Patent Publication Number: US-8543446-B2

Title: Methods and apparatus to predict new product performance metrics

Description:
RELATED APPLICATION 
     This patent claims the benefit of U.S. Provisional Patent Application Ser. No. 61/447,503, which was filed on Feb. 28, 2011, and is hereby incorporated herein by reference in its entirety. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure relates generally to market research, and, more particularly, to methods and apparatus to predict new product performance metrics. 
     BACKGROUND 
     Retailers or consultants to retailers consider which product(s) to place in an aisle, and determine which competitive or complimentary products should accompany such product(s). In some examples, proximity of a product of interest to a competitive product will cause one of the two products to increase its sales volume at the expense of the other product. This effect is sometimes referred to as cannibalization. In such examples, a retailer or a consultant to retailers may choose to remove certain products from one or more shelves or aisles. In other examples, aggregate volumes of total sales, including the product of interest and one or more competitive products, improve based on the arrangement of the products displayed in the aisle (e.g., displayed on a shelf of the aisle). Thus, the retailer is typically concerned with creating an assortment of products that reduces cross product cannibalization while increasing aggregate sales of products. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a relationship matrix representative of example product relationships to be used by the system of  FIG. 3 . 
         FIG. 2  is a tree diagram representative of example hierarchical relationships within a product category. 
         FIG. 3  is a schematic illustration of an example system to predict new product performance metrics. 
         FIG. 4  is a schematic illustration of an example new product manager of the example system of  FIG. 3 . 
         FIG. 5  is an example modified tree diagram generated by the example system of  FIG. 3  that includes a new product 
         FIG. 6  is an example modified attribute matrix generated by the example system of  FIG. 3  that includes a new product. 
         FIGS. 7-9  are flowcharts representative of example machine readable instructions that may be executed to implement the example system shown in  FIGS. 3 and 4 . 
         FIG. 10  is a schematic illustration of an example processor platform that may execute the instructions of  FIGS. 7-9  to implement the example systems and apparatus of  FIGS. 3 and 4 . 
     
    
    
     DETAILED DESCRIPTION 
     Methods and apparatus are disclosed to predict new product performance metrics. An example disclosed method includes selecting a product hierarchy tree based on a distribution measure for a new product, identifying a segment location of the product hierarchy tree for the new product, retrieving a relationship matrix associated with the segment location of the product hierarchy tree, the relationship matrix comprising available product performance coefficients and attribute information, identifying a spatial location for the new product in the relationship matrix based on the attribute information, calculating, using a programmed processor, an impact parameter based on the spatial location within the relationship matrix, and calculating the performance metric for the new product based on the impact parameter and a channel sales value. 
     Attempts to introduce a new product to an existing shelf in an aisle of a retailer may be met with resistance by the retailer based on, in part, a concern that any new product may have an adverse effect on adjacent products on the shelf and, thus, reduce overall sales revenue. For example, consumer goods companies frequently attempt to introduce new products into the market, but must negotiate with retailers to de-list products (preferably of competitors) to make room for the new product(s). Absent reliable data and/or predictions of performance for the new product, the retailer may be unwilling to accept a new product. The success or failure of decisions of which products) to adjust shelf allocation, such as de-listing and/or shelf space adjustment(s), translate into improved or diminished sales profits and/or revenue performance for the retailer in general. Unfortunately, in the event the decision to de-list a product or adjust a shelf allocation result in poor sales for the new product or one or more adjacent products, the diminished aggregate sales may remain for an extended time period. Thus, it is important to accurately predict the success or failure of new product(s) and the impact such new product(s) will have on other product(s). 
     Example systems, methods, apparatus and/or articles of manufacture disclosed herein may be used to predict the performance of a new product that has not yet been available in a consumer market. At least one benefit of example systems, methods, apparatus and/or articles of manufacture disclosed herein is an opportunity to predict new product performance before deciding which other product(s) to modify (e.g., de-list, modify shelf allocation, etc.), thereby avoiding the overall diminished sales profits and/or revenue effects of a poor modification decision. Although identifying different product assortments to reduce cannibalization and ensure aggregate sales, profits and/or revenue is possible for existing products based on, in part, analysis of historical sales data, new products do not have such historical sales data to facilitate forecasting efforts. 
     Example systems, methods, apparatus and/or articles of manufacture disclosed herein forecast a performance metric of products to be displayed in a store. The forecast may be affected by, in part, how much of the product will be available in the market and/or how much of the product will be available in a particular channel of the market (e.g., a grocery store channel, a convenience store channel, a gas station channel, etc.). Generally speaking, a measure of how much of the product is available in a market channel is referred to as a “distribution measure” of the product. For example, a product associated with the dental products category may be offered for consumer purchase at grocery store and pharmacy channels. A distribution measure of 50% indicates that only half of the channels expected to carry the product of interest in the dental products category will actually make that dental product available to consumers. On the other hand, a distribution measure of 100% indicates that the product of interest is available in all of the expected channels. 
     Measures of distribution vary from one geography to another based on, in part, the types of retailers present in the geography, the types of products historically sold in the geography, cultural effects, etc. For example, a category related to barbecue condiments may exhibit greater distribution in Southern geographies of the United States when compared to Northern geographies. Additionally, for a given distribution measure of a geography, the presence of specialty retailers (e.g., barbecue supply stores, boutique cooking supply stores, office supply stores, etc.) may affect sourcing of products within the category of interest. Differences in geographic distribution may also cause differences in the manner in which products affect each other on a display shelf in terms of proportional volume shifting. In some examples, the addition of a competitive product shifts volume away from the other products adjacent to the competitive product in a manner that can be characterized empirically. Empirical observations over time are cultivated by example methods, systems, apparatus and/or articles of manufacture disclosed herein to allow differential weights (impact parameters) to be derived between products of one or more categories and/or specific to one or more channels. The empirical observations are aggregated to derive the differential weights and generate one or more relationship matrices. 
     In the illustrated example of  FIG. 1 , a relationship matrix  100  includes a row of available products  102  and a column having the same available products  104 . Each cell within the example relationship matrix  100  includes data derived from empirical observations indicative of the effect a product in the row  102  has on a product in the column  104 . Products along the diagonal of the example relationship matrix  100  reflect a direct impact (“DI”), which is the impact of the product on itself. Products not along the diagonal reflect a cross impact (“CI”), which include performance coefficients and product attributes at the product level to allow one or more calculations of the effect on one product to another product when placed on a retail shelf. To identify an effect that changing an assortment of products may have on sales, the product pairs may be selected at different rows/columns to calculate impact parameters from the intersecting cell coefficient value(s). 
     The example relationship matrix  100  of  FIG. 1  may be one of many relationship matrices employed by example systems, methods, apparatus and/or articles of manufacture disclosed herein when forecasting the effects of changing product assortment option(s) and/or delisting product(s). For example, a relationship matrix may be tailored to include geographic attributes that reflect interaction behavior for particular geographic regions within a market and/or sub-market. Changing geographic regions exhibit different impacts from one product to another product, different impacts from one product to a category of products, different proportions of sales, etc. As such, the relationship matrix illustrates how products give and/or take away volume when introduced on a retail shelf in a given geographic market, which may be further influenced by the geographic attributes, such as, but not limited to distribution measures, product attributes, channel placement, etc. 
     Prior to developing one or more relationship matrices, such as the example relationship matrix  100  of  FIG. 1 , example systems, methods, apparatus and/or articles of manufacture disclosed herein develop one or more attribute trees to visualize a hierarchy of products (sometimes referred to as a “hierarchy tree”). In the illustrated example of  FIG. 2 , the example attribute tree  200  includes any number of products at a first level detail (i.e., most granular level)  202 . In the example of  FIG. 2 , levels of detail below the highest level include nodes (segments) having a greater degree of detail, and the lowest level of an attribute tree typically contains specific products. An example second level detail  204  includes all of the products or other items below it, and may have one or more adjacent types of second level detail. In the illustrated example of  FIG. 2 , flavors are a second level detail  204 . An example third level detail  206  is an abstraction of the level beneath it (i.e., the second level detail  204 ), and an example fourth level detail  208  represents a highest abstraction of the example attribute tree  200 , such as a category. Although the illustrated example of  FIG. 2  includes four levels of detail, other numbers of levels may alternatively be employed. The example levels of detail of  FIG. 2  are described herein for purposes of explanation and not limitation. 
     Without limitation, the example attribute tree  200  may be stored in a database or memory and accessed by the analyst via a graphical user interface to explore and/or examine any level of attribute detail in the hierarchy. The example fourth level detail  208  of  FIG. 2  represents a category of sports drinks  210 , which may further be described and/or otherwise considered by the example systems, methods, apparatus and/or articles of manufacture disclosed herein to have sub-categories. In the illustrated example of  FIG. 2 , the third level of detail  206  identifies additional granularity of the level above it, such as type of sports drinks  210  (e.g., low calorie  212 , zero calorie  214 , or performance  216 ). After selecting one or more levels of detail from the example attribute tree  200  (e.g., via a user interface, a graphical user interface (GUI), etc.), associated information may be visualized that is related to the preceding level. In the illustrated example of  FIG. 2 , the example zero calorie  214  attribute in the third level of detail  206  includes three sub-attributes in the second level detail  204 ; apple flavor  218 , grape flavor  220  and orange flavor  222 . Additionally, after selecting and/or otherwise navigating to one or more attributes in the example second level of detail  204 , corresponding sub-attributes are displayed and/or otherwise revealed in the example attribute tree  200  such as size X  224 , size Y  226  and size Z  228 . Each attribute tree, such as the example attribute tree  200  of  FIG. 2 , may be associated with a corresponding relationship matrix, such as the example relationship matrix  100  of  FIG. 1 . 
     Additionally, a unique attribute tree may be associated with each geographic region of interest to reflect particular regional influences of one product in view of other products. For example, a first geographic region may include low calorie sports drinks  212 , zero calorie sports drinks  214  and performance sports drinks  216 , while a second geographic region may not sell and/or otherwise make available the zero calorie sports drinks  214 . In such examples, the interaction between low calorie sports drinks  212  and performance sports drinks  216  is different in the first geographic region as compared to the second geographic region. In still other examples, because the second geographic region includes only two types of sports drinks (i.e., low calorie  212  and performance  216 ), a relatively greater degree of product sourcing and/or influence may occur therebetween than compared to the first geographic region, which includes a greater degree of distribution variety. Generally speaking, the attribute tree  200  allows the analyst to appreciate how products may be physically related, and maps each product of interest to a corresponding hierarchy of attributes. 
     While the example attribute tree  200  of  FIG. 2  and the example relationship matrix  100  of  FIG. 1  allows the analyst to appreciate the effects of placing competitive products on a shelf, the analyst is not able to identify such effects with respect to new products that do not have a corresponding sales history. Example methods, apparatus and/or articles of manufacture disclosed herein may be used to tailor the relationship matrix in a manner that adds the new product of interest and identifies effect(s) of introducing such a product (with or without disturbing other product(s)). Some such examples derive impact parameters associated with the introduction of the new product by leveraging impact parameters and/or coefficients of rows and columns of the relationship matrix against the new row and/or column added for the new product. The derivation of these impact parameters further enable calculations to reveal the impact of introducing the new product on: (1) sales allocated to a geography of interest, (2) distribution in the geography, and/or (3) shelf space in the geography. 
       FIG. 3  is a schematic illustration of a system  300  to predict one or more new product performance metrics. As used herein, “product performance metrics” include, but are not limited to, sales (e.g., volume, profit, revenue and/or other sales measures) allocated to a geography, distribution effects in the geography, a measure of shelf space occupied by the new product in the geography, and/or market forecasting of the effect(s) the new product has on other products. In the illustrated example of  FIG. 3 , the system  300  includes a product information database  302 , a channel information database  304 , and a relationship matrix generator  306 . In operation, the example relationship matrix generator  306  generates one or more relationship matrices based on information from the product information database  302  and/or the channel information database  304 . The example product information database  302  of  FIG. 3  includes product specific attribute information such as, but not limited to product name, manufacturer name, brand, packaging type, product size, flavor, lot number, serial number, nutritional information and/or a corresponding universal product code (UPC). The Nielsen® Product Reference Library (PRL) that codes more than 700,000 items, in which each item includes an average of forty (40) descriptive characteristics, is an example source for such product information. 
     The example product information database  302  of  FIG. 3  also includes historical point of sale (POS) information for the products in the PRL. POS information may be obtained in any manner including, but not limited to panelist based consumer monitoring, such as the Nielsen® Homescan® system, and/or POS UPC scanning equipment at retailer locations, such as the Nielsen® Scantrack® system. Information related to one or more channels in the example channel information database  304  may be obtained in any manner including, but not limited to the Nielsen® TDLinx® system, which tracks and stores information related to retailer stores, parent company information, parent company marketing group(s), a number of store(s) in operation, a number of employees per store, an address of the store, and/or the channel(s) serviced by the store(s) (e.g., a grocery channel, a pharmacy channel, etc.). 
     The example relationship matrix generator  306  of  FIG. 3  uses the information indicative of product attributes, sales, and/or geographic channel activity to generate one or more relationship matrices, such as the example matrix  100  of  FIG. 1 . The matrices and/or associated attribute tree(s) may be stored in a memory for later retrieval and use, such as in a market intelligence database  308 . 
     Although a new product (which, by definition, has not yet been released to the relevant marketplace of interest) does not have associated sales activity data in one or more geograph(ies) and/or channel(s) of interest, an example new product manager  310  receives information related to the new product and modifies one or more relationship matrices to leverage impact parameters of adjacent products (e.g., sibling products). Using the impact parameters from the adjacent products, the example new product manager  310  generates one or more product metrics  312  related to sales allocated to a geography of interest, distribution effects in the geography of interest, and/or a measure of shelf space in the geography of interest. The information related to the new product received by the example new product manager  310  includes, but is not limited to, sales estimates  314 , distribution information  316  and/or hierarchy information  318 . 
       FIG. 4  is a schematic illustration of an example implementation of the new product manager  310  of  FIG. 3 . In the illustrated example of  FIG. 4 , the new product manager  310  includes a tree manager  402 , a matrix modifier  404 , a proximity weighting engine  406 , and a ratio engine  408 . In operation, the example tree manager  402  receives the distribution information and the hierarchy information to generate a modified attribute tree that includes the new product. For example, considering the example attribute tree  200  of  FIG. 2  and a new product related to a zero calorie sports drink having a grape flavor and an extra large size of 20 ounces, the tree manager  402  identifies a spatial placement of the new product within the hierarchy of the already existing attribute tree  200 . Turning to  FIG. 5 , a modified attribute tree  500  is shown that is substantially similar to the attribute tree  200  shown in  FIG. 2 . Based on the distribution information and hierarchy information, the tree manager  402  places the new product at a branch  530  (segment) of the tree  500 . In some examples, a manufacturer of the new product will identify a proper location (e.g., hierarchy information  318 ) for the new product based on their understanding of appropriate similar products, channels and/or attributes. In other examples, the example tree manager  402  may determine that, based on, for example, geographical distribution information  316 , the new product should be placed at an alternate location within the tree  500  because of, for example, expected channel influences within the geography. 
     Returning to  FIG. 4 , the example matrix modifier  404  tailors an existing relationship matrix, such as the example relationship matrix  100  of  FIG. 1 , to incorporate a new product row and a new product column based on the modifications to the example tree  500 , the distribution information  316  and/or the hierarchy information  318 . As described above, a relationship matrix includes information indicative of historical sales activity related to existing products in the market. Additionally, the cells of the relationship matrix may include product attribute information and coefficient values that facilitate calculation of impact parameters between existing products. However, prior to using one or more coefficient values to calculate impact parameters, the example matrix modifier  404  of  FIG. 4  inserts a new product row and a new product column into the relationship matrix in a manner that conforms to the distribution information  316  and/or hierarchy information  318  for the new product of interest. 
     Continuing with the sports drink example, the matrix modifier  404  enters the new sports drink in a column and row adjacent to a sports drink from competitor “A” because, in the geographic region identified by the distribution information  316 , competitor “A” has a substantial presence. In some examples, the substantial presence may be based on a threshold value, such as a threshold percentage value of one or more products in a geographic region of interest (e.g., a 50% distribution, a 75% distribution, etc.). On the other hand, one or more additional and/or alternate relationship matrices may be generated by the matrix modifier  404  based on available competitors in the alternate geographies. As described above, knowledge of the available competitors in a geographic region of interest may reveal which existing products will be sourced by the new product(s). For example, the matrix modifier  404  may enter the new sports drink in a column and row adjacent to a sports drink from competitor “B” because, in the other geographic region identified by the distribution information  316 , competitor “B” has a threshold presence. In still other examples, the matrix modifier  404  may enter the new sports drink in a column and row adjacent to multiple sports drinks from both competitors “A” and “B” in the event that both have a substantial presence and/or observed sales volume in the other geography. While the above examples refer to geographic factors and/or geographical distribution information, example methods and apparatus disclosed herein are not limited thereto. One or more alternate and/or additional factors may be employed with the example matrix modifier  404 , such as, but not limited to one or more product characteristics (size, packaging, color, shape, etc.), similar items in distribution, shelf characteristics, volumetric space characteristics (e.g., shelf dimensions), etc. 
     Turning briefly to  FIG. 6 , an example modified relationship matrix  600  is shown that is substantially similar to the example relationship matrix  100  of  FIG. 1 . As described above, the received hierarchy information  318  and/or modifications to the example attribute tree  200  assist in identifying where a new product row and column should be placed. In the illustrated example of  FIG. 6 , the new zero calorie grape flavored sports drink of size W  530  is inserted into the modified relationship matrix  600  at row  602  and column  604 . While row  602  and column  604  represent the same newly added product (e.g., zero calorie grape flavored sports drink of size W), the intersecting values between the row(s) and column(s) may be different. In this example, the example matrix modifier  404  identified neighboring products deemed most similar to the new zero calorie grape flavored sports drink of size W  530  based on, in part, the received hierarchy information  318 , which depending on, in part, geography information (what types of stores, what they keep on shelves, etc.) and the received distribution information  316 . While the illustrated example of  FIG. 6  shows a single modified relationship matrix  600 , the matrix modifier  404  may generate any number of modified relationship matrices tailored for one or more market geographies identified by the distribution information  316 . As described above, each relationship matrix may include information indicative of geographic characteristic(s) related to the marketing channel, distribution, and/or product attribute(s) (e.g., an amount of linear units of space consumed by the product on a shelf). For example, in a first market geography, the new product  530  is placed in a new row and column adjacent to a first set of competitive products because, in part, that first set of competitive products has a presence (e.g., 85% distribution in the first market geography) in the first market geography. However, that same modified relationship matrix may not be appropriate for a second market geography due to, in part, differing competitive players in that second geography. 
     The example proximity weighting engine  406  of  FIG. 4  populates the new cells in the modified relationship matrix  600  of the newly added row  602  and column  604  that are associated with the new product. Coefficients from cells associated with existing products that surround the cells associated with the new row  602  and column  604  may be used to generate new coefficients for the new cells (shown shaded in  FIG. 6 ). The new coefficients may be generated by the proximity weighting engine  406  in any manner including, but not limited to, averaging, curve fitting and/or geometric weighting techniques. In other examples, the new coefficients are the result of a weighted mathematical average of sibling matrix cells. Additionally, the example proximity weighting engine  406  creates impact parameters from one or more cell coefficient value(s) in view of one or more comparisons between the new product and adjacent competitive product(s). The values in the new cells may be calculated based on immediately adjacent cells and/or on cells spaced from the new cells by one or more intervening cells. The new cell coefficient values in the cells associated with the new product of interest may be used to calculate product level performance metrics of the new products by using an assortment optimization model, such as the Assortman Optimizer® by Nielsen®. 
     The example ratio engine  408  of  FIG. 4  employs the modified attribute tree, such as the example modified attribute tree of  FIG. 5 , and sales estimates for a channel of interest, such as the sales estimates  314 , to calculate sales allocated to a geography of interest, a distribution effect in the geography of interest, and/or a measure of shelf space in that geography. In other examples, the ratio engine  408  employs information from the attribute tree to calculate sales allocated to the geography of interest, and the modified relationship matrix may be further employed to calculate specific volumetric effects of introducing the new product to the market on other products (e.g., sibling products). The concept of aiding or hampering sales volume of an existing product by introducing a new product is sometimes referred to as “cannibalization.” 
     Using the sales estimate information  314 , which is an estimated sales value for a channel of the new product, category sales in a target geography, and/or channel sales in the target geography, the example ratio engine calculates estimated sales of the new product in the target geography. In other words, if a sales estimate is 10,000 units in a grocery store channel at a 50% distribution, then the example ratio engine  408  facilitates calculating how much of those 10,000 units will sell in the geography of interest. A ratio of the category sales within the target geography over the overall category sales within the channel of interest is calculated and referred to herein as a “category ratio.” A category is a type of product (e.g., sports drinks, board games, etc.). Additionally, a ratio of channel sales in the target geography over the overall channel sales is calculated and referred to herein as a “channel ratio.” A channel is a category of distribution (e.g., retail stores, grocery stores, hardware stores, etc.). Furthermore, because some new products will exhibit different influences on category and channel sales due to the nature of the product, the popularity of the product, regional preferences for the product, and/or the popularity of the brand, impact parameters are applied to the channel ratio and the category ratio based on how the new product fits within the category. In some examples, information regarding how the new product fits within the category is obtained from attribute tree information, such as the example attribute tree  500  of  FIG. 5 . 
     Generally speaking, when determining an impact parameter (weight) the proximity weighting engine  406  may consider whether category activity for the new product is believed to be more influential than channel activity. For example, pizza sales are not likely to have distinguishing channel activity differences from one geographic market to another. In most geographical markets, as evidenced by the historical POS data from the product information database  302  and/or the channel information database  304 , pizza sales are substantially homogeneous. However, as more detailed levels of granularity in the example attribute tree for the pizza category are propagated (e.g., advancing from a high level tree category toward a lower level of the tree having greater granularity and/or product specificity), geographic (regional) differences become more apparent. For example, a sub-category of pizza may include pan pizza versus thin-crust pizza. In the event that the new product is related to pan pizza and a target geography of interest is Chicago, then channel activity is likely to be an influential component of sales allocated to the geography of interest. On the other hand, if a second geography of interest includes Seattle, the channel activity may also be an influential component of sales based on an observed preference for thin-crust pizza in the Northwest United States. 
     An example manner of calculating sales allocated to a geography of interest is shown in Equation 1. 
     
       
         
           
             
               
                 
                   
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                   = 
                   
                     
                       
                         SalesEstimate 
                         
                           α 
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                       ⁡ 
                       
                         [ 
                         
                           
                             A 
                             B 
                           
                           + 
                           
                             
                               α 
                               2 
                             
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                   Equation 
                   ⁢ 
                   
                       
                   
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                   1 
                 
               
             
           
         
       
     
     In the illustrated example of Equation 1, which may be implemented by the example ratio engine  408 , V(g) represents the volume sales of the new product allocated to the target geography (g), SalesEstimate represents an estimated number of new products to be sold in the target geography of a particular channel (e.g., 10,000 units), such as the example sales estimate  314  of  FIG. 3 , α 1  and α 2  represent first and second impact parameters, the ratio A/B represents the category ratio, and the ratio C/D represents the channel ratio. In particular, A represents category sales in the target geography (g), B represents overall category sales in the channel. The value of A and B may be obtained from the product information database  302  and/or the channel information database  304 . C represents channel sales in the target geography, and D represents overall channel sales. As described above, because one or more modified relationship matrices, such as the example modified relationship matrix  600  of  FIG. 6 , are tailored by the example matrix modifier  404  to geographic market characteristics, the resulting sales allocated to the target geography for the new product of interest and sibling product(s) may be calculated in the geography of interest based on the coefficients in the new row and column. Additionally, one or more forecasts may be generated in view of scenarios where distribution factors are changed to view corresponding effects on products within the relationship matrices. 
     A measure of the allocation or spread of items (distribution) over one or more geographies of interest may also be calculated by the example ratio engine  408  to facilitate comparison of items in the target geography to items in the channel geography. An example manner of calculating the measure of the distribution over a geography of interest is shown in Equation 2. 
     
       
         
           
             
               
                 
                   
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                         Dist 
                         
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                   Equation 
                   ⁢ 
                   
                       
                   
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                   2 
                 
               
             
           
         
       
     
     In the illustrated example of Equation 2, D(g) represents the items allocated to geography (g), Dist represents an estimated distribution for the new products for the target geography, such as the example distribution information  316  of  FIG. 3 , α 1  and α 2  represent first and second impact parameters, the ratio A/B represents the category ratio, and the ratio C/D represents the channel ratio. In particular, A represents category distribution in the target geography (g), B represents overall category distribution in the channel. The values of A and B may be obtained from the product information database  302  and/or the channel information database  304 . C represents channel distribution in the target geography. D represents overall channel distribution. 
     While an example manner of implementing an example system  300  to predict new product performance metrics has been illustrated in  FIGS. 3 and 4 , one or more of the elements, processes and/or devices illustrated in  FIGS. 3 and 4  may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example product information database  302 , the example channel information database  304 , the example relationship matrix generator  306 , the example market intelligence database  308 , the example new product manager  310 , the example tree manager  402 , the example matrix modifier  404 , the example proximity weighting engine  406  and/or the example ratio engine  408  of  FIGS. 3 and 4  may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example product information database  302 , the example channel information database  304 , the example relationship matrix generator  306 , the example market intelligence database  308 , the example new product manager  310 , the example tree manager  402 , the example matrix modifier  404 , the example proximity weighting engine  406  and/or the example ratio engine  408  of  FIGS. 3 and 4  could be implemented by one or more circuit(s), programmable processsor(s), application specific integrated circuits) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)), etc. When any of the apparatus or system claims of this patent are read to cover a purely software and/or firmware implementation, at least one of the example product information database  302 , the example channel information database  304 , the example relationship matrix generator  306 , the example market intelligence database  308 , the example new product manager  310 , the example tree manager  402 , the example matrix modifier  404 , the example proximity weighting engine  406  and/or the example ratio engine  408  of  FIGS. 3 and 4  are hereby expressly defined to include a tangible computer readable medium such as a memory, DVD, CD, Blu-ray, etc. storing the software and/or firmware. Further still, the example system  300  of  FIG. 3  may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIGS. 3 and 4 , and/or may include more than one of any or all of the illustrated elements, processes and devices. 
     Flowcharts representative of example machine readable instructions for implementing the system  300  of  FIGS. 3 and 4  are shown in  FIGS. 7-9 . In these examples, the machine readable instructions comprise a program for execution by a processor such as the processor  1012  shown in the example computer  1000  discussed below in connection with  FIG. 10 . The program may be embodied in software stored on a tangible computer readable 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  1012 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  1012  and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowcharts illustrated in  FIGS. 7-9 , many other methods of implementing the example system  300  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. 7-9  may be implemented using coded instructions (e.g., computer readable instructions) stored on a tangible computer readable 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 media in which information is stored for any duration (e.g., for extended time periods, permanently, brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term tangible computer readable medium is expressly defined to include any type of computer readable storage and to exclude propagating signals. Additionally or alternatively, the example processes of  FIGS. 7-9  may be implemented using coded instructions (e.g., computer readable instructions) stored on a non-transitory computer 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 media in which information is stored for any duration (e.g., for extended time periods, permanently, 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 medium and to exclude propagating signals. 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. Thus, a claim using “at least” as the transition term in its preamble may include elements in addition to those expressly recited in the claim. 
     The program  700  of  FIG. 7  begins at block  702  where the example new product manager  310  receives and/or otherwise retrieves the sales estimate information associated with a specified channel (e.g., number of unit sales in grocery channel, number of unit sales in pharmacy channel, etc.)  314 , the distribution information  316  (e.g., 50% distribution for the identified channel) and/or the hierarchy information  318 . As described above, while a new product for a region or channel of interest does not have associated historical sales and distribution data associated with it for that region or channel, information from a user of the example system  300  may provide estimates of expected and/or targeted sales and distribution goals for the new product. To help constrain the received estimate information with a degree of confidence, such received distribution information  316  and/or hierarchy information  318  is used by the example tree manager  402  to identify one or more candidate attribute tree(s) (block  704 ). The example market intelligence database  308  includes, in part, candidate attribute trees generated by the example tree manager  402  over time for exhibited sales and distribution behavior of existing market products. Example attribute trees include, but are not limited to, one or more hierarchical arrangements of categories specific to one or more geographies. Example attribute trees may additionally or alternatively include hierarchical information related to one or more particular channels for one or more geographies. 
     Generally speaking, an attribute tree may have different hierarchical arrangements based on the geography and/or channel with which it is associated. For example, in some geographies a category has an established set of competitors that compete for shelf space. In other geographies, a particular category may include a substantial sub-category preference (e.g., deep-dish pizza), while still other geographies include relatively few manufacturers for such sub-categories based on observed sales data. After selecting a candidate attribute tree based on the received distribution  316  and product hierarchy information  318 , the example tree manager  402  tailors the candidate tree to include the new product within one or more of its branches (block  706 ). In some examples, a manufacturer of the new product identifies (a) a spatial location within the candidate tree where the new product is believed to be most similar to other products, (b) other sub-categories and/or (c) marketing channel(s). 
     As described above, because some products have different influences from geography to geography, from channel to channel, and/or from category to category, the example proximity weighting engine  406  establishes one or more impact parameters associated with the new product (block  708 ). In the event that the modified attribute tree indicates that a channel (e.g., grocery store, pharmacy, specialty store, etc.) associated with the geography of interest is likely to be an influential component of the new product distribution, an impact parameter may be weighted accordingly. For example, if the new product is related to edamame and the geography of interest includes a substantial Japanese culture, then the impact parameter associated with a specialty store channel may be weighted higher in view of a density of grocery stores that provide Japanese products. On the other hand, if the new product of interest is a thin-crust pizza, then the impact parameter may not be weighted as heavily based on a historically homogeneous level of sales in grocery channels from one geography to another. 
     In other examples, a new product may elicit a greater influence on a category, thereby causing the proximity weighting engine  406  to calculate an impact parameter having a greater weight. For example, while an overall pizza category may not exhibit a substantial channel influence from one geography to the next, the pizza category may differ substantially in view of products within sub-categories. Deep-dish pizza, for example, has a greater category influence in some geographic markets than in others, thereby warranting a corresponding impact parameter weight assigned by the proximity weighting engine  406 . 
     The example ratio engine  408  of  FIG. 4  uses the one or more impact parameters to calculate where sales are likely to manifest in the market and the distribution of the new products within the geography (block  710 ). For example, if 10,000 units of the new product are estimated to be sold in a grocery store channel, then the example ratio engine  408  identifies how many of those 10,000 units will actually sell in the target geography of interest. As described above, the ratio engine  408  of the illustrated example employs example Equations 1 and/or 2 to establish a category ratio and a channel ratio in view of received sales estimate information  314 , received distribution information  316 , information from the historical market intelligence database  308 , and the impact parameter(s) to calculate an allocation of sales, or an allocation of distribution over one or more geographies of interest. 
     While the calculated allocation of sales and/or allocation of distribution (block  710 ) provides insight into the general sales effects from a channel of interest in a geography of interest, the example relationship matrix generator  306  further tailors a candidate relationship matrix to allow more granular forecasting of how the new product of interest will likely affect sibling products (block  712 ). As described in further detail below, a modified relationship matrix, such as the example modified relationship matrix  600  of  FIG. 6 , may be tailored and used with a model (e.g., the Assortman Optimizer® by Nielsen®) to calculate performance metrics of the new product (block  714 ). Performance metrics may include, but are not limited to, a proportional spread of volume of the new product in the geography of interest and/or a proportional distribution of the new product in the geography of interest. 
     The program  712  of  FIG. 8  illustrates further detail related to building product relationships for each geography of interest (block  712 ) described in  FIG. 7 . In the illustrated example of  FIG. 8 , the program  712  begins at block  802  where the relationship matrix generator  306  identifies one or more candidate relationship matrices based on the geography of interest. As described above, each geography may have a different assortment of sibling products based on, in part, the presence of certain competitors. Sibling products in the candidate matrix selected for the geography of interest are identified by the example matrix modifier  404  that have a match to products within the modified attribute tree (block  804 ). A new column and row are inserted to the relationship matrix at the identified locations having matching products (block  806 ). The new column and row correspond to the new product of interest. Cell coefficients for the cells associated with the new column and row are calculated by the example proximity weighting engine  406  and/or the example matrix modifier  404  based on the cell coefficients of the sibling products (block  808 ). As described above, the new cell coefficients may be generated in any manner, such as, but not limited to a mathematical average of the sibling cells. The new cell coefficients now allow one or more models to be applied that calculate a relative effect of the new product on the sibling products. 
     Turning to  FIG. 9 , the program  714  of  FIG. 9  illustrates further detail related to calculating performance metrics of new products described in  FIG. 7 . In the illustrated example of  FIG. 9 , the program  714  begins at block  902  where the new product manager  310  selects an assortment of product(s) related to the new product of interest. As described above, the example modified relationship matrix  600  includes a row/column associated with the new product of interest and each cell of the matrix  600  includes coefficients indicative of product shifting effects (proportional volume shifting effects). As each product has a measurable effect on every other product in the example matrix  600 , different assortments of products in a store (e.g., on a shelf in an aisle) impose different effects on each other. Using the allocation of sales (or an allocation of distribution) calculated by the example ratio engine  408 , the effects on existing products may be identified in terms of volume sold in the geography of interest (block  904 ). Additionally, an aggregate number of stores, chains and/or other channel aspect may be calculated to determine an overall sales effect (block  906 ). 
     Results may be stored in a memory (block  908 ), such as in the example market intelligence database  308  of  FIG. 3 , and another combination of product assortment may be considered (block  910 ). When any number of product assortment(s) combinations have been analyzed (block  910 ), the results may be compared to identify which product(s) are candidates for modification (e.g., a de-listing candidate) (block  912 ). For example, one or more analyses may indicate that a first product frequently illustrates poor performance when placed on a shelf proximate to the new product. The one or more analyses may alternatively conclude that, despite the poor performance of the first product, an overall store sales increase for the category and/or channel of interest increases in the aggregate, thereby confirming that the first product is not a candidate for modification (e.g., de-listing). 
       FIG. 10  is a block diagram of an example computer  1000  capable of executing the instructions of  FIGS. 7 ,  8  and/or  9  to implement the example product information database  302 , the example channel information database  304 , the example relationship matrix generator  306 , the example market intelligence database  308 , the example new product manager  310 , the example tree manager  402 , the example matrix modifier  404 , the example proximity weighting engine  406  and/or the example ratio engine  408  of  FIGS. 3 and 4 . The computer  1000  can be, for example, a server, a personal computer, an Internet appliance, or any other type of computing device. 
     The system  1000  of the instant example includes a processor  1012 . For example, the processor  1012  can be implemented by one or more microprocessors or controllers from any desired family or manufacturer. 
     The processor  1012  includes a local memory  1013  (e.g., a cache) and is in communication with a main memory including a volatile memory  1014  and a non-volatile memory  1016  via a bus  1018 . The volatile memory  1014  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  1016  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  1014 ,  1016  is controlled by a memory controller. 
     The computer  1000  also includes an interface circuit  1020 . The interface circuit  1020  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. 
     One or more input devices  1022  are connected to the interface circuit  1020 . The input device(s)  1022  permit a user to enter data and commands into the processor  1012 . The input device(s) can be implemented by, for example, a keyboard, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system. 
     One or more output devices  1024  are also connected to the interface circuit  1020 . The output devices  1024  can be implemented, for example, by display devices (e.g., a liquid crystal display, a cathode ray tube display (CRT), a printer and/or speakers). The interface circuit  1020 , thus, typically includes a graphics driver card. 
     The interface circuit  1020  also includes a communication device such as a modem or network interface card to facilitate exchange of data with external computers via a network  1026  (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.). 
     The computer  1000  also includes one or more mass storage devices  1028  for storing software and data. Examples of such mass storage devices  1028  include floppy disk drives, hard drive disks, compact disk drives and digital versatile disk (DVD) drives. The mass storage device  1028  may implement the example product information database  302 , the example channel information database  304  and/or the example market intelligence database  308 . 
     The coded instructions  1032  of  FIGS. 7-9  may be stored in the mass storage device  1028 , in the volatile memory  1014 , in the non-volatile memory  1016 , and/or on a removable storage medium such as a CD or DVD. 
     From the foregoing, it will be appreciated that the above disclosed systems, methods, apparatus and articles of manufacture facilitate prediction of new product performance metrics within one or more geographies and/or channels of interest when, for example, no prior historical sales data is available for the new product in the corresponding geography or channel. 
     Although certain example methods, apparatus and articles of manufacture have been described 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 either literally or under the doctrine of equivalents.