Patent Publication Number: US-2021166249-A1

Title: Methods and Apparatus to Generate Consumer Data

Description:
RELATED APPLICATION 
     This patent claims the benefit of U.S. Provisional Application Ser. No. 62/141,246, filed on Mar. 31, 2015, entitled “Method and Apparatus to Generate Consumer Data,” which is hereby incorporated by reference in its entirety. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure relates generally to consumer research and, more particularly, to methods and apparatus to generate consumer data. 
     BACKGROUND 
     In some consumer market research systems, groups of panelists agree to passively and/or actively submit information about their demographics and/or their behavior to a data collection entity that uses the information to develop reports about populations of interest. The information submitted by the panelist may include data related to, for example, purchased products, media exposure, demographics (e.g., age, gender, race, income, home location, occupation, etc.) advertisement exposure, etc. The data collected from the panelists can be extrapolated to provide estimations of behaviors of a broader population, such as a demographic group sharing certain traits with the panelists. The data collection entity, or some other entity with access to the submitted information, typically generates reports using the submitted information and/or the extrapolated estimations and provides the reports and/or the data to, for example, providers of goods and services that advertise to targeted audiences. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is an illustration of an example consumer research system including a consumer data generator constructed in accordance with teachings of this disclosure. 
         FIG. 2  is a block diagram showing additional detail of an example implementation of the example consumer data generator of  FIG. 1 . 
         FIG. 3A  is a flowchart representative of machine readable instructions that may be executed to implement the example membership sample selector of  FIG. 2 . 
         FIG. 3B  is a flow chart representative of machine readable instructions for setting the targets of  FIG. 3A . 
         FIG. 4  is a table illustrating example linkages associated with the example data fuser of  FIG. 2 . 
         FIG. 5  is a flowchart representative of example machine readable instructions that may be executed to implement the example linkage updater of  FIG. 2 . 
         FIG. 6  is a flowchart representative of example machine readable instructions that may be executed to implement the example projection generator of  FIG. 2 . 
         FIG. 7  is a flowchart representative of example machine readable instructions that may be executed to implement the example volumetric adjustment projection system (VAPS) of  FIG. 6 . 
         FIG. 8  is a block diagram of an example processing system implementing the example consumer data generator of  FIG. 1 , the example membership sample selector of  FIG. 2  by executing the example machine readable instructions of  FIG. 3A , the example data fuser of  FIG. 2 , the example linkage updater of  FIG. 2  by executing the example machine readable instructions of  FIG. 5 , the example projection generator of  FIG. 2  by executing the example machine readable instructions of  FIG. 6 , and/or the example VAPS of  FIG. 6  by executing the example machine readable instructions of  FIG. 7 . 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is a schematic illustration of an example market research system  100  including a data collection facility  102 . The example data collection facility  102  of  FIG. 1  collects panel data from a plurality of panelists who have agreed to actively and/or passively submit data to the data collection facility  102 . In the example of  FIG. 1 , a household  104  including a panelist  106  is illustrated. However, multiple panelists and households provide panel data to the example data collection facility of  FIG. 1 . In the illustrated example of  FIG. 1 , the panelist  106  is a member of a panel (e.g., a Nielsen Homescan® panel) that has agreed to scan codes (e.g., barcodes such as traditional barcodes, tag barcodes, QR codes, etc.) of obtained (e.g., purchased) merchandise using a scanning device (e.g., a barcode scanner) provided by an entity (e.g., The Nielsen Company) associated with the example data collection facility  102 . However, other types of panels (e.g., television panels, radio panels, Internet panels, etc.) to measure additional or other activities may likewise be employed. 
     The household  104  and/or the panelist  106  may be statistically selected (e.g., by the entity operating the data collection facility  102 ) according to, for example, one or more demographic factors, geographic location, answers provided in response to a survey, previous purchase behavior, etc. In the example of  FIG. 1 , the data collection facility  102  includes demographic information associated with the household  104  and the panelist  106 . In some examples, the demographic information of the household  104  and/or the demographic information of the panelist are represented by a profile generated by the entity managing the data collection facility  102 . For example, the demographic profile of the household  104  may include an age profile, a geographic location profile, a household income profile, etc. Information representative of such characteristics of the household  104  and/or the panelist  106  is referred to herein as panelist profile data and may be stored in demographic data storage  108  at the data collection facility  102 . Demographic data storage  108  may also be used to store other types of demographic data, for example regional demographic data, a demographic data repository and the like, as describe further below. 
     In the illustrated example of  FIG. 1 , the panelist  106  has agreed to submit information related to obtained items (e.g., purchased products) to the data collection facility  102 . Information representative of the obtained items that is provided to that data collection facility  102  is referred herein as panel product data  110 , which is stored at the data collection facility  102 . In the example of  FIG. 1 , the panel product data  110  includes information representative of, for example, products obtained by a single household member (e.g., the panelist  106 ), the household as a whole, and/or any subset of household members (e.g., children, parents, males, females, an eldest child, etc.) and point of sale data (e.g., retailer name and address, wholesaler name and address, etc.). Furthermore, the panel product data  110  submitted to the data collection facility  102  includes, for example, any type of products, a designated type of product (e.g., groceries, clothing, media (e.g., music or movies), electronics, housekeeping products, home improvement products, etc.), a designated brand, and/or a plurality of types of products. 
     The panelist  106  conveys the panel product data  110  to the data collection facility  102  in any suitable manner. In the illustrated example of  FIG. 1 , the panelist  106  is provided with a scanner  112  (e.g., a barcode scanner) that is adapted to collect data (e.g., via the barcode reader) and to communicate the collected data to the data collection facility  102  either directly and/or via a network (e.g., a local-area network, a wide-area network, a metropolitan-area network, the Internet, the Plain Old Telephone System (POTS), a digital subscriber line (DSL) network, a cable network, a power line network, a wireless communication network, a wireless mobile phone network, a Wi-Fi network, and/or a satellite network). 
     After purchasing and/or otherwise obtaining a product  114 , the panelist  106  uses the example scanner  112  to scan identifying information  116  on the product  114 . In the illustrated example of  FIG. 1 , the identifying information  116  on the product  114  is a barcode. However, any type of identifying information may be utilized. For example, the scanner  112  may be provided with a key pad or other user device to enable the user to manually enter the product information (e.g., product name, point of sale, etc.). The identifying information  116  is unique to the product  114 , such that the product  114  is identifiable using data obtained from the identifying information  116 . For example, the identifying information  116  corresponds to a SKU (stock-keeping unit) associated with the product  114 . The example scanner  112  of  FIG. 1  electronically stores data corresponding to the scanned identifying information  116  in a memory. 
     The example scanner  112  of  FIG. 1  transmits (e.g., periodically, after scanning new information, and/or in response to an event or prompt) the stored panel product data corresponding to the identifying information  116  to the data collection facility  102  in any suitable manner. In some examples, the scanner  112  includes a wireless communication module (e.g., a cellular module) capable of wirelessly transmitting stored data to the data collection facility  102 . In some examples, the scanner  112  is coupled to a personal computer  118 , which is in communication with the data collection facility  102  (e.g., via a wired and/or wireless network). In some examples, the scanner  112  is coupled to a docking station  120 , which is in communication with the data collection facility  102  (e.g., via a wired, a wireless network, and/or via the personal computer  118 ), and which may charge the scanner  112 . While the example of  FIG. 1  includes the personal computer  118  and the docking station  120 , any other type of device capable of interacting with the scanner  112  to transmit the stored data to the data collection facility  102  may be employed. The scanner  112  may be able to use one, some, or all of these communication technologies. 
     In addition to the product panel data received from the household  104 , the example data collection facility  102  of  FIG. 1  receives data representative of transactions occurring at retailers  122 . In the example of  FIG. 1 , the retailers  122  collect and store transaction data in a point of sale (POS) database  124 . The information stored in the POS database  124  includes POS data related to purchases made via the retailers  122  such as, for example, time and date of purchase, product identifying information, retailer identifying information, location identifying information, etc. 
     In the example of  FIG. 1 , the retailers  122  implement membership programs for which people can register. As used herein, the terms membership and membership program refer to any suitable type of program for which consumers actively register with a retailer to create a persistent shopper ID, or a retailer creates a persistent shopper ID using transactional data available to the retailer (e.g., this may include using a traceable tender as opposed to a membership ID). Examples include: programs that requires a fee, programs that do not require a fee, programs that require and/or request use of a card when making purchases, programs that do not require or request use of a card when making purchases, programs that make use of a traceable tender (e.g., a retailer associating a particular credit or debit card to a persistent shopper ID), etc. In some examples, the membership programs reward registered people when those people use, for example, a card or code when making a purchase. In some instances, the membership programs are referred to as loyalty programs, the cards used by registered people are referred to herein as loyalty cards, and the data associated with the transactions involving the loyalty cards is referred to herein as loyalty data. In the illustrated example, the retailers  122  collect and store membership card data in a membership card database  126 . 
     The information stored in the membership card database  126  includes data related to purchases made at the retailers  122  by holders of membership cards associated with, for example, the membership programs offered and/or administered by the retailers  122 . For example, the membership card database  126  includes information representative of purchases made by a person and/or household registered with a membership program offered by a grocery store. When the registered person and/or household makes a purchase at the grocery store, data representative of the purchase is tagged as membership card data and stored in the membership card database  126 . 
     In the illustrated example of  FIG. 1 , the retailers  122  provide the data collection facility  102  with access to the POS database  124  and the membership card database  126 . For example, the retailers  122  may enable the data collection facility  102  to retrieve information via direct access, enable the data collection facility  102  to submit queries to the POS database  124  and/or the membership card database  126 , and/or may implement any other suitable access arrangement. 
     As disclosed in detail below, the example data collection facility  102  of  FIG. 1  includes a consumer data generator  128  constructed in accordance with teachings of this disclosure. The example consumer data generator  128  of  FIG. 1  selects a sample from the membership card data, combines the panel product data  110  and the membership card data to generate fused datasets, generates projections based on the fused datasets, and updates linkages used to generate the fused dataset. 
       FIG. 2  is a block diagram of an example implementation of the example consumer data generator  128  of  FIG. 1 . In the example of  FIG. 2 , user identifying information from the membership card database  126  is provided to a matcher  200 . The user identifying information provided to the matcher  200  includes names and addresses of membership card holders. The example matcher  200  of  FIG. 2  queries a demographic data repository (e.g., a collection of demographic profiles each known to correspond to a particular address) with the addresses of the user identifying information. In some examples, the demographic data repository is stored in demographic data storage  108 . Additionally, the example matcher  200  of  FIG. 2  assigns an anonymous household-level identifier to each of the households that are membership cardholders. The example matcher  200  appends the demographic profile of household retrieved from the demographic data repository to the corresponding anonymous household-level identifier. In some examples, the matcher  200  links together households that shop in more than one of the membership retailers. Accordingly, the example matcher  200  of  FIG. 2  creates entries in an anonymous transaction database  202  of  FIG. 2  that have an anonymous identifier and demographic profile(s), but do not include personally identifying information (e.g., names). The anonymous transaction database  202  stores transaction data from the membership card database  126  and the example matcher  200  assigns the anonymous household-level identifier to the transaction data. 
     In the example of  FIG. 2 , the anonymous transaction database  202  receives transaction data from the membership card database  126 . The transaction data represents actual purchase information and the corresponding anonymous identifiers. Using the identifiers to link the purchase information with the entries created by the matcher  200 , the anonymous transaction database  202  is populated with purchase information and the associated anonymous household-level identifier. 
     In the example of  FIG. 2 , the information of the anonymous transaction database  202  is provided to a membership sample selector  204  of the example consumer data generator  128 . The example membership sample selector  204  of  FIG. 2  uses the information from the anonymous transaction database  202 , point of sale (POS) data  124 , demographic data storage  108 , and panel product data  110  to generate a sample of the membership card data provided by the example retailers  122  of  FIG. 1 . In some examples the sample of the membership card data is stored in memory, for example memory  206 . In particular, the example membership sample selector  204  selects the sample of the membership card data such that the sample is representative of a total shopper universe, rather than only membership cardholders. The operation of the example membership sample selector  204  is disclosed in detail below in connection with  FIG. 3A . 
     The example consumer data generator  128  of  FIG. 2  includes a data fuser  208  to integrate the membership card data of the generated membership sample with the panel product data  110  provided by the panelist  106  (and other panelists of, for example, a Nielsen Homescan® panel). The fusion of the membership card data and the panel product data performed by the example data fuser  208  of  FIG. 2  involves supplementing membership card data associated with particular characteristics (e.g., demographics) with panel product data associated with the same or similar characteristics (e.g., demographics). The panel product data associated with the same or similar characteristics provides information regarding purchasing activity at a plurality of retailers, rather than the single-retailer information provided by the membership data. Accordingly, the example data fuser  208  of  FIG. 2  attributes the panel product data to the membership card data according to one or more linkages between the two datasets. For example, membership card data associated with a 35 year old Caucasian who resides in Chicago, Ill., is fused with panel product data associated with other 35 year old Caucasians who reside in Chicago, Ill., such that the panel product data is projected onto the membership card data. In particular, the panel product data associated with the other 35 year old Caucasians who reside in Chicago, Ill., includes information related purchases made at a plurality of different retailers, while the membership card data for a particular retailer includes information related to purchases made at that retailer. As such, the dataset generated by the example data fuser  208  of  FIG. 2  expands the membership card data, which, by itself reflects purchases at one retailer, to be representative of purchasing behavior at multiple retailers. The example data fuser  208  is disclosed in detail below in connection with  FIGS. 4 AND 5 . 
     The example consumer data generator  128  of  FIG. 2  includes a linkage updater  210  to maintain and/or improve result(s) of the fusion performed by the example data fuser  208 . The example linkage updater  210  of  FIG. 2  periodically evaluates newly available information of the panel product data  110  provided by the panelist  106  and other panelists to determine whether improved linkages (e.g., demographic linkages) between the members of the panel and the membership card users are available. If such information is available, the example linkage updater  210  of  FIG. 2  triggers the data fuser  208  to perform an updated fusion of the panel product data  110  and the membership card data, thereby maintaining and/or improving the performance of the data fusion. Operation of the example linkage updater  210  is disclosed in detail below in connection with  FIG. 6 . 
     The example consumer data generator  128  of  FIG. 2  includes a projection generator  212  to generate one or more projections using the data provided by the example data fuser  208 . The projection(s) generated by the example projection generator  212  tailor (e.g., weight) the fused dataset according to, for example, expected market share information. Additionally, the example projection generator  212  of  FIG. 2  stratifies the fused dataset to include one or more stratifications such as, for example, membership and non-membership strata. Thus, the example projection generator  212  of  FIG. 2  generates an enhanced version of the fused dataset generated by the data fuser  208 . The example projection generator  212  of  FIG. 2  may store the enhanced version of the fused dataset in memory, for example memory  206 . Operation of the example projection generator  212  is disclosed in detail below in connection with  FIG. 7 . 
     In the example of  FIG. 2 , the projection generator  212  provides an enhanced fused dataset to a reporting system  214 , for example, by storing the enhanced fused dataset into memory  206 . The example reporting system  214  provides accessibility to the enhanced fused dataset. In some examples, the reporting system  214  conditions, formats, and/or customizes the raw data of the enhanced fused dataset to provide a user-friendly presentation of the information. 
     While an example manner of implementing the consumer data generator  128  of  FIG. 1  is illustrated in  FIG. 2 , one or more of the elements, processes and/or devices illustrated in  FIG. 2  may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example matcher  200 , the example membership sample selector  204 , the example data fuser  208 , the example linkage updater  210 , the example projection generator  212 , the example reporting system  214  and/or, more generally, the example consumer data generator  128  of  FIG. 2  may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example matcher  200 , the example membership sample selector  204 , the example data fuser  208 , the example linkage updater  210 , the example projection generator  212 , the example reporting system  214  and/or, more generally, the example consumer data generator  128  of  FIG. 2  could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When reading any of the appended claims to cover a purely software and/or firmware implementation, at least one of the example matcher  200 , the example membership sample selector  204 , the example data fuser  208 , the example linkage updater  210 , the example projection generator  212 , the example reporting system  214  and/or, more generally, the example consumer data generator  128  of  FIG. 2  is/are hereby expressly defined to include a tangible computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. storing the software and/or firmware. Further still, the example consumer data generator  128  of  FIG. 1  may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIG. 2 , and/or may include more than one of any or all of the illustrated elements, processes and devices. 
       FIG. 3A  is a flowchart representative of machine readable instructions for implementing a process  300  of the example membership sample selector  204  of  FIG. 2 . The example membership sample selector  204  of  FIG. 2  uses membership data from the anonymous transaction database  202 , the POS data in the POS database  124 , regional demographic data stored in the demographic data storage  108 , and the panel product data  110  to generate the membership sample. In some examples, the example membership sample selector  204  stores the membership sample in memory, for example memory  206 . The example membership sample selector  204  of  FIG. 2  generates the membership sample such that the membership sample is representative of membership card holders as well as shoppers that do not have a membership card. 
     In the example of  FIG. 3A , the membership sample selector  204  of receives membership card data from the anonymous transaction database  202  of  FIG. 2 . As described above, the membership card data from the anonymous transaction database  202  includes data representative of transactions involving a membership card and includes information indicative of, for example, a location of the corresponding transaction and/or demographic information associated with the transaction. In the example of  FIG. 3A , the membership sample selector  204  applies a purchase filter to the membership card data to eliminate certain transactional data from consideration for the membership sample (block  301 ). In the illustrated example, the purchase filter identifies and filters out membership card data corresponding to households that do not use the corresponding membership card, households that do not use the corresponding membership card at a threshold frequency, and/or households that do not use the corresponding membership card for a threshold amount of purchases as measured by dollars. As such, the example the membership sample selector  204  of  FIG. 3A  directs the membership sample toward repeat shoppers and/or repeat shopping households, rather than people that have the card but do not use the card or barely use the card. 
     In the example of  FIG. 3A , the membership sample selector  204  applies a geographic filter to the membership card data to eliminate certain transactional data from consideration for the membership sample (block  302 ). In the illustrated example, the geographic filter identifies and filters out membership card data corresponding to transactions outside a particular geographic region that likely correspond to card holders that have moved from the known address that was used to register with the membership card program. In some examples, the geographic area is defined according to a location of the corresponding retailer. For example, transactions occurring in the geographical county of the corresponding retailer and/or adjacent counties. Accordingly, the example the membership sample selector  204  of  FIG. 3A  enables the membership sample to include data for which the corresponding address is known and/or confirmed. 
     In the example of  FIG. 3A , the membership sample selector  204  stratifies the membership card data according single banner and multi-banner households (block  304 ). In the example of  FIG. 3A , the stratification performed by the membership sample selector  204  includes creating separate datasets according to whether the corresponding household is a single banner household or a multi-banner household. As used herein, a banner refers to an identifier for a retailer that corresponds to a sign (e.g., a physical sign or an electronic display item) display that a shopper views when visiting (e.g., physically and/or electronically) a retailer. For example, some households buy groceries at a single banner, while other household buy groceries at more than one banner. In the illustrated example, the membership sample selector  204  creates the different datasets while requiring distinct sampling rates based on differing proportions of membership records relative to a total shopper universe. In some examples, membership sample selector  204  of  FIG. 3A  is aware of probably distributions across a population according to single banner and multi-banner households and the stratification performed by the membership sample selector  204  includes tailoring the membership card data according to those distributions or sampling rates. That is, the example membership sample selector  204  of  FIG. 3A  requires the membership sample to adhere to expected and/or known distributions of single banner and multi-banner households by, for example, requiring the membership sample to include a certain percentage of single banner households and/or a certain percentage of multi-banner households. 
     In the example of  FIG. 3A , the membership sample selector  204  computes cross-banner probabilities such that the membership sample is representative of likelihoods of households being cross-banner households (block  306 ). For example, the membership sample selector  204  identifies combinations of retailer banners that show purchasing across common households in a population (e.g., a shopper universe). The computation of the cross-banner probabilities enables the membership sample to appropriately represent population proportions for shoppers frequenting more than one participating banner. 
     In the example of  FIG. 3A , the membership sample selector  204  utilizes the POS database  124  to determine an overall sample size according to banner information (block  308 ). In the illustrated example, the membership sample selector  204  uses the POS database  124  to supplement distribution selection for the membership sample when, for example, an insufficient amount of data (e.g., below a threshold amount of transactions) is available in the membership card data for one or more categories. In particular, the POS database  124  provides purchase information regarding all purchases (e.g., at a particular banner) and indicates how much category spend occurred in particular categories. Thus, when a random sample selection would result in the membership card data over- or under-representing one or more of the categories, the POS database  124  is used to determine an appropriate selection of households to achieve representativeness of category purchasing for each participating retailer banner. 
     In the example of  FIG. 3A , the membership sample selector  204  uses regional demographic data, the POS data, and the panel product data  110  to set one or more targets for the membership sample (block  310 ). The targets may include demographic targets, geographic targets, department and/or category targets, and/or purchasing behavior targets. The setting of the targets is describe below with respect to  FIG. 3B   
     In the example of  FIG. 3A , with the membership data filtered and the sample sizes selected (blocks  300 - 308 ) and the targets defined (block  310 ), the membership sample selector  204  selects cross-banner households for the membership sample in accordance with the corresponding sample sizes and the corresponding targets (block  312 ). As described above, the membership data is stratified and the example membership sample selector  204  of  FIG. 3A  selects the cross-banner households such that each stratum within each retailer banner is appropriately represented in the membership sample. Additionally, the selection of the cross-banner households performed by the example membership sample selector  204  of  FIG. 3A  optimizes distances to the corresponding demographic targets, the corresponding geographic targets, the corresponding department and/or category targets, and/or the corresponding purchasing behavior targets. In the example of  FIG. 3A , the selected the cross-banner households (e.g., membership card holders from the membership data) are added to the membership sample. 
     In the example of  FIG. 3A , the membership sample selector  204  uses the selection of the cross-banner households to re-compute the targets determined in blocks  312  such that different targets are used for a selection of single banner households (block  314 ). That is, the example membership sample selector  204  of  FIG. 3A  recalculates the demographic targets, the geographic targets, the department and/or category targets, and/or the purchasing behavior targets with the households selected to fulfill the cross-banner strata being removed from the consideration of the targets. Thus, the updated targets are based on the membership card data less the membership card data selected for the cross-banner households at block  308 . 
     In the example of  FIG. 3A , the membership sample selector  204  selects the single banner households such that each stratum within each banner is appropriately represented in the membership sample (block  316 ). The example membership sample selector  204  of  FIG. 3A  merges the single banner selections and the cross-banner selections to generate the membership sample. In some examples, the example membership sample selector  204  of  FIG. 3A  stores the membership sample in memory, for example memory  206 . 
       FIG. 3B  is a flow chart representative of machine readable instructions for setting the targets (block  310 ) of  FIG. 3A . In the illustrated example, the membership sample selector  204  uses the regional demographic data located in the demographic data storage  108  to determine demographic and/or geographic target(s) for the membership sample (block  318 ). In the illustrated example, the regional demographic data is provided by, for example, an entity (e.g., The Nielsen Company (US), LLC) associated with the data collection facility  102 . The regional demographic data is demographic profile information associated with aggregations of people such as, for example, particular geographic regions (e.g., counties, towns, etc.). Thus, the example membership sample selector  204  uses the regional demographic data to set target(s) for the membership sample such that the membership sample appropriately represents shoppers in the trade areas of the corresponding banners according to demographic and county-level geographic profiles. 
     In the example of  FIG. 3B , the membership sample selector  204  uses the POS database  124  to set department and category targets for the membership sample (block  320 ). In particular, the membership sample selector  204  analyzes the POS data to identify categories and/or departments of, for example, different banners that demonstrate biases in the membership data. In the illustrated example, the membership sample selector  204  defines targets across a number (e.g., thirty) of categories custom to each banner to appropriately represent total store purchasing. That is, the example membership sample selector  204  of  FIG. 3B  defines the department and/or category targets such that the categorical and/or departmental biases in the membership card data are accounted for. 
     In the example of  FIG. 3B , the membership sample selector  204  uses the panel product data  110  to set purchasing behavior targets (block  322 ). For example, the membership sample selector  204  of  FIG. 3B  determines an average spend target and a spend distribution target for the membership sample. However, the example membership sample selector  204  of  FIG. 3B  can set targets for any suitable type of purchasing behavior. In the illustrated example of  FIG. 3B , the membership sample selector  204 , determines an average spend within a particular banner and a spend distribution within that banner and uses that information to tailor the membership sample accordingly. As such, the membership sample selector  204  enables the membership sample to represent a full range of spending behaviors. 
     As described above in connection with the example of  FIG. 2 , the membership sample is provided to the example data fuser  208 . In some examples, the example data fuser  208  of  FIG. 2  accesses the membership sample from memory, for example memory  206 . In other examples, the membership sample may be passed directly to the example data fuser  208  by the example membership sample selector  204 . The example data fuser  208  of  FIG. 2  performs a fusion of the membership sample and the panel product data  110 . While the membership sample provides information regarding purchases made at individual ones of the retailers  122 , the example consumer data generator  128  is tasked with generating consumer data representative of purchasing behavior across a plurality of retailers. Therefore, the example consumer data generator  128  uses the example data fuser  208  to supplement the membership sample with information from the panel product data  110 . As described above, the panel product data  110  corresponds to purchases made by panelists regardless of which retailer at which the purchases occur. Accordingly, the panel product data  110  represents purchasing behavior of the corresponding panelists across retailers. 
     The example data fuser  208  of  FIG. 2  uses the panel product data  110  as donor data, and the example data fuser  208  of  FIG. 2  uses the membership sample as recipient data. In the illustrated example, the data fuser  208  matches one or more of the panelists (e.g., the panelist  106  of  FIG. 1 ) with one of the households of the membership sample. The example data fuser  208  of  FIG. 2  matches the donor(s) (e.g., from the panel product data  110 ) with the recipients (e.g., from the membership data) based on, for example, demographic and/or geographic characteristics and/or any other suitable linkage(s). Put another way, the example data fuser  208  utilizes one or more linkage variables to match donor(s) from the panel product data  110  with recipients from the membership data. In the illustrated example, the linking variables focus on geography, demography and purchasing behavior. The linkage variables are correlated with rest-of-market purchasing to fuse the two datasets. The linkages used by the example data fuser  208  of  FIG. 2  based on purchasing behavior enables identification of robust statistical associations between donors and recipients. 
       FIG. 4  illustrates an example matching performed by the example data fuser  208  of  FIG. 2 . In the example of  FIG. 4 , the data fuser  208  obtains profiles of the panelist households of the panel product data  110 . In the example of  FIG. 4 , a first panelist household  400  is matched with and, thus, acts as a donor for first, second and third households  402 - 406  of the membership sample. The matching between the first panelist household  400  and the first, second and third households  402 - 406  of the membership sample is based on one or more similarities (e.g., having values within corresponding thresholds) between one or more aspects of the corresponding profiles. In the example of  FIG. 4 , the aspects of the profiles that are similar are a frequented retailer, an average basket spend, a household size, and a deal sensitive designation. Additional or alternative aspects and/or characteristics of the profiles can be considered such as, for example, presence and/or age of children, occupation, household age, ethnicity and/or race, presence of pets, education levels, income, ownership of home, renter, basket size, basket assortment, cross category sales rates, PL affinity, primary shopper location, primary banners, household geography, etc. In the illustrated example, the data fuser  208  assigns a weight to individual aspects and/or characteristics based on, for example, how well the respective aspect or characteristic predicts rest-of-market purchasing behavior. For each recipient of the membership sample, the example data fuser  208  of  FIG. 2 , the best one or more donors is identified based on, for example, the characteristics, optimization of distance scores between the characteristics, and/or optimization of the number of times each donor is used. The example data fuser  208  of  FIG. 2  selects a second one  408  of the panelist households as a donor for fourth and fifth ones  410  and  412  of the membership sample households  410 . The example data fuser  208  of  FIG. 2  selects a third one  414  of the panelist households as a donor for a sixth one  416  of the membership households. 
     Accordingly, the example data fuser  208  of  FIG. 2  attributes the data associated with the donor(s) from the panel product data  110  to the data recipients of the membership sample. In the illustrated example, the fusion performed by the example data fuser  208  includes the panel product data  110  from the donor(s) being appended to the data of the membership sample. As such, the fused data represents the membership transaction data corresponding to single retailers supplemented by transactional data corresponding to similar households (e.g., according to demographics, geographic factors, purchasing behaviors, etc.). 
     In some examples, the data fuser  208  of  FIG. 2  takes into account ongoing research indicative of which linkage variable to use and/or which weights to assign to the linkage variables that more appropriately represent rest-of-market purchasing behavior. In some examples, the data fuser  208  generates and maintains scorecards for the linkage variable that are maintained and updated to track performance of the selected linkage variables. The scorecards used by the example data fuser  208  include, for example, profile metrics and/or buy metrics that measure how well the fused rest-of-market aligns with expectations. In some examples, the evaluation of the linkage variables used by the data fuser  208  includes a split sample validation. For example, as part of the evaluation, the data fuser  208  performs the data fusion on only the panel product data  110 , rather than fusing the panel product data  110  and the membership data, by splitting the panel product data  110  into two groups, donors (20%) and recipients (80%). This enables the evaluation performed by the data fuser  208  to analyze one or more metrics for fused recipient data and compare the actual panel product data  110  to determine how well the model is performing. That is, how closely the matching performed by the data fuser  208  is representing expectations and/or how well the matching is preserving relationships in the fused data. For example, for the profile metrics, the data fuser  208  tracks correlation of a percentage point difference between actual and fused distributions across, many different category and/or demographic combinations. For examples, for the buy metrics, the data fuser  208  compares fused data frequencies and/or penetrations in the fused data with data from the original dataset to determine how well the fusion is performing. That is, the example data fuser  208  determines whether one or more profiles of, for example, the retailers, are being maintained through the fusion process. 
     Thus, the data fuser  208  of  FIG. 2  provides a fused dataset that represents the membership sample as supplemented by the panel product data  110 . In some examples, the fused dataset is stored in memory, for example memory  206 . 
     As described above in connection with  FIG. 2 , the linkage updater  210  performs a maintenance on the linkages utilizes by the data fuser  208 .  FIG. 5  is a flowchart representative of example machine readable instructions for implementing a process  500  of the example linkage updater  210  of  FIG. 2 . In the example of  FIG. 5 , the linkage updater  210  periodically checks the linkages utilized by the example data fuser  208  to determine whether one or more of the matches between donor and recipient are no longer the best available matches. A better donor for a particular recipient may become available due to, for example, panelist households being added to the panel, changes in purchasing behavior of one or more donors and/or recipients, geography and/or demographic updates, one or more of the membership households no longer being filtered out of the membership sample by the membership sample selector  204 , etc. The example linkage updater  210  determines whether better candidates are now available and/or whether performance of the linkages (e.g., in maintaining one or more profiles through the fusion process) is unsatisfactory (e.g., according the profile metrics and/or buy metrics used by the data fuser  208  to evaluate the linkage variables and additional metrics designed to detect deterioration in linkages based on increasing distance scores). In the example of  FIG. 5 , TIME 1 corresponds to the status of the available data at a time of the previous data fusion, while TIME 2 corresponds to a current status of the available data at a time of the periodic maintenance. 
     In the example of  FIG. 5 , the linkage updater  210  obtains a linkage file indicative of the linkage variables used at TIME 1, updated recipient data corresponding to the membership sample at TIME 2, and updated donor data corresponding to the panel product data  110  at TIME 2. In some examples, the linkage file, the updated recipient data, and the updated donor data are retrieved from memory, for example memory  206  of  FIG. 2 . In the example of  FIG. 5 , the linkage updater  210  analyzes the linkage file to determine, based on the updated recipient data and the updated donor data, whether any of the linkages between donors and recipient in the linkage file  500  is broken (block  501 ). For example, a donor from the linkage file may no longer be available as a donor. Additional or alternative changes may cause a linkage to be broken. 
     In the example of  FIG. 5 , for those of linkages that are broken, the example linkage updater  210  classifies the broken linkages into one of a plurality of scenarios (block  502 ). The following table (TABLE I) illustrates example scenarios into which the example linkage updater  210  classifies the broken linkages: 
     
       
         
           
               
             
               
                 TABLE I 
               
             
            
               
                   
               
               
                 Linkage Scenarios 
               
            
           
           
               
               
               
            
               
                 Donor 
                 Recipient 
                 Action 
               
               
                   
               
               
                 Time 1 &amp; 
                 Time 1 
                 Reduce TU field in TimesUsed - Time 1 file 
               
               
                 Time 2 
                 only 
                 (donor is used one less time for each 
               
               
                   
                   
                 associated recipient that is no longer 
               
               
                   
                   
                 present in Time 2), store in TimesUsed - 
               
               
                   
                   
                 Time 2 file 
               
               
                 Time 1 
                 Time 1 &amp; 
                 Include recipient in Submit_Recipient List - 
               
               
                 only 
                 Time 2 
                 Time 2 
               
               
                   
                   
                 Remove donor from TimesUsed - Time 2 
               
               
                   
                   
                 (donor no longer present) 
               
               
                 Time 2 
                 N/A 
                 Append to TimesUsed - Time 2 file, set TU = 
               
               
                 only 
                   
                 0 (e.g., brand new donor, used for the first time) 
               
               
                 N/A 
                 Time 2 
                 Include in Submit_Recipient List - Time 2 
               
               
                   
                 only 
                 (e.g., brand new recipient, used for the first 
               
               
                   
                   
                 time) 
               
               
                   
               
            
           
         
       
     
     As illustrated in TABLE I, the example linkage updater  210  performs one or more updates to data and/or variables used in the data fusion process depending on which scenario is encountered. According to the example of TABLE I, when the linkage updater  210  determines that a particular donor of the panel product data  110  was available at TIME 1 and TIME 2 and that the corresponding recipient from the linkage file is no longer available, the linkage updater  210  reduces a TimesUsed value associated with that donor and stores the updated value for the TimesUsed for that donor in the TIME 2 data. That is, for TIME 2, that donor is used for one less recipient, which may affect how the data fuser  208  selects the donors for the recipients in the data fusion process of TIME 2. 
     According to the example of TABLE I, when the linkage updater  210  determines that a particular donor was available at TIME 1 but is no longer available at TIME 2, the linkage updater  210  adds the corresponding recipient to a list of recipients that requires a donor for the data fusion process of TIME 2. Further, the example linkage updater  210  removes the donor from consideration for the TIME 2 data fusion process. 
     According to the example of TABLE I, when the linkage updater  210  determines that a new donor is available for TIME 2 (e.g., a donor not available at TIME 1), the linkage updater  210  adds the donor for consideration for the data fusion process of TIME 2 and sets the corresponding TimesUsed value to zero. 
     According to the example of TABLE I, when the linkage updater  210  determines that a new recipient is available for TIME 2 (e.g., a recipient not available at TIME 1), the linkage updater  210  adds the recipient to the list of recipients that requires a donor. 
     In the example of  FIG. 5 , the linkage updater  210  updates a TimesUsed file associated with the TIME 2 data fusion process and a recipients list file associated with the TIME 2 data fusion process to reflect the appropriate changes from the example of TABLE 1 (block  504 ). 
     Returning to block  501 , for the linkages that are unbroken, the example linkage updater  210  evaluates a quality of the corresponding linkage (block  510 ). In the illustrated example of  FIG. 5 , the linkage updater  210  generates pairwise donor/recipient distance scores for TIME 2. As described above, the characteristics (e.g., demographic, geographic, and/or purchasing behavior) of the donor and/or recipient may have changed since TIME 1 and, thus, the quality of the linkage may have changed. The example linkage updater  210  determines whether any such changes warrant a new, better suited donor being assigned to the recipient. In some examples, this determination by the linkage updater  210  involves comparing a linkage quality metric (e.g., a distance score) to one or more thresholds. In some examples, the linkage updater  210  calculates a distribution of the distance scores and determines which linkages exceed a threshold (e.g., a top one percent of a distribution for three primary views of the data). Put another way, the example linkage updater  210  breaks the linkages that are the most dissimilar such that better suited matches can be made. In the example of  FIG. 5 , if the linkage does not meet the quality threshold (block  512 ), the linkage updater  210  breaks the linkage and updates the corresponding TimesUsed file and the recipient list accordingly (block  514 ). 
     In the example of  FIG. 5 , for linkages that meet the quality threshold (block  512 ), the linkage updater  210  maintains those linkages and sets those linkage apart in a base linkage file. That is, in the illustrated example of  FIG. 5 , the unbroken linkages that meet the quality threshold do not undergo the data fusion process of TIME 2. 
     In the example of  FIG. 5 , the linkage updater  210  maintains importance weights that are used to link the donors to the recipients. In the example of  FIG. 5 , the linkage updater  210  provides the importance weights to the data fusion process of TIME 2. Using the updated TimesUsed file, the updated recipients list, the updated donor list, and the importance weights, the linkage updater  210  determines updated linkages for recipients requiring a donor using the list of donors and the corresponding numbers of times the donors have been used (e.g., paired with a recipient) (block  522 ). In the example of  FIG. 5 , the linkage updater  210  combines the base linkage file and the updated linkages to generate a final linkage file for TIME 2. In some examples, the final linkage file is saved in memory, for example, memory  206  of  FIG. 2 . 
       FIG. 6  is a flowchart representative of example machine readable instructions for implementing a process  600  of the example projection generator  212 . The example projection generator  212  accounts for differences in, for example, sampling rate and purchase levels in the membership data  126  and the panel product data  110 . To ensure that retailer penetration and market shares are balanced (e.g., not skewed by differential sampling rates for the membership data and non-membership data), the example projection generator  212  employs a stratification between membership and non-membership data. 
     In the example of  FIG. 6 , the projection generator  212  stratifies the panel data associated with the panelist  106  (and other panelists of the panel) into membership households and panel households (block  602 ). In the example of  FIG. 6 , the projection generator  212  analyzes the panel households to quantify an amount of shopping each panelist conducts at respective ones of the retailers  122  that implement a membership program (e.g., a loyalty program) (block  604 ). In the example of  FIG. 6 , the projection generator  212  treats panel households that do a majority of their shopping at a same one of the retailer  118  as membership households (e.g., even though those households are not technically members in a loyalty program). The example projection generator  212  creates a first stratum (Stratum A) to include the membership households and the panel households performing a majority of their shopping at one retailer. In some examples, stratum A is saved in memory, for example, memory  206 . The example projection generator  212  creates a second stratum (Stratum B) to include those of the panel households that do not perform a majority of their shopping at a single retailer. In some examples, stratum B is saved in memory, for example, memory  206 . The example projection generator  212  combines the first and second strata (Stratum A and Stratum B) to form a panel sample (block  606 ), which includes the corresponding household characteristics and the strata designations. In some examples the panel sample is stored in memory, for example memory  206 . 
     In the example of  FIG. 6 , the projection generator  212  creates share targets for the different strata based on, for example, membership retailer dollar share within a market (block  608 ). In the example of  FIG. 6 , the share targets are used to allocate percentages of a market demographic to the first and second strata (Stratum A and Stratum B). In the example of  FIG. 6 , the projection generator  212  applies the share targets to universe estimates, which are indicative of, for example, estimated demographic and/or geographic distributions in one or more markets. In some examples the universe estimates are stored in memory, for example memory  206 . In particular, the projection generator  212  uses the share targets and the universe estimates to generate targets for a RIM (Random Iterative Method) weighting algorithm. Further, the example projection generator  212  formats the targets according to a protocol associated with the RIM weighting algorithm (block  610 ). The example projection generator  212  executes the RIM weighting algorithm to generate geographic and/or demographic weights for all households in the panel associated with the panelist  106  (block  612 ). In some examples, the geographic and/or demographic weights are stored in memory, for example memory  206 . In the illustrated example, households in the first stratum (Stratum A) are likely to have smaller or lesser weights than households in the second stratum (Stratum B). The geographic and/or demographic weights are provided to a volumetric adjustment projection system (VAPS)  614 , which is disclosed in detail below in connection with  FIG. 7 . 
     The example projection generator  212  uses the POS data to create volumetric targets for the VAPS system  614  by stratifying the POS data based on, for example, product, channel, geography, time, and market dimensions associated with the first and second strata (Stratum A and Stratum B) (block  616 ). The following table (TABLE II) illustrates example cells for volumetric targets generated by the example projection generator  212 . 
     
       
         
           
               
               
             
               
                   
                 TABLE II 
               
             
            
               
                   
                   
               
               
                   
                 Product Group 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Non- 
                 Health &amp; 
                 General 
               
               
                 Channel 
                 Food 
                 Food 
                 Beauty 
                 Merchandise 
               
               
                   
               
            
           
           
               
               
            
               
                 Grocery/Loyalty 
                 POS Volumetric Targets 
               
            
           
           
               
               
               
               
               
            
               
                 Grocery/Non-Loyalty 
                   
                   
                   
                   
               
               
                 Drug 
               
               
                 Remaining All Outlets 
               
               
                   
               
            
           
         
       
     
     In the example of TABLE II, the cells for the volumetric targets are broken down according to product group and channel. However, additional or alternative bases are possible for the volumetric target cells. The example VAPS  614  of  FIG. 6  uses the volumetric targets for a calibration process implemented by the VAPS  614 , as disclosed in detail below in connection with  FIG. 7 , to ensure that the data is weighted in accordance with known distributions among the POS data. Further, the example projection generator  212  obtains membership transaction data corresponding to, for example, purchases made in connection with loyalty programs. The membership transaction data is provided to the example VAPS  614 . In some examples, the membership transaction data is stored in memory, for example memory  206 . As disclosed below in connection with  FIG. 7 , the example VAPS  614  uses the provided information to adjust, if necessary, weights of the demographically and/or geographically weighted panel data. The resulting information generated by the example VAPS is a plurality of final weights assigned to, for example, the different households associated with the panel product data  110  and the membership data. In some examples, the final weights are stored in memory, for example memory  206 . 
       FIG. 7  is a flowchart representative of example machine readable instructions for implementing the example VAPS  614  of  FIG. 6 . As described above, the VAPS  614  is provided with volumetric targets (e.g., the targets of TABLE II), the membership transaction data, and the weights generated from the RIM weighting algorithm at block  612 . In some examples, the volumetric targets, the membership transaction data, and the weights generated from the RIM weighting algorithm are located in memory, for example memory  206 . The example VAPS  614  calculates targets for each geographic stratum (block  702 ). Accordingly, the example VAPS  614  has a plurality of targets to achieve by determining appropriate weights for the purchasing data such that market distributions are appropriately represented. In the example of  FIG. 7 , the VAPS  614  modifies one or more household-level weights according to the targets (block  704 ). When the process of modifying the household-level weights is not complete (block  706 ), the VAPS  614  iteratively continues the modification(s) (block  704 ). Otherwise, if the process is complete (block  706 ), the VAPS  614  determines whether the targets have been met (block  708 ). For example, the VAPS  614  determines whether the household-level data has converged to the targets (e.g., the volumetric targets and/or the targets for the each geographic stratum). If the data has not converged (block  710 ), the VAPS  614  re-evaluates the targets (block  712 ). For example, the VAPS  614  determines if adjustments are necessary to one or more the targets based on, for example, an inability of the data to converge. In the example of  FIG. 7 , control returns to block  702 . 
     Referring back to block  708 , if the targets have been met, the example VAPS  614  outputs the corresponding weights. Accordingly, the VAPS  614  generates adjusted or modified weights for the household-level data such that the volumetric targets are represented appropriately in the projections. In some examples, the adjusted or modified weights are stored in memory, for example memory  206 . 
     Referring back to  FIG. 2 , the reporting system  214  uses weighted fused dataset, which includes the weights generated by the VAPS  614  to, for example, generate one or more projections for larger populations using the membership data and the panel product data  110  as described above. 
     As described above,  FIGS. 3 and 5-7  are flowcharts representative of example machine readable instructions for implementing the example consumer data generator  128  of  FIGS. 1 and/or 2 . In the examples of  FIGS. 3 and 5-7 , the machine readable instructions comprise one or more programs for execution by a processor such as the processor  812  shown in the example processor platform  800  discussed below in connection with  FIG. 8 . The programs may be embodied in software stored on a tangible computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or a memory associated with the processor  812 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  812  and/or embodied in firmware or dedicated hardware. Further, although the example programs are described with reference to the flowcharts illustrated in  FIGS. 3 and/or 5-7 , many other methods of implementing the example consumer data generator  128  of  FIGS. 1 and/or 2  may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined. 
     As mentioned above, the example processes of  FIGS. 3 and/or 5-7  may be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a tangible computer readable storage medium such as a hard disk drive, a flash memory, a read-only memory (ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, a random-access memory (RAM) and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term tangible computer readable storage medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, “tangible computer readable storage medium” and “tangible machine readable storage medium” are used interchangeably. Additionally or alternatively, the example processes of  FIGS. 3 and/or 5-7  may be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium such as a hard disk drive, a flash memory, a read-only memory, a compact disk, a digital versatile disk, a cache, a random-access memory and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term non-transitory computer readable medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, when the phrase “at least” is used as the transition term in a preamble of a claim, it is open-ended in the same manner as the term “comprising” is open ended. 
       FIG. 8  is a block diagram of an example processor platform  800  capable of executing the instructions of  FIGS. 3 and/or 5-7  to implement the example consumer data generator  128  of  FIGS. 1 and/or 2 . The processor platform  800  can be, for example, a server, a personal computer, a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad®), a personal digital assistant (PDA), an Internet appliance, a media player (e.g., a DVD player, a CD player, a digital video recorder, a Blu-ray player, etc.), a gaming console, or any other type of computing device. 
     The processor platform  800  of the illustrated example includes a processor  812 . The processor  812  of the illustrated example is hardware. For example, the processor  812  can be implemented by one or more integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer. In some examples, the processor  812  implements the example consumer data generator  128  of  FIGS. 1 and/or 2 , the example matcher  200  of  FIG. 2 , the example membership sample selector  204  of  FIG. 2 , the example data fuser  208  of  FIG. 2 , the example projection generator  212  of  FIG. 2 , the example linkage updater  210  of  FIG. 2 , and/or the example reporting system  214  of  FIG. 2 , the example VAPS  614  of  FIG. 6 . 
     The processor  812  of the illustrated example includes a local memory  813  (e.g., a cache). The processor  812  of the illustrated example is in communication with a main memory including a volatile memory  814  and a non-volatile memory  816  via a bus  818 . The volatile memory  814  may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory  816  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  814 ,  816  is controlled by a memory controller. 
     The processor platform  800  of the illustrated example also includes an interface circuit  820 . The interface circuit  820  may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface. 
     In the illustrated example, one or more input devices  822  are connected to the interface circuit  820 . The input device(s)  822  permit(s) a user to enter data and commands into the processor  812 . The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system. 
     One or more output devices  824  are also connected to the interface circuit  820  of the illustrated example. The output devices  824  can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touchscreen, a tactile output device, a printer and/or speakers). The interface circuit  820  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip or a graphics driver processor. 
     The interface circuit  820  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem and/or network interface card to facilitate exchange of data with external machines via a network  826  (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.). 
     The processor platform  800  of the illustrated example also includes one or more mass storage devices  828  for storing software and/or data. Examples of such mass storage devices  828  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives. 
     Coded instructions  832  of  FIGS. 3 , and/or  5 - 7  may be stored in the mass storage device  828 , in the volatile memory  814 , in the non-volatile memory  816 , and/or on a removable tangible computer readable storage medium such as a CD or DVD. 
     Although certain example methods, apparatus and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the claims of this patent.