activities. Therefore, additive synergy tends to
be positive.
For the same reason, subtractive synergy tends to be negative since turning
off an
individual activity not only loses the lift from that activity but also makes
the
remaining activities somewhat less effective.
[0059] In one embodiment and depending on the relative size of incremental
volume to total volume and the relative lift of different activities, the
choice of
decomposition scheme has an impact on raw incremental volume. For example, the
subtractive method can allocate a relatively higher volume contribution to the
"small"
effects than the additive method.
[0060] Furthermore, at processing block 420 and in one embodiment, processing
logic allocates the computed synergy to each of the raw volume contributions.
By
allocating the synergy to the raw volume contributions, processing logic can
calculate
a volume contribution for an activity that models the actual volume
contribution. In
one embodiment, processing logic allocates synergy for each activity in the
set of
activities. In this embodiment, the calculated synergy is the result of the
interaction of
all the activities. In another embodiment, processing logic excludes one or
more of the
activities from the synergy allocation. In this alternative embodiment, an
activity that
is excluded from the synergy allocation will have the final volume
contribution equal
to the raw volume contribution. For example, an activity is excluded if the
activity
enters the response model in an additive fashion (e.g., supplemental volume)
or
activities that will be combined with base volume later to form the reported
base
volume.
[0061] In alternate embodiments, amongst those activities included into the
synergy allocation, synergy is allocated based on different formulae. Examples
are
proportionate allocation, allocation proportionate to the absolute size of a
volume
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contribution, allocation in equal portions, or any allocation scheme that
results in the
sum synergy portions allocated to each activity being equal to total synergy'
[0062] Before discussing allocation of synergy, it is useful to give an
example of
an overall volume decomposition calculation. In one embodiment, processing
logic
allocates synergy with the following properties: raw and final volume
contributions
have the same sign (no sign flipping); final volume contributions be as close
to the
raw contributions a possible; and the relative size of final contributions be
as close as
possible to the relative size of the raw contributions. Allocating synergy
with this
embodiment is further described in Figure 6, below.
[0063] Figure 5 is a table illustrating one embodiment of a volume
decomposition
calculation using the subtractive scheme. In Figure 5, processing logic
computes a
volume decomposition 522 using activities 524A-F. Activities 524A-F comprise
TV
advertising 524A, print advertising 524B, coupons 524C, display 524D, feature
524E,
and competition 524F. Processing logic uses a response model (not shown) that
models this market volume using drivers 510A-J. Each of the activities 524A-F
comprises one or more of drivers 510A-J. For example, TV advertising 524A
comprises TV gross ratings points driver 510A, print advertising 524B
comprises
print circulation driver 510H, coupons 524C comprises coupon circulation
driver
510F, display 524D comprises percent base volume on display driver 510B and
display price driver 510C, feature activity 524E comprises percent base volume
on
feature driver 510D and feature price driver 510E, and competition activity
524F
comprises percent volume on trade competition driver 510J. As illustrated in
Figure 5,
activities 524A-F comprise one or more of drivers 510A-J. However, not all of
drivers
510A-J are included in one of activities 524A-F. For example, base price
driver 510I
and radio gross ratings point 510G are not included in one of activities 524A-
F.
Instead, these drivers add to the base volume 508 and are not changed during a
simulation to determine the raw volume contribution of activities 524A-F.
[0064] Furthermore, as described above, each of drivers 510A-J has a reference
value and an executed value. Processing logic uses the executed value to
calculate the
expected volume, whereas processing logic uses drivers 510A-J reference values
to
calculate the base volume. Processing logic uses either the reference or
expected
values at times to calculate an activity's raw volume contribution. Using
these values,
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processing logic calculates an expected volume of 1000 and a base volume of
700.
The incremental volume is 300.
[0065] Processing logic uses this model described above to calculate a raw
volume contribution for each of activities 524A-F. For example, for TV
advertising
524A, processing logic calculates a volume with TV gross rating point driver
510A
changed from its expected value of 20 to the reference value of 0. This
scenario gives
a predicted volume of 950, meaning that the raw volume for TV advertising 524A
is
50. For the print activity 524B, processing logic turns off the print activity
driver
510H to 0 from 1,000,000. This calculation gives a raw volume contribution for
print
activity 524B of 20. Similarly, processing logic calculates raw volume
contributions
for coupons 524C, display 524D, feature 524E, and competition 524F of 10, 150,
100,
and -5, respectively, using the reference values for the associated driver
illustrated in
Figure 5.
[0066] From the raw volume contributions above, processing logic calculates
the
absolute value of the raw volume contributions, which are 50, 20, 10, 150,
100, and 5
for activities 524A-F, respectively. As will be described below with respect
to Figure
6, the allocated synergy for each activity is -3.73, -1.49, -0.75, -11.19, -
7.46, and -0.37
for activities 524A-F, respectively. This leads to a final volume contribution
of 700
for base volume 508 and activity 524A-F contributions of 46.27, 18.51, 9.25,
138.81,
92.54, and -5.37, respectively.
[0067] In the volume decomposition described above, processing logic allocated
synergy based on the absolute values of the raw volume contributions. Figure 6
is a
flow diagram of one embodiment of a process 600 for calculating synergy
allocations
based on the absolute value of the raw volume. The process may be performed by
processing logic that may comprise hardware (e.g., circuitry, dedicated logic,
programmable logic, microcode, etc.), software (such as run on a general
purpose
computer system or a dedicated machine), or a combination of both. In one
embodiment, process 600 is performed by data processing system 1800 of Figure
18.
[0068] In Figure 6, at processing block 602, the process begins by processing
logic summing the activity raw volume contributions calculated in Figure 4,
processing blocks. Processing logic sets the amount of synergy equal to the
incremental volume minus the raw volume sum at processing block 604.
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[0069] At processing block 606, processing logic allocates a portion of the
calculated synergy for each of the activity volume contributions based on the
absolute
values of the raw volume contributions. In one embodiment, let V,, V2,..., Va
be the
raw volume contributions of those activities that are being allocated a
portion of the
calculated synergy and let S be the calculated synergy to be allocated. In one
embodiment, the final volume contribution for each activity i is computed
using Eq.
(12):
V Final = V + Vi S
Vi a n
n=1 vJ
(12).
where ViFinal is the final volume contribution for an activity i, Vi is the
raw volume
contribution for activity i, and S is the total calculated synergy.
Furthermore, because
synergy S satisfies the inequality, - In V < S In VJ . , V Final and V. will
have
j=1 J j 1
the same sign. In addition, the raw contributions with the same sign are
scaled by the
factor with a relative size, preserving volume contribution relative size.
[0070] As an example of synergy allocation, Figure 7 is a table illustrating
one
embodiment of a synergy calculation. The volume numbers in Figure 7 are
derived
from the expected/base volumes and volume contributions in Figure 5. For
example,
incremental volume 706 has a value of 300 that is the difference of the
expected
volume 704 and the base volume 508. Summing the raw volume contributions 758
gives a total of 325. The difference between this sum and the incremental
volume is
25, which is the synergy 710. In one embodiment, synergy 710 is allocated to
the raw
volume contribution using Eq. (7) above.
[0071] As described above, processing logic allocates the synergy based on the
absolute value of the raw volume contribution. In an alternate embodiment,
processing
logic allocates synergy based on the actual raw volume contributions. However,
allocating synergy based on raw volume contributions has drawbacks. For
example,
for negative synergy, allocating based on raw volume contributions can flip
the sign of
an activity's volume contribution. Sign flipping can change an activity from a
positive
volume contribution to a negative volume contribution or vice versa. Thus,
sign
flipping obscures the qualitative contribution as activity has to the volume.
As
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described above, allocating synergy based on the absolute value of the raw
volume
contribution does not have the sign flipping problem. Furthermore, raw volume
synergy allocation can lead to large amounts of scaling to get a small amount
of
synergy. As will be described below, this can arise for raw volume
contributions of
opposite signs. Figures 8AB are block diagrams that illustrate synergy
allocation
based on raw volume contributions and the absolute value of the raw volume
contributions. In Figure 8A, diagram 800 comprises volume contributions of two
activities 802A-B. In this diagram, raw volume contribution 802A is positive
and
larger than the negative raw volume contribution. Synergy allocations 804 A-B
are
allocations that adjust raw volume contribution 802 A-B in opposite
directions. The
overall allocated synergy is positive because lallocation 804AI > lallocation
804B1. In
comparison, in Figure 8B, diagram 880 comprises raw volume contributions 852A-
B
and synergy allocations 854A-B. Because the overall synergy allocated is
positive and
the synergy is allocated based on the absolute value of raw volume
contributions
852A-B, synergy allocation 854A-B are both positive and smaller than the
corresponding synergy allocation 804A-B in Figure 8A.
[0072] The method described above calculates a volume decomposition for a
single predicted volume, e.g., for a single product in a single week and a
single
location. If a volume decomposition is desired for a set of volumes (multiple
Products/Weeks/Locations), the volume decompositions for all individual
volumes are
calculated and the volume contributions to the respective activities are
added.
[0073] As described above, in one embodiment, a volume decomposition is
computed that is independent of that response model by toggling on/off
activities.
While the decomposition process is described in terms of decomposing a volume,
this
process, in alternate embodiments, can be used to decompose other measurable
business metrics (e.g., revenue, profit or market share, etc.). For example,
in one
embodiment, processing logic decomposes another measurable business metric and
allocates synergy as described in Figures 4 and 6 above.
Atomic Decompositions and Decompositions Hierarchies
[0074] The volume decomposition described above illustrates a decomposition at
one level of granularity, namely the granularity based on the response model
and the
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set of activities. However, businesses are often interested in seeing
decompositions at
different levels of granularity. For example, what is considered a single
activity for the
purpose of one report (e.g. Trade Promotions) might be considered as a
collection of
multiple activities for another business purpose (e.g. Display, Feature,
Feature and
Display, and TPR). As another example, a single encompassing activity of TV is
broken down into one or more individual activities of national TV, local TV,
cable,
broadcast, daytime, nighttime, etc. However, due to the potential allocation
of synergy
at different levels of granularity, the level at which activities are defined
will have an
impact on the volume contribution attributed to a collection of activities.
This can lead
to inconsistencies between different decomposition reports. Referring back to
the
example activity groupings, one report for trade can give a different volume
contribution overall than the sum of display, feature, feature and display,
TPR activity
volume contributions.
[0075] To avoid these inconsistencies, a fundamental set of "indivisible" or
"atomic" activities is defined along with a set of trees describing how to
roll up the
volume contributions from these atomic activities into aggregate activities.
Each level
of the tree is called a decomposition level with the level for the leaf nodes
being the
atomic decomposition level. The sequence of levels starting at the atomic
decomposition level is called a decomposition hierarchy. Volume contributions
for an
activity are obtained by summing the volume contributions of all the atomic
activities
(leaf nodes) underneath the node associated with this activity. The hierarchy
of atomic
decomposition level and higher decomposition levels is called a volume
decomposition hierarchy.
[0076] Figure 9 is a block diagram illustrating one embodiment of a volume
decomposition hierarchy 900. In Figure 9, volume decomposition hierarchy 900
comprises three decomposition levels: summary level 902, detailed level 904,
and
atomic level 906. While volume decomposition hierarchy 900 is illustrated with
three
levels, in alternate embodiments, volume decomposition hierarchy 900 has more
or
less decomposition levels with the same and/or different volume decomposition
levels. In particular, volume decomposition hierarchy 900 can have more than
one
summary level and/or detailed level.
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[0077] Atomic decomposition level 906 is the lowest level of volume
decomposition hierarchy 900 and comprises the finest granularity of
activities. Atomic
decomposition level 906 comprises activities 912A-K which are %ACV 912A,
number of items 912B, feature 912C, display 912D, feature and display 912E,
TPR
912F, National TV 912G, local TV 912H, print 9121, radio 912J, and FSI 912K.
This
decomposition level serves as a base for the detailed 904 and summary 902
decomposition levels.
[0078] Detailed decomposition level 904 is a volume decomposition level that
is
an aggregation of the activities in the atomic decomposition level 906.
Detailed
decomposition level 904 comprises activities 910A-I which are distribution
910A,
feature 910B, display 910C, feature and display 910D, TPR 910E, TV 910F, print
910G, radio 910H, and FSI 910I. The activities 910A-I in detailed
decomposition
level 904 are composed of one or more activities 912A-K from the atomic
decomposition level 906. For example, distribution 910A comprises %ACV 912A
and
number of items 912B. Furthermore, feature 910B, display 910C, feature and
display
910D, TPR 910E comprise each of feature 912C, display 912D, feature and
display
912E, and TPR 912F, respectively. TV 910F comprises national TV 912G and local
TV 912H.
[0079] Summary decomposition level 902 is the highest decomposition level in
the volume decomposition hierarchy and presents a volume decomposition of the
least
number of activities. In one embodiment, summary decomposition level 902
represents a volume decomposition due to a category of broad activities 908A-
D. In
one embodiment, summary decomposition level 902 comprises activities 908A-D,
which are distribution 908A, trade 908B, media 908C, and coupons 908D. Each of
these activities 908A-D are composed of finer granular activities from the
decomposition level 904 that is below the summary decomposition level 902. For
example, distribution 908A comprises distribution 910A. Trade 908B comprises
feature 910B, display 910C, feature and display 910D, and TPR 910E activities.
Media 908C summarizes media advertising activities and comprises TV 910F,
print
910G, and radio 910H activities. Coupons 908D summarizes coupon activity and
comprises FSI 9101.
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[0080] Using this hierarchy of volume decomposition 900, an analyst can use
the
high level decomposition 902 to get an overview of which activities 908A-D
give
which contributions to the changes in the volume. This analyst or a different
analyst
can drill down to the detailed 904 or atomic 906 volume decompositions to get
a finer
granularity of the activity contributions. In addition, because the volume
decompositions are built upon atomic volume decomposition 902, each of volume
decompositions 904 and 906 are consistent with each other and atomic volume
decomposition 902. Thus, an analyst can choose the volume decomposition
granularity level that best suits the needs of the analyst. In one embodiment,
these
volume decomposition levels are consistent because the volume contributions at
each
level have the same total synergy and add to the same total volume
contribution.
[0081] Figure 10 is a flow diagram of one embodiment of a process 1000 for
calculating a volume decomposition report for the atomic decomposition level.
The
process may be performed by processing logic that may comprise hardware (e.g.,
circuitry, dedicated logic, programmable logic, microcode, etc.), software
(such as run
on a general purpose computer system or a dedicated machine), or a combination
of
both. In one embodiment, process 1000 is performed by data processing system
1800
of Figure 18.
[0082] Referring to Figure 10, at processing block 1002, the process begins by
processing logic accessing the defined set of atomic activities. As per above,
the
defined set of atomic activities is the indivisible set of activities for this
response
model. The defined set of activities is further described in Figure 11 below.
Processing logic calculates raw volume contribution for each of the atomic
activities
at processing block 1004. In one embodiment, processing logic calculates the
raw
volume contributions using additive or subtractive schemes, as described above
in
Figure 4 at processing blocks 410-418.
[0083] At processing block 1006, processing logic calculates the base volume
using the response model. In one embodiment, processing logic calculates the
base
volume by turning all the atomic activities off as described in Figure 4,
processing
block 408 described above. Processing logic calculates the incremental volume
from
the base and expected volumes at processing block 1008. In one embodiment,
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processing logic calculates the incremental volume as described in Figure 4,
processing block 406.
[0084] At processing block 1010, processing logic calculates the final volume
contributions for each of the atomic activities. In one embodiment, processing
logic
calculates the final volume contributions by allocating the calculated synergy
as
described in Figure 4, processing block 420 above. Processing logic presents
the base
volume and final volume contributions at processing block 1012. In one
embodiment,
processing logic presents this data graphically, in a table, or in another
scheme known
in the art.
[0085] With the atomic decomposition level calculated, higher volume
decomposition levels is defined and/or calculated. Figure 11 is a flow diagram
of one
embodiment of a process 1100 for determining higher volume decomposition
levels
based on the atomic volume decomposition levels. The process may be performed
by
processing logic that may comprise hardware (e.g., circuitry, dedicated logic,
programmable logic, microcode, etc.), software (such as run on a general
purpose
computer system or a dedicated machine), or a combination of both. In one
embodiment, process 1100 is performed by data processing system 1800 of Figure
18.
[0086] Referring to Figure 11, at processing block 1102, the process begins by
processing logic accessing the set of activities for which volume
contributions are to
be broken out. Processing logic executes a processing loop (processing blocks
1104-
1110) to calculate a raw volume contribution for each activity. At processing
block
1106, processing logic defines higher level volume decomposition groups. In
one
embodiment, processing logic defines a higher volume decomposition group by
defining a new set of activities from a lower set of activities. The lower set
of
activities is the atomic set of activities or a set of activities higher in
the volume
decomposition hierarchy than the atomic volume decomposition and lower than
the
set of activities being defined. For example, in Figure 9, processing logic
defines
activities 910A-I for detailed volume decomposition level 904 based on
activities
912A-K from the atomic volume decomposition level 906. Processing logic
defines a
set of reference values for each of the drivers in the defined volume
decomposition
group. The processing loop ends at processing block 1110.
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[0087] With the different volume decomposition levels defined, the volume
decomposition hierarchy is calculated as described in Figures 12 and 13 below.
Figure
12 is a flow diagram of one embodiment of a process 1200 for calculating a
volume
decomposition report for aggregate scopes for the atomic decomposition level.
The
process may be performed by processing logic that may comprise hardware (e.g.,
circuitry, dedicated logic, programmable logic, microcode, etc.), software
(such as run
on a general purpose computer system or a dedicated machine), or a combination
of
both. In one embodiment, process 1200 is performed by data processing system
1800
of Figure 18.
[0088] Referring to Figure 12, at processing block 1202, the process begins by
processing logic calculating the base volume and the final volume
contributions for
the atomic volume decomposition level. In one embodiment, processing logic
calculates the base volume and final volume contribution as described Figure 4
above.
Processing logic sums the respective volume contributions for each activity
for the
base across product/week/locations in the scope at processing block 1204.
Processing
logic presents the base volume and final volume contributions at processing
block
1012. In one embodiment, processing logic presents this data graphically, in a
table, or
in another scheme known in the art.
[0089] Figure 13 is a flow diagram of one embodiment of a process 1300 for
calculating a volume decomposition report for aggregate scopes for
decomposition
levels higher in the decomposition hierarchy. The process may be performed by
processing logic that may comprise hardware (e.g., circuitry, dedicated logic,
programmable logic, microcode, etc.), software (such as run on a general
purpose
computer system or a dedicated machine), or a combination of both. In one
embodiment, process 400 is performed by data processing system 1800 of Figure
18.
[0090] Referring to Figure 13, at processing block 1302, the process begins by
processing logic calculating the base volume and the final volume
contributions for
the atomic volume decomposition level. In one embodiment, processing logic
calculates the base volume and final volume contribution as described Figure 4
above.
[0091] At processing block 1304, for each volume decomposition group,
processing logic sums the volume contributions of the atomic volume
decomposition
groups in the leaf nodes belonging to the volume decomposition group in the
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hierarchy. In one embodiment, each leaf nodes is an activity of one of the
volume
decomposition levels. Furthermore, processing logic assigns this sum to this
volume
decomposition group. Processing logic presents the base volume and final
volume
contributions at processing block 1012. In one embodiment, processing logic
presents
this data graphically, in a table, or in another scheme known in the art.
[0092] In one embodiment, an atomic decomposition level and decomposition
hierarchy of volume decomposition levels is described that results in a set of
internally
consistent set of volume decomposition levels. The atomic decomposition level
represents a set of activities that are indivisible and are used to build upon
other sets
of activities and volume decomposition levels that are internally consistent
with the
atomic decomposition level.
[0093] While the atomic decomposition and decomposition hierarchy is described
in terms of decomposing a volume, this process, in alternate embodiments, can
be
used to calculate atomic decompositions and decomposition hierarchies for
other
measurable business metrics (e.g., revenue, profit or market share, etc.). For
example,
in one embodiment, processing logic calculates an atomic decomposition and/or
decomposition hierarchy for another measurable business metric as described in
Figures 10-13 above.
Hybrid Due-To Reports
[0094] The volume decomposition and volume decomposition hierarchy described
above attempt to answer the question "What activities contribute to a sales
volume (or
other measurable business metrics)?" While the volume decomposition is applied
to
one or more products, time period and/or locations, the volume decomposition
is
typically applied when modeling a single sales volume figure. Another type of
report,
a "due-to," determines the volume contributions due to differences in volumes
between two different time periods. Thus, a due-to attempts to answer the
question
"Why is the volume up/down?" and determine how much of the volume change is
attributable to each of the specific activities. In one embodiment, the due-to
report is
particularly useful when analyzing changes in volume for the same set of
products
over different time periods, such as comparing year-to-year sales volumes.
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[0095] An analyst typically would want to know which activity change caused
the
volume changes. Figure 15 is a chart illustrating one embodiment of a due-to
report
1500. In Fig. 15, due-to report 1500 comprises a starting volume 1502, ending
volume
1504, volume contribution changes 1506, base volume change 1512, and model
error
1510. Starting volume 1502 is the volume from the starting time period and
ending
volume 1504 is the volume from the ending time period. Due-to report 1500
presents
the change in starting 1502 and ending 1504 volumes as comprising changes in
each
of the activities volume contribution and the change in the base volume
contribution.
For example, in Figure 15, the change in the base volume 1512 adds +2.98% to
starting volume 1502.
[0096] Furthermore, in Figure 15, changes in the activities volumes 1506 can
be
positive, negative, and/or zero, and range from -4% to +3.48%. For example,
the
volume change attributable to activity Numltems is +3.48%. The model error
1510
add -0.4% and -1.1% to the change in volume contribution.
[0097] As is known in the art, one scheme to calculate a due-to reports is to
calculate volume decomposition reports for the starting and ending volumes and
determine the differences in each activity from these reports. This scheme is
known in
the art as difference decomposition due-to report. The difference
decomposition due-
to asks the question "how did the volume contributions from my activities
change?"
[0098] Difference decompositions are calculated as the difference - activity
by
activity - of volume contributions in a decomposition in time period two and a
decomposition in time period one. However, difference decomposition due-tos
have
the disadvantage that certain activities that cannot naturally be decomposed
and are
therefore part of the base volume in decomposition. In one embodiment, some
activities do not have a natural reference value and cannot be naturally
included in a
volume decomposition. Examples of such activities are base price or
distribution. As a
result, difference decomposition due-tos show these effects as base changes
without
breaking them out.
[0099] Independent of whether an activity is based on drivers with or without
reference values, a due-to is calculated using the start and end value of
these activities.
This is called a hybrid due-to. The hybrid due-to asks the question "How did
the
change in the activity levels change the volume?" It compares what would have
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happened had the analyst not changed the plan to what happened under the
changed
plan and attributes the difference to the changes in activities.
[00100] The first step in calculating a hybrid due-to is to determining the
change in
base volume between the two time periods. This change in base volume results
from
temporal fluctuations in sales volume that occur without being directly caused
by a
firm's marketing activities and/or from activities that are not modeled in the
response
model. Note that volume contributions can change even if activities do not
change.
For example, volume may increase based on word of mouth advertising by a
client
base or the popularity of a set of products may increase/decrease naturally.
This is due
to changes in volume that resulted from driver changes not associated with
activities,
i.e. base changes.
[00101] Figure 14 is a block diagram 1400 illustrating one embodiment of
different
predicted volumes for different time periods along with the changes
attributable to a
change in base volume and activities. In Figure 14, starting volume 1402 is
the
volume in period 1 (P1) and ending volume 1406 is the volume period 2 (P2),
both
predicted based on the activities that were executed in periods 1 and 2
respectively. In
diagram 1400, ending volume 1406 is greater than starting volume 1402. We can
also
predict the volume for period 2 had we executed the same activities as we
executed in
period2, resulting in the volume noted by 1404. The difference between volumes
1404
and 1402 is the change in base volume between the two periods. The changes in
volume 1406 and 1402 are attributable to changes in the base volume 1404 and
changes due to the activities 1410. Volume 1404 represents the volume 1402
corrected for the change in base. After adjusting for the change in base
volume, the
rest of the volume change is attributable to the change in activities 1410.
[00102] The raw volumes to be attributed to the changes in activity is
calculated by
using the driver values in P1 as the decomposition reference values and
executing one
of the decomposition algorithms (e.g., additive, subtractive). In addition,
synergy is
allocated in any of the ways described above. The changes in volume
contribution are
also called a volume variance.
[00103] In this embodiment, the drivers in this model have a value defined in
period 1. For that reason, even drivers without an explicit reference value
have a
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reference value in the hybrid due-to algorithm and, therefore, will have a
volume
contribution broken out.
[00104] Figure 16 is a flow diagram of one embodiment of a process 1600 for
calculating a hybrid due-to and allocating synergy. The process may be
performed by
processing logic that may comprise hardware (e.g., circuitry, dedicated logic,
programmable logic, microcode, etc.), software (such as run on a general
purpose
computer system or a dedicated machine), or a combination of both. In one
embodiment, process 1600 is performed by data processing system 1800 of Figure
18.
[00105] Referring to Figure 16, at processing block 1602, the process begins
by
processing logic accessing the start/end volume and activity values and other
input
information used in this process (response model, etc.). Processing logic
calculates the
start and end volumes at processing block 1604. In one embodiment, processing
logic
calculates the start and end volumes as described in Figure 4, processing
block 406.
[00106] At processing block 1606, processing logic calculates the change in
base
volume. As described above, the change in base volume can result from temporal
fluctuation in sales volume that occurs naturally and/or from activities that
are not
modeled in the response model. In one embodiment, processing logic calculates
the
change in base volume by calculating one the predicted volume for one period
(e.g.,
end or start volume) using the executed activities of the other period (e.g.,
start or end
executed activities). The difference between these two predicted volumes is
the
change in base volume.
[00107] Processing logic executes a processing loop (processing blocks 1608 -
1614) to calculate the change in volume contribution for each of the
activities. At
processing block 1610, processing logic sets an activity to one of the start
and end
value and all of the other activities to the other sets of values. For
example, if
processing logic sets one activity to the start value, processing logic sets
all the other
activities to the end value. Using this setup, processing logic calculates the
volume
contribution for the activity that has the different start/end value. In one
embodiment,
processing logic calculates the volume contribution by taking the difference
between
the volume calculated with the different start/end value and the corresponding
start/end volume. Examples of this type of calculation are further described
in
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reference with Tables 1 - 10 below. The processing loop ends at processing
block
1614.
[00108] At processing block 1616, processing logic allocates the synergy to
the set
of volume contributions calculated in the processing loop above. In one
embodiment,
processing logic allocates a portion of the calculated synergy for each of the
activity
volume variances based on the absolute values of the raw volume variances. In
one
embodiment, let V,, V2,..., Va be the raw volume variances of those activities
that are
being allocated a portion of the calculated synergy and let S be the
calculated synergy
to be allocated. In one embodiment, the final volume variance for each
activity i is
computed using Eq. (13):
V Final = V + Vi S
i y1_1 VJ
(13).
where ViFinal is the final volume variance for an activity i, Vi is the raw
volume
variance for activity i, and S is the total calculated synergy. Processing
logic calculates
the model error at processing block 1618.
[00109] The example given below illustrates, in one embodiment, how processing
logic determines volume "due-to" changes by creating cubes that contain some
drivers
from both Start and End states. In this example, the set of activities modeled
includes
marketing, trade, price, and distribution. The marketing activity comprises
the driver
TV. The trade activity comprises drivers TPR_Price and TPR_ACV. The Price
activity comprises drivers NoPromoPrice and AverageNoPromoPrice. The
distribution activity comprise driver ACV. The following model is used with
these
activities to calculate volume (Eq. (14)):
Volume = (1.5) * (TV) +
(2000) * (AverageNoPromoPrice - TPR_Price) * TPR_ACV/ACV +
(1000) * (AverageNoPromoPrice - NoPromoPrice) * (ACV -
TPR_ACV)/ACV +
(150) * (ACV)
(14)
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Furthermore, in this example, the subtractive form of the decomposition scheme
is
used along with the absolute synergy allocation scheme. In other embodiments,
other
schemes are used.
[00110] Table 1 illustrates the comparison of the start and end driver values
for the
activities of marketing, trade, price, and distribution.
Activity Drivers Start End Cube
Cube Drivers
Drivers
Marketing TV 100 60
Trade TPR Price 2.1 2.30
TPR_ACV 20 15
Price NoPromoPrice 3.90 4.20
AverageNoPromoPrice 4.10 4.10
Distribution ACV 40 50
Table 1. Start and End Driver Values.
[00111] With the end driver values, processing logic calculates the end
volume.
The end volume is 8,600 units of volume as illustrated in Table 2.
Activity Driver Names Driver Result
Values
Marketing TV 60 90
Trade TPR_Price 2.30 1,080
TPR_ACV 15 0
Price NoPromoPrice 4.20 -70
AverageNoPromoPrice 4.10 0
Distribution ACV 50 7,500
Volume 8,600
Table 2. Calculating the End Volume.
[00112] Processing logic calculates each of the raw volume contributions by
toggling each activity from the end value to the start value and recomputing
the
volume with this configuration. For example, processing logic toggles the
market
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value by setting TV driver to 100. The resulting volume is 8660, or a change
of -60
(Table 3).
Activity Driver Names Driver Result EndCubeResult -
Values CurrentResult
Marketing TV 100 150 -60
Trade TPR_Price 2.30 1,080 0
TPR_ACV 15 0 0
Price NoPromoPrice 4.20 -70 0
AverageNoPromoPrice 4.10 0 0
Distribution ACV 50 7,500 0
Volume 8,660 -60
Table 3. Calculating the Volume for the Marketing Activity with the Starting
Driver
Value.
[00113] For the trade activity raw volume contribution, processing logic
restores
the marketing activity to the end driver value and sets the trade activity to
the start
value. In this example, processing logic sets the drivers of trade, TPR_Price
and
TPR_ACV, to 2.1 and 20, respectively. The calculated volume is 9,130 which is
a
change of -530 (Table 4).
Activity Driver Names Driver Result EndCubeResult -
Values CurrentResult
Marketing TV 60 90 0
Trade TPR_Price 2.1 1,600 -520
TPR_ACV 20 0 0
Price NoPromoPrice 4.20 -60 -10
AverageNoPromoPrice 4.10 0 0
Distribution ACV 50 7,500 0
Volume 9,130 -530
Table 4. Calculating the Volume for the Trade Activity with the Starting
Driver
Values .
[00114] For the price activity, processing logic restores the trade activity
to the end
driver values and sets the price activity to the start driver values. In this
example,
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processing logic sets the price drivers, NoPromoPrice and AverageNoPromoPrice,
to
3.90 and 4.10, respectively. The calculated volume is 8,810, which is a change
of -210
(Table 5).
Activity Driver Names Driver Result EndCubeResult -
Values CurrentResult
Marketing TV 60 90 0
Trade TPR_Price 2.30 1,080 0
TPR_ACV 15 0 0
Price NoPromoPrice 3.90 140 -210
AverageNoPromoPrice 4.10 0 0
Distribution ACV 50 7,500 0
Volume 8,810 -210
Table 5. Calculating the Volume for the Price Activity with the Starting
Driver
Values.
[00115] For the distribution activity, processing logic restores the price
activity to
the end driver values and sets the distribution activity to the start driver
values. In this
example, processing logic sets the price driver, ACV, to 40. The calculated
volume is
7,378, which is a change of 1223 (Table 6).
Activity Driver Names Driver Result EndCubeResult -
Values CurrentResult
Marketing TV 60 90 0
Trade TPR_Price 2.30 1,350 -270
TPR_ACV 15 0 0
Price NoPromoPrice 4.20 -63 -8
AverageNoPromoPrice 4.10 0 0
Distribution ACV 40 6,000 1,500
Volume 7,378 1,223
Table 6. Calculating the Volume for the Distribution Activity with the
Starting Driver
Values.
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[00116] Furthermore, processing logic calculates the start volume using the
start
driver values, which results in a volume of 8,250 and a difference of 350 from
the end
volume (Table 7).
Activity Driver Names Driver Result EndCubeResult -
Values CurrentResult
Marketing TV 100 150 -60
Trade TPR_Price 2.1 2,000 -920
TPR_ACV 20 0 0
Price NoPromoPrice 3.90 100 -170
AverageNoPromoPrice 4.10 0 0
Distribution ACV 40 6,000 1,500
Volume 8,250 350
Table 7. Start Volume and Start Volume/End Volume Difference.
However, the sum of the initial volume contributions is 423 (Table 8). This is
indicates there is 73 units of synergy that is allocated to the individual
volume
contributions.
Activity Driver Names Per
Activity
Volume
Delta
Marketing TV -60
Trade TPR Price -530
TPR_ACV
Price NoPromoPrice -210
AverageNoPromoPrice
Distribution ACV 1223
Total 423
Table 8. Individual Volume Contributions, no Synergy Allocated.
[00117] Using the absolute synergy allocation scheme as described above in
Figure
6, the final volume contributions are listed in Table 9. For example, for the
marketing
activity, the incremental volume is -60, absolute value is 60, sum of the
absolute value
for each activity is 2023, and the total synergy is -73. The allocated portion
of the total
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synergy is -2.2 using Eq. (7) to calculate the synergy allocation for the
marketing
activity. The final incremental volume for the marketing activity is -62.2.
Activity Driver Names Per End Synergy ABS(Activity) Per Per
Activity Total Activity Activity
Volume - Start Adjust. Volume
Delta Total Delta
Marketing TV -60 60 -2.2 -62.2
Trade TPR_Price -530 530 -19.1 -549.1
TPR_ACV 0.0 0.0
Price NoPromoPrice -210 210 -7.6 -217.6
AverageNoPromoPrice 0.0 0.0
Distribution ACV 1223 1223 -44.1 1,178.9
Total 423 350 -73 2023 -73 350
Table 9. Synergy Allocation and Final Volume Contributions including Synergy.
The Final results are listed in Table 10.
Activity Driver Names Start Gross Net Per End
Cube Per Activity Cube
Activity Delta
Delta
Marketing TV -60 -62.2
Trade TPR Price -530 -549.1
TPR_ACV 0.0
Price NoPromoPrice -210 -217.6
AverageNoPromoPrice 0.0
Distribution ACV 1223 1,178.9
Total 8,250 350 8,600
Table 10. Final Results.
[00118] As described above, in one embodiment, a hybrid due-to report is
calculated that determines the volume variance between two different volumes
for a
set of activities that do not have a reference values. Furthermore, in other
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embodiments, the hybrid due-to is applied to other sets of activities
(activities that do
have a reference value, distribution activities, etc.).
[00119] In another embodiment, multiple levels of volume variances are
computed
based on a defined set of atomic activities, a decomposition hierarchy that
includes a
tree of activities, and a hybrid due-to. In this embodiment, processing logic
computes
an atomic volume variance level using the defined set of atomic activities as
described
in Figure 16 above. Using this atomic volume variance level, processing logic
computes higher levels of volume variance levels based on other sets of
activities that
are based on the defined set of atomic activities as described in Figures 11-
13. In this
embodiment, the volume variances are summed instead of volume contributions.
[00120] While the hybrid due-to is described in terms of calculating a volume
variance, this process, in alternate embodiments, can be used to calculate
calculating
variances for other measurable business metrics (e.g., revenue, profit or
market share,
etc.). For example, in one embodiment, processing logic calculates a volume
variance
for another measurable business metric as described in Figure 16 above.
Compound Due-to Reports
[00121] As described above, due-to reports are calculated using difference
decomposition or hybrid schemes. Difference decompositions due-to reports is
calculated only for activities whose drivers have an appropriate reference
value,
whereas hybrid due-to reports is used for activities that do or do not have an
appropriate reference value. In some cases, users prefer the interpretation of
"change
in volume contribution of an activity" provided by the difference of
decompositions
method over the interpretation "change of volume due to change of activity" of
the
hybrid method. In order to retain the ability to provide volume variance
reports across
these sets of activities, in one embodiment, these two schemes are combined
for
calculating the due-to reports. In this embodiment, the difference
decomposition is
applied to activities that have drivers with a natural reference value, hybrid
is applied
to non-distribution activities that do not have drivers with a natural
reference value,
and volume changes resulting from the distribution activities are calculated
by
subtraction. Figure 17 is a flow diagram of one embodiment of a process 1700
for
calculating a compound due-to. The process may be performed by processing
logic
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that may comprise hardware (e.g., circuitry, dedicated logic, programmable
logic,
microcode, etc.), software (such as run on a general purpose computer system
or a
dedicated machine), or a combination of both. In one embodiment, process 700
is
performed by data processing system 1800 of Figure 18.
[00122] Referring to Figure 17, at processing block 1702, the process begins
by
processing logic determining the sets of activities that will be calculated by
difference
decomposition, hybrid, or other. In one embodiment, processing logic
calculates the
volume contributions for activities with drivers with a natural reference
value using
difference decomposition. Furthermore, processing logic calculates the volume
contributions for all other non-distribution activities using the hybrid
scheme.
Distribution activities will be calculated by subtracting the difference
decomposition
and hybrid results from the start/end volume difference.
[00123] For example, in one embodiment, consider a response model consisting
of
the following activities: TV, trade, price, base price, and distribution. For
this set of
activities, TV and trade have a reference value, whereas price, base price,
and
distribution do not. In a compound scheme to calculate the due-to report, TV
and trade
would be calculated using difference decomposition, price and base price would
be
calculated using hybrid, and distribution would be calculated by subtraction
(Table
11).
"Bucket" Calculation Phase
TV DD
Trade DD
Price H
Base H
Distribution Subtraction
Table 11. Calculation Scheme for Each of the Activities.
[00124] During each of the phases of calculation, the drivers not active
during that
phase are treated as if they were not of interest to the due-to report. As a
result, effects
of those drivers will be part of the base volume as described in (Table 12).
The overall
base volume is the set of unassigned activities as declared in the
decomposition level.
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An effective base is the set of activities that are be treated in a base-like
fashion
during each phase of the calculation. Activities that are not in the effective
base for a
phase of the calculation will toggle their driver values. In the difference
decomposition phase, the declared base gets lumped into the effective base. In
this
embodiment, the drivers that are part of the effective base are not toggle in
calculating
the respective decompositions. In the hybrid phase, the declared base is
treated
differently from the declared base in the decomposition phase. This means that
the
drivers in the hybrid declared base are toggled between the two different
values.
Buckets as Buckets as Buckets as
Declared used in DiffD used in Hybrid
TV TVDD EffectiveBaseH
Trade TradeDD EffectiveBaseH
Price EffectiveBaseDD PriceH
Base EffectiveBaseDD BaseH
Distribution EffectiveBaseDD EffectiveBaseH
Table 12. Effective Bases for each of the Activities.
[00125] At processing block 1704, processing logic applies difference
decomposition for the activities that have a natural reference value. In one
embodiment, processing logic calculates a volume decomposition for each of the
start
and end scenarios. Using these two volume decompositions, processing logic
calculates a volume variance for each of the activities by taking the
difference of the
two volume decompositions. Furthermore, processing logic could allocate a
portion of
any calculated synergy for each of the volume variances.
[00126] In the example of activities given in Table 11, processing logic would
use
the difference decomposition interpretation of the decomposition level (TVDD,
TradeDD, and EffectiveBaseDD) and perform calculation using difference
decomposition. Note that for this calculation that activities in
EffectiveBaseDD (price
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base, and distribution) do not toggle. Processing logic calculates the synergy
using Eq.
(15):
(VTVDD +VTradeDD )-VTVHH-and-TradeDD
(15)
where V71,DD is the calculated volume with the TV in the reference value,
VTradeDD is the
calculated volume with trade in the reference value, and V7VDD-and-TradeDD is
the
calculated volume with TV and trade in the reference values. Processing logic
can
allocate the synergy in this step or in a later step.
[00127] At processing block 1706, processing logic applies the hybrid scheme
for
the activities that do not have a natural reference value and are not
distribution
activities. In one embodiment, processing logic calculates a volume variance
using the
hybrid scheme as described in Figure 16. As applied to the activities in Table
11,
processing logic would use the hybrid scheme for price and base price and
perform the
hybrid calculation. In this embodiment, the activities (TV and trade) in
EffectiveBaseH
do not toggle. Processing logic calculates the synergy using Eq. (16):
(VP, iceH + VBaseH / - VPr iceH -and -BaseH
(16)
where VPr iceH is the calculated volume for the price in one of the start/end
state,
VBaseH is the calculated volume for base price in one of the start/end state,
and
VPriceH -and -BaseH is the calculated volume with price and base price in one
of the
start/end state. Processing logic can allocate the synergy in this step or in
a later step.
[00128] At processing block 1708, processing logic calculates volume variance
for
the distribution activities by subtracting the other volume variances
calculated in
processing blocks 1704 and 1706 from the total predicted volume as in Eq.
(17):
WDist Predicted -I VV, - SynergyDD - SynergyH
(17)
where WDist is the distribution volume variance, VPredicted is the predicted
volume,
YWi is sum of the volume variances calculated using the difference
decomposition
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and hybrid schemes, Synergy DD is the synergy calculated using difference
decomposition, and Synergy, is the synergy calculated using the hybrid scheme.
[00129] In an alternate embodiment, processing logic calculates a compound due-
to
without using the subtraction phase. In this embodiment, processing logic
calculates
the volume contributions from those activities that are assigned to the
difference of
decomposition group (DD) using the difference of decomposition method.
"Bucket" Calculation Phase
TV DD
Trade DD
Price H
Distribution H
Table 13. Calculation Scheme for Each of the Activities in a
Compound Due-to without Subtraction.
The drivers associated with the activities in DD are set to their
decomposition values
in the two time periods. The result is a new model predicting volume without
the DD
activities' contribution. Applying a hybrid due-to algorithm to this new model
with
respect to those activities not assigned to DD provides both the volume
contributions
of the non-DD activities as well as the change in base volume.
[00130] In more detail, the compound due-to without subtraction is calculated
in the following three steps:
1. Calculate the decompositions for both time periods with respect to the
activities in DD. Taking the differences between the corresponding raw
volume contributions gives the raw volume contributions for these activities
2. Setting all the drivers that correspond to the activities in DD to their
reference
values provides a new model. A hybrid due-to is calculated based on this
model. For example, the hybrid due-to is calculated as described in Figure 16
above. This due-to provides the raw volume contributions for the activities in
the hybrid due-to as well as the change in base volume.
3. Synergy (the difference between the sum of all the raw volume contributions
and the difference in predicted volumes) is allocated by one of the mechanisms
described above.
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[00131] In another embodiment, multiple levels of volume variances are
computed
based on a defined set of atomic activities, a decomposition hierarchy that
includes a
tree of activities, and a compound due-to. In this embodiment, processing
logic
computes an atomic volume variance level using the defined set of atomic
activities as
described in Figure 17 above. Using this atomic volume variance level,
processing
logic computes higher levels of volume variance levels based on other sets of
activities that are based on the defined set of atomic activities as described
in Figures
11-13. In this embodiment, the volume variances are summed instead of volume
contributions.
[00132] While the compound due-to is described in terms of calculating a
volume
variance, this process, in alternate embodiments, can be used to calculate
atomic
decompositions and decomposition hierarchies for other measurable business
metrics
(e.g., revenue, profit or market share, etc.). For example, in one embodiment,
processing logic calculates an atomic decomposition and/or decomposition
hierarchy
for another measurable business metric as described in Figures 10-13 above.
[00133] Figure 18 is a block diagram of a data processing system 800 that
calculates volume decompositions, atomic decompositions/volume decomposition
hierarchies, hybrid due-tos, and/or compound due-tos. Data processing system
is, but
not limited to, a general-purpose computer, a multi-processor computer,
several
computers coupled by a network, etc. In Figure 18, system 1800 comprises
volume
decomposition module 1802, decomposition hierarchy module 1804, synergy module
1806, compound due-to module 1808, volume module 1810, and hybrid due-to
module 1812. Volume module 1810 accesses inputs and calculates a volume
results.
In one embodiment, inputs comprise the response model, the set of activities
and the
values for each of those activities as described in Figure 2. In one
embodiment,
volume decomposition module 1802 and hybrid due-to module 1812 direct volume
module to calculate one or more of the base volume, expected volumes, volume
contributions, etc. Synergy module 1806 calculates and allocates the synergy
as
described in Figure 7. In one embodiment, volume decomposition module 1802 and
hybrid due-to module 1812 direct volume module to calculate and allocate the
synergy.
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[00134] Volume decomposition module 1802 comprises base volume module
1820, raw volume contribution module 1822, expected volume module 1824, final
volume contribution module 1826, and input module 1828. Base volume module
1820
calculates the base volume as described in Figure 1, processing block 406. Raw
volume contribution module 1822 calculates the raw volume contributions for
each of
the activities with a reference value as described in Figure 4, processing
blocks 410-
418. Expected volume module 1824 calculates the expected volume as described
in
Figure 4, processing block 406. In one embodiment, base volume module 1820,
raw
volume contribution module 1822, and expected volume modules direct volume
module 1810 to calculate the appropriate volume. Final volume contribution
1826
adds determines the allocated synergy and adds it to each of the raw volume
contributions as described Figure 4, processing block 420. In one embodiment,
final
volume module 1826 uses synergy module 1806 to determine the synergy
allocations.
Input module accesses the inputs as described in Figure 4, processing block
402.
[00135] Decomposition hierarchy module 1804 comprises atomic decomposition
module 1830 and higher level decomposition module 1832. Atomic decomposition
module 1830 defines and calculates an atomic decomposition level as described
in
Figure 10. Higher level decomposition module 1832 calculates levels of volume
decompositions based on the atomic decomposition as described in Figures 11-
13.
[00136] Synergy module 1806 comprises total synergy module 1840, and synergy
contribution module 1842. Total synergy module 1840 calculates the total
synergy
based on an incremental volume and raw volume contributions as described in
Figure
6, processing block 604. Synergy contribution module 1842 determines the
individual
synergy contribution for each of the input activities as described in Figure
6,
processing block 606. In one embodiment, synergy contribution module 1842
determines the individual synergy contribution based on the absolute value of
each
raw volume contribution.
[00137] Compound due-to module 1810 comprises hybrid module 1850, difference
decomposition module 1852, and distribution due-to module 1854. Hybrid module
calculates the volume variance for non-distribution activities that do not
have a
reference value as described in Figure 17, processing block 1706. Difference
decomposition module 1852 calculates the volume variance for non-distribution
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activities that have a reference values as described in Figure 17, processing
block
1704. Distribution due-to module 1854 calculates the volume variance for
distribution
activities as described in Figure 17, processing block 1708.
[00138] Hybrid Due-to module 1812 comprises start/end volume module 1860,
base volume module change module 1862, raw volume contribution module 1864,
synergy module 1866, model error module 1868, and input module 1870. Start/end
volume module calculates the start and end volume as described in Figure 16,
processing block 1604. Base change volume module 1862 calculates the base
volume
change as described in Figure 16, processing block 1606. Raw volume
contribution
module calculates the raw volume contribution change as described in Figure
16,
processing blocks 1608-1614. Synergy module 1866 calculates the synergy for
each
of the activities as described in Figure 16, processing block 1616. Model
error module
1868 calculates the model error as described in Figure 16, processing block
1618.
Input module 1870 access the input parameters as described in Figure 16,
processing
block 1602.
[00139] The method described above calculates a due-to report for a single
matched pair of predicted volumes (scenarios), e.g. for a single product in
single
location for two different weeks. If a due-to report is desired for a set
matched pairs of
scenarios (e.g. multiple Products/Locations for two different weeks), the due-
to report
for all individual volumes are calculated and the volume contributions to the
respective activities are added.
[00140] The processes described herein may constitute one or more programs
made
up of machine-executable instructions. Describing the process with reference
to the
flow diagrams in Figures 4, 6, 10-13, 16, and 17 enables one skilled in the
art to
develop such programs, including such instructions to carry out the operations
(acts)
represented by logical processing blocks on suitably configured machines (the
processor of the machine executing the instructions from machine-readable
media,
such as RAM (e.g. DRAM), ROM, nonvolatile storage media (e.g. hard drive or CD-
ROM), etc.). The machine-executable instructions may be written in a computer
programming language or may be embodied in firmware logic or in hardware
circuitry. If written in a programming language conforming to a recognized
standard,
such instructions are executed on a variety of hardware platforms and for
interface to a
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variety of operating systems. In addition, the present invention is not
described with
reference to any particular programming language. It will be appreciated that
a variety
of programming languages may be used to implement the teachings of the
invention
as described herein. Furthermore, it is common in the art to speak of
software, in one
form or another (e.g., program, procedure, process, application, module,
logic...), as
taking an action or causing a result. Such expressions are merely a shorthand
way of
saying that execution of the software by a machine causes the processor of the
machine to perform an action or produce a result. It will be further
appreciated that
more or fewer processes may be incorporated into the processes illustrated in
the flow
diagrams without departing from the scope of the invention and that no
particular
order is implied by the arrangement of blocks shown and described herein.
[00141] Figure 19 shows several computer systems 1900 that are coupled
together
through a network 1902, such as the Internet. The term "Internet" as used
herein
refers to a network of networks which uses certain protocols, such as the
TCP/IP
protocol, and possibly other protocols such as the hypertext transfer protocol
(HTTP)
for hypertext markup language (HTML) documents that make up the World Wide
Web (web). The physical connections of the Internet and the protocols and
communication procedures of the Internet are well known to those of skill in
the art.
Access to the Internet 1902 is typically provided by Internet service
providers (ISP),
such as the ISPs 1904 and 1906. Users on client systems, such as client
computer
systems 1912, 1916, 1924, and 1926 obtain access to the Internet through the
Internet
service providers, such as ISPs 1904 and 1906. Access to the Internet allows
users of
the client computer systems to exchange information, receive and send e-mails,
and
view documents, such as documents which have been prepared in the HTML format.
These documents are often provided by web servers, such as web server 1908
which is
considered to be "on" the Internet. Often these web servers are provided by
the ISPs,
such as ISP 1904, although a computer system can be set up and connected to
the
Internet without that system being also an ISP as is well known in the art.
[00142] The web server 1908 is typically at least one computer system which
operates as a server computer system and is configured to operate with the
protocols
of the World Wide Web and is coupled to the Internet. Optionally, the web
server
1908 can be part of an ISP which provides access to the Internet for client
systems.
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The web server 1908 is shown coupled to the server computer system 1910 which
itself is coupled to web content 1912, which can be considered a form of a
media
database. It will be appreciated that while two computer systems 1908 and 1910
are
shown in Figure 19, the web server system 1908 and the server computer system
1910
can be one computer system having different software components providing the
web
server functionality and the server functionality provided by the server
computer
system 1910 which will be described further below.
[00143] Client computer systems 1912, 1916, 1924, and 1926 can each, with the
appropriate web browsing software, view HTML pages provided by the web server
1908. The ISP 1904 provides Internet connectivity to the client computer
system
1912 through the modem interface 1914 which can be considered part of the
client
computer system 1912. The client computer system can be a personal computer
system, a network computer, a Web TV system, a handheld device, or other such
computer system. Similarly, the ISP 1906 provides Internet connectivity for
client
systems 1916, 1924, and 1926, although as shown in Figure 19, the connections
are
not the same for these three computer systems. Client computer system 1916 is
coupled through a modem interface 1918 while client computer systems 1924 and
1926 are part of a LAN. While Figure 19 shows the interfaces 1914 and 1918 as
generically as a "modem," it will be appreciated that each of these interfaces
can be an
analog modem, ISDN modem, cable modem, satellite transmission interface, or
other
interfaces for coupling a computer system to other computer systems. Client
computer systems 1924 and 1916 are coupled to a LAN 1922 through network
interfaces 1930 and 1932, which can be Ethernet network or other network
interfaces.
The LAN 1922 is also coupled to a gateway computer system 1920 which can
provide
firewall and other Internet related services for the local area network. This
gateway
computer system 1920 is coupled to the ISP 1906 to provide Internet
connectivity to
the client computer systems 1924 and 1926. The gateway computer system 1920
can
be a conventional server computer system. Also, the web server system 1908 can
be a
conventional server computer system.
[00144] Alternatively, as well-known, a server computer system 1928 can be
directly coupled to the LAN 1922 through a network interface 1934 to provide
files
1936 and other services to the clients 1924, 1926, without the need to connect
to the
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Internet through the gateway system 1920. Furthermore, any combination of
client
systems 1912, 1916, 1924, 1926 may be connected together in a peer-to-peer
network
using LAN 1922, Internet 1902 or a combination as a communications medium.
Generally, a peer-to-peer network distributes data across a network of
multiple
machines for storage and retrieval without the use of a central server or
servers. Thus,
each peer network node may incorporate the functions of both the client and
the server
described above.
[00145] The following description of Figure 20 is intended to provide an
overview
of computer hardware and other operating components suitable for performing
the
processes of the invention described above, but are not intended to limit the
applicable
environments. One of skill in the art will immediately appreciate that the
embodiments of the invention can be practiced with other computer system
configurations, including set-top boxes, hand-held devices, multiprocessor
systems,
microprocessor-based or programmable consumer electronics, network PCs,
minicomputers, mainframe computers, and the like. The embodiments of the
invention can also be practiced in distributed computing environments where
tasks are
performed by remote processing devices that are linked through a
communications
network, such as peer-to-peer network infrastructure.
[00146] Figure 20 shows one example of a conventional computer system that can
be used in one or more aspects of the invention. The computer system 2000
interfaces
to external systems through the modem or network interface 2002. It will be
appreciated that the modem or network interface 2002 can be considered to be
part of
the computer system 2000. This interface 2002 can be an analog modem, ISDN
modem, cable modem, token ring interface, satellite transmission interface, or
other
interfaces for coupling a computer system to other computer systems. The
computer
system 2002 includes a processing unit 2004, which can be a conventional
microprocessor such as an Intel Pentium microprocessor or Motorola Power PC
microprocessor. Memory 2008 is coupled to the processor 2004 by a bus 2006.
Memory 2008 can be dynamic random access memory (DRAM) and can also include
static RAM (SRAM). The bus 2006 couples the processor 2004 to the memory 2008
and also to non-volatile storage 2014 and to display controller 2010 and to
the
input/output (UO) controller 2016. The display controller 2010 controls in the
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conventional manner a display on a display device 2012 which can be a cathode
ray
tube (CRT) or liquid crystal display (LCD). The input/output devices 2018 can
include a keyboard, disk drives, printers, a scanner, and other input and
output
devices, including a mouse or other pointing device. The display controller
2010 and
the UO controller 2016 can be implemented with conventional well known
technology. A digital image input device 2020 can be a digital camera which is
coupled to an UO controller 2016 in order to allow images from the digital
camera to
be input into the computer system 2000. The non-volatile storage 2014 is often
a
magnetic hard disk, an optical disk, or another form of storage for large
amounts of
data. Some of this data is often written, by a direct memory access process,
into
memory 2008 during execution of software in the computer system 2000. One of
skill
in the art will immediately recognize that the terms "computer-readable
medium" and
"machine-readable medium" include any type of storage device that is
accessible by
the processor 2004 or by other data processing systems such as cellular
telephones or
personal digital assistants or MP3 players, etc. and also encompass a carrier
wave that
encodes a data signal.
[00147] Network computers are another type of computer system that can be used
with the embodiments of the present invention. Network computers do not
usually
include a hard disk or other mass storage, and the executable programs are
loaded
from a network connection into the memory 2008 for execution by the processor
2004. A Web TV system, which is known in the art, is also considered to be a
computer system according to the embodiments of the present invention, but it
may
lack some of the features shown in Figure 20, such as certain input or output
devices.
A typical computer system will usually include at least a processor, memory,
and a
bus coupling the memory to the processor.
[00148] It will be appreciated that the computer system 2000 is one example of
many possible computer systems, which have different architectures. For
example,
personal computers based on an Intel microprocessor often have multiple buses,
one
of which can be an input/output (UO) bus for the peripherals and one that
directly
connects the processor 2004 and the memory 2008 (often referred to as a memory
bus). The buses are connected together through bridge components that perform
any
necessary translation due to differing bus protocols.
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[00149] It will also be appreciated that the computer system 2000 is
controlled by
operating system software, which includes a file management system, such as a
disk
operating system, which is part of the operating system software. One example
of an
operating system software with its associated file management system software
is the
family of operating systems known as WINDOWS OPERATING SYSTEM from
Microsoft Corporation in Redmond, Washington, and their associated file
management systems. The file management system is typically stored in the non-
volatile storage 2014 and causes the processor 2004 to execute the various
acts
required by the operating system to input and output data and to store data in
memory,
including storing files on the non-volatile storage 2014.
Alternative Embodiments
[00150] A computer implemented method comprising accessing a response model
and a first plurality of activities, the response model and first plurality of
activities
used to compute a business metric decomposition, wherein the first plurality
of
activities defines an atomic decomposition level of the business metric, and
wherein
the atomic decomposition level is a base level of a set of different
decompositions
using different pluralities of activities and the set of different
decompositions is
consistent with the atomic decomposition level.
[00151] The computer implemented method, further comprising computing a base
business metric value using the response model and the first plurality of
activities,
wherein each of the first plurality of activities is set to a corresponding
reference
value.
[00152] The computer implemented method, further comprising computing a first
contribution to the business metric for each of the first plurality of
activities using the
response model by setting that activity to one of a corresponding reference
and current
values and setting others of the first plurality of activities to a value
state opposite of
that activity.
[00153] The computer implemented method, further comprising computing a
second decomposition level of the business metric using a second plurality of
activities, wherein the second plurality of activities is based on the first
plurality of
activities and the second decomposition is based on the first decomposition.
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[00154] The computer implemented method, further comprising computing a third
decomposition level of the business metric using a third plurality of
activities, wherein
the third plurality of activities is based on a fourth plurality of activities
and the third
decomposition is based on a lower level decomposition, wherein the fourth
plurality
of activities corresponds to the lower level decomposition.
[00155] The computer implemented method, wherein each of the first plurality
of
activities has a reference value.
[00156] The computer implemented method, wherein the reference value for each
of the first plurality of activities adds a zero contribution to the atomic
decomposition
level for that activity.
[00157] The computer implemented method, wherein each of the first plurality
of
activities is an indivisible set of activities.
[00158] The computer implemented method, wherein the business metric is one of
sales volume, revenue, profit, and market share.
[00159] While various embodiments of the invention have been described,
alternative embodiments of the invention can operate differently. For
instance, while
the flow diagrams in the figures show a particular order of operations
performed by
certain embodiments of the invention, it should be understood that such order
is
exemplary (e.g., alternative embodiments may perform the operations in a
different
order, combine certain operations, overlap certain operations, etc.).
[00160] While the invention has been described in terms of several
embodiments,
those skilled in the art will recognize that the invention is not limited to
the
embodiments described, can be practiced with modification and alteration
within the
spirit and scope of the appended claims. The description is thus to be
regarded as
illustrative instead of limiting.
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