# US 8,543,446 B2 · Methods and apparatus to predict new product performance metrics > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,543,446 B2 - Document ID: US8543446B2 - Status: Granted - Kind: B2 - Application number: 13/405,856 - Filed: 2012-02-27 - Published: 2012-11-22 - Granted: 2013-09-24 - Earliest claimed: 2011-02-28 - Assignees: The Nielsen Company (US), LLC - Applicants: Bruce C. Richardson; Martin Quinn; Jeffrey S. Long - Inventors: Bruce C. Richardson; Martin Quinn; Jeffrey S. Long - Web page: https://patentgrep.com/patent/US8543446B2 ## CPC - G06Q 30/0201: Market modelling; Market analysis; Collecting market data - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract 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. ## Claims ### Claim 1 A method to predict performance metrics of a new product, comprising: identifying, with a programmed processor, a segment location of a product hierarchy tree based on a projected distribution measure for the new product; retrieving, with the programmed processor, a relationship matrix associated with the segment location of the product hierarchy tree, the relationship matrix comprising available product performance coefficients and attribute information; calculating, using the programmed processor, an impact parameter based on a spatial location within the relationship matrix, the spatial location based on the attribute information; calculating the performance metrics for the new product for first and second assortments of related products on a store shelf, the first and second assortments associated with first and second proportional volume shifting effects, respectively, the performance metric calculated based on the impact parameter and a sum of (a) a ratio of category sales in a target geography and overall category sales in a sales channel associated with the new product, and (b) a ratio of channel sales in the target geography and overall channel sales in the sales channel associated with the new product; and selecting a candidate product to de-list based on sales effects with the first and second assortment of products on the store shelf. ### Claim 2 (depends on 1) A method as described in claim 1, further comprising identifying a plurality of product hierarchy trees related to at least one of a market channel or a market category associated with the new product. ### Claim 3 (depends on 1) A method as described in claim 1, further comprising retrieving a second relationship matrix associated with the projected distribution measure. ### Claim 4 (depends on 3) A method as described in claim 3, wherein the projected distribution measure includes a geographic indicator of at least one of a category or a channel associated with the new product. ### Claim 5 (depends on 4) A method as described in claim 4, further comprising identifying products in the relationship matrix that match sibling products in the product hierarchy tree. ### Claim 6 (depends on 5) A method as described in claim 5, further comprising adding a row and column associated with the new product to the relationship matrix adjacent to the matched sibling products. ### Claim 7 (depends on 6) A method as described in claim 6, further comprising calculating cell coefficient values for a plurality of cells in the row and column associated with the new product based on the matched sibling products. ### Claim 8 (depends on 1) A method as described in claim 1, further comprising tailoring the relationship matrix based on geographic attributes associated with a geography of interest. ### Claim 9 (depends on 1) A method as described in claim 1, further comprising de-listing the candidate related product when overall category sales for a store decrease and sales of the candidate related product decrease. ### Claim 10 (depends on 1) A method as described in claim 1, further comprising refraining from de-listing the candidate related product when sales of the candidate related product decrease while overall category sales for a store increase. ### Claim 11 An apparatus to predict performance metrics of a new product, comprising: a tree manager to select a product hierarchy tree based on a distribution measure for a new product, and to identify a segment location of the product hierarchy tree based on the distribution measure for the new product; a matrix modifier to retrieve a relationship matrix associated with the segment location of the product hierarchy tree, the relationship matrix comprising available product performance coefficients and attribute information, the matrix modifier to identify a spatial location for the new product in the relationship matrix based on the attribute information; and a proximity weighting engine to calculate an impact parameter based on the spatial location within the relationship matrix, and to calculate the performance metric for the new product for a first and second assortment of related products on a store shelf, the first and second assortments to be associated with first and second proportional volume shifting effects, respectively, the performance metric calculated based on the impact parameter and a sum of (a) a ratio of category sales in a target geography and overall category sales in a sales channel associated with the new product, and (b) a ratio of channel sales in the target geography and overall channel sales in the sales channel associated with the new product; and a new product manager to select a candidate product to de-list based on a sales effect with the first and second assortment of products on the store shelf; and wherein the tree manager, the matrix modifier, the proximity weighting engine and the new product manager comprising a logic circuit. ### Claim 12 (depends on 11) An apparatus as described in claim 11, wherein the tree manager is to select the distribution measure based on a channel and a category. ### Claim 13 A tangible machine readable storage device or storage disk comprising machine-accessible instructions, that, when executed, cause a machine to, at least: identify a segment location of a product hierarchy tree based on a projected distribution measure for the new product; retrieve a relationship matrix associated with the segment location of the product hierarchy tree, the relationship matrix comprising available product performance coefficients and attribute information; calculate an impact parameter based on a spatial location within the relationship matrix, the spatial location based on the attribute information; calculate performance metrics for the new product for a first and second assortment of related products on a store shelf, the first and second assortments associated with first and second proportional volume shifting effects, respectively, the performance metrics calculated based on the impact parameter and a sum of (a) a ratio of category sales in a target geography and overall category sales in a sales channel associated with the new product, and (b) a ratio of channel sales in the target geography and overall channel sales in the sales channel associated with the new product; and select a candidate product to de-list based on sales effects with the first and second assortment of products on the store shelf. ### Claim 14 (depends on 13) A tangible storage device or storage disk as described in claim 13, wherein the machine-accessible instructions, when executed, cause the machine to identify the projected distribution measure is indicative of a channel and a category. ### Claim 15 (depends on 13) A tangible storage device or storage disk as described in claim 13, wherein the machine-accessible instructions, when executed, cause the machine to receive a candidate segment location in response to identifying the segment location of the product hierarchy tree. ### Claim 16 (depends on 15) A tangible storage device or storage disk as described in claim 15, wherein the machine-accessible instructions, when executed, cause the machine to select the candidate segment location based on a product similarity input from a user. ### Claim 17 (depends on 13) A tangible storage device or storage disk as described in claim 13, wherein the machine-accessible instructions, when executed, cause the machine to retrieve a second relationship matrix associated with the projected distribution measure. ### Claim 18 (depends on 17) A tangible storage device or storage disk as described in claim 17, wherein the machine-accessible instructions, when executed, cause the machine to identify at least one of a category or a channel associated with the new product. ### Claim 19 (depends on 18) A tangible storage device or storage disk as described in claim 18, wherein the machine-accessible instructions, when executed, cause the machine to identify products in the relationship matrix that match sibling products in the product hierarchy tree. ### Claim 20 (depends on 19) A tangible storage device or storage disk as described in claim 19, wherein the machine-accessible instructions, when executed, cause the machine to add a row and column associated with the new product to the relationship matrix adjacent to the matched sibling products. ### Claim 21 (depends on 20) A tangible storage device or storage disk as described in claim 20, wherein the machine-accessible instructions, when executed, cause the machine to calculate cell coefficient values for a plurality of cells in the row and column associated with the new product based on the matched sibling products.