U.S. patent application number 11/926320 was filed with the patent office on 2008-06-26 for system and method for analyzing and correcting retail data.
Invention is credited to CHERYL G. BERGEON, ARVID C. JOHNSON, MICHAEL W. KRUGER.
Application Number | 20080154843 11/926320 |
Document ID | / |
Family ID | 34861204 |
Filed Date | 2008-06-26 |
United States Patent
Application |
20080154843 |
Kind Code |
A1 |
KRUGER; MICHAEL W. ; et
al. |
June 26, 2008 |
SYSTEM AND METHOD FOR ANALYZING AND CORRECTING RETAIL DATA
Abstract
A computer system and method is disclosed that analyzes and
corrects retail data. The system and method includes several client
workstations and one or more servers coupled together over a
network. A database stores various data used by the system. A
business logic server uses competitive and complementary fusion to
analyze and correct some of the data sources stored in database
server. The data fusion process itself is an iterative
one--utilizing both competitive and complementary fusion methods.
In competitive fusion, two or more data sources that provide
overlapping attributes are compared against each other. More
accurate/reliable sources are used to correct less
accurate/reliable sources. In complementary fusion, relationships
modeled where data sources overlap are projected to areas of the
data framework in which fewer sources exist--enhancing the
accuracy/reliability of those fewer sources even in the absence of
the other sources upon which the models were based.
Inventors: |
KRUGER; MICHAEL W.;
(GLENVIEW, IL) ; BERGEON; CHERYL G.; (ARLINGTON
HEIGHTS, IL) ; JOHNSON; ARVID C.; (FRANKFORT,
IL) |
Correspondence
Address: |
STRATEGIC PATENTS P.C..
C/O PORTFOLIOIP, P.O. BOX 52050
MINNEAPOLIS
MN
55402
US
|
Family ID: |
34861204 |
Appl. No.: |
11/926320 |
Filed: |
October 29, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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10783323 |
Feb 20, 2004 |
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11926320 |
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Current U.S.
Class: |
1/1 ;
707/999.002; 707/E17.017 |
Current CPC
Class: |
G06Q 30/0204 20130101;
G06Q 10/0637 20130101; G06Q 30/02 20130101; G06Q 10/063 20130101;
G06Q 30/0201 20130101 |
Class at
Publication: |
707/2 ;
707/E17.017 |
International
Class: |
G06F 7/06 20060101
G06F007/06; G06F 17/30 20060101 G06F017/30 |
Claims
1. A method comprising: identifying a plurality of data sources,
wherein at least a first data source is more accurate than a second
data source; identifying a plurality of overlapping attribute
segments to use for comparing the data sources; calculating a
factor as a function of each of the plurality of overlapping
attribute segments; and using the factors to update a first group
of values in the second data source to reduce bias.
Description
BACKGROUND
[0001] The present invention relates to computer software, and more
particularly, but not exclusively, relates to systems and methods
for analyzing and correcting retail data.
[0002] The measurement of sales in retail channels can be done via
a variety of methods. Initially, sample-based audits of consumer
purchases at check-out were extensively utilized--but were costly
and subject to significant potential inaccuracies. With the advent
and accuracy improvement in scanner-based point of sale (POS) data,
tracking services such as those offered by Information Resources,
Inc. (IRI), and A.C. Nielsen (ACN) are able to provide
highly-granular (in terms of item, venue, and time),
highly-accurate measurement of sales in several retail
channels--including food/grocery, drug, mass merchandise,
convenience, and military commissary. These POS-based offerings can
be sample-based--i.e., rely on a statistically determined subset of
the target population--or census-based--i.e., use all available
data from all available venues.
[0003] While POS-based measurement offerings do an excellent job of
reporting "what" sold, they provide little insight into "why"
something sold--since they provide no consumer-level data. To fill
this need, market research companies such as IRI and ACN have
recruited national consumer panels--in which panelists report their
households' purchases on a regular basis. This longitudinal sample
allows the development of much deeper consumer insights (e.g.,
brand switching, trial and repeat, etc.).
[0004] However, consumer panels are not without their problems. As
with any sample-based survey, consumer panels are subject to two
types of errors--i.e., sampling errors and biases--where the total
error is given by the sum: (Total Error).sup.2=(Sampling
Error).sup.2+(Bias).sup.2.
[0005] Sampling errors are those errors attributable to the normal
(random) variation that would be expected due to the fact that, by
the very act of sampling, measurements are not being taken from the
entire population. Sampling errors can be reduced by increasing the
sample size since the standard deviation of the sampling
distribution (often referred to as the "standard error") decreases
with the square root of the sample size.
[0006] Biases are systematic errors that affect any sample taken by
a particular sampling method. Because these errors are systematic,
they are not affected by the size of the sample. Examples of panel
biases include, but are not limited to: [0007] Recruitment Bias--in
which households recruited to participate in the panel are not
representative of the target population (e.g., the overall
population of the United States); [0008] Self-Selection Bias--in
which households who choose to participate in the panel have
slightly different buying habits than the average household (e.g.,
an orientation toward using promotions or adopting new products);
[0009] Panelist Turnover Bias--in which the reporting effectiveness
(accuracy and consistency) of panelists may vary over the time
period in which they participate in the panel; [0010] Hereditary
Bias--in which individuals within a household share a tendency
toward certain behaviors or medical conditions; [0011] Compliance
Bias--in which certain purchases or purchase occasions are
consistently underreported by panelists; [0012] Item Placement
Bias--in which panelists report products purchased that have not
been accurately captured and/or classified in the hierarchy
maintained by the data collector; and [0013] Projection Bias--in
which the weighting or projection system cannot fully adjust all
geo-demographics or is stressed by over- or under-sampled segments
of the target population.
[0014] While both bias and sampling error are present in consumer
panel data, for panels of a size significant enough to be of use in
tracking consumer purchases (e.g., the IRI and ACN panels), the
vast majority of the error that is present is due to bias. Further,
since bias is unaffected by sample size, the negative impact of
bias relative to the negative impact of sampling error worsens as
the panel size increases.
[0015] The negative impact of bias is substantially larger than
that of sampling error for most products. Increasing the size of
the sample (i.e., the size of the panel) will reduce only the
sampling error and may, in fact, worsen any bias that may be
present. Given the sizes of today's consumer panels, there is
limited advantage to be gained by increasing the size of the
panel--since over 90% of the total error is often due to
non-sampling errors (i.e., bias).
[0016] There has been little progress in the area of developing a
systematic method of identifying and quantifying these biases.
Further advancements are needed in this area.
[0017] Another area of concern in retail sales measurement is
"coverage". Coverage includes both the number of channels in which
measurements are reported and the business usefulness of those
measurements. While Information Resources, Inc.'s (IRI's)
point-of-sale (POS) based services provide excellent coverage of
the Food/Grocery, Drug, Mass (excluding WALMART.RTM.), Convenience,
and Military channels, these channels may account for only 50% of a
manufacturer's sales--and as little as 20% of its sales growth.
Non-tracked, growth channels--e.g., Club, Dollar,
WALMART.RTM.--are, thus, becoming an increasingly important part of
manufacturers' businesses while at the same time having little data
available in the way of actionable sales measurement information.
Further advancements are also needed in this area.
SUMMARY
[0018] One form of the present invention is a unique system for
analyzing and correcting retail data.
[0019] Other forms include unique systems and methods to identify,
quantify, and correct consumer panel biases. Yet another form
includes unique systems and methods to model relationships where
data sources overlap to project values in areas in which fewer
sources exist.
[0020] Another form includes operating a computer system that has
several client workstations and servers coupled together over a
network. At least one server is a database server that stores sale
data for various data sources, product identifier and attribute
categorizations, calculated factors, and other data. External
sources can be used to feed the data store on a scheduled or
on-demand basis. At least one server is a server that contains
business logic for analyzing and correcting some of the data
sources stored in database server. Some client workstations can be
used to administer settings used in process of analyzing and
correcting the data sources. Other client workstations can be used
to view the corrected and/or uncorrected data in a
multi-dimensional format using a graphical user interface.
[0021] Another form includes providing a computer system that uses
multiple data sources to support inferences that would not be
feasible based upon any single data source when used alone. Sales
are positioned along product, venue, and time dimension
hierarchies. Characteristics of the data source determine the level
of aggregation at which the data can be positioned in the
framework. For example, POS data may be available weekly in a
particular channel; however, direct store delivery (DSD) data may
be available at a daily level, and still other measures may be
available only at a monthly or quarterly level. The situation is
similar along the product and venue dimensions--ranging from the
specificity of the sale of a particular UPC-coded item at a
particular store to the generality of total category sales within a
channel (across all geographies).
[0022] Once this data framework is populated, the data fusion
process itself is an iterative one, utilizing both competitive and
complementary fusion methods. In "competitive fusion", two or more
data sources that provide overlapping measurements along at least
one dimension are compared ("competed") against each other at some
level of aggregation along the product, venue, and time dimensions.
More accurate/reliable sources are used to correct less
accurate/reliable sources. In "complementary fusion", relationships
modeled where data sources overlap are projected to areas of the
data framework in which fewer (or even a single) sources
exist--enhancing the accuracy/reliability of those fewer (or
single) sources even in domains where data from of the other
sources upon which the models were based do not exist. The process
is iterative in that the competitive and complementary fusion
methodologies can be repeated at varying level of aggregation of
the data framework.
[0023] Another form includes providing a method for identifying and
quantifying biases in consumer panel data so that the inherent
utility of the consumer panel data may be enhanced. This method is
termed competitive fusion. At least two data sources are used, with
at least one assumed to be more accurate than the other--e.g.,
scanner-based POS data and consumer panel purchase data. The data
sources are aligned along a common framework (i.e., data model or
hierarchy) along the dimensions of product (item), venue (channel
and/or geography), and/or time, with aggregation along these
dimensions as necessary. The attributes associated with the
framework are identified along which the framework may be
characterized. The data sources are compared along these
attributes--quantifying the impact of the attributes on the
less-accurate data source.
[0024] After these biases have been identified and quantified, the
usefulness of the consumer panel data may be enhanced. The effect
of the biases may be corrected for via modeling; i.e., the raw data
may be adjusted to reduce or eliminate the effect of the biases.
Furthermore, as appropriate, panel management practices may be
changed in order to remove or lessen the source of bias in the
panel itself.
[0025] Yet another form of the present invention includes providing
a method for using complementary fusion to "project" the results
and relationships from the competitive fusion method onto consumer
panel data in a channel with incomplete/less data than desired
(e.g. data from WALMART.RTM.) to help enhance the accuracy of the
Panel data source. At this point, competitive fusion may be used
again in several possible ways and at several levels of aggregation
along the venue, time, and/or product dimensions in order to
develop independent estimates against which the complementary-fused
estimate may be competed: [0026] Publicly available data about the
incomplete channel (e.g., channel reports, reported sales and
financials, store databases, geo-demographics, etc.) may be used to
develop an independent venue (channel) estimate. [0027] Publicly
available data about the category of interest (e.g., category
studies, industry reports, reported sales/financials, etc.) may be
used to develop an independent category estimate. [0028] Private
data from manufacturer-partners (e.g., shipment data, delivery
data, retailer-supplied data, etc.) may be used to develop
independent channel and category estimates. Due to the potentially
sensitive nature of some of these data sources, this competitive
fusion may be performed inside a manufacturer's facility--as an
auxiliary input to the baseline model. [0029] Private data from
retailer-partners within a Collaborative Retail Exchange may be
used in some venues to develop independent channel and category
estimates.
[0030] Yet other forms, embodiments, objects, advantages, benefits,
features, and aspects of the present invention will become apparent
from the detailed description and drawings contained herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0031] FIG. 1 is a diagrammatic view of a computer system of one
embodiment of the present invention.
[0032] FIG. 2 is a multi-dimensional diagram illustrating the data
space used by the system of FIG. 1.
[0033] FIG. 3 is a block diagram illustrating selected data sources
that are used by the system of FIG. 1.
[0034] FIG. 4 is a high-level process flow diagram for the system
of FIG. 1.
[0035] FIG. 5A is a first part process flow diagram for the system
of FIG. 1 demonstrating the stages involved in performing
competitive and complementary fusion.
[0036] FIG. 5B is a second part process flow diagram for the system
of FIG. 1 demonstrating the stages involved in performing
competitive and complementary fusion.
[0037] FIG. 6A is a first part process flow diagram for the system
of FIG. 1 demonstrating a preferred process for calculating and
applying factors in competitive fusion.
[0038] FIG. 6B is a second part process flow diagram for the system
of FIG. 1 demonstrating a preferred process for calculating and
applying factors in competitive fusion.
[0039] FIG. 6C is a third part process flow diagram for the system
of FIG. 1 demonstrating a preferred process for calculating and
applying factors in competitive fusion.
[0040] FIG. 7A is a first part process flow diagram for the system
of FIG. 1 demonstrating an alternate process for calculating and
applying factors in competitive fusion.
[0041] FIG. 7B is a second part process flow diagram for the system
of FIG. 1 demonstrating an alternate process for calculating and
applying factors in competitive fusion.
[0042] FIG. 7C is a third part process flow diagram for the system
of FIG. 1 demonstrating an alternate process for calculating and
applying factors in competitive fusion.
[0043] FIG. 8 is a process flow diagram for the system of FIG. 1
demonstrating the stages involved in performing complementary
fusion.
[0044] FIG. 9 is a process flow diagram for the system of FIG. 1
demonstrating the stages involved in iteratively performing
competitive and complementary fusion steps.
[0045] FIG. 10 is a process flow diagram for the system of FIG. 1
demonstrating the stages involved in calculating blended factors
where multiple factor measures are available for the same
factor.
[0046] FIG. 11 is a data table illustrating hypothetical data
elements stored in the database of FIG. 1 to be used in accordance
with the procedure of FIG. 6.
[0047] FIG. 12 is a data table illustrating hypothetical data
elements that are stored in the database of FIG. 1 and are adjusted
according to factors for a first attribute in accordance with the
procedure of FIG. 6.
[0048] FIG. 13 is a data table illustrating hypothetical data
elements that are stored in the database of FIG. 1 and are adjusted
according to factors for a second attribute in accordance with the
procedure of FIG. 6.
[0049] FIG. 14 is a data table illustrating hypothetical data
elements that are stored in the database of FIG. 1 and are adjusted
according to factors for a third attribute in accordance with the
procedure of FIG. 6.
[0050] FIG. 15 is a data table illustrating hypothetical data
elements stored in the database of FIG. 1, with attribute
summaries, and used in accordance with the procedure of FIG. 7.
[0051] FIG. 16 is a data table illustrating hypothetical data
elements that are stored in the database of FIG. 1 and are adjusted
according to factors for three attributes in accordance with the
procedure of FIG. 7.
[0052] FIG. 17 is a data table illustrating hypothetical data
elements by retailer that are stored in the database of FIG. 1 and
used in accordance with the complementary fusion procedure of FIG.
8.
[0053] FIG. 18 is a data table illustrating hypothetical data
elements by retailer that are stored in the database of FIG. 1,
adjusted using complementary fusion according to the factors
calculated in accordance with the procedure of FIG. 7, as described
in the procedure of FIG. 8.
[0054] FIG. 19 is a data table illustrating hypothetical data
elements by retailer that are stored in the database of FIG. 1 and
are used to perform another iteration of competitive fusion,
including calculating blended factors, as described in the
procedures of FIG. 9 and FIG. 10.
[0055] FIG. 20 is a data table illustrating hypothetical data
elements by retailer that are stored in the database of FIG. 1 and
updated based upon the blended factor, as described in the
procedures of FIG. 9 and FIG. 10.
[0056] FIG. 21 is a data table illustrating hypothetical real,
original, and corrected values stored in the database of FIG. 1 to
show how the competitive and complementary fusion process helped
improve the data, as described in the procedures of FIG. 9.
[0057] FIG. 22 is a simulated screen of a user interface for one or
more client workstations of FIG. 1 that allows a user to view the
multi-dimensional elements in the database, as described in the
procedures of FIG. 4 and FIG. 5.
DETAILED DESCRIPTION OF SELECTED EMBODIMENTS
[0058] For the purposes of promoting an understanding of the
principles of the invention, reference will now be made to the
embodiment illustrated in the drawings and specific language will
be used to describe the same. It will nevertheless be understood
that no limitation of the scope of the invention is thereby
intended. Any alterations and further modifications in the
described embodiments, and any further applications of the
principles of the invention as described herein are contemplated as
would normally occur to one skilled in the art to which the
invention relates.
[0059] One embodiment of the present invention includes a unique
system for identifying, quantifying, and correcting consumer panel
biases, and then using overlapping areas of the data sources to
project values in areas where fewer or less complete sources exist.
FIG. 1 is a diagrammatic view of computer system 20 of one
embodiment of the present invention. Computer system 20 includes
computer network 22. Computer network 22 couples together a number
of computers 21 over network pathways 23a-e. More specifically,
system 20 includes several servers, namely business logic server 24
and database server 25. System 20 also includes external data
sources 26, which in various embodiments include other computers,
files, electronic and/or paper data sources. External data sources
26 are optionally coupled to network over pathway 23f. System 20
also includes client workstations 30a, 30b, and 30c (collectively
client workstations 30). While computers 21 are each illustrated as
being either a server or a client, it should be understood that any
of computers 21 may be arranged to provide both a client and server
functionality, solely a client functionality, or solely a server
functionality. Furthermore, it should be understood that while six
computers 21 are illustrated, more or fewer may be utilized in
alternative embodiments.
[0060] Computers 21 include one or more processors or CPUs (50a,
50b, 50c, 50d, and 50e, respectively) and one or more types of
memory (52a, 52b, 52c, 52d, and 52e, respectively). Each memory
52a, 52b, 52c, 52d, and 52e includes a removable memory device.
Each processor may be comprised of one or more components
configured as a single unit. Alternatively, when of a
multi-component form, a processor may have one or more components
located remotely relative to the others. One or more components of
each processor may be of the electronic variety defining digital
circuitry, analog circuitry, or both. In one embodiment, each
processor is of a conventional, integrated circuit microprocessor
arrangement, such as one or more PENTIUM III or PENTIUM 4
processors supplied by INTEL Corporation of 2200 Mission College
Boulevard, Santa Clara, Calif. 95052, USA.
[0061] Each memory (removable or generic) is one form of
computer-readable device. Each memory may include one or more types
of solid-state electronic memory, magnetic memory, or optical
memory, just to name a few. By way of non-limiting example, each
memory may include solid-state electronic Random Access Memory
(RAM), Sequentially Accessible Memory (SAM) (such as the First-In,
First-Out (FIFO) variety or the Last-In-First-Out (LIFO) variety),
Programmable Read-Only Memory (PROM), Electronically Programmable
Read-Only Memory (EPROM), or Electrically Erasable Programmable
Read-Only Memory (EEPROM); an optical disc memory (such as a DVD or
CD ROM); a magnetically encoded hard disc, floppy disc, tape, or
cartridge media; or a combination of any of these memory types.
Also, each memory may be volatile, nonvolatile, or a hybrid
combination of volatile and nonvolatile varieties.
[0062] Although not shown in FIG. 1 to preserve clarity, in one
embodiment each computer 21 is coupled to a display. Computers 21
may be of the same type, or be a heterogeneous combination of
different computing devices. Likewise, the displays may be of the
same type, or a heterogeneous combination of different visual
devices. Although again not shown to preserve clarity, each
computer 21 may also include one or more operator input devices
such as a keyboard, mouse, track ball, light pen, and/or
microtelecommunicator, to name just a few representative examples.
Also, besides display, one or more other output devices may be
included such as loudspeaker(s) and/or a printer. Various display
and input device arrangements are possible.
[0063] Computer network 22 can be in the form of a wired or
wireless Local Area Network (LAN), Municipal Area Network (MAN),
Wide Area Network (WAN) such as the Internet, a combination of
these, or such other network arrangement as would occur to those
skilled in the art. The operating logic of system 20 can be
embodied in signals transmitted over network 22, in programming
instructions, dedicated hardware, or a combination of these. It
should be understood that more or fewer computers 21 can be coupled
together by computer network 22.
[0064] In one embodiment, system 20 operates at one or more
physical locations where business logic server 24 is configured as
a server that hosts and runs application business logic 33,
database server 25 is configured as a database 34 that stores
reference data 35 (e.g. product identifiers 36a, attributes 36b,
and a dictionary 36c), at least two retail data sources (such as
point-of-sale and panel data) 38, calculated factors 39, and other
data 40. In one embodiment, external data 26 is imported to
database server 25 from a mainframe extract file that is generated
on a periodic basis. Various other scenarios are also possible for
using and importing external data to database server 25. In another
embodiment, external data sources are not used. In one embodiment,
database 34 of database server 25 is a relational database and/or a
data warehouse. Alternatively or additionally, database 34 can be a
series of files, a combination of database tables and external
files, calls to external web or other services that return data,
and various other arrangements for accessing data for use in a
program as would occur to one of ordinary skill in the art. Client
workstations 30 are configured for providing one or more user
interfaces to allow a user to modify settings used by business
logic 33 and/or to view the retail data sources 38 of database 34
in a multi-dimensional format. Typical applications of system 20
would include more or fewer client workstations of this type at one
or more physical locations, but three have been illustrated in FIG.
1 to preserve clarity. Furthermore, although two servers are shown,
it will be appreciated by those of ordinary skill in the art that
the one or more features provided by business logic server 24 and
database server 25 could be provided on the same computer or
varying other arrangements of computers at one or more physical
locations and still be within the spirit of the invention. Farms of
dedicated servers could also be provided to support the specific
features if desired.
[0065] FIG. 2 is a multi-dimensional cube 60 that illustrates a way
of conceptually thinking about the elements stored in database 34
of system 20. Cube 60 contains three dimensions: complexity 62,
sources 64, and aggregation 66. In one embodiment, at least part of
the data in database 34 is categorized according to complexity 62,
sources 64, and aggregation 66 axes of multi-dimensional cube 60
for analysis, viewing, and/or reporting. Cube 60 helps illustrate
the concept that the aggregation dimension 66 is multi-dimensional,
although other dimensions could be used than illustrated. Examples
of elements of the source dimension 64 includes client (internal)
data 65a, scanning (point-of-sale) data 65b, panel data 65c, audit
data 66d, and other (external) data 66e, as a few examples.
Examples of elements of the aggregation dimension 66 include time
67a, item (product) 67b, channel (venue) 67c, geography (venue)
67d, and other 67e, to name a few examples. Various dimensions of
cube 60 are used in the competitive fusion and complementary fusion
processes described herein.
[0066] FIG. 3 is a block diagram illustrating further examples of
the one or more retail data sources (36 in FIG. 1 and 64 in FIG. 2)
that can be used by the system of FIG. 1 in the competitive fusion
and complementary fusion processes described herein. Point-of-sale
data 70, consumer panel data 72, audit/survey data 74 including
causal (promotional) data, shipment data 76 from anywhere in supply
chain, population census data 78 including geo-demographic data,
store universe data 80, other data sources 82, and specialty panels
84 are examples of the types of data that can be used with system
20. The types of data that can be used with system 20 are not
limited to traditional retailers. For example, data collected
during any part of the supply chain could be used as a data
source.
[0067] Referring also to FIG. 4, one embodiment for implementing
system 20 is illustrated in flow chart form as procedure 150, which
demonstrates a high-level process for the system of FIG. 1 and will
be discussed in more detail below. FIG. 4 illustrates the
high-level procedures for performing "competitive fusion" and
"complementary fusion". In "competitive fusion", two or more data
sources that provide overlapping measurements along at least one
dimension are compared ("competed") against each other at some
level of aggregation along the product, venue, and/or time
dimensions. More accurate/reliable sources are used to correct less
accurate/reliable sources. In "complementary fusion", relationships
modeled where data sources overlap are projected to areas of the
data framework in which fewer (or even a single) sources
exist--enhancing the accuracy/reliability of those fewer (or
single) sources even in domains where data from of the other
sources upon which the models were based do not exist. The process
is iterative in that the competitive and complementary fusion
methodologies can be repeated at varying level of aggregation of
the data framework.
[0068] In one form, procedure 150 is at least partially implemented
in the operating logic of system 20. Procedure 150 begins with
business logic server 24 identifying at least two data sources,
with at least one data source being more accurate than another
(stage 152). At least one data source (see e.g. 36 in FIG. 1 and 64
in FIG. 2) is used as the "reference" data source and another is
used as the "target" data source with the biases to be identified
and quantified. In one embodiment, the reference data source is
more accurate than the target data source. For purposes of the
tracking of sales in retail channels, scanner-based point-of-sale
(POS) data is typically a good "reference" source, due to its
inherent accuracy and high level of granularity along the
dimensions of time, venue, and product. Alternatively or
additionally, manufacturer-supplied shipment data, especially where
such data is based upon direct store delivery (DSD) information,
may be utilized as a "reference" source. As yet another
alternative, retailer-specific data sources (e.g., "frequent
shopper" program data from loyalty cards) are also appropriate.
[0069] Various examples herein illustrate using consumer panel
purchase data as the target data source to be corrected. However,
the current invention can be used with other data sources, such as
sample-based or survey-based data sources whose overall accuracy is
limited by the presence of biases, to name a few non-limiting
examples.
[0070] The product characteristics of the data sources should
ideally be available at the item level, where "item" is by UPC,
SKU, or another unique product identifier. In terms of the venue
characteristics of the data sources, they should ideally be
available at the retailer and market level, where "retailer" is a
store (or chain of stores) within a particular retail channel and
"market" is a geographic construct (e.g., Chicago area). In terms
of the time characteristics of the data sources, they should
ideally be available at the weekly level (or even daily in some
cases), although monthly data (or 4-week "quad" data) or various
other time frames are also acceptable. Where these levels of
granularity are not possible, more aggregated levels of the product
(e.g., "brand"), venue (e.g., "food" or "mass" channel for retailer
and/or "region" or "total U.S." for market), and/or time (e.g.,
quarterly or annual data) dimensions may be used.
[0071] After the data sources have been identified (stage 152),
they are next aligned along a common framework (stage 154), such as
along the item, venue, and/or time dimensions. Depending upon the
characteristics (and quality) of the data sources, some aggregation
along these dimensions may be required in order for the alignment
to be possible. For example, UPC-level POS data may need to be
aggregated at the SKU or even brand level in order to be aligned
with data from other sources (particularly in the cases in which
venue-specific UPCs are involved). Similarly, store-level data may
need to be aggregated at the local market or even regional level in
order to be aligned with consumer panel purchase data. Finally,
weekly (or even daily) POS data may need to be aggregated at the
4-week quad level in order to be aligned with shipment/delivery
data. Various other arrangements for aligning the data along a
common framework are also possible.
[0072] In one embodiment, the item structure is provided by a
multiple-level hierarchy, in which UPCs are the lowest level and
are aggregated along category-related characteristics. Venue
structure is provided along both geographical and channel
dimensions, with FIPS-code-level transactions being aligned along
market and regions and store locations being part of a sub-chain,
chain, and parent store hierarchy. Time structure is presently
provided at the weekly level at the lowest level of aggregation,
with daily data being aggregated at the weekly level before
placement into the structure, although a daily data compatible
structure or other variation is also possible.
[0073] As a result of aligning the data sources along a common
framework (stage 154), overlapping attribute segments of at least
one dimension are available to use for data comparison and
correction. Certain attributes associated with the data sources are
identified along which more detailed comparisons may be made. In
one embodiment, product attributes are available in from reference
data 35 of database 34. For example, one or more pieces of
information from product identifier 36a, attributes 36b, and
dictionary 36c references can be used to access or modify
attributes, attribute hierarchies, and mappings. These attributes
represent category-specific dimensions along which products in that
category may be characterized (e.g., diet vs. regular in carbonated
soft drinks, active ingredient in internal analgesics, product size
in most categories). The term attribute used herein is meant in the
generic sense to cover various types of descriptors.
[0074] Business logic server 24 compares the data sources and
calculates factors for the attributes of at least one element of
the common framework (stage 158). Each segment of a given attribute
will have its own factor, as described in detail herein. The
presence of attribute-related bias may be identified by comparison
of the data sources. In the examples illustrated herein, volumetric
comparisons are made (e.g., equivalent units); however, various
other measures (e.g., dollar sales, actual units) could also be
utilized, as long as the same type of measure is being used for the
comparison. For example, it would not be useful to compare dollar
sales to actual units, but it would be useful to compare dollars to
dollars. The comparison itself is between the value of the target
data source (e.g., projected panel volume) and that of the
reference data source (e.g., POS data). This comparison can be by
way of two-sample inference, regression analysis, or other
statistical tests appropriate for determining whether any
differences between the two data sources are associated with the
attributes along which they have been characterized at a
statistically significant level. Where such differences (biases)
are identified, they are quantified, and factors are calculated for
use in bias correction/adjustment.
[0075] The factors are used to correct bias in the less accurate
data source (stage 160), which in this example is consumer panel
data. By using the factors to correct the bias in the less accurate
"target" data source, the effect of these biases is reduced or
eliminated. These biases can be corrected by adjusting the raw
data, or by way of post-adjustment.
[0076] In "complementary fusion", the factors are also used to
supplement the data that is incomplete in the less complete data
source (stage 162), such as consumer panel data. Incomplete data is
used in a general sense to mean that less data was provided than
desired or that the data is less accurate than desired, to name a
few non-limiting examples. Where highly accurate data (e.g. POS
data) is not provided, less accurate data (e.g. panel data) becomes
more important to analyze and correct. Relationships modeled where
data sources overlap are projected to areas of the data framework
in which fewer (or even a single) sources exist, enhancing the
accuracy and reliability of those fewer (or single) sources even in
domains where data from of the other sources upon which the models
were based do not exist.
[0077] Users and/or reports can access database 34 from one of
client workstations 30 to view/analyze the corrected and adjusted
data (stage 164). Users and/or reports can also access database 34
from one of client workstations 30 to view and/or modify settings
used by system 20 to make data corrections. The steps are repeated
as desired (stage 166). The process then ends at stage 168.
[0078] FIGS. 5A-5B are first and second parts of a process flow
diagram for the system of FIG. 1 demonstrating the stages involved
in performing competitive and complementary fusion using POS and
panel data as the data sources. While in this and other figures,
the first data source (the "source" data source) is described as
being POS data and the second data source (the "target" data
source) is described as being panel data, it will be appreciated
that the system and methodologies can be used with other data
sources as appropriate. In one form, procedure 170 is at least
partially implemented in the operating logic of system 20.
Procedure 170 begins in FIG. 5A with receiving updates for
reference data 35 and/or data sources 38 on a periodic basis (stage
172).
[0079] In one embodiment, a parameter specification for the number
of weeks used in calculating the factors is thirteen, and the
minimum week range included in database 34 is then set to be
thirteen weeks prior to the update week. Database 34 may be built
and maintained using various data sources and can include various
types of data, as would occur to one of ordinary skill in the art.
In one embodiment, system 20 supports the option to pull the
desired period (e.g. all thirteen weeks) of the data sources 38,
append the recent period (e.g. four weeks) needed since the last
factor update to the existing database 34, and/or be able to
recreate the data a week at a time. In such a scenario, for space
conservation, the system can optionally drop the same number of
weeks from the start week of database 34 as were appended to the
end week. For example, if the option was chosen to append the four
weeks needed since the last factor update, the system should drop
the four oldest weeks from the existing database 34 when appending
the four new weeks.
[0080] The received updates to reference data 35 and/or data
sources 38 are stored in database 34 (stage 174). At some point in
time, such as on a scheduled or as-requested basis, the system
determines that data adjustments should be made to correct bias
(decision point 175). Application business logic 33 ensures
reference data 35 and data sources 38 are up to date, and if not,
updates them accordingly (stage 176). Optionally, reference data 35
is reviewed to ensure that the default attributes for the current
category will be appropriate for the client or scenario, and
adjustments are made to reference data 35 as appropriate (stage
177). As one non-limiting example, attribute segments may be
reviewed and translated to more succinct segmentations that better
classify the product identifiers. Other variations are also
possible.
[0081] A product-identifier-to-attribute-segment mapping is
prepared for the product identifiers (e.g. UPC's) (stage 178). If
the attributes are determined to be irrelevant, they can be removed
from further consideration in this process. The attribute table 36b
is a reference table that maps each product identifier 36a to a set
of attribute variables. While UPC's are described as a common
product identifier, other identifiers could also be used. For
example, not every dataset has a UPC, but may have a product
identifier at a higher, lower, or equivalent level. Rules are used
to determine supportable attribute segments and relevant
attributes. In one embodiment, if segment assignment is missing
then the UPC is assigned to a new segment "not supportable." All
segments with less than a 5% share are assigned to "not
supportable." Furthermore, in one embodiment, if the final "not
supportable" category accounts for >50% of the category share,
then the attribute is designated as "irrelevant." Other ways for
determining relevance can also be used, or relevance can simply be
ignored. Stage 178 can be repeated to arrive at the final level of
segments to use (rolled-up or drilled-down) as appropriate.
[0082] Continuing with FIG. 5B, source (e.g. POS) and target (e.g.
panel) data 38 are retrieved from database 34 and summarized by
attribute segments (stage 180). Factors are calculated for
attribute segments (stage 181). The significance of the attribute
segments is determined (stage 182). If any non-significant factors
are determined, the significant attribute factors can be re-aligned
(stage 183). The factors for each attribute segment are applied to
the target (panel) data to correct bias (stage 184). The factors
are also applied to the target (panel) data to correct data that is
incomplete (e.g. less available) (stage 186). The competitive
and/or complementary data fusion steps can be repeated as desired
or appropriate (stage 187). Users and/or reports can access
database 34 from one of client workstations 30 to view/analyze the
corrected and adjusted data (stage 188). The procedure 170 then
ends at stage 190. FIGS. 6-10 illustrate the competitive and
complementary fusion stages in further detail.
[0083] FIGS. 6A-6C are first, second, and third parts of a process
flow diagram for the system of FIG. 1 demonstrating a preferred
process for iteratively calculating and applying factors in
competitive fusion. In one form, procedure 200 is at least
partially implemented in the operating logic of system 20.
Procedure 200 begins on FIG. 6A with summing source (POS) data by
the most granular product and time dimension (e.g. UPC) (stage 202)
and summing target (panel) data by the most granular product and
time dimension (e.g. UPC) (stage 204). In one embodiment, they are
both summed to weekly (e.g. 52) totals. Business logic server 24
determines the period of time to use in the analysis (stage 206),
such as to use all of the weekly totals summed in the prior step or
to use only part of the weekly totals that cover a desired time
period, such as the most recent 13 weeks, to name a few examples.
Outliers are also eliminated (stage 207) at this point or another
appropriate point before final calculations. For example, in one
embodiment, although thirteen weeks are contained in the dataset,
only 11 weeks are actually used in calculations. Research indicates
that panel volume is extremely vulnerable to outliers. To minimize
the potential impact of outliers, the week with the lowest coverage
and the week with the highest coverage are eliminated from further
use in calculations for the current update. In one embodiment,
although the outlier weeks are eliminated from further use in
calculations for the current update, they are not removed from the
dataset as they may be used in subsequent updates. Business logic
server 24 then merges the source (POS) data, target (panel) data,
and product identifier to attribute segment mapping reference data
(stage 208). Attributes can optionally be sorted in order by
importance (stage 210). In one embodiment, the least important is
first and the most important is last. If factors for the most
important attribute segments are the last ones applied, it usually
has the most significant mathematical effect because no lesser
important attribute segment factor will be applied after that last
calculation to further skew the results.
[0084] An initial factor of 1.0 is assigned to all attribute
segment (stage 212). Continuing with FIG. 6B, source (POS) and
target (panel) data are then summarized for the segments of the
current attribute (stage 214). A factor is calculated for each
attribute segment of the current attribute as source data volume
divided by target data volume (stage 216). Other mathematical
variations could also be used. For each segment of the current
attribute, determine whether the attribute segment is significant
(stage 218). In one embodiment, shares are calculated for the the
attribute segments, such as by dividing the Calculation Period
Segment Total U.S. POS volume by the Calculation Period Category
Total U.S. POS volume. Significance is then determined by first
analyzing a confidence interval (CI) for each share to determine if
there is overlap between the POS share CI and the panel share CI.
If there is overlap, then the difference between source and target
shares is not significant and the attribute segment will be
designated as "nonsignificant." Other ways for determining
significance can also be used, or significance can be assumed.
[0085] In one embodiment, if two or more segments for the current
attribute were nonsignificant (stage 220), then the significant
factors (that remain) will need to be re-aligned to account for
non-significant segment factors being removed (stage 222). At the
product identifier-level target (POS) data, each volume is
multiplied by the factor for the corresponding segment (stage 224).
Again, other mathematical variations could also be used. The
factors for each attribute segment are then saved to factor data
store 39 of database 34 (stage 226). If another attribute is
present (decision point 228), the next attribute is made the
current attribute (stage 230) and stages 214-226 are repeated.
These stages are repeated until all attributes are processed.
Continuing with FIG. 6C, a category adjustment factor is applied to
all product identifiers as necessary (stage 232) to adjust for the
level of coverage. In one embodiment, the use of a category
adjustment factor depends on the type of measure being used. For
example, where volume is used, coverage adjustments may not be
necessary, but where shares are used, further coverage adjustments
may be necessary. Any final factors for the category adjustment
factor are saved to the factor data store 39 of database 34 (stage
234). The process 200 then ends at stage 236.
[0086] FIGS. 7A-&C are first, second, and third parts of a
process flow diagram for the system of FIG. 1 demonstrating an
alternate process for calculating and applying factors in
competitive fusion. In one form, procedure 250 is at least
partially implemented in the operating logic of system 20.
Procedure 250 begins on FIG. 7A with summing the more reliable
(source) data source (e.g., POS data) by the most granular product
and time dimension (e.g. UPC) (stage 252) and summing the less
accurate (target) data source (e.g., panel data) by the most
granular product and time dimension (stage 254). Business logic
server 24 determines the period of time to use in the analysis
(stage 256) and eliminates outliers (stage 257), as discussed in
FIG. 6. Source data, target data, and product identifiers to
attribute segment mapping data are merged (stage 258). An initial
factor of 1.0 is assigned to each attribute segment (stage 260).
Source and target data are summarized to the segments for all
attributes (stage 262).
[0087] Continuing with FIG. 7B, factors are calculated for each
attribute segment as source volume divided by target volume (stage
264). Business logic server 24 determines whether the attribute
segment is significant (stage 266), as described in FIG. 6. Where
two or more segments for any particular attribute are insignificant
(decision point 268), then the significant factors are re-aligned
to account for the elimination of the insignificant segment factors
in the particular attribute (stage 270). At the product
identifier-level target data, each volume is multiplied by the
factor for each corresponding segment (stage 272). In other words,
all of the factors applicable to the volume are applied
simultaneously, as opposed to iteratively as shown in FIG. 6. The
factors are then saved to factor data store 39 for each attribute
segment (stage 274).
[0088] Continuing with FIG. 7C, a category adjustment factor is
applied to all product identifiers as necessary (stage 276), as
described in FIG. 6. The final factors for the category adjustment
factor are saved to the factor data store 39 of database 34 (stage
277). The procedure 250 then ends at stage 278. Procedure 250
should only be used in the appropriate circumstances, such as when
the attributes are not affected by each other and iteration is not
needed for greater accuracy, to name one example. If attributes are
affected by each other and procedure 250 is used instead of the
iterative procedure of FIG. 6, then the results will be
mathematically different, with the procedure of FIG. 6 producing a
more accurate result.
[0089] FIG. 8 is a process flow diagram for the system of FIG. 1
demonstrating the stages involved in performing complementary
fusion. In one form, procedure 280 is at least partially
implemented in the operating logic of system 20. Procedure 280
begins with merging source data, target data, and product
identifier data to attribute segment mapping data (stage 282). The
factors previously calculated in accordance with FIG. 6 or FIG. 7
are applied to the product identifier-level target data based on
the attribute segment mapping to correct the data for
incompleteness (e.g. less data than desired) (stage 286). The
target data elements that are corrected in this process can be the
same, different, or overlapping from the target data that was used
to help calculate the factors. The procedure 280 then ends at stage
288.
[0090] FIG. 9 is a process flow diagram for the system of FIG. 1
demonstrating the stages involved in performing repeating
competitive and complementary fusion steps multiple times. In one
form, procedure 290 is at least partially implemented in the
operating logic of system 20. Procedure 290 begins with determining
what additional public or private data sources are available to use
for competitive fusion along venue, time, and/or product dimensions
(stage 292). Using one or more of those data sources, additional
factors are calculated that are independent estimates against which
the complementary-fused estimate may be competed (stage 294). The
newly calculated factors are applied to the product
identifier-level target data (e.g. POS data) to further adjust the
data (stage 296). The competitive and complementary fusion steps
can be repeated as desired and/or appropriate (stage 298). The
procedure 290 then ends at stage 299.
[0091] FIG. 10 is a process flow diagram for the system of FIG. 1
demonstrating the stages involved in calculating blended factors
where multiple factor measures are available for the same factor.
In one form, procedure 300 is at least partially implemented in the
operating logic of system 20. Procedure 300 can be used when
competitive fusion is being performed and at least two data sources
are available for the same factor (stage 302). For each aggregation
(venue, time, or product) that has at least two factor measures,
calculate specific totals are calculated across attributes (stage
304). Factors for each aggregation of the current data source are
calculated by dividing source data volume by target data volume
(stage 305). If there are more data sources (decision point 306),
then move to the next data source (stage 307) and repeat stages
304-305. Then, calculate a blended factor (stage 308) where the
more accurate source is given a higher weight and the less accurate
source is given a lower weight. One simple way of calculating a
blended factor is to calculate a central tendency--e.g., mean or
median--of the various factors as the overall factor. This treats
all estimates as of equal value (reliability, accuracy, precision),
which in reality may or may not be the case. In a preferred
embodiment, the "blended factor" uses an
"inverse-variance-weighted" method (see 444 on FIG. 19 as an
example). This name originates from the fact that more "reliable"
estimates--i.e., those with more precision and, thus, less
variability--are given more weight than those that are less
"reliable" (more variable). Once the blended estimate has been
calculated, multiply each volume of the product identifier-level
target data by the blended factor (stage 310). The procedure 300
then ends at stage 312.
[0092] A hypothetical example will now be described in FIGS. 11-21
to with reference to the procedures described in FIGS. 6-10. FIG.
11 is a data table illustrating hypothetical data elements that are
adjusted according to the preferred embodiment competitive fusion
procedure of FIG. 6. POS data 320, panel data 322, and attribute
information 324 are shown in a summarized form by UPC 326. For each
attribute and its corresponding segments, various steps are
performed as discussed below.
[0093] Turning to FIG. 12, the data is assumed to be relevant and
the POS and panel data shown in table 330 are then summarized for
the segments of the current attribute (stage 214), which in the
current iteration is manufacturer 332. Private brand label
summaries 334 and non-private brand label summaries 336 for POS 338
and panel data 340 are calculated from table 330 as illustrated. A
factor 342 for each attribute segment of the current attribute, in
this case private label manufacturer 334 and non-private label
manufacturer 336 segments, is calculated as POS volume 338 divided
by panel volume 340 (stage 216). Business logic server 24
determines whether the current attribute segment is significant
(stage 218). For purposes of illustrating the current example, all
attribute segments are also assumed significant. At the UPC level
panel data, each panel volume 344 is multiplied by the factor 342
for its corresponding segment (stage 224) to arrive at an adjusted
panel value 346. Factors 342 are saved to the factor data store 39
of database 34 (stage 226).
[0094] As shown in FIGS. 13 and 14, stages 214 to 226 repeat for
each attribute, with previously adjusted data being used in the
calculation. FIG. 13 illustrates data elements being adjusted
according to factors calculated for a second attribute in
accordance with the procedure of FIG. 6. The POS and panel data
shown in table 350 are then summarized for the segments of the
current attribute (stage 214), which in the current iteration is
type 352. Summaries for regular type 354 and special type 356 for
POS 358 and panel data 360 are calculated from table 350 as
illustrated. A factor 362 for each attribute segment of the current
attribute, in this case regular type 354 and special type 356
segments, is calculated as POS Volume 358 divided by panel volume
360 (stage 216). At the UPC level panel data, the previously
adjusted panel volume 364 is multiplied by the factor 362 for its
corresponding segment (stage 224) to arrive at yet another adjusted
panel value 366. Factors 362 are saved to the factor data store 39
of database 34 (stage 226).
[0095] FIG. 14 illustrates data elements being adjusted according
to factors calculated for a third attribute in accordance with the
procedure of FIG. 6. The POS and panel data shown in table 370 are
then summarized for the segments of the current attribute (stage
214), which in the current iteration is size 372. Summaries for
size big 374, size medium 375, and size small 376 for POS 378 and
panel data 380 are calculated from table 370 as illustrated. A
factor 382 for each attribute segment of the current attribute, in
this case size big 374, medium 375, and small 376 segments, is
calculated as POS Volume 378 divided by panel volume 380 (stage
216). At the UPC level panel data, each previously adjusted panel
volume 384 is multiplied by the factor 382 for its corresponding
segment (stage 224) to arrive at yet another adjusted panel value
386. Factors 382 are saved to the factor data store 39 of database
34 (stage 226). After processing all attributes, the final factors
are saved to the factor data store 39 of database 34 (stage 234).
The process then ends at stage 236.
[0096] FIGS. 15 and 16 illustrate data elements being adjusted
according to factors calculated according to an alternative
embodiment competitive fusion process in accordance with the
procedure of FIG. 7. Business logic server 24 determines the period
of time to use in the analysis (stage 256), and merges POS, panel,
and attribute information by UPC as shown in table 390 (stage 258).
POS data 392 and panel data 394 are summarized for all attribute
segments (stage 262), in this case by manufacturer 396, type 398,
and size 400. As shown in FIG. 16, factors for each attribute
segment 402 are calculated as each respective POS volume 404
divided by each respective panel volume 406 (stage 264). Each panel
volume 407 is multiplied by the factors 408a-408c appropriate for
its corresponding segment (stage 272) to calculate an adjusted
panel value 410. The process then ends at stage 278.
[0097] FIG. 17 is a data table illustrating hypothetical data
elements by retailer that are stored in the database of FIG. 1 and
used in accordance with the complementary fusion procedure of FIG.
8. POS, panel and attribute information are merged by UPC (stage
282) for multiple retailers, as shown in table 420. Client shipment
data 424, another data source available, is also merged by UPC.
Shares are calculated for POS data 420a-420b and panel data
422a-422c for the segments of each attribute (stage 284). As shown
in FIG. 18, the previously calculated factors 430a-430c (408a-408c
in FIG. 16) are applied to the UPC level panel data 432a-432c to
further adjust the data to correct for incompleteness (stage 286)
and arrive at an adjusted panel value 434a-434c. The complementary
fusion process then ends at stage 288.
[0098] FIGS. 19 and 20 illustrate performing another iteration of
competitive fusion, including calculating blended factors, as
described in the procedures of FIG. 9 and FIG. 10. Additional
public or private data sources are identified as available to use
for competitive fusion (stage 292). As shown in table 438, channel
specific totals 440a-440f across attributes have been identified
for use in competitive fusion. In addition to POS and Panel totals
for retailers 1 and 2 (440a-440d), client shipment total 440e and
panel total 440f can also be used for comparison. Using these
totals 440a-440f, additional factors 442 have been calculated that
are independent estimates against which the complementary-fused
data from FIG. 18 may be competed (stage 294). A blended factor 444
has been calculated since multiple data sources were available for
the same factor (stages 302-308 in FIG. 10). As shown in FIGS. 19
and 20, each volume 446a-446c of the previously adjusted UPC-level
panel data is then multiplied by the blended factor to arrive at
the newly adjusted panel values 450a-450c (stage 298 in FIG. 9, and
stage 310 in FIG. 10).
[0099] FIG. 21 is a data table illustrating hypothetical table 460
of end results for POS data elements by retailers 2 and 3, with a
comparison to reality FIGS. 462a-462b, pre-fusion FIGS. 464a-464b,
and post-fusion FIGS. 466a-466b to show how the competitive and
complementary fusion processes according to FIGS. 4-10 and
illustrated in the hypothetical of FIGS. 11-20 helped improve the
data accuracy.
[0100] FIG. 22 is a simulated screen of a user interface for one or
more client workstations 30 that allows a user to view the
multi-dimensional elements in the database, as described in the
procedures of FIG. 4 and FIG. 5.
[0101] Alternatively or additionally, once data fusion has been
performed as described herein, the updated data can be used by
various systems, users, and/or reports as appropriate.
[0102] In one embodiment of the present invention, a method is
disclosed comprising identifying a plurality of data sources,
wherein at least a first data source is more accurate than a second
data source; identifying a plurality of overlapping attribute
segments to use for comparing the data sources; calculating a
factor as a function of each of the plurality of overlapping
attribute segments; and using the factors to update a first group
of values in the second data source to reduce bias.
[0103] In another embodiment of the present invention, a method is
disclosed comprising receiving point-of-sale data and panel data on
a periodic basis; identifying a plurality of product identifiers
and a plurality of attributes to analyze; retrieving and
summarizing the point-of-sale data and the panel data by the
plurality of product identifiers, the plurality of attributes, and
a plurality of corresponding attribute segments for a specified
time period; calculating a factor for each attribute segment of a
particular attribute; and applying the factors for the particular
attribute segment to the panel data to correct panel bias.
[0104] In yet another embodiment, a method is disclosed comprising
receiving point-of-sale data and panel data on a periodic basis;
identifying a plurality of product identifiers and a plurality of
attributes to analyze; retrieving and summarizing the point-of-sale
data and the panel data by the plurality of product identifiers,
the plurality of attributes, and a plurality of corresponding
attribute segments for a specified time period; calculating a
plurality of factors, wherein one factor is calculated for each
attribute segment of the plurality of attributes; and applying the
factors to the second data source to reduce bias; and applying the
factors to the second data source to reduce incompleteness.
[0105] In yet a further embodiment, a method is disclosed
comprising identifying a plurality of product identifiers and a
plurality of attributes to analyze for at least two data sources,
wherein at least a first data source is more accurate than a second
data source; retrieving and summarizing the first data source and
the second data source by the plurality of product identifiers, the
plurality of attributes, and a plurality of corresponding attribute
segments for a specified time period; calculating a plurality of
factors, wherein one factor is calculated for each attribute
segment of the plurality of attributes; applying the factors to the
second data source to reduce bias; and applying the factors to a
different or overlapping dataset of the the second data source to
reduce incompleteness.
[0106] In another embodiment, a system is disclosed that comprises
one or more servers being operable to store retail data from at
least two data sources, store product identifier and attribute
categorizations, and store a plurality of factor calculations;
wherein the at least two data sources includes a first data source
that is more accurate than a second data source; and wherein one or
more of said servers contains business logic that is operable to
identify and retrieve a plurality of overlapping attribute segments
to use for comparing the at least two data sources, compare each of
the overlapping attribute segments, calculate a factor for each of
the overlapping attribute segments, and use the factors to update a
first group of values in the second data source to reduce bias.
[0107] In yet a further embodiment, an apparatus is disclosed that
comprises a device encoded with logic executable by one or more
processors to: identify and retrieve a plurality of overlapping
attribute segments to use for comparing at least two data sources,
wherein the at least two data sources includes a first data source
that is more accurate than a second data source, compare each of
the overlapping attribute segments, calculate a factor for each of
the overlapping attribute segments, and use the factors to update a
first group of values in the second data source to reduce bias.
[0108] A person of ordinary skill in the computer software art will
recognize that the client and/or server arrangements, user
interface screen content, and data layouts could be organized
differently to include fewer or additional options or features than
as portrayed in the illustrations and still be within the spirit of
the invention.
[0109] While the invention has been illustrated and described in
detail in the drawings and foregoing description, the same is to be
considered as illustrative and not restrictive in character, it
being understood that only the preferred embodiment has been shown
and described and that all equivalents, changes, and modifications
that come within the spirit of the inventions as described herein
and/or by the following claims are desired to be protected.
* * * * *