My chapter from the book: Product Innovation Toolbox: A Field Guide to Consumer Understanding and Research, ISBN 978-0813823973
http://www.amazon.com/Product-Innovation-Toolbox-Consumer-Understanding/dp/0813823978/
Learn all about conjoint analysis in this guide by Survey Analytics. While we focus on choice-based conjoint because it is the most common, you can also learn about what it can be used for and how to conduct it in your research.
Conjoint analysis is a market research tool used to develop effective product designs. It identifies the key attributes that influence consumer decisions by presenting choices between products defined by attribute sets. Conjoint analysis uses multivariate analysis to understand consumer preferences for different attribute levels of a product. An example shows how conjoint analysis could be used to understand consumer preferences for different cleanliness, shine, smell, quantity, packaging, and design attributes of dishwashing products. Conjoint analysis is commonly used in marketing, product management, operations research, and other fields to test consumer acceptance of new products and advertisements.
The document discusses consumer attitude formation and change. It defines an attitude as a learned predisposition to behave favorably or unfavorably towards an object. There are three main components that make up attitudes: the cognitive component involving knowledge and perceptions, the affective component involving feelings, and the conative component involving the likelihood of taking action. Models of attitudes include the tricomponent model and multiattribute models examining how attitudes are formed based on evaluations of product attributes or beliefs. Strategies for changing attitudes include associating products with admired groups, resolving conflicting attitudes, and altering components of multiattribute models.
This document discusses methods for measuring attitudes. It defines attitude as an enduring disposition to respond consistently to various aspects of the world, composed of affective, cognitive, and behavioral components. Attitudes are hypothetical constructs that are not directly observable but can be measured through indirect indicators like verbal expressions or behaviors. Some common techniques for measuring attitudes discussed are ranking, rating, sorting, and choice methods. Specific scales discussed include category scales and Likert scales, which allow respondents to indicate positions or levels of agreement on continuums.
Learn how to do a conjoint analysis project in 1 hrQuestionPro
Survey Analytics provides conjoint analysis software to help companies evaluate new products and variations of existing products. The software allows users to define product attributes and levels, conduct surveys to assess consumer preferences, and analyze results. Key features include an intuitive interface for setting up studies, previewing concepts, and reviewing utility values and relative importance of attributes. The software also includes a market segmentation simulator that allows predicting how changes to products may impact market share. Conjoint analysis provides a cost-effective way to test concepts without full product development and can help optimize offerings to meet consumer demands.
Can business decision taken purely on data go wrong? What details and techniques should be employed in market research that can capture the key aspect of consumer behavior in decision making?
Absolutdata incorporates behavioral economics in decision making through conjoint analysis! A very simple presentation combining Neoclassical Economics and Behavioral Economics with Conjoint. Ask us for more details: marketing@absolutdata.com
Conjoint Analysis Alternatives in Questionnaire DesignLaurie Gelb
Conjoint analysis in survey research is outmoded: static, closed, attribute-based in a real-time, turn-on-a-dime, conversational world. Heuristic methods offer a cheaper, faster, more actionable framework for both qualitative and quantitative work. This deck briefly outlines the quantitative framework.
How to Run Discrete Choice Conjoint AnalysisQuestionPro
This webinar provided an overview of how to conduct discrete choice conjoint analysis. It discussed the theory and logic behind conjoint analysis, when to use it, how to design a conjoint study including attributes and levels, how to write the questionnaire, how to analyze results to determine relative attribute importance and utility values, and tips for a successful project including using qualitative research first, sample size considerations, and best practices for surveys. The webinar also demonstrated how to set up a conjoint analysis question, different design types, and how to use the market simulator tool to predict how new product concepts may perform.
Learn all about conjoint analysis in this guide by Survey Analytics. While we focus on choice-based conjoint because it is the most common, you can also learn about what it can be used for and how to conduct it in your research.
Conjoint analysis is a market research tool used to develop effective product designs. It identifies the key attributes that influence consumer decisions by presenting choices between products defined by attribute sets. Conjoint analysis uses multivariate analysis to understand consumer preferences for different attribute levels of a product. An example shows how conjoint analysis could be used to understand consumer preferences for different cleanliness, shine, smell, quantity, packaging, and design attributes of dishwashing products. Conjoint analysis is commonly used in marketing, product management, operations research, and other fields to test consumer acceptance of new products and advertisements.
The document discusses consumer attitude formation and change. It defines an attitude as a learned predisposition to behave favorably or unfavorably towards an object. There are three main components that make up attitudes: the cognitive component involving knowledge and perceptions, the affective component involving feelings, and the conative component involving the likelihood of taking action. Models of attitudes include the tricomponent model and multiattribute models examining how attitudes are formed based on evaluations of product attributes or beliefs. Strategies for changing attitudes include associating products with admired groups, resolving conflicting attitudes, and altering components of multiattribute models.
This document discusses methods for measuring attitudes. It defines attitude as an enduring disposition to respond consistently to various aspects of the world, composed of affective, cognitive, and behavioral components. Attitudes are hypothetical constructs that are not directly observable but can be measured through indirect indicators like verbal expressions or behaviors. Some common techniques for measuring attitudes discussed are ranking, rating, sorting, and choice methods. Specific scales discussed include category scales and Likert scales, which allow respondents to indicate positions or levels of agreement on continuums.
Learn how to do a conjoint analysis project in 1 hrQuestionPro
Survey Analytics provides conjoint analysis software to help companies evaluate new products and variations of existing products. The software allows users to define product attributes and levels, conduct surveys to assess consumer preferences, and analyze results. Key features include an intuitive interface for setting up studies, previewing concepts, and reviewing utility values and relative importance of attributes. The software also includes a market segmentation simulator that allows predicting how changes to products may impact market share. Conjoint analysis provides a cost-effective way to test concepts without full product development and can help optimize offerings to meet consumer demands.
Can business decision taken purely on data go wrong? What details and techniques should be employed in market research that can capture the key aspect of consumer behavior in decision making?
Absolutdata incorporates behavioral economics in decision making through conjoint analysis! A very simple presentation combining Neoclassical Economics and Behavioral Economics with Conjoint. Ask us for more details: marketing@absolutdata.com
Conjoint Analysis Alternatives in Questionnaire DesignLaurie Gelb
Conjoint analysis in survey research is outmoded: static, closed, attribute-based in a real-time, turn-on-a-dime, conversational world. Heuristic methods offer a cheaper, faster, more actionable framework for both qualitative and quantitative work. This deck briefly outlines the quantitative framework.
How to Run Discrete Choice Conjoint AnalysisQuestionPro
This webinar provided an overview of how to conduct discrete choice conjoint analysis. It discussed the theory and logic behind conjoint analysis, when to use it, how to design a conjoint study including attributes and levels, how to write the questionnaire, how to analyze results to determine relative attribute importance and utility values, and tips for a successful project including using qualitative research first, sample size considerations, and best practices for surveys. The webinar also demonstrated how to set up a conjoint analysis question, different design types, and how to use the market simulator tool to predict how new product concepts may perform.
Conjoint analysis is a market research technique used to understand customers' preferences for different product features. It involves assessing customers' ratings, rankings, or choices between hypothetical products defined by various attribute levels to estimate the value or "part-worth" that customers place on each attribute level. This helps companies design products with optimal combinations of features to satisfy customers. The method originated in psychology in the 1970s and uses computer surveys to consider many attributes and levels simultaneously, simulating real purchase decisions involving trade-offs between features.
Why Customers Buy | Conjoint Analysis: Unlocking the Secret to What Your Cu...Qualtrics
Conjoint analysis is the key to unlocking the value customers place on different feature of a given product, service, or experience. Join us as we explore five different types of conjoint analysis and discuss how you can use them to let your customers do the talking.
Res 351Education Specialist / snaptutorial.comMcdonaldRyan120
For more classes visit
www.snaptutorial.com
RES 351 Week 1 Discussion Question 1
RES 351 Week 1 Discussion Question 2
RES 351 Week 1 Individual Assignment Current Events in Business Research Paper
RES 351 Week 2 Discussion Question 1
RES 351 Week 2 Discussion Question 2
Evaluation metric plays a critical role in achieving the optimal classifier during the classification training.
Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the
optimal classifier. This paper systematically reviewed the related evaluation metrics that are specifically
designed as a discriminator for optimizing generative classifier. Generally, many generative classifiers
employ accuracy as a measure to discriminate the optimal solution during the classification training.
However, the accuracy has several weaknesses which are less distinctiveness, less discriminability, less
informativeness and bias to majority class data. This paper also briefly discusses other metrics that are
specifically designed for discriminating the optimal solution. The shortcomings of these alternative metrics
are also discussed. Finally, this paper suggests five important aspects that must be taken into consideration
in constructing a new discriminator metric.
MBC gives more power to consumers by allowing consumers to select one to multiple options from a menu to indicate their preference. It is most useful in in markets where customers have freedom to customize an existing product or build their own package.
Personal customized recommendation system reflecting purchase criteria and pr...IJECEIAES
As the size of the e-commerce market grows, the consequences of it are appearing throughout society. The business environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the user‟s subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user.
This chapter discusses noncomparative scaling techniques used in marketing research to measure attitudes, preferences and perceptions. It describes continuous rating scales, itemized rating scales like the Likert scale, semantic differential scale and Stapel scale. Key decisions in developing itemized scales include the number of categories, balanced vs unbalanced scales, and scale configuration. The chapter also covers developing multi-item scales, evaluating scales based on reliability, validity and generalizability, and choosing the appropriate scaling technique.
Attitude measurement and scaling techniquesCharu Rastogi
This document discusses various techniques for measuring consumer attitudes, including non-structured methods like in-depth interviews and focus groups, as well as structured methods like scales. It describes several types of scales such as graphic rating scales, semantic differential scales, Thurstone scales, and Likert scales. Additionally, it covers multidimensional scaling as a technique to analyze consumer perceptions of products or brands based on multiple attributes simultaneously. The goal of these various attitude measurement methods is to better understand consumers' beliefs, feelings, and likelihood of purchasing particular products or services.
There are several common methods for measuring attitudes, including Thurstone scales, Likert scales, Guttman scales, and semantic differentials. Thurstone scales were the first to apply scaling principles to attitudes but have been replaced in popularity by Likert scales, which are easier to construct and more valid. Factor analysis is often used to develop attitude scales and ensure items are measuring only one construct.
Analyzing the spillover roles of user-generated reviews on purchases: Evidenc...Gene Moo Lee
This document summarizes a research study on how online product reviews of related products can influence individual purchase decisions of a focal product. The researchers analyze clickstream data from a UK retailer to examine how reviews of substitute and complementary products impact the probability that a consumer will purchase a focal product. They develop hypotheses about how substitutability, brand, and device channel may moderate these spillover effects. Topic modeling is used to measure product similarity and identify substitutes and complements. The results support most hypotheses and find both positive and negative spillover effects that vary based on product relationships and other moderators.
This document summarizes a study that examines consumer preferences for certified pork chops when presented with branded versus non-branded options. The study uses choice experiment surveys and conditional and mixed logit regression models to analyze how branding influences willingness to pay for certification attributes. The results suggest consumers are willing to pay more for certification when products are branded, and there is greater variation in willingness to pay for non-branded products. This may be due to increased uncertainty about quality consistency without brand information. The findings have implications for producers, retailers, and policymakers regarding branding strategies and credence attributes.
Attitude measurement and scales amiya 26 th march 2012Amiyakumar Sahoo
Attitude measurement and scales are important for understanding customer thinking and predicting future behavior. There are four main types of scales used in measurement: nominal scales assign categories, ordinal scales rank objects, interval scales measure on a continuum with an arbitrary zero point, and ratio scales have a true zero and allow comparisons of absolute magnitudes. The appropriate scale must be selected based on the attribute being measured.
The document discusses the consumer decision-making process of evaluating alternatives. It is the third step where consumers compare different solutions to a problem by looking at attributes such as performance, design, price and value for computers. Consumers will evaluate each brand based on these attributes and form beliefs about how each brand rates. They may consult others or salespeople for information. Consumers will compare brands in their consideration set based on relevant criteria to make their choice. Common criteria include country of origin, price, and brand reputation. Consumers may use decision heuristics or rules of thumb to make choices quickly.
The document discusses the consumer decision-making process of evaluating alternatives. It is the third step where consumers compare different solutions to a problem by looking at attributes such as performance, design, price and value for computers. Consumers will evaluate each brand based on attributes and form beliefs, consulting others or salespeople for information. They will consider brands in their evoked set and evaluate them based on determinate attributes that products differ on. Common evaluative criteria include country of origin, price, and brand reputation. Consumers may use heuristics, decision rules or cognitive models like compensatory or non-compensatory models to evaluate alternatives quickly and make a decision.
1. Consumer behavior is the study of how individuals select, purchase, use, and dispose of products and services to satisfy their needs. It examines psychological and social factors that influence buying decisions.
2. The purchase decision process typically involves problem recognition, information search, evaluation of alternatives, purchase, and post-purchase evaluation. Consumers progress through these stages to varying degrees depending on the type of purchase.
3. Marketers aim to understand the purchase process and influence consumers at each stage. Tools like advertising, promotions, and sales techniques can increase brand awareness, shape consideration sets, and encourage purchase. Marketers also analyze post-purchase feedback to enhance future marketing strategies.
Study to investigate which analysis is the best equipped to understand how co...Charm Rammandala
The purpose of this study is to identify the best method of analysis to deploy to understand how consumers develop preferences for products or services using combination of different attributes.
After conducting a detailed literature review, it was proven that conjoint analysis is the best method to associate for the type of research needed to be carry-out. This study take an in-depth look in to the conjoint analysis method to understand how it use to achieve the intended results
The document discusses consumer loyalty and decision making. It explains that consumer decision making involves selecting an option from multiple alternatives. There are three main models used to analyze consumer decisions: economic models based on rationality; psychological models based on needs and influences; and consumer behavior models that blend economic and psychological approaches. The document also discusses complex decision making, the factors that influence brand and store choice, and how marketing organizations can increase the likelihood of their brand being considered.
Assignment needs to make sure it relates to this week’s readings a.docxrock73
Assignment needs to make sure it relates to this week’s readings and lecture. The assignment also needs demonstrate an understanding of lesson concept and clearly present well-reasoned ideas and concepts.
Consumer Decision Making
Welcome class to week six of the course, we only have three weeks left to go, yeah. We have officially covered over the last five weeks the foundations of what marketing is, along with discussing in depth the elements (four P’s) of the marketing mix. This week, we are going to discover what I like to call the fifth P of the marketing mix, “people.” We are going to learn all about the consumer this week and the vital role “People,” also known as the consumers play within the wonderful world of marketing. Let’s not waste anymore time and jump right into the role of a consumer.
The Importance of Understanding Consumer Behavior
Consumers’ product and service preferences are constantly changing. Marketing managers must understand these desires in order to create a proper marketing mix for a well-defined market. So it is critical that marketing managers have a thorough knowledge of consumer behavior. Consumer behavior describes how consumers make purchase decisions and how they use and dispose of the purchased goods or services. Understanding how consumers make purchase decisions can help marketing managers know how to meet the demands, needs, and criterion of the consumer.
The Consumer Decision Making Process
When buying products, particularly new or expensive items, consumers generally follow the consumer decision-making process, a five-step process used by consumers when buying goods or services. The five steps of the consumer decision-making process are: (1) need recognition, (2) information search, (3) evaluation of alternatives, (4) purchase, and (5) post-purchase behavior.
These five steps represent a general process that can be used as a guide for studying how consumers make decisions. It is important to note, though that consumers’ decisions do not always proceed in order through all of these steps. In fact, the consumer may end the process at any time or may not even make a purchase. Let’s discuss the five steps of the consumer decision-making process in greater detail.
1. Need Recognition
The first stage in the consumer decision-making process is need recognition. Need recognition is the result of an imbalance between actual and desired states. The imbalance arouses and activates the consumer decision-making process. Need recognition is triggered when a consumer is exposed to either an internal or an external stimulus, which is any unit of input affecting one or more of the five senses: sight, smell, taste, touch, and hearing.
Internal stimuli are occurrences you experience such as hunger or thirst. External stimuli are influences from an outside source such as someone’s recommendation of a new restaurant, the color of an automobile, the design of a package, a brand name mentioned by a friend, or a ...
Consumer reactions to unexpected prices were studied for low and high involvement products. An exploratory market research study with 40 respondents of varying ages and incomes was conducted. For high involvement products like computers and motorcycles, consumers were willing to pay higher unexpected prices by changing their attitude or trivializing the price increase. For low involvement products like snacks and medicines, consumers typically accepted small unexpected price increases or sought substitutes. The results provide implications for marketers on how to reduce consumer cognitive dissonance caused by unexpected price rises through communication strategies like justifying higher prices or avoiding direct price comparisons.
Conjoint analysis is a market research technique used to understand customers' preferences for different product features. It involves assessing customers' ratings, rankings, or choices between hypothetical products defined by various attribute levels to estimate the value or "part-worth" that customers place on each attribute level. This helps companies design products with optimal combinations of features to satisfy customers. The method originated in psychology in the 1970s and uses computer surveys to consider many attributes and levels simultaneously, simulating real purchase decisions involving trade-offs between features.
Why Customers Buy | Conjoint Analysis: Unlocking the Secret to What Your Cu...Qualtrics
Conjoint analysis is the key to unlocking the value customers place on different feature of a given product, service, or experience. Join us as we explore five different types of conjoint analysis and discuss how you can use them to let your customers do the talking.
Res 351Education Specialist / snaptutorial.comMcdonaldRyan120
For more classes visit
www.snaptutorial.com
RES 351 Week 1 Discussion Question 1
RES 351 Week 1 Discussion Question 2
RES 351 Week 1 Individual Assignment Current Events in Business Research Paper
RES 351 Week 2 Discussion Question 1
RES 351 Week 2 Discussion Question 2
Evaluation metric plays a critical role in achieving the optimal classifier during the classification training.
Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the
optimal classifier. This paper systematically reviewed the related evaluation metrics that are specifically
designed as a discriminator for optimizing generative classifier. Generally, many generative classifiers
employ accuracy as a measure to discriminate the optimal solution during the classification training.
However, the accuracy has several weaknesses which are less distinctiveness, less discriminability, less
informativeness and bias to majority class data. This paper also briefly discusses other metrics that are
specifically designed for discriminating the optimal solution. The shortcomings of these alternative metrics
are also discussed. Finally, this paper suggests five important aspects that must be taken into consideration
in constructing a new discriminator metric.
MBC gives more power to consumers by allowing consumers to select one to multiple options from a menu to indicate their preference. It is most useful in in markets where customers have freedom to customize an existing product or build their own package.
Personal customized recommendation system reflecting purchase criteria and pr...IJECEIAES
As the size of the e-commerce market grows, the consequences of it are appearing throughout society. The business environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the user‟s subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user.
This chapter discusses noncomparative scaling techniques used in marketing research to measure attitudes, preferences and perceptions. It describes continuous rating scales, itemized rating scales like the Likert scale, semantic differential scale and Stapel scale. Key decisions in developing itemized scales include the number of categories, balanced vs unbalanced scales, and scale configuration. The chapter also covers developing multi-item scales, evaluating scales based on reliability, validity and generalizability, and choosing the appropriate scaling technique.
Attitude measurement and scaling techniquesCharu Rastogi
This document discusses various techniques for measuring consumer attitudes, including non-structured methods like in-depth interviews and focus groups, as well as structured methods like scales. It describes several types of scales such as graphic rating scales, semantic differential scales, Thurstone scales, and Likert scales. Additionally, it covers multidimensional scaling as a technique to analyze consumer perceptions of products or brands based on multiple attributes simultaneously. The goal of these various attitude measurement methods is to better understand consumers' beliefs, feelings, and likelihood of purchasing particular products or services.
There are several common methods for measuring attitudes, including Thurstone scales, Likert scales, Guttman scales, and semantic differentials. Thurstone scales were the first to apply scaling principles to attitudes but have been replaced in popularity by Likert scales, which are easier to construct and more valid. Factor analysis is often used to develop attitude scales and ensure items are measuring only one construct.
Analyzing the spillover roles of user-generated reviews on purchases: Evidenc...Gene Moo Lee
This document summarizes a research study on how online product reviews of related products can influence individual purchase decisions of a focal product. The researchers analyze clickstream data from a UK retailer to examine how reviews of substitute and complementary products impact the probability that a consumer will purchase a focal product. They develop hypotheses about how substitutability, brand, and device channel may moderate these spillover effects. Topic modeling is used to measure product similarity and identify substitutes and complements. The results support most hypotheses and find both positive and negative spillover effects that vary based on product relationships and other moderators.
This document summarizes a study that examines consumer preferences for certified pork chops when presented with branded versus non-branded options. The study uses choice experiment surveys and conditional and mixed logit regression models to analyze how branding influences willingness to pay for certification attributes. The results suggest consumers are willing to pay more for certification when products are branded, and there is greater variation in willingness to pay for non-branded products. This may be due to increased uncertainty about quality consistency without brand information. The findings have implications for producers, retailers, and policymakers regarding branding strategies and credence attributes.
Attitude measurement and scales amiya 26 th march 2012Amiyakumar Sahoo
Attitude measurement and scales are important for understanding customer thinking and predicting future behavior. There are four main types of scales used in measurement: nominal scales assign categories, ordinal scales rank objects, interval scales measure on a continuum with an arbitrary zero point, and ratio scales have a true zero and allow comparisons of absolute magnitudes. The appropriate scale must be selected based on the attribute being measured.
The document discusses the consumer decision-making process of evaluating alternatives. It is the third step where consumers compare different solutions to a problem by looking at attributes such as performance, design, price and value for computers. Consumers will evaluate each brand based on these attributes and form beliefs about how each brand rates. They may consult others or salespeople for information. Consumers will compare brands in their consideration set based on relevant criteria to make their choice. Common criteria include country of origin, price, and brand reputation. Consumers may use decision heuristics or rules of thumb to make choices quickly.
The document discusses the consumer decision-making process of evaluating alternatives. It is the third step where consumers compare different solutions to a problem by looking at attributes such as performance, design, price and value for computers. Consumers will evaluate each brand based on attributes and form beliefs, consulting others or salespeople for information. They will consider brands in their evoked set and evaluate them based on determinate attributes that products differ on. Common evaluative criteria include country of origin, price, and brand reputation. Consumers may use heuristics, decision rules or cognitive models like compensatory or non-compensatory models to evaluate alternatives quickly and make a decision.
1. Consumer behavior is the study of how individuals select, purchase, use, and dispose of products and services to satisfy their needs. It examines psychological and social factors that influence buying decisions.
2. The purchase decision process typically involves problem recognition, information search, evaluation of alternatives, purchase, and post-purchase evaluation. Consumers progress through these stages to varying degrees depending on the type of purchase.
3. Marketers aim to understand the purchase process and influence consumers at each stage. Tools like advertising, promotions, and sales techniques can increase brand awareness, shape consideration sets, and encourage purchase. Marketers also analyze post-purchase feedback to enhance future marketing strategies.
Study to investigate which analysis is the best equipped to understand how co...Charm Rammandala
The purpose of this study is to identify the best method of analysis to deploy to understand how consumers develop preferences for products or services using combination of different attributes.
After conducting a detailed literature review, it was proven that conjoint analysis is the best method to associate for the type of research needed to be carry-out. This study take an in-depth look in to the conjoint analysis method to understand how it use to achieve the intended results
The document discusses consumer loyalty and decision making. It explains that consumer decision making involves selecting an option from multiple alternatives. There are three main models used to analyze consumer decisions: economic models based on rationality; psychological models based on needs and influences; and consumer behavior models that blend economic and psychological approaches. The document also discusses complex decision making, the factors that influence brand and store choice, and how marketing organizations can increase the likelihood of their brand being considered.
Assignment needs to make sure it relates to this week’s readings a.docxrock73
Assignment needs to make sure it relates to this week’s readings and lecture. The assignment also needs demonstrate an understanding of lesson concept and clearly present well-reasoned ideas and concepts.
Consumer Decision Making
Welcome class to week six of the course, we only have three weeks left to go, yeah. We have officially covered over the last five weeks the foundations of what marketing is, along with discussing in depth the elements (four P’s) of the marketing mix. This week, we are going to discover what I like to call the fifth P of the marketing mix, “people.” We are going to learn all about the consumer this week and the vital role “People,” also known as the consumers play within the wonderful world of marketing. Let’s not waste anymore time and jump right into the role of a consumer.
The Importance of Understanding Consumer Behavior
Consumers’ product and service preferences are constantly changing. Marketing managers must understand these desires in order to create a proper marketing mix for a well-defined market. So it is critical that marketing managers have a thorough knowledge of consumer behavior. Consumer behavior describes how consumers make purchase decisions and how they use and dispose of the purchased goods or services. Understanding how consumers make purchase decisions can help marketing managers know how to meet the demands, needs, and criterion of the consumer.
The Consumer Decision Making Process
When buying products, particularly new or expensive items, consumers generally follow the consumer decision-making process, a five-step process used by consumers when buying goods or services. The five steps of the consumer decision-making process are: (1) need recognition, (2) information search, (3) evaluation of alternatives, (4) purchase, and (5) post-purchase behavior.
These five steps represent a general process that can be used as a guide for studying how consumers make decisions. It is important to note, though that consumers’ decisions do not always proceed in order through all of these steps. In fact, the consumer may end the process at any time or may not even make a purchase. Let’s discuss the five steps of the consumer decision-making process in greater detail.
1. Need Recognition
The first stage in the consumer decision-making process is need recognition. Need recognition is the result of an imbalance between actual and desired states. The imbalance arouses and activates the consumer decision-making process. Need recognition is triggered when a consumer is exposed to either an internal or an external stimulus, which is any unit of input affecting one or more of the five senses: sight, smell, taste, touch, and hearing.
Internal stimuli are occurrences you experience such as hunger or thirst. External stimuli are influences from an outside source such as someone’s recommendation of a new restaurant, the color of an automobile, the design of a package, a brand name mentioned by a friend, or a ...
Consumer reactions to unexpected prices were studied for low and high involvement products. An exploratory market research study with 40 respondents of varying ages and incomes was conducted. For high involvement products like computers and motorcycles, consumers were willing to pay higher unexpected prices by changing their attitude or trivializing the price increase. For low involvement products like snacks and medicines, consumers typically accepted small unexpected price increases or sought substitutes. The results provide implications for marketers on how to reduce consumer cognitive dissonance caused by unexpected price rises through communication strategies like justifying higher prices or avoiding direct price comparisons.
This document summarizes a thesis about analyzing human behavior and decision making when shopping at supermarkets. Specifically, it examines how factors like risk tolerance and product placement influence whether people buy branded or unbranded items. The thesis will use data and statistical analysis to test theories of irrational human behavior and see how variables affect purchasing choices. The goal is to better understand supermarket shopping behavior and how brands can target customers more effectively through product positioning.
Brand image and Consumer psyche by continuous change in Brand Packaging. Hasnain Iqbal
The document summarizes a research report on the effect of continuous changes in brand packaging on consumer buying behavior, brand image, and consumer psyche. It includes an introduction outlining the study, literature review on key concepts, research methodology using a questionnaire for 30 respondents, results of data analysis in SPSS accepting all 3 hypotheses, and conclusion with recommendations for future research.
The document discusses consumer behavior and the consumer decision-making process. It describes how consumers search for information both internally and externally, evaluate alternatives, and make purchase decisions. The summary also explains how behavioral and cognitive learning theories influence consumer behavior. External factors like culture, subculture, reference groups, and family roles also impact consumer decisions. Understanding these concepts can help marketers develop effective advertising and promotion strategies.
1. The document provides an overview of a course on consumer education, including the course description, units, outcomes, and modules.
2. Module 1 focuses on consumer behavior and includes lessons on introduction to consumer behavior and perception.
3. The lesson on introduction to consumer behavior defines consumer behavior and discusses the consumer decision-making process and models used to evaluate alternatives.
This document provides an overview of pricing research techniques. It discusses the economic and psychological dimensions of price and how they influence consumer behavior. Various types of pricing research are described, including willingness to pay research, sensitivity research, and value research. Specific methods like transaction data analysis, surveys, and contingent valuation are outlined. The goal of pricing research is to understand how consumers perceive and evaluate price so companies can set prices that maximize perceived value and profits.
Experimental research designs are considered the standard for research. They involve assigning one or more dependent variables to different treatments and observing the results to draw conclusions. Experimental research has both advantages and disadvantages. It allows full researcher control but can be resource-intensive. It aims to determine relationships between dependent and independent variables by supporting or rejecting hypotheses. Data must be quantifiable and include measurements of variables like area, weight, temperature etc. Qualitative observations also supplement the research. Overall, experimental research uses a scientific approach to test business matters and understand customer behavior through product testing and experiments.
This document discusses market segmentation and how conjoint analysis can be used for market segmentation. It presents five approaches to using conjoint analysis for market segmentation: 1) focusing on buyer background characteristics to define segments and design products for each, 2) focusing on attribute preferences from conjoint analysis to define segments, 3) using a "stepwise segmentation" approach to design individual products, 4) all approaches eventually produce product profiles and descriptions of buyer segments, and 5) additional considerations include allowing all products to be available to all buyers and using weights on buyer characteristics.
This document summarizes a research study that analyzed the influence of attitude and brand awareness on purchase intention of private label brands. The study surveyed 150 customers in Jakarta who purchase private label products from major retailers. The results of the multiple regression analysis found that consumer attitude had a significant positive influence on purchase intention, but brand awareness did not have a significant influence. The study concludes that building positive consumer attitudes can positively impact purchase intentions for private label brands.
1Recycling of Products as a Marketing ProblemA.docxeugeniadean34240
1
Recycling of Products as a Marketing Problem
Alper Antebi
MAR 322
Charles Lawry
Pace University
October 16, 2014
Recycling of Products as a Marketing Problem
Introduction
For my research proposal I have chosen recycling of products as a marketing problem. Recycling is the best way to reuse a material or product. People utilize many products, gadgets, appliances etc. and constant use of any products or electronic devices causes wear and tear, the product gets old and the consumers had to ultimately throw them (Sodhi et al 2001). If we keep on throwing used material, the waste accumulation and garbage dump will be overcrowded with these materials and products and it can lead to many unfavorable consequences or problems (Oskamp, 1995). So my company thought an innovative way to recycle these materials so it can be reused. Recycling has many benefits for our environment as well as customers too. It reduces air, water pollution, helps in conservation of natural resources, and saves energy consumption.
Although recycled product has many benefits but a major marketing problem arises while selling these products to customers or consumers. Various researches have been done to analyze the problem in detail and find the best possible solution to overcome it. The recycling research has been divided into many categories such as pricing research, consumer behavior research, selling techniques and strategy, promotional research etc. Thus we need to build a strategy or framework to integrate all these research aspects and design a new method or strategy to launch these recycled products in markets and how to increase its sales.
A literature review will help understand the topic better and understand the different aspects of recycling. For creating recycled products we need to distinguish the items which can be recycled, then we need to find out the method which can be used to recycle items. Various methods or recyclers are available, but we will try to choose which cost effective, eco is friendly and fast. We need to do a market survey and statistical analysis. We need to find out dependent as well as independent variables and find their correlation. Then we need to find its impact on various variables such as recycling behavior, problems, limitations, solutions etc. (Sirdeshmukh et al 2002).
Pricing Research
Cost is also a factor in conducting market survey and research. Thorough analysis of cost must be done as different recycling programs. During market survey we will also determine the prices of similar products in market such that similar recycled products can be created and will be sold at considerably very low prices.
Promotion research
A better promotion requires different communication tactics which include advertising, personal selling, publicity, sales promotions etc. It is designed to increase the sales. This research investigates effects of various strategies which are focused on increasing rec.
Consumer research aims to understand consumer preferences, attitudes, and behaviors. It uses both quantitative methods like surveys and experiments as well as qualitative methods like interviews and focus groups. The findings from consumer research help companies design effective marketing strategies by providing insights into product development, advertising, pricing, and market trends. While consumer research benefits both consumers and businesses, it also faces challenges in areas like respecting cultural differences, managing costs, protecting privacy, and preventing deceptive practices.
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Accenture’s Digital Strategy & Enterprise Frameworks
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Cisco’s Digital Transformation Framework
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Innovation Management Frameworks: Your Guide to Creativity & Innovation
Benefit Hierarchy Analysis
1. Benefit Hierarchy Analysis
The steps in the new product development process entail defining the product concept, identifying the
consumer needs and product benefits, and determining the target consumer demographics. Then, an
optimal product formulation (or several alternative formulations) is developed that can satisfy potential
consumer needs, at a manufacturing cost that is low enough to justify a reasonable price.
In every step of the new product development process, researchers are trying to determine what
product benefits, consumer or sensory attributes, ingredients (including their different levels and
combinations) drive product liking, purchase intent or preference. Hierarchy Analysis is a
relatively new data analysis technique that allows researchers to answer these questions by
organizing benefits, attributes or different ingredient levels into hierarchies according to their
relative impact on consumer choice and preference.
The most noticeable difference between Hierarchy Analysis and traditional approaches to product
optimization is the choice of optimization criterion. Let’s consider a typical study where each
respondent tastes several similar products sequentially and uses the following 9-Point Hedonic
Overall Liking Scale to evaluate each product:
9 - Like Extremely
8 - Like Very Much
7 - Like Moderately
6 - Like Slightly
5 - Neither Like nor Dislike
4 - Dislike Slightly
3 - Dislike Moderately
2 - Dislike Very Much
1 - Dislike Extremely
Traditional data analysis methodologies will either calculate the mean Overall Liking score for
each product and use it as a criterion for decision making, thus implying that the best product is
the one with the highest mean Overall Liking score; or calculate for each product a percent of
respondents who rated the product as Like Extremely or Like Very Much, the so called Top 2 Box
2. score, and use it as a criterion for decision making, thus implying that the best product is the one
with the highest Top 2 Box Overall Liking score.
In contrast, Hierarchy Analysis uses the criterion that the best product is the most preferred
product. Let’s consider the example presented in Figure 1, which are the results from ten
respondents who rated two products using a 9-point Overall Liking scale.
Figure 1 – Product Ratings
Respondent
Product A
Rating
Product B
Rating
Preferred
Product
1 8 9 B
2 8 9 B
3 8 9 B
4 8 9 B
5 8 9 B
6 8 9 B
7 8 9 B
8 8 9 B
9 9 2 A
10 9 1 A
Mean Score 8.2 7.5
Top 2 Box Score 100% 80%
Preference 20% 80%
Using either the mean Overall Liking score or the Top 2 Box Overall Liking score, we would come
to the conclusion that product A is better than product B. However, when analyzing individual
preferences on a respondent by respondent basis, 80% of the respondents preferred product B
3. over product A. Thus, according to the criterion that the best product is the most preferred
product, we would infer that product B is better than product A.
The main source of discrepancies between the outcomes of different criteria usage comes from
the way that the three different methods use the original 9-Point Hedonic Overall Liking scale:
Mean Overall Liking score criterion treats the scale as an interval scale, presuming that all
differences between numeric tags assigned to each verbal statement are equidistant.
Top 2 Box Overall Liking score treats the 9-Point Hedonic Scale as binomial, recognizing
only the difference between a “good rating” (Like Extremely or Like Very Much) and a “bad
rating,” but neglecting all the other differences.
Preference criterion treats the 9-Point Hedonic Overall Liking scale as ordinal, assuming
that the rating 9 is better than the rating 8, that the rating 8 is better than the rating 7, etc.,
without any assumptions regarding distances between verbal statements and without any
loss of information resulting from aggregating the statements into a “good” and a “bad”
category.
From the measurement theory view point [1], preference criterion is the only correct criterion,
corresponding to the nature of the measurement scale used.
The theoretical behavior background of the technique is based on a model of consumer behavior
known as “bounded rationality.” The term and the concept were originally introduced by Herbert
A. Simon [2], who in 1978 was awarded the Nobel Prize in economics “for his pioneering research
into the decision-making process.” Ideas of bounded rationality were further expanded by Daniel
Kahneman [3], who in 2002 received the Nobel Prize in economics "for having integrated insights
from psychological research into economic science, especially concerning human judgment and
decision-making under uncertainty."
4. The main distinction of “bounded rationality” from “full rationality” (which is assumed is such
popular method as conjoint analysis) lies in the recognition that consumers have limited cognitive
abilities and limited time to make decision. Therefore, consumers are not able to evaluate all
product benefits, attributes or ingredients at once, than immediately construct a utility function and
maximize its expected value. There is overwhelming experimental evidence for substantial
deviation of actual consumer behavior from what is predicted by traditional rationality models [3].
Some authors call it “irrationality”, but, in our opinion, the problem is not that people behave
irrationally, but that elegant and beautiful mathematical rationality models do not adequately
explain the consumer’s decisions and choices. According to [4, p.9}, “The greatest weakness of
unbounded rationality is that it does not describe the way real people think.”
According to the bounded rationality concept, consumers employ the use of heuristics or schemas
to make decisions rather than strict rigid rules of decision optimization [5]. A schema is a mental
structure we use to organize and simplify our knowledge of the world around us. We have
schemas just about everything, including ourselves, other people, cars, phones, food, etc.
Schemas affect what we notice, how we interpret things and how we make decisions and act. We
use them to classify things, such as when we ‘pigeon-hole’ people. They also help us to forecast
and predict what will happen in the future. We even remember and recall things via schemas,
using them to ‘encode’ memories. Schemas are often shared within cultures and allow
communication to be shortened. Every word is, in fact, a schema, that we can interpret in our own
way. We tend to have favorite schemas which we use often. They act like filters, accentuating
and downplaying various aspects of the things surrounding us, including different product
attributes and benefits. Schemas are also self-sustaining, and persist even in the face of
disconfirming evidence. If something does not match the schema, such as evidence against it, the
contradictory evidence is often consciously or subconsciously ignored. Some schemas are easier
to change than others, and some people are more open to changing their schemas than others.
5. Schemas are also referred to in literature as mental models, mental concepts, mental
representations and knowledge structures. The basic proposition of the bounded rationality theory
applied to consumer behavior is that consumers are rational, and when they make choices or
preferences between products, they have some conscious or subconscious reasons for those
choices or preferences that are realized trough their individual schemas.
Hierarchy Analysis presumes that each consumer uses an individual schema for evaluating a
particular category of products and makes choices between products within the category based on
this schema. Hierarchy Analysis represents consumer schema in the form of a hierarchy of
benefits, attributes or ingredient levels arranged in the order of likelihood of their impact on
consumer decisions. By aggregating schemas among random probability sample of consumers,
Hierarchy Analysis allows us to determine the prevalent schema in a population. On other hand,
Hierarchy Analysis methodology allows us to group consumers into clusters based on similarities
or dissimilarities of their individual schemas to discover market segmentation based on consumer
schemas. In addition, Hierarchy Analysis methodology includes procedures for testing statistical
hypotheses related to consumer schemas, for example, if a particular product benefit is more
important than another benefit, or if a particular product benefit is more important for one
consumer group than for another consumer group, or if a particular product benefit is more
important for choice of one product than for choice of another product.
Bounded rationality concept assumes that consumers evaluate product in three steps [5]:
First they search for some familiar cues.
When consumers have found enough cues, they stop searching and start evaluating and
organizing these cues in some order of importance to them or the magnitude of the
differences between products.
Then they make judgments regarding “overall liking,” “purchase intent” and the choice of
product.
6. The Hierarchy Analysis model relies on the assumption that some of the cues recognized by
consumers are related directly or indirectly, consciously or subconsciously, to the set of product
benefits and attributes that we ask consumers to evaluate (or to the levels and the combinations of
the ingredients and the sensory attributes that are associated with the products, evaluated by
consumers).
Boundedly rational consumers do not necessarily make quantitative choices between alternative
options based on their perceived utilities. Instead, they rely on qualitative expectations regarding
directional changes. For each pair of products, one product could be evaluated by a consumer as
better than or worse than another, or the differences between two products could be negligible.
In this model of consumer behavior, the actual magnitude of the differences between products
does not affect the product choice, only the directional differences matter. On other hand, the
greater the magnitude of the differences between products, the more consumers will recognize the
differences as noticeable and express their preferences. Therefore, the strength of preferences is
measured, not in the magnitude of the differences between products or their utilities, as in the
case of conjoint analysis, but by the proportion of consumers who evaluated the product as
preferred over the alternatives. By considering only the directional differences between products
and benefits, this method essentially treats all scales of measurement used in consumer research
as ordinal, not interval. This corresponds to the actual nature of the scales and makes this
technique conceptually more valid in comparison with traditional statistical methods based on
means and correlations that treat all consumer research scales as if they were interval.
Another important advantage of this approach over traditional statistical techniques is an
acknowledgment of the fact that each respondent has an individual interpretation of the meanings
of different values on psycholinguistic scales. Traditional statistical methods compare ratings
given by an individual respondent to sample averages. This implies that all respondents interpret
7. scales in the same manner. But, individual interpretations of scales might differ between
respondents based on cultural background, education, age, gender, personal experiences, etc.
Hierarchy Analysis deals with data on a respondent by respondent basis, assuming that each
respondent interprets the scales in an individual manner but consistently across various products,
benefits, attributes, or concepts.
There are multitudes of articles in marketing research literature related to the affect of cross-
cultural differences on scale item interpretations. This issue taints inferences based on the
comparison of mean scores for the same product or benefit across different countries, languages
or cultures. By analyzing data on a respondent by respondent basis, Hierarchy Analysis is free
from this problem and allows the direct comparison of results across countries, languages and
cultures.
Traditional statistical methods usually assume the normal distribution of answers among
respondents for all attributes and criterion ratings. Even if this is not stated explicitly, the mere fact
that traditional statistical methods use only means and standard deviations to describe the
statistical distribution of answers, characterizes the distribution as normal. Moreover, assuming
normality implies that the distributions must be symmetric. In fact, we practically never observe
symmetrical normal distribution in marketing research studies; in many cases answers are skewed
toward high ratings, limited by range, and do not have a symmetrical normal distribution. Also, as
we stated above, a normal distribution could be applied only if we treat all scales as interval, which
actually contradicts the ordinal nature of the scales used. Hierarchy Analysis methodology does
not rely on any assumptions about distributions and accepts all actual distributions “as is”, which
makes it a robust statistical method by definition.
Most of the traditional statistical techniques are based on linear relationships between criterion
and factors (regression and correlation analysis) or linear additive models (conjoint analysis) or
8. polynomial models (response surface analysis). Hierarchy Analysis presumes only probabilistic
directional relationships between criterion and factors, which makes it independent from the
researcher’s assumptions regarding data.
The integral part of Hierarchy Analysis is the philosophy of Exploratory Data Analysis (EDA),
which was introduced by John W. Tukey [6]. The exploratory approach to data analysis calls for
the exploration of the data with an open mind. According to Tukey, the goal of EDA is to discover
patterns in data. He often likened EDA to detective work; Tukey suggested thinking of exploratory
analysis as the first step in a two-step process similar to that utilized in criminal investigations. In
the first step, the researcher searches for evidence using all of the investigative tools that are
available. In the second step, that of confirmatory data analysis, the researcher evaluates the
strength of the evidence and judges its merits and applicability.
In the classical analysis framework, the data collection is followed by the imposition of a model
(normality, linearity, etc.), and then the analysis that follows is focused on the parameters of that
model. For EDA, the data collection is followed immediately by an analysis that has the goal of
inferring which models are appropriate. Hence, the EDA approach allows the data to suggest
models that best fit the data. Following the spirit of EDA, Benefit Hierarchy Analysis evaluates all
the possible multimodal relationships between product preferences and benefits and estimates the
likelihood that each benefit has an impact on product preference. The result is a hierarchy of
benefits, arranged in the order of likelihood of their impact on product choice and preference.
Another cornerstone of Benefit Hierarchy Analysis is the concept of Probabilistic Causality [7]. A
probabilistic causality approach applied to the analysis of consumer choice and preference data
assumes the following:
9. The observed choices and preferences are not spontaneous, but are the results of the
conscious or subconscious use of schemas by consumers in their decision making
process.
The actual product characteristics, such as various ingredient levels or sensory attributes
could be related to cues discovered by consumers and used in their schemas.
The perceived product benefits and attributes could be related to cues discovered by
consumers and used in their schemas.
Consumer schemas represent reasons or causes for their choices.
Consumers do not use their schemas deterministically and always consistently.
Consumers do not use their schemas stochastically or completely randomly.
For each of the possible product benefits, attributes or ingredients, there is an objective
probability that consumers use this particular component in determining their choices and
preferences.
This causal probability could be estimated from the data.
The process of estimating causal probabilities from observed data starts with the assumption that
all attributes or benefits are mutually independent and a-priori each have an equal chance to be a
cause for the consumer’s choices or preferences. Then, by analyzing evidence of all pairwise
relationships between benefits from the data, and testing, for each pair of benefits, two alternative
hypotheses: (1) that benefit A is more likely to be a cause for the choice and preference between
products than benefit B, and (2) that benefit B is more likely to be a cause for the choice and
preference between products than benefit A, we can estimate for every benefit, the a-posteriori
likelihood that the benefit is a cause of choice and preference between products. The result is a
hierarchy of benefits, arranged in the order of this a-posteriori likelihood of the impact on product
choice and preference.
10. The following examples illustrate several practical uses of Hierarchy Analysis in consumer
research. The company wanted to develop a new kind of fresh baked bread to sell in stores
nationwide. Their product developers created nine prototypes for the bread using Taguchi
experimental design for four three-level (High-Medium-Low) design factors, as outlined below in
Figure 2.
Figure 2 – Design Factors
Product Factor 1 Factor 2 Factor 3 Factor 4
1 3 1 3 2
2 2 2 3 1
3 2 3 1 2
4 3 2 1 3
5 1 1 1 1
6 1 2 2 2
7 2 1 2 3
8 3 3 2 1
9 1 3 3 3
To identify which of the nine product prototypes is the most preferred by consumers, a nationally
representative sample of 450 consumers were interviewed in 25 locations. Each respondent
tasted 4 of the 9 samples of bread (incomplete block design). To avoid order bias, we
implemented a random balanced rotation algorithm. As a result of the random balanced rotations,
each respondent tasted a unique set of four products. Each product was tasted an equal number
of times in each position balanced by location and each pair of products was tasted an equal
number of times on each sequential position. For each product, respondents were asked Overall
Liking, using a 9-point scale, and 13 diagnostic attributes. The following Figure 3 shows the
results of the Hierarchy Analysis.
Figure 3: Hierarchy Analysis of Products
11. 5.4
7.5
33.9 H
38.0 H
51.9 F
62.6 F
67.1 E
89.0 C
94.6 C
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0
PRODUCT 1 (A)
PRODUCT 7 (B)
PRODUCT 8 (C)
PRODUCT 6 (D)
PRODUCT 2 (E)
PRODUCT 4 (F)
PRODUCT 9 (G)
PRODUCT 3 (H)
PRODUCT 5 (I)
In the Hierarchy Analysis, all products are arranged in the order of their preference and labeled
alphabetically, so “A” is a label for the most preferred or best product, while “I” is a label for the
least preferred or worst product. The bars for each product represent the likelihood that the
product is the most preferred by consumers in comparison to the other products being considered.
For PRODUCT 1, which is labeled with the letter “A,” the 94.6% denotes, that based on the
evidence in the data, we have a 94.6% confidence that PRODUCT 1 is the most preferred
product. The letter “C” after the confidence signifies that this product is more preferred than any
product labeled with the letter “C” or below, with at least 94.6% confidence. PRODUCT 7, which is
labeled with the letter “B,” is the second most preferred product. The likelihood that PRODUCT 7
is the most preferred product is equal to 89.0%, which is greater than all the products labeled with
the letter “C” or below. Statistically, PRODUCT 1 and PRODUCT 7 are at parity, despite the fact
that PRODUCT 1 has a numerically greater likelihood of being the most preferred product.
12. Now, when we know the hierarchy of product preference, we can define the optimal levels of four
Taguchi design factors using a procedure called Non-Parametric Response Surface Analysis. The
principal difference of this analysis from the traditional Response Surface Analysis is the fact that
we do not restrict a set of possible functions describing the relationships between the design
factors and the overall criterion to being the subset of polynomial regression functions, but we
build the response surface as a multitude of points of interest. The following Figure 4 shows the
results of the Non-Parametric Response Surface Analysis.
Figure 4– Non-Parametric Response Surface Analysis
PRODUCT Factor 1 Factor 2 Factor 3 Factor 4
1 3 1 3 2
2 2 2 3 1
3 2 3 1 2
4 3 2 1 3
5 1 1 1 1
6 1 2 2 2
7 2 1 2 3
8 3 3 2 1
9 1 3 3 3
Optimal
Level 3 1 2 3
Confidence 98.2 91.1 88.4 76.5
Cells with the optimal levels of the corresponding factors are highlighted
PRODUCT 1 has the optimal levels for factors 1 and 2, while PRODUCT 7 has the optimal levels
for factors 2, 3, and 4. A product with a high level of factors 1 and 4, a low level of factor 2, and a
medium level of factor 3, which was not part of the original design, could potentially be the best
product.
Figures 5, 6, 7 and 8 represent the non-parametric response surfaces for the factors. The
numbers in the tables represent the likelihood that the corresponding level of the factor is
preferred by consumers over the other levels of the same factor.
Figure 5 - Non-Parametric Response Surface Analysis of Factor 1
14. Figure 7 - Non-Parametric Response Surface Analysis of Factor 3
0.0
88.4
61.6
0.0
25.0
50.0
75.0
100.0
Low Medium High
Figure 8 - Non-Parametric Response Surface Analysis of Factor 4
6.7
66.8
76.5
0.0
25.0
50.0
75.0
100.0
Low Medium High
15. As we can see from the results of the four main effect analyses for the four design factors above,
the gradient of differences between the optimal factor level and the second best factor level is
51.6% for factor 1; for factor 2 it is 35.2%, for factor 3 it is 26.8% and, for factor 4 it is 9.6%.
Therefore, by deviation from the optimal factor level, we would be exposed to the highest risk for
factor 1, followed by factor 2, and then factor 3, with factor 4 representing the lowest risk.
Another useful application of Hierarchy Analysis involves linking the consumer preferences to the
sensory attributes of the products. This methodology evaluates, for each sensory attribute, the
likelihood that consumers can recognize different levels of the attribute for different products and
make choices or express preferences between products based on this information. If consumers
do not express preferences between two products with different levels of a sensory attribute, then
we might conclude that the difference between these two levels of a sensory attribute is not
noticeable to the average consumer, but can be discriminated by a trained sensory panel.
In the bread optimization project described above, a sensory panel evaluated 55 various sensory
attributes for each bread sample;
32 attributes are related to the taste of the bread,
10 attributes are related to the texture of the bread,
13 attributes are related to the aroma of the bread.
As a result of applying the Hierarchy Analysis methodology to all 55 attributes, we discovered 11
sensory attributes that affect consumer choices with at least an 80% likelihood. Each of these 11
attributes has a larger impact on consumer preferences with at least a 95% confidence level than
any of remaining 44 attributes. The following Figure 9 illustrates the results of the application of
the Hierarchy Analysis methodology to the sensory attributes.
Figure 9 – Hierarchy Analysis of Sensory Attributes
16. 77.7 oQ
80.9 L
81.6 L
85.5 J
86.0 J
89.0 H
91.0 G
93.5 F
95.5 F
95.5 F
95.5 F
99.2 B
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0
FLAVOR27 (A)
FLAVOR6 (B)
TEXTURE5 (C)
TEXTURE9 (D)
FLAVOR26 (E)
FLAVOR9 (F)
FLAVOR23 (G)
AROMA8 (H)
AROMA2 (I)
AROMA7 (J)
TEXTURE7 (K)
FLAVOR4 (L)
The sensory flavor attribute FLAVOR27 has the singular highest impact on consumer choice, with
a 99.2% confidence level. The four attributes, FLAVOR6, TEXTURE5, TEXTURE9, and
FLAVOR26, are statistically at parity on their likelihood to impact consumer preferences, with
confidence levels ranging from 95.5% to 93.5%. Overall, flavor and texture sensory attributes
have a greater impact on consumer choices and preferences between the nine samples of bread
than the aroma related attributes, because the most impactful of the aroma attributes is ranked
only eighth in the hierarchy.
The Hierarchy Analysis for sensory attributes not only identifies which sensory attributes have an
impact on consumer choice, but defines the optimal range for each sensory attribute. The
following Figure 10 illustrates the optimal sensory attribute range for the most impactful sensory
attribute FLAVOR27.
17. Figure 10 – Hierarchy Analysis for the Most Impactful Sensory Attribute
PRODUCT 9
PRODUCT 8
PRODUCT 7
PRODUCT 6
PRODUCT 5
PRODUCT 4
PRODUCT 3
PRODUCT 2
PRODUCT 1
R2
= 0.4551
0.0
25.0
50.0
75.0
100.0
8 9 10 11 12 13 14 15 16
The optimal range for this attribute is below 9.5. Only two the most preferred products, PRODUCT
1 and PRODUCT 7, have this sensory attribute in the optimal range. As we can see from Figure
10, in this case, the application of the standard polynomial regression to the data would give a
similar conclusion: products with smaller levels of the sensory attribute are more preferred;
however, the Hierarchy Analysis reveals the two ranges of the attribute that are recognisable by
consumers. Products in the optimal range have relatively high average likelihood (91.8%) of being
the most preferred product, while products with sensory attribute levels higher than 9.5 have a low
average likelihood of being the most preferred product (only 38.1%).
During the product evaluation, respondents were asked the overall liking rating for each product
and the ratings of 13 diagnostic attributes, using the same 9-point scale mentioned above. The
following Figure 11 demonstrates the application of the Hierarchy Analysis to the ratings of the 13
bread diagnostic attributes.
18. Figure 11 – Hierarchy Analysis of Diagnostic Attributes
99.8 B
85.6 D
82.8 D
61.2 hI
60.2 hI
56.4 hJ
55.5 hJ
48.1 J
44.8 J
30.4 K
13.5 L
6.3
5.4
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0
Taste of bread (A)
Texture of bread (B)
Crust of bread (C)
Appearance of bread (D)
Moistness of bread (E)
Aroma of bread (F)
Thickness/denseness of bread (G)
Crispiness/crunchiness of crust (H)
Color of bread crust (I)
Color of bread interior (J)
Liking of particulates (K)
Amount of crumbs from bread (L)
Amount of particulates within bread (M)
The liking of the taste of the bread is the singular best predictor of overall product preferences,
with 99.8% likelihood. The liking of the texture of the bread and the liking of the crust of the bread
are statistically at parity with an 85.6% and 82.8% likelihood, respectively. These results closely
match the sensory attribute Hierarchy Analysis, where the two sensory attributes with the highest
likelihood of impact were the flavor attributes and five out of the top seven attributes were the
flavor related sensory attributes, while the remaining two were the texture related attributes.
Figure 11 represents the prevalent schema in a population for choosing between samples of
bread. As mentioned above, while assessing the prevalent schema in a population, we calculated
the individual schema for each respondent. Now we can use these results to evaluate the
homogeneity of the consumer schemas. Applying traditional Ward’s algorithm of cluster analysis
to individual schemas, we discovered two different consumer segments with different schemas.
The following Figure 12 illustrates the statistical comparative analysis of two schemas.
19. Figure 12 – Comparative Schema Analysis
COMPARATIVE SCHEMA ANALYSIS
95% Significant Differences
Amount of particulates
within bread
Liking of particulates
Aroma of bread
Amount of crumbs from
bread
Moistness of bread
Thickness/ denseness of
bread
Crispiness/
crunchiness of crust
Crust of bread
Texture of bread
Color of bread interior
Color of bread crust
Appearance of bread
Taste of bread
0.0
25.0
50.0
75.0
100.0
0 25 50 75 100
SEGMENT 2 (48%)
SEGMENT1(52%)
For all consumers, the most impactful product attribute is the taste of the bread. However for 52%
of consumers (SEGMENT 1), the second most impactful attribute is the aroma of the bread. For
the other 48% of consumers (SEGMENT 2), the aroma of the bread is ranked very low on the
schema hierarchy; this is why aroma was not placed high on an average consumer schema
presented on Figure 11. We can clearly see that the taste and the crust of the bread are equally
important for both consumer segments. However, the aroma of the bread is significantly more
impactful for SEGMENT 1, while the texture of the bread and the crispiness/crunchiness of the
crust are significantly more impactful for SEGMENT 2, with at least 95% confidence.
As result of applying two different schemas to the product evaluation, consumers belonging to the
different segments prefer different products. The following Figure 13 illustrates the results of the
statistical comparative product choice analysis.
20. Figure 13 – Comparative Choice Analysis
COMPARATIVE CHOICE ANALYSIS
95% Significant Differences
PRODUCT 1
PRODUCT 2
PRODUCT 3
PRODUCT 4
PRODUCT 5
PRODUCT 6
PRODUCT 7
PRODUCT 8
PRODUCT 9
0.0
25.0
50.0
75.0
100.0
0.0 25.0 50.0 75.0 100.0
SEGMENT 2 (48%)
SEGMENT1(52%)
Consumers in SEGMENT 1 preferred PRODUCT 8, PRODUCT 4, and PRODUCT 9 with a
significantly greater likelihood than the consumers in SEGMENT 2, with PRODUCT 8 being the
most preferred product in SEGMENT 1, with 93.4% likelihood. Consumers in SEGMENT 2
preferred PRODUCT 1, PRODUCT 6, and PRODUCT 2 with a significantly greater likelihood than
consumers in SEGMENT 1, with PRODUCT 1 being the most proffered product in SEGMENT 2,
with 98.5% likelihood. Interestingly, PRODUCT 7 is the second most preferred choice for both
segments, and should be chosen if the manufacturer decides to introduce just one new product to
the market. Alternatively, the introduction of two new products corresponding to PRODUCT 1 and
PRODUCT 8 will better satisfy both segments. Product optimization, based on experimental
design and sensory attributes, illustrated above, could be performed for every segment for more
insight.
21. Hierarchy Analysis is a versatile and robust statistical methodology that helps to solve many tasks
of consumer research. It has more than 15 years of history of usage for hundreds of consumer
research projects by leading consumer packaged goods manufacturers. It is based on the
bounded rationality consumer behavior theory and treats all consumer research scales as ordinal.
The analyses are performed on a respondent by respondent basis, without unjustified
assumptions of interval scales, respondent uniformity, linearity and normality. It provides
quantifiable recommendations for choosing the best product prototype and the best levels of
design factors or sensory attributes. It reveals the reasons for consumer choice and preference
between products (known as consumer schemas), provides statistical tests for homogeneity of
schemas in the population and discovers consumer segments in the cases of heterogeneous
consumer schemas.
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