�ݺ�ߣshows by User: Compusense

�ݺ�ߣshows by User: Compusense / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: Compusense / Fri, 08 Mar 2019 19:47:15 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: Compusense Global Collaboration Case Study /slideshow/global-collaboration-case-study-135284434/135284434 globalcollaborationmpf2-190308194715
In this case study, see how one global company used Compusense Cloud to streamline their business practices, increase cohesiveness amongst their global locations and saved valuable time and budget on their sensory testing.]]>
In this case study, see how one global company used Compusense Cloud to streamline their business practices, increase cohesiveness amongst their global locations and saved valuable time and budget on their sensory testing.]]> Fri, 08 Mar 2019 19:47:15 GMT /slideshow/global-collaboration-case-study-135284434/135284434 Compusense@slideshare.net(Compusense) Global Collaboration Case Study Compusense In this case study, see how one global company used Compusense Cloud to streamline their business practices, increase cohesiveness amongst their global locations and saved valuable time and budget on their sensory testing. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/globalcollaborationmpf2-190308194715-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this case study, see how one global company used Compusense Cloud to streamline their business practices, increase cohesiveness amongst their global locations and saved valuable time and budget on their sensory testing.

Global Collaboration Case Study from Compusense Inc.

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0 Packaging and Concept Testing Case Study /slideshow/packaging-and-concept-testing-case-study-135262570/135262570 packagingandconcepttesting2015-190308180103
This case study demonstrates how one large flavours and seasoning company used Compusense Cloud to gain valuable consumer insights before taking their new product packaging/concepts to market.]]>
This case study demonstrates how one large flavours and seasoning company used Compusense Cloud to gain valuable consumer insights before taking their new product packaging/concepts to market.]]> Fri, 08 Mar 2019 18:01:03 GMT /slideshow/packaging-and-concept-testing-case-study-135262570/135262570 Compusense@slideshare.net(Compusense) Packaging and Concept Testing Case Study Compusense This case study demonstrates how one large flavours and seasoning company used Compusense Cloud to gain valuable consumer insights before taking their new product packaging/concepts to market. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/packagingandconcepttesting2015-190308180103-thumbnail.jpg?width=120&height=120&fit=bounds" /> This case study demonstrates how one large flavours and seasoning company used Compusense Cloud to gain valuable consumer insights before taking their new product packaging/concepts to market.

Packaging and Concept Testing Case Study from Compusense Inc.

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0 Instore-testing-case-study /slideshow/in-storetestingcasestudy/134995783 in-store-testing-case-study-190306203235
This case study highlights how a major, multi-national grocer used in-store testing at their various locations to gather valuable demographic data and discover regional preference differences.]]>
This case study highlights how a major, multi-national grocer used in-store testing at their various locations to gather valuable demographic data and discover regional preference differences.]]> Wed, 06 Mar 2019 20:32:35 GMT /slideshow/in-storetestingcasestudy/134995783 Compusense@slideshare.net(Compusense) Instore-testing-case-study Compusense This case study highlights how a major, multi-national grocer used in-store testing at their various locations to gather valuable demographic data and discover regional preference differences. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/in-store-testing-case-study-190306203235-thumbnail.jpg?width=120&height=120&fit=bounds" /> This case study highlights how a major, multi-national grocer used in-store testing at their various locations to gather valuable demographic data and discover regional preference differences.

Instore-testing-case-study from Compusense Inc.

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0 Central location testing /slideshow/central-location-testing/134986118 centrallocationtesting2015-rev-190306195307
This case study examines how a global quick server restaurant (QSR) used CLT testing to ensure consistency and efficiency while employing a third-party supplier to conduct tests at multiple locations.]]>
This case study examines how a global quick server restaurant (QSR) used CLT testing to ensure consistency and efficiency while employing a third-party supplier to conduct tests at multiple locations.]]> Wed, 06 Mar 2019 19:53:07 GMT /slideshow/central-location-testing/134986118 Compusense@slideshare.net(Compusense) Central location testing Compusense This case study examines how a global quick server restaurant (QSR) used CLT testing to ensure consistency and efficiency while employing a third-party supplier to conduct tests at multiple locations. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/centrallocationtesting2015-rev-190306195307-thumbnail.jpg?width=120&height=120&fit=bounds" /> This case study examines how a global quick server restaurant (QSR) used CLT testing to ensure consistency and efficiency while employing a third-party supplier to conduct tests at multiple locations.

Central location testing from Compusense Inc.

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0 Panel Recruitment and Scheduling Case Study /slideshow/panel-recruitment-and-scheduling-case-study/46169420 panelrecruitmentandschedulingcasestudy-150323083029-conversion-gate01
This case study explores how Compusense's Sensory department used Scheduling to dramatically reduce the time dedicated to panel recruitment, saving them valuable time, resources and budget.]]>
This case study explores how Compusense's Sensory department used Scheduling to dramatically reduce the time dedicated to panel recruitment, saving them valuable time, resources and budget.]]> Mon, 23 Mar 2015 08:30:29 GMT /slideshow/panel-recruitment-and-scheduling-case-study/46169420 Compusense@slideshare.net(Compusense) Panel Recruitment and Scheduling Case Study Compusense This case study explores how Compusense's Sensory department used Scheduling to dramatically reduce the time dedicated to panel recruitment, saving them valuable time, resources and budget. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/panelrecruitmentandschedulingcasestudy-150323083029-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This case study explores how Compusense's Sensory department used Scheduling to dramatically reduce the time dedicated to panel recruitment, saving them valuable time, resources and budget.

Panel Recruitment and Scheduling Case Study from Compusense Inc.

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0 Consumer Driven Product Development for Export Markets /slideshow/findlay-feb-18-2015/45476835 findlayfeb182015-150305095449-conversion-gate01
Compusense's Dr. Chris Findlay presents "Consumer Driven Product Development for Export Markets".]]>
Compusense's Dr. Chris Findlay presents "Consumer Driven Product Development for Export Markets".]]> Thu, 05 Mar 2015 09:54:49 GMT /slideshow/findlay-feb-18-2015/45476835 Compusense@slideshare.net(Compusense) Consumer Driven Product Development for Export Markets Compusense Compusense's Dr. Chris Findlay presents "Consumer Driven Product Development for Export Markets". <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/findlayfeb182015-150305095449-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Compusense's Dr. Chris Findlay presents "Consumer Driven Product Development for Export Markets".

Consumer Driven Product Development for Export Markets from Compusense Inc.

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0 A Preliminary Review of Multiple Group Principal Component Analysis for Descriptive Sensory Data /slideshow/pangborn2013-a-preliminaryreviewofmgpcafordescriptivesensorydata/34964990 pangborn2013apreliminaryreviewofmgpcafordescriptivesensorydata-140521131841-phpapp02
Principal component analysis (PCA) is frequently used to analyse sensory descriptive analysis data to better understand the multivariate sensory space. Consider that even well-trained descriptive sensory panelists might retain some distinctive characteristics, including a tendency to use somewhat different scale levels and ranges than other panelists. Panelists might also show other innate differences in sensitivity to particular attributes or differences in response patterns due to attribute understanding. Often these differences are averaged out prior to conducting PCA. We explored multiple group principal component analysis (MGPCA; Thorpe, 1988) as an alternative multivariate approach. MGPCA is a relatively simple technique related to canonical variate analysis (CVA; Hotelling, 1936; Thorpe, 1988). Where PCA might perform singular value decomposition on the variance-covariance matrix obtained (conventionally) from panel averages, MGPCA can be performed by singular value decomposition of a pooled variance-covariance matrix derived from the weighted average of the panelists' variance-covariance matrices. MGPCA provides a within-class analysis that derives a consensus sensory space in which the individual panellist responses for products are also represented. Agreement amongst panelists is readily evaluated by inspection. In this respect, MGPCA provides richer output than PCA. It derives a similar consensus space as generalized Procrustes analysis (GPA) without performing translation, rotation, isotropic scaling transformations. This preliminary investigation reveals some advantages to MGPCA for sensory data, and interpreted results from previous descriptive analysis studies were comparable to those obtained from other multivariate approaches, indicating that the MGPCA approach warrants further investigation.]]>
Principal component analysis (PCA) is frequently used to analyse sensory descriptive analysis data to better understand the multivariate sensory space. Consider that even well-trained descriptive sensory panelists might retain some distinctive characteristics, including a tendency to use somewhat different scale levels and ranges than other panelists. Panelists might also show other innate differences in sensitivity to particular attributes or differences in response patterns due to attribute understanding. Often these differences are averaged out prior to conducting PCA. We explored multiple group principal component analysis (MGPCA; Thorpe, 1988) as an alternative multivariate approach. MGPCA is a relatively simple technique related to canonical variate analysis (CVA; Hotelling, 1936; Thorpe, 1988). Where PCA might perform singular value decomposition on the variance-covariance matrix obtained (conventionally) from panel averages, MGPCA can be performed by singular value decomposition of a pooled variance-covariance matrix derived from the weighted average of the panelists' variance-covariance matrices. MGPCA provides a within-class analysis that derives a consensus sensory space in which the individual panellist responses for products are also represented. Agreement amongst panelists is readily evaluated by inspection. In this respect, MGPCA provides richer output than PCA. It derives a similar consensus space as generalized Procrustes analysis (GPA) without performing translation, rotation, isotropic scaling transformations. This preliminary investigation reveals some advantages to MGPCA for sensory data, and interpreted results from previous descriptive analysis studies were comparable to those obtained from other multivariate approaches, indicating that the MGPCA approach warrants further investigation.]]> Wed, 21 May 2014 13:18:41 GMT /slideshow/pangborn2013-a-preliminaryreviewofmgpcafordescriptivesensorydata/34964990 Compusense@slideshare.net(Compusense) A Preliminary Review of Multiple Group Principal Component Analysis for Descriptive Sensory Data Compusense Principal component analysis (PCA) is frequently used to analyse sensory descriptive analysis data to better understand the multivariate sensory space. Consider that even well-trained descriptive sensory panelists might retain some distinctive characteristics, including a tendency to use somewhat different scale levels and ranges than other panelists. Panelists might also show other innate differences in sensitivity to particular attributes or differences in response patterns due to attribute understanding. Often these differences are averaged out prior to conducting PCA. We explored multiple group principal component analysis (MGPCA; Thorpe, 1988) as an alternative multivariate approach. MGPCA is a relatively simple technique related to canonical variate analysis (CVA; Hotelling, 1936; Thorpe, 1988). Where PCA might perform singular value decomposition on the variance-covariance matrix obtained (conventionally) from panel averages, MGPCA can be performed by singular value decomposition of a pooled variance-covariance matrix derived from the weighted average of the panelists' variance-covariance matrices. MGPCA provides a within-class analysis that derives a consensus sensory space in which the individual panellist responses for products are also represented. Agreement amongst panelists is readily evaluated by inspection. In this respect, MGPCA provides richer output than PCA. It derives a similar consensus space as generalized Procrustes analysis (GPA) without performing translation, rotation, isotropic scaling transformations. This preliminary investigation reveals some advantages to MGPCA for sensory data, and interpreted results from previous descriptive analysis studies were comparable to those obtained from other multivariate approaches, indicating that the MGPCA approach warrants further investigation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pangborn2013apreliminaryreviewofmgpcafordescriptivesensorydata-140521131841-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Principal component analysis (PCA) is frequently used to analyse sensory descriptive analysis data to better understand the multivariate sensory space. Consider that even well-trained descriptive sensory panelists might retain some distinctive characteristics, including a tendency to use somewhat different scale levels and ranges than other panelists. Panelists might also show other innate differences in sensitivity to particular attributes or differences in response patterns due to attribute understanding. Often these differences are averaged out prior to conducting PCA. We explored multiple group principal component analysis (MGPCA; Thorpe, 1988) as an alternative multivariate approach. MGPCA is a relatively simple technique related to canonical variate analysis (CVA; Hotelling, 1936; Thorpe, 1988). Where PCA might perform singular value decomposition on the variance-covariance matrix obtained (conventionally) from panel averages, MGPCA can be performed by singular value decomposition of a pooled variance-covariance matrix derived from the weighted average of the panelists' variance-covariance matrices. MGPCA provides a within-class analysis that derives a consensus sensory space in which the individual panellist responses for products are also represented. Agreement amongst panelists is readily evaluated by inspection. In this respect, MGPCA provides richer output than PCA. It derives a similar consensus space as generalized Procrustes analysis (GPA) without performing translation, rotation, isotropic scaling transformations. This preliminary investigation reveals some advantages to MGPCA for sensory data, and interpreted results from previous descriptive analysis studies were comparable to those obtained from other multivariate approaches, indicating that the MGPCA approach warrants further investigation.

A Preliminary Review of Multiple Group Principal Component Analysis for Descriptive Sensory Data from Compusense Inc.

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0 Panel Tracking via Thurstonian Modeling /slideshow/pangborn2013-panel-trackingviathurstonianmodeling/34964877 pangborn2013paneltrackingviathurstonianmodeling-140521131557-phpapp02
Sensory professionals continue to face the challenge of quantifying and accounting for differences in individual panelist performance within their difference testing programs. In this presentation we discuss how recent developments in Thurstonian modeling can be used to track panelist performance over a series of (potentially non-replicated) difference tests within the same product category. In particular, we show how Thurstonian modeling can assign individual sensitivity parameters to each panelist and can update estimates of these parameters as difference tests are conducted over time. This approach both monitors differences in panel members and provides more precise estimates of product differences. The ideas in this presentation build on recent advances in the use of Thurstonian modeling to either analyze results from several experiments or to model data from replicated testing, and constitute an additional practical contribution of Thurstonian modeling to industry.]]>
Sensory professionals continue to face the challenge of quantifying and accounting for differences in individual panelist performance within their difference testing programs. In this presentation we discuss how recent developments in Thurstonian modeling can be used to track panelist performance over a series of (potentially non-replicated) difference tests within the same product category. In particular, we show how Thurstonian modeling can assign individual sensitivity parameters to each panelist and can update estimates of these parameters as difference tests are conducted over time. This approach both monitors differences in panel members and provides more precise estimates of product differences. The ideas in this presentation build on recent advances in the use of Thurstonian modeling to either analyze results from several experiments or to model data from replicated testing, and constitute an additional practical contribution of Thurstonian modeling to industry.]]> Wed, 21 May 2014 13:15:57 GMT /slideshow/pangborn2013-panel-trackingviathurstonianmodeling/34964877 Compusense@slideshare.net(Compusense) Panel Tracking via Thurstonian Modeling Compusense Sensory professionals continue to face the challenge of quantifying and accounting for differences in individual panelist performance within their difference testing programs. In this presentation we discuss how recent developments in Thurstonian modeling can be used to track panelist performance over a series of (potentially non-replicated) difference tests within the same product category. In particular, we show how Thurstonian modeling can assign individual sensitivity parameters to each panelist and can update estimates of these parameters as difference tests are conducted over time. This approach both monitors differences in panel members and provides more precise estimates of product differences. The ideas in this presentation build on recent advances in the use of Thurstonian modeling to either analyze results from several experiments or to model data from replicated testing, and constitute an additional practical contribution of Thurstonian modeling to industry. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pangborn2013paneltrackingviathurstonianmodeling-140521131557-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Sensory professionals continue to face the challenge of quantifying and accounting for differences in individual panelist performance within their difference testing programs. In this presentation we discuss how recent developments in Thurstonian modeling can be used to track panelist performance over a series of (potentially non-replicated) difference tests within the same product category. In particular, we show how Thurstonian modeling can assign individual sensitivity parameters to each panelist and can update estimates of these parameters as difference tests are conducted over time. This approach both monitors differences in panel members and provides more precise estimates of product differences. The ideas in this presentation build on recent advances in the use of Thurstonian modeling to either analyze results from several experiments or to model data from replicated testing, and constitute an additional practical contribution of Thurstonian modeling to industry.

Panel Tracking via Thurstonian Modeling from Compusense Inc.

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0 Existing and new approaches for analysing data from Check All That Apply questions /slideshow/pangborn2013-ws6-exisitingandnewapproachesforcatadata/34964790 pangborn2013ws6exisitingandnewapproachesforcatadata-140521131348-phpapp02
Check-All-That-Apply (CATA) questions are increasingly being incorporated into consumer tests because they provide a simple mechanism for consumers to communicate their perceptions of products being evaluated. We review existing and propose new approaches for analysing data obtained from such a study. Contingency tables are well known, and can be pictured using mosaic plots. Correspondence analysis (CA) using the χ2 distance provides dimensionality reduction, but Hellinger's distance is often preferred where rarely cited attributes skew results. Word clouds can be used to determine citation frequency for responses that might be entered in open comment format by consumers (e.g. upon checking "other" in a CATA question). Cochran's Q test provides a univariate test for differences between 3 or more products, and the sign test can be used to assess pairwise differences. To our knowledge no omnibus hypothesis test is available for assessing global differences. We propose such a test, based on randomization and Cochran's Q statistics, in which the null distribution is formed from data re-randomizations. Multidimensional alignment (MDA) is suggested to investigate the relationship between products and CATA attributes. The φ-coefficients, proposed to understand relationships between CATA attributes, are readily visualized using MDS. Consumers can be asked to evaluate an ideal product, and the gaps between the real and ideal products can inform product improvements. Penalty and penalty-lift analyses can reveal (positive and negative) hedonic drivers. Methods are illustrated by means of CATA study on whole grain breads.]]>
Check-All-That-Apply (CATA) questions are increasingly being incorporated into consumer tests because they provide a simple mechanism for consumers to communicate their perceptions of products being evaluated. We review existing and propose new approaches for analysing data obtained from such a study. Contingency tables are well known, and can be pictured using mosaic plots. Correspondence analysis (CA) using the χ2 distance provides dimensionality reduction, but Hellinger's distance is often preferred where rarely cited attributes skew results. Word clouds can be used to determine citation frequency for responses that might be entered in open comment format by consumers (e.g. upon checking "other" in a CATA question). Cochran's Q test provides a univariate test for differences between 3 or more products, and the sign test can be used to assess pairwise differences. To our knowledge no omnibus hypothesis test is available for assessing global differences. We propose such a test, based on randomization and Cochran's Q statistics, in which the null distribution is formed from data re-randomizations. Multidimensional alignment (MDA) is suggested to investigate the relationship between products and CATA attributes. The φ-coefficients, proposed to understand relationships between CATA attributes, are readily visualized using MDS. Consumers can be asked to evaluate an ideal product, and the gaps between the real and ideal products can inform product improvements. Penalty and penalty-lift analyses can reveal (positive and negative) hedonic drivers. Methods are illustrated by means of CATA study on whole grain breads.]]> Wed, 21 May 2014 13:13:48 GMT /slideshow/pangborn2013-ws6-exisitingandnewapproachesforcatadata/34964790 Compusense@slideshare.net(Compusense) Existing and new approaches for analysing data from Check All That Apply questions Compusense Check-All-That-Apply (CATA) questions are increasingly being incorporated into consumer tests because they provide a simple mechanism for consumers to communicate their perceptions of products being evaluated. We review existing and propose new approaches for analysing data obtained from such a study. Contingency tables are well known, and can be pictured using mosaic plots. Correspondence analysis (CA) using the χ2 distance provides dimensionality reduction, but Hellinger's distance is often preferred where rarely cited attributes skew results. Word clouds can be used to determine citation frequency for responses that might be entered in open comment format by consumers (e.g. upon checking "other" in a CATA question). Cochran's Q test provides a univariate test for differences between 3 or more products, and the sign test can be used to assess pairwise differences. To our knowledge no omnibus hypothesis test is available for assessing global differences. We propose such a test, based on randomization and Cochran's Q statistics, in which the null distribution is formed from data re-randomizations. Multidimensional alignment (MDA) is suggested to investigate the relationship between products and CATA attributes. The φ-coefficients, proposed to understand relationships between CATA attributes, are readily visualized using MDS. Consumers can be asked to evaluate an ideal product, and the gaps between the real and ideal products can inform product improvements. Penalty and penalty-lift analyses can reveal (positive and negative) hedonic drivers. Methods are illustrated by means of CATA study on whole grain breads. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pangborn2013ws6exisitingandnewapproachesforcatadata-140521131348-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Check-All-That-Apply (CATA) questions are increasingly being incorporated into consumer tests because they provide a simple mechanism for consumers to communicate their perceptions of products being evaluated. We review existing and propose new approaches for analysing data obtained from such a study. Contingency tables are well known, and can be pictured using mosaic plots. Correspondence analysis (CA) using the χ2 distance provides dimensionality reduction, but Hellinger's distance is often preferred where rarely cited attributes skew results. Word clouds can be used to determine citation frequency for responses that might be entered in open comment format by consumers (e.g. upon checking "other" in a CATA question). Cochran's Q test provides a univariate test for differences between 3 or more products, and the sign test can be used to assess pairwise differences. To our knowledge no omnibus hypothesis test is available for assessing global differences. We propose such a test, based on randomization and Cochran's Q statistics, in which the null distribution is formed from data re-randomizations. Multidimensional alignment (MDA) is suggested to investigate the relationship between products and CATA attributes. The φ-coefficients, proposed to understand relationships between CATA attributes, are readily visualized using MDS. Consumers can be asked to evaluate an ideal product, and the gaps between the real and ideal products can inform product improvements. Penalty and penalty-lift analyses can reveal (positive and negative) hedonic drivers. Methods are illustrated by means of CATA study on whole grain breads.

Existing and new approaches for analysing data from Check All That Apply questions from Compusense Inc.

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0 Sensory Informed Design: An effective clustering of incomplete block consumer data /Compusense/pangborn-oral-1003findlay pangbornoral10-140521130626-phpapp02
Consumer research has advanced its business relevance through segmenting consumer populations into clusters based upon liking. Products designed to meet the expectations and desires of specific niche markets have demonstrated commercial success. The studies that are typically designed to reveal liking segments require a relatively large number of products and a large sample of consumers in a complete block design. A study of 12 Cabernet Sauvignon wines was conducted using over 600 consumers recruited and tested for liking of 3 of the 12 wines in a BIB design. The data were subsequently analyzed for liking clusters with missing data replaced with the consumer’s individual mean. Four liking clusters successfully demonstrated different sensory liking profiles. The method was not robust. Consequently, a research program was initiated to develop a systematic approach to building designs using sensory information to ensure contrast. The Sensory Informed Design (SID) approach was applied to a 12-present-6 study of white breads. All breads were profiled using calibrated descriptive analysis. The results of the DA were used to construct a balanced experimental design (12:6) that included two smaller-sized SIDs (12:3 and 12:4) nested within the experiment to evaluate the efficiency and stability. Consumer data (n=400) were collected and missing data were imputed as part of a novel EM approach for mixture model-based clustering; the one latent factor model gave a six-cluster solution. In 2012, a study of whole grain breads was conducted with 570 consumers using an improved SID of 16:6, with nested designs of 16:3 and 16:4. The nested designs demonstrated stable clusters, provided internal validation and supported the results of previous work. The application of SID, EM imputation and model-based cluster analysis can dramatically reduce the resources required to conduct large category appraisals and deliver effective consumer clusters. ]]>
Consumer research has advanced its business relevance through segmenting consumer populations into clusters based upon liking. Products designed to meet the expectations and desires of specific niche markets have demonstrated commercial success. The studies that are typically designed to reveal liking segments require a relatively large number of products and a large sample of consumers in a complete block design. A study of 12 Cabernet Sauvignon wines was conducted using over 600 consumers recruited and tested for liking of 3 of the 12 wines in a BIB design. The data were subsequently analyzed for liking clusters with missing data replaced with the consumer’s individual mean. Four liking clusters successfully demonstrated different sensory liking profiles. The method was not robust. Consequently, a research program was initiated to develop a systematic approach to building designs using sensory information to ensure contrast. The Sensory Informed Design (SID) approach was applied to a 12-present-6 study of white breads. All breads were profiled using calibrated descriptive analysis. The results of the DA were used to construct a balanced experimental design (12:6) that included two smaller-sized SIDs (12:3 and 12:4) nested within the experiment to evaluate the efficiency and stability. Consumer data (n=400) were collected and missing data were imputed as part of a novel EM approach for mixture model-based clustering; the one latent factor model gave a six-cluster solution. In 2012, a study of whole grain breads was conducted with 570 consumers using an improved SID of 16:6, with nested designs of 16:3 and 16:4. The nested designs demonstrated stable clusters, provided internal validation and supported the results of previous work. The application of SID, EM imputation and model-based cluster analysis can dramatically reduce the resources required to conduct large category appraisals and deliver effective consumer clusters. ]]> Wed, 21 May 2014 13:06:26 GMT /Compusense/pangborn-oral-1003findlay Compusense@slideshare.net(Compusense) Sensory Informed Design: An effective clustering of incomplete block consumer data Compusense Consumer research has advanced its business relevance through segmenting consumer populations into clusters based upon liking. Products designed to meet the expectations and desires of specific niche markets have demonstrated commercial success. The studies that are typically designed to reveal liking segments require a relatively large number of products and a large sample of consumers in a complete block design. A study of 12 Cabernet Sauvignon wines was conducted using over 600 consumers recruited and tested for liking of 3 of the 12 wines in a BIB design. The data were subsequently analyzed for liking clusters with missing data replaced with the consumer’s individual mean. Four liking clusters successfully demonstrated different sensory liking profiles. The method was not robust. Consequently, a research program was initiated to develop a systematic approach to building designs using sensory information to ensure contrast. The Sensory Informed Design (SID) approach was applied to a 12-present-6 study of white breads. All breads were profiled using calibrated descriptive analysis. The results of the DA were used to construct a balanced experimental design (12:6) that included two smaller-sized SIDs (12:3 and 12:4) nested within the experiment to evaluate the efficiency and stability. Consumer data (n=400) were collected and missing data were imputed as part of a novel EM approach for mixture model-based clustering; the one latent factor model gave a six-cluster solution. In 2012, a study of whole grain breads was conducted with 570 consumers using an improved SID of 16:6, with nested designs of 16:3 and 16:4. The nested designs demonstrated stable clusters, provided internal validation and supported the results of previous work. The application of SID, EM imputation and model-based cluster analysis can dramatically reduce the resources required to conduct large category appraisals and deliver effective consumer clusters. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pangbornoral10-140521130626-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Consumer research has advanced its business relevance through segmenting consumer populations into clusters based upon liking. Products designed to meet the expectations and desires of specific niche markets have demonstrated commercial success. The studies that are typically designed to reveal liking segments require a relatively large number of products and a large sample of consumers in a complete block design. A study of 12 Cabernet Sauvignon wines was conducted using over 600 consumers recruited and tested for liking of 3 of the 12 wines in a BIB design. The data were subsequently analyzed for liking clusters with missing data replaced with the consumer’s individual mean. Four liking clusters successfully demonstrated different sensory liking profiles. The method was not robust. Consequently, a research program was initiated to develop a systematic approach to building designs using sensory information to ensure contrast. The Sensory Informed Design (SID) approach was applied to a 12-present-6 study of white breads. All breads were profiled using calibrated descriptive analysis. The results of the DA were used to construct a balanced experimental design (12:6) that included two smaller-sized SIDs (12:3 and 12:4) nested within the experiment to evaluate the efficiency and stability. Consumer data (n=400) were collected and missing data were imputed as part of a novel EM approach for mixture model-based clustering; the one latent factor model gave a six-cluster solution. In 2012, a study of whole grain breads was conducted with 570 consumers using an improved SID of 16:6, with nested designs of 16:3 and 16:4. The nested designs demonstrated stable clusters, provided internal validation and supported the results of previous work. The application of SID, EM imputation and model-based cluster analysis can dramatically reduce the resources required to conduct large category appraisals and deliver effective consumer clusters.

Sensory Informed Design: An effective clustering of incomplete block consumer data from Compusense Inc.

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0 The power of calibrated descriptive sensory panels /Compusense/iufosto1432-presentation20100825 iufost-o1432presentation-2010-08-25-101013153028-phpapp02
Presented at IUFoST 2010 in Cape Town, South Africa.]]>
Presented at IUFoST 2010 in Cape Town, South Africa.]]> Wed, 13 Oct 2010 15:30:24 GMT /Compusense/iufosto1432-presentation20100825 Compusense@slideshare.net(Compusense) The power of calibrated descriptive sensory panels Compusense Presented at IUFoST 2010 in Cape Town, South Africa. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iufost-o1432presentation-2010-08-25-101013153028-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presented at IUFoST 2010 in Cape Town, South Africa.

The power of calibrated descriptive sensory panels from Compusense Inc.

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0 Best Practices in Equivalence Testing /slideshow/007-054castura-2010/5436657 007-054-castura2010-101013153032-phpapp01
Presented by John Castura at 10th Sensometrics, Rotterdam, July 2010]]>
Presented by John Castura at 10th Sensometrics, Rotterdam, July 2010]]> Wed, 13 Oct 2010 15:30:21 GMT /slideshow/007-054castura-2010/5436657 Compusense@slideshare.net(Compusense) Best Practices in Equivalence Testing Compusense Presented by John Castura at 10th Sensometrics, Rotterdam, July 2010 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/007-054-castura2010-101013153032-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presented by John Castura at 10th Sensometrics, Rotterdam, July 2010

Best Practices in Equivalence Testing from Compusense Inc.

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0 Sensory Considerations in BIB Design /slideshow/011-000findlay-2010/5436656 011-000-findlay2010-101013153030-phpapp02
Presented at 10th Sensometrics Meeting in Rotterdam, July 2010]]>
Presented at 10th Sensometrics Meeting in Rotterdam, July 2010]]> Wed, 13 Oct 2010 15:30:19 GMT /slideshow/011-000findlay-2010/5436656 Compusense@slideshare.net(Compusense) Sensory Considerations in BIB Design Compusense Presented at 10th Sensometrics Meeting in Rotterdam, July 2010 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/011-000-findlay2010-101013153030-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presented at 10th Sensometrics Meeting in Rotterdam, July 2010

Sensory Considerations in BIB Design from Compusense Inc.

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0 Segmentation of BIB Consumer Liking Data of 12 Cabernet Sauvignon Wines /slideshow/stellenbosch-university-presentation20100826-rev2/5436654 stellenboschuniversity-presentation-2010-08-26rev2-101013153033-phpapp02
Presented at University of Stellenbosch, September 2010]]>
Presented at University of Stellenbosch, September 2010]]> Wed, 13 Oct 2010 15:30:18 GMT /slideshow/stellenbosch-university-presentation20100826-rev2/5436654 Compusense@slideshare.net(Compusense) Segmentation of BIB Consumer Liking Data of 12 Cabernet Sauvignon Wines Compusense Presented at University of Stellenbosch, September 2010 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stellenboschuniversity-presentation-2010-08-26rev2-101013153033-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presented at University of Stellenbosch, September 2010

Segmentation of BIB Consumer Liking Data of 12 Cabernet Sauvignon Wines from Compusense Inc.

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