This document summarizes the results of a conjoint analysis study examining consumer preferences for smartphone attributes. The study used a survey of 162 respondents to rank 16 smartphone profiles varying price, battery capacity, operating system, memory, camera, processor, resolution, and quick charging. The conjoint analysis found that battery capacity was the most important attribute, followed by operating system, internal memory, resolution, and quick charging. The results imply that a smartphone priced 10000-12000 INR that emphasizes battery capacity of 3001-4000 mAh, Android operating system, 16GB or 32GB memory, 8-12MP camera, resolution of 4K, and quick charging could appeal to price-sensitive consumers.
Customer Clustering Based on Customer Purchasing Sequence DataIJERA Editor
Customer clustering has become a priority for enterprises because of the importance of customer relationship management. Customer clustering can improve understanding of the composition and characteristics of customers, thereby enabling the creation of appropriate marketing strategies for each customer group. Previously, different customer clustering approaches have been proposed according to data type, namely customer profile data, customer value data, customer transaction data, and customer purchasing sequence data. This paper considers the customer clustering problem in the context of customer purchasing sequence data. However, two major aspects distinguish this paper from past research: (1) in our model, a customer sequence contains itemsets, which is a more realistic configuration than previous models, which assume a customer sequence would merely consist of items; and (2) in our model, a customer may belong to multiple clusters or no cluster, whereas in existing models a customer is limited to only one cluster. The second difference implies that each cluster discovered using our model represents a crucial type of customer behavior and that a customer can exhibit several types of behavior simultaneously. Finally, extensive experiments are conducted through a retail data set, and the results show that the clusters obtained by our model can provide more accurate descriptions of customer purchasing behaviors.
Data analysis involves editing, coding, sorting, and entering data to discover useful information. The process includes gathering data from various sources, reviewing it, and analyzing it to form conclusions. Specifically, editing ensures quality by reviewing collected data, coding categorizes data for computer analysis, sorting arranges data in a meaningful order for comprehension, and data entry inputs collected information into a computer. Finally, a master chart compiles all essential data in one format.
An impact of knowledge mining on satisfaction of consumers in super bazaarsIAEME Publication
This document summarizes research on using knowledge mining techniques to study customer satisfaction levels in super bazaars. It first introduces the importance of customer satisfaction for super bazaars and defines knowledge mining. It then describes various knowledge mining techniques that can be applied, including classification, regression, time series analysis, clustering, and association rule mining. The document proposes a model for conducting customer satisfaction surveys, applying knowledge mining techniques to the data, and using the results to enhance customer satisfaction. The goal of the research is to better understand customer preferences and behaviors to improve business performance for super bazaars.
Demand forecasting involves estimating future demand for a product or service. There are two main approaches: obtaining expert opinions or consumer surveys, or using past sales data through statistical techniques. Simple survey methods include expert opinion polls, the Delphi technique to reach consensus among experts, and consumer surveys using complete or sample enumeration. More complex statistical methods include time series analysis to identify trends, using leading economic indicators to forecast changes, and correlation/regression analysis to determine relationships between demand and influencing factors like price, income, and advertising. The most sophisticated method is simultaneous equation modeling or developing an econometric model of an entire economy.
This document discusses various techniques for analyzing quantitative and qualitative data. It describes editing, coding, classification, and tabulation as methods for processing qualitative data. For quantitative data, it covers univariate analyses like measures of central tendency and dispersion. It also discusses bivariate analyses like correlation and regression, as well as multivariate techniques including multidimensional analysis, factor analysis, and cluster analysis. The goal of data analysis is to discover useful information and support decision making.
The document summarizes a study examining the relationship between risk aversion and behavioral loyalty in the Pakistani telecom sector. The study used a questionnaire to collect data from 300 mobile phone users. The results of path analyses using SPSS and AMOS found that:
1) Risk aversion has a direct positive effect on attitudinal loyalty but no direct effect on behavioral loyalty.
2) Risk aversion has an indirect effect on behavioral loyalty through brand affects and attitudinal loyalty acting as mediators.
3) Practitioners should focus on loyalty programs to strengthen customer relationships given high switching rates in the telecom industry.
This document defines and classifies different types of marketing research designs. It discusses exploratory research design, which aims to provide insights and understanding through unstructured processes like expert surveys, case studies, and focus groups. Descriptive research design attempts to describe market characteristics and functions using secondary data analysis and surveys. Causal research design tests hypotheses about cause-and-effect relationships through experiments to understand independent and dependent variables. The document provides objectives, characteristics, methods and uses for each type of research design.
This document summarizes strategies for improving the effectiveness of marketing and sales discussed in various research papers. It first introduces common data mining algorithms like Apriori, FP-growth, and Eclat that are used to find frequently purchased item sets by analyzing customer transaction data. This information can then be used to better target products to customers. The document then summarizes 11 research papers that examine techniques like sentiment analysis of reviews to predict sales, using price indexes to understand market impacts, and designing marketing management systems. The goal of these strategies is to enhance customer relationships and make more informed decisions about product placement, pricing, and promotions.
Customer Clustering Based on Customer Purchasing Sequence DataIJERA Editor
Customer clustering has become a priority for enterprises because of the importance of customer relationship management. Customer clustering can improve understanding of the composition and characteristics of customers, thereby enabling the creation of appropriate marketing strategies for each customer group. Previously, different customer clustering approaches have been proposed according to data type, namely customer profile data, customer value data, customer transaction data, and customer purchasing sequence data. This paper considers the customer clustering problem in the context of customer purchasing sequence data. However, two major aspects distinguish this paper from past research: (1) in our model, a customer sequence contains itemsets, which is a more realistic configuration than previous models, which assume a customer sequence would merely consist of items; and (2) in our model, a customer may belong to multiple clusters or no cluster, whereas in existing models a customer is limited to only one cluster. The second difference implies that each cluster discovered using our model represents a crucial type of customer behavior and that a customer can exhibit several types of behavior simultaneously. Finally, extensive experiments are conducted through a retail data set, and the results show that the clusters obtained by our model can provide more accurate descriptions of customer purchasing behaviors.
Data analysis involves editing, coding, sorting, and entering data to discover useful information. The process includes gathering data from various sources, reviewing it, and analyzing it to form conclusions. Specifically, editing ensures quality by reviewing collected data, coding categorizes data for computer analysis, sorting arranges data in a meaningful order for comprehension, and data entry inputs collected information into a computer. Finally, a master chart compiles all essential data in one format.
An impact of knowledge mining on satisfaction of consumers in super bazaarsIAEME Publication
This document summarizes research on using knowledge mining techniques to study customer satisfaction levels in super bazaars. It first introduces the importance of customer satisfaction for super bazaars and defines knowledge mining. It then describes various knowledge mining techniques that can be applied, including classification, regression, time series analysis, clustering, and association rule mining. The document proposes a model for conducting customer satisfaction surveys, applying knowledge mining techniques to the data, and using the results to enhance customer satisfaction. The goal of the research is to better understand customer preferences and behaviors to improve business performance for super bazaars.
Demand forecasting involves estimating future demand for a product or service. There are two main approaches: obtaining expert opinions or consumer surveys, or using past sales data through statistical techniques. Simple survey methods include expert opinion polls, the Delphi technique to reach consensus among experts, and consumer surveys using complete or sample enumeration. More complex statistical methods include time series analysis to identify trends, using leading economic indicators to forecast changes, and correlation/regression analysis to determine relationships between demand and influencing factors like price, income, and advertising. The most sophisticated method is simultaneous equation modeling or developing an econometric model of an entire economy.
This document discusses various techniques for analyzing quantitative and qualitative data. It describes editing, coding, classification, and tabulation as methods for processing qualitative data. For quantitative data, it covers univariate analyses like measures of central tendency and dispersion. It also discusses bivariate analyses like correlation and regression, as well as multivariate techniques including multidimensional analysis, factor analysis, and cluster analysis. The goal of data analysis is to discover useful information and support decision making.
The document summarizes a study examining the relationship between risk aversion and behavioral loyalty in the Pakistani telecom sector. The study used a questionnaire to collect data from 300 mobile phone users. The results of path analyses using SPSS and AMOS found that:
1) Risk aversion has a direct positive effect on attitudinal loyalty but no direct effect on behavioral loyalty.
2) Risk aversion has an indirect effect on behavioral loyalty through brand affects and attitudinal loyalty acting as mediators.
3) Practitioners should focus on loyalty programs to strengthen customer relationships given high switching rates in the telecom industry.
This document defines and classifies different types of marketing research designs. It discusses exploratory research design, which aims to provide insights and understanding through unstructured processes like expert surveys, case studies, and focus groups. Descriptive research design attempts to describe market characteristics and functions using secondary data analysis and surveys. Causal research design tests hypotheses about cause-and-effect relationships through experiments to understand independent and dependent variables. The document provides objectives, characteristics, methods and uses for each type of research design.
This document summarizes strategies for improving the effectiveness of marketing and sales discussed in various research papers. It first introduces common data mining algorithms like Apriori, FP-growth, and Eclat that are used to find frequently purchased item sets by analyzing customer transaction data. This information can then be used to better target products to customers. The document then summarizes 11 research papers that examine techniques like sentiment analysis of reviews to predict sales, using price indexes to understand market impacts, and designing marketing management systems. The goal of these strategies is to enhance customer relationships and make more informed decisions about product placement, pricing, and promotions.
IRJET- Customer Buying Prediction using Machine-Learning Techniques: A SurveyIRJET Journal
1) The document discusses using machine learning techniques to predict customer purchasing and churn based on their personal and behavioral data.
2) It reviews several machine learning algorithms that have been used for prediction, including random forest, logistic regression, naive bayes, and support vector machines.
3) Deep learning techniques are also discussed, including the use of convolutional neural networks to reveal hidden patterns in customer data and predict purchases and churn.
11.direct marketing with the application of data miningAlexander Decker
This document summarizes a research paper on using data mining techniques for direct marketing. It discusses how direct marketing focuses on specific customer groups rather than mass marketing. Data mining algorithms like decision trees are used to classify customers as loyal or unloyal based on attributes in customer data. This helps direct marketing efforts towards the most beneficial customers. The document also outlines some common problems in classification for direct marketing like imbalanced data and issues with predictive accuracy, and provides solutions like lift analysis.
Direct marketing with the application of data miningAlexander Decker
1. Direct marketing involves sending targeted messages directly to consumers through methods like email, telemarketing, and mail. It is more effective than mass marketing as it focuses on specific customer groups.
2. Data mining techniques like supervised classification can be used to analyze customer data and classify customers as loyal or unloyal for direct marketing purposes. Decision trees are a popular technique to visualize customer classifications.
3. Building accurate customer classifications is challenging due to issues like imbalanced class distributions in the data. Ranking and lift analysis can help address these issues and identify the most promising potential customers to target.
The observation method is the most commonly used method for behavioral science studies. It involves direct observation by the investigator without asking questions to respondents. This allows the investigator to obtain information about current behaviors. It is less demanding than interview or questionnaire methods as it does not require active cooperation from respondents. Some common observation techniques include warranty cards, pantry audits, and distributor audits.
A widely used approach for gaining insight into the heterogeneity of consumer’s buying behavior is market segmentation. Conventional market segmentation models often ignore the fact that consumers’ behavior may evolve over time. Therefore retailers consume limited resources attempting to service unprofitable consumers. This study looks into the integration between enhanced Recency, Frequency, Monetary (RFM) scores and Consumer Lifetime Value (CLV) matrix for a medium size retailer in the State of Kuwait. A modified regression algorithm investigates the consumer purchase trend gaining knowledge from a pointof-sales data warehouse. In addition, this study applies enhanced normal distribution formula to remove outliers, followed by soft clustering Fuzzy C-Means and hard clustering Expectation Maximization (EM) algorithms to the analysis of consumer buying behavior. Using cluster quality assessment shows EM algorithm scales much better than Fuzzy C-Means algorithm with its ability to assign good initial points in the smaller dataset.
Opinion pattern mining based on probabilistic principle component analysis re...eSAT Journals
This document summarizes an article that proposes a new method called Opinion Pattern Mining Segmentation (OPMS) based on Probabilistic Principal Component Analysis (PPCA). The method segments user profiles and behavior patterns from product reviews more efficiently compared to traditional methods like random forests. It reduces dimensionality using a covariance matrix in the PPCA process, improving segmentation efficiency by up to 9% and decreasing false positive rates. The method was tested on product review data and showed improvements in segmentation efficiency, user product preference accuracy, and reduced opinion pattern mining time compared to other methods.
Predicting Bank Customer Churn Using ClassificationVishva Abeyrathne
This document describes a study that used classification models to predict customer churn for a bank. The authors collected a dataset of 10,000 bank customers from Kaggle and preprocessed the data. They then explored relationships between features and the target variable of whether a customer churned. Two classification models were tested - KNN and Decision Tree. After hyperparameter tuning, Decision Tree achieved the best accuracy of 84.25%, outperforming KNN. However, both models struggled to accurately predict customers who would churn. The authors concluded Decision Tree was the best model but recommend collecting more data on churning customers.
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.
Machine Learning - Algorithms and simple business casesClaudio Mirti
Linear regression, logistic regression, and decision trees are commonly used supervised learning algorithms. Linear regression models the relationship between input and output variables to predict future values, logistic regression is used for binary classification tasks, and decision trees split data into branches to make predictions. Unsupervised learning algorithms like k-means clustering group unlabeled data into clusters with similar characteristics. Reinforcement learning optimizes strategies through trial-and-error interactions like optimizing inventory levels or self-driving cars. Convolutional neural networks in deep learning can diagnose diseases from scans, detect logos in images, and understand customer perception through visual data analysis.
This document provides an overview of key statistical concepts for data analysis, including:
- Variables can be quantitative or categorical, and it is important to understand the type of variable to perform appropriate analyses.
- A population includes all relevant data, while a sample is a subset used to make inferences. Statistical formulas differ for populations and samples.
- Measures of central tendency like the mean, median, and mode summarize typical values in a dataset. Measures of variability like the range and standard deviation describe how spread out the values are.
- Understanding these fundamental statistical concepts is necessary to properly analyze and interpret data through techniques like hypothesis testing, correlation analysis, regression, and comparisons of group means.
AHP Based Data Mining for Customer Segmentation Based on Customer Lifetime ValueIIRindia
Data mining techniques are widely used in various areas of marketing management for extracting useful information.Particularly in a business-to-customer (B2C) setting, it plays an important role in customer segmentation. A retailernot only tries to improve its relationship with its customers,but also enhances its business in a manufacturer-retailer-consumer chainwith respect to this information.Although there are various approaches for customer segmentation, we have used an analytic hierarchical process based data mining technique in this regard. Customers are segmented into six clusters based on Davis-Bouldin (DB) index and K-Means algorithm.Customer lifetime value (CLV)along four dimensions, viz., Length (L), Recency(R), Frequency (F) and Monetary value (M) are considered for these clusters. Then, we apply Saaty’s analytical hierarchical process (AHP) to determine the weights of these criteria, which in turn, helps in computing the CLV value for each of the clusters and their individual rankings. This information is quite important for a retailer to design promotional strategies for improving relationship between the retailer and its customers. To demonstrate the effectiveness of this methodology, we have implemented the model, taking a real life data-base of customers of an organization in the context of an Indian retail industry.
Conjoint analysis is a technique used to understand what attributes of a product are most important to consumers. It involves having consumers rank hypothetical products that combine different levels of attributes. This allows researchers to determine the partial utilities (or part-worths) that consumers assign to each attribute level and understand their relative importance. Conjoint analysis provides insight into consumer preferences while maintaining realism by considering attributes together rather than separately. It estimates the tradeoffs consumers make between attributes and can uncover important drivers of choice.
FHCC: A SOFT HIERARCHICAL CLUSTERING APPROACH FOR COLLABORATIVE FILTERING REC...IJDKP
This summary provides the key details about a new soft hierarchical clustering algorithm called Fuzzy Hierarchical Co-clustering (FHCC) that is proposed for collaborative filtering recommendation. FHCC simultaneously generates hierarchical clusters of users and products based on a user-product rating matrix to detect potential user-product joint groups. It uses a fuzzy set approach to allow each user and product to belong to multiple clusters rather than a single cluster. The algorithm works by initially forming singleton co-clusters of individual users and products, then repeatedly merging the most similar pair of co-clusters until a single cluster remains. A hybrid similarity measure is used to calculate similarity between co-clusters based on their user, product, and rating components. The algorithm is intended to provide
A review and integration of the partial models of consumer behaviourAlexander Decker
This document provides an overview of consumer behavior models. It reviews four partial models: the economic model, which views consumers as rational actors seeking to maximize utility within a budget constraint; the linear experimental model, which sees consumers progressing through stages of awareness, interest, evaluation, trial, and adoption; the psychoanalytic model, which examines unconscious motivations; and the sociological model, which considers external social and cultural influences. The document then proposes integrating aspects of these partial models into a more comprehensive consumer behavior model.
A review and integration of the partial models of consumer behaviourAlexander Decker
This document provides an overview of consumer behavior models. It reviews four partial models: the economic model, which views consumers as rational actors seeking to maximize utility within a budget constraint; the linear experimental model, which sees consumers progressing through stages of awareness, interest, evaluation, trial, and adoption; the psychoanalytic model, which examines unconscious motivations; and the sociological model, which considers external social and cultural influences. The document then proposes integrating aspects of these partial models into a more comprehensive consumer behavior model.
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
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.
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/
A study on after sales and services in tvsProjects Kart
A comprehensive study on providing the after sales and services with one of dealers of TVS motors in Hassan city. This project report covers all the aspects of after sales and services on increasing the brand visibility by providing the services. Visit http://www.projectskart.com/p/contact-us.html for more information
STOCK MARKET PREDICTION USING MACHINE LEARNING METHODSIAEME Publication
Stock price forecasting is a popular and important topic in financial and academic
studies. Share market is an volatile place for predicting since there are no significant
rules to estimate or predict the price of a share in the share market. Many methods
like technical analysis, fundamental analysis, time series analysis and statistical
analysis etc. are used to predict the price in tie share market but none of these
methods are proved as a consistently acceptable prediction tool. In this paper, we
implemented a Random Forest approach to predict stock market prices. Random
Forests are very effectively implemented in forecasting stock prices, returns, and stock
modeling. We outline the design of the Random Forest with its salient features and
customizable parameters. We focus on a certain group of parameters with a relatively
significant impact on the share price of a company. With the help of sentiment
analysis, we found the polarity score of the new article and that helped in forecasting
accurate result. Although share market can never be predicted with hundred per-cent
accuracy due to its vague domain, this paper aims at proving the efficiency of Random
forest for forecasting the stock prices
IRJET- Customer Buying Prediction using Machine-Learning Techniques: A SurveyIRJET Journal
1) The document discusses using machine learning techniques to predict customer purchasing and churn based on their personal and behavioral data.
2) It reviews several machine learning algorithms that have been used for prediction, including random forest, logistic regression, naive bayes, and support vector machines.
3) Deep learning techniques are also discussed, including the use of convolutional neural networks to reveal hidden patterns in customer data and predict purchases and churn.
11.direct marketing with the application of data miningAlexander Decker
This document summarizes a research paper on using data mining techniques for direct marketing. It discusses how direct marketing focuses on specific customer groups rather than mass marketing. Data mining algorithms like decision trees are used to classify customers as loyal or unloyal based on attributes in customer data. This helps direct marketing efforts towards the most beneficial customers. The document also outlines some common problems in classification for direct marketing like imbalanced data and issues with predictive accuracy, and provides solutions like lift analysis.
Direct marketing with the application of data miningAlexander Decker
1. Direct marketing involves sending targeted messages directly to consumers through methods like email, telemarketing, and mail. It is more effective than mass marketing as it focuses on specific customer groups.
2. Data mining techniques like supervised classification can be used to analyze customer data and classify customers as loyal or unloyal for direct marketing purposes. Decision trees are a popular technique to visualize customer classifications.
3. Building accurate customer classifications is challenging due to issues like imbalanced class distributions in the data. Ranking and lift analysis can help address these issues and identify the most promising potential customers to target.
The observation method is the most commonly used method for behavioral science studies. It involves direct observation by the investigator without asking questions to respondents. This allows the investigator to obtain information about current behaviors. It is less demanding than interview or questionnaire methods as it does not require active cooperation from respondents. Some common observation techniques include warranty cards, pantry audits, and distributor audits.
A widely used approach for gaining insight into the heterogeneity of consumer’s buying behavior is market segmentation. Conventional market segmentation models often ignore the fact that consumers’ behavior may evolve over time. Therefore retailers consume limited resources attempting to service unprofitable consumers. This study looks into the integration between enhanced Recency, Frequency, Monetary (RFM) scores and Consumer Lifetime Value (CLV) matrix for a medium size retailer in the State of Kuwait. A modified regression algorithm investigates the consumer purchase trend gaining knowledge from a pointof-sales data warehouse. In addition, this study applies enhanced normal distribution formula to remove outliers, followed by soft clustering Fuzzy C-Means and hard clustering Expectation Maximization (EM) algorithms to the analysis of consumer buying behavior. Using cluster quality assessment shows EM algorithm scales much better than Fuzzy C-Means algorithm with its ability to assign good initial points in the smaller dataset.
Opinion pattern mining based on probabilistic principle component analysis re...eSAT Journals
This document summarizes an article that proposes a new method called Opinion Pattern Mining Segmentation (OPMS) based on Probabilistic Principal Component Analysis (PPCA). The method segments user profiles and behavior patterns from product reviews more efficiently compared to traditional methods like random forests. It reduces dimensionality using a covariance matrix in the PPCA process, improving segmentation efficiency by up to 9% and decreasing false positive rates. The method was tested on product review data and showed improvements in segmentation efficiency, user product preference accuracy, and reduced opinion pattern mining time compared to other methods.
Predicting Bank Customer Churn Using ClassificationVishva Abeyrathne
This document describes a study that used classification models to predict customer churn for a bank. The authors collected a dataset of 10,000 bank customers from Kaggle and preprocessed the data. They then explored relationships between features and the target variable of whether a customer churned. Two classification models were tested - KNN and Decision Tree. After hyperparameter tuning, Decision Tree achieved the best accuracy of 84.25%, outperforming KNN. However, both models struggled to accurately predict customers who would churn. The authors concluded Decision Tree was the best model but recommend collecting more data on churning customers.
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.
Machine Learning - Algorithms and simple business casesClaudio Mirti
Linear regression, logistic regression, and decision trees are commonly used supervised learning algorithms. Linear regression models the relationship between input and output variables to predict future values, logistic regression is used for binary classification tasks, and decision trees split data into branches to make predictions. Unsupervised learning algorithms like k-means clustering group unlabeled data into clusters with similar characteristics. Reinforcement learning optimizes strategies through trial-and-error interactions like optimizing inventory levels or self-driving cars. Convolutional neural networks in deep learning can diagnose diseases from scans, detect logos in images, and understand customer perception through visual data analysis.
This document provides an overview of key statistical concepts for data analysis, including:
- Variables can be quantitative or categorical, and it is important to understand the type of variable to perform appropriate analyses.
- A population includes all relevant data, while a sample is a subset used to make inferences. Statistical formulas differ for populations and samples.
- Measures of central tendency like the mean, median, and mode summarize typical values in a dataset. Measures of variability like the range and standard deviation describe how spread out the values are.
- Understanding these fundamental statistical concepts is necessary to properly analyze and interpret data through techniques like hypothesis testing, correlation analysis, regression, and comparisons of group means.
AHP Based Data Mining for Customer Segmentation Based on Customer Lifetime ValueIIRindia
Data mining techniques are widely used in various areas of marketing management for extracting useful information.Particularly in a business-to-customer (B2C) setting, it plays an important role in customer segmentation. A retailernot only tries to improve its relationship with its customers,but also enhances its business in a manufacturer-retailer-consumer chainwith respect to this information.Although there are various approaches for customer segmentation, we have used an analytic hierarchical process based data mining technique in this regard. Customers are segmented into six clusters based on Davis-Bouldin (DB) index and K-Means algorithm.Customer lifetime value (CLV)along four dimensions, viz., Length (L), Recency(R), Frequency (F) and Monetary value (M) are considered for these clusters. Then, we apply Saaty’s analytical hierarchical process (AHP) to determine the weights of these criteria, which in turn, helps in computing the CLV value for each of the clusters and their individual rankings. This information is quite important for a retailer to design promotional strategies for improving relationship between the retailer and its customers. To demonstrate the effectiveness of this methodology, we have implemented the model, taking a real life data-base of customers of an organization in the context of an Indian retail industry.
Conjoint analysis is a technique used to understand what attributes of a product are most important to consumers. It involves having consumers rank hypothetical products that combine different levels of attributes. This allows researchers to determine the partial utilities (or part-worths) that consumers assign to each attribute level and understand their relative importance. Conjoint analysis provides insight into consumer preferences while maintaining realism by considering attributes together rather than separately. It estimates the tradeoffs consumers make between attributes and can uncover important drivers of choice.
FHCC: A SOFT HIERARCHICAL CLUSTERING APPROACH FOR COLLABORATIVE FILTERING REC...IJDKP
This summary provides the key details about a new soft hierarchical clustering algorithm called Fuzzy Hierarchical Co-clustering (FHCC) that is proposed for collaborative filtering recommendation. FHCC simultaneously generates hierarchical clusters of users and products based on a user-product rating matrix to detect potential user-product joint groups. It uses a fuzzy set approach to allow each user and product to belong to multiple clusters rather than a single cluster. The algorithm works by initially forming singleton co-clusters of individual users and products, then repeatedly merging the most similar pair of co-clusters until a single cluster remains. A hybrid similarity measure is used to calculate similarity between co-clusters based on their user, product, and rating components. The algorithm is intended to provide
A review and integration of the partial models of consumer behaviourAlexander Decker
This document provides an overview of consumer behavior models. It reviews four partial models: the economic model, which views consumers as rational actors seeking to maximize utility within a budget constraint; the linear experimental model, which sees consumers progressing through stages of awareness, interest, evaluation, trial, and adoption; the psychoanalytic model, which examines unconscious motivations; and the sociological model, which considers external social and cultural influences. The document then proposes integrating aspects of these partial models into a more comprehensive consumer behavior model.
A review and integration of the partial models of consumer behaviourAlexander Decker
This document provides an overview of consumer behavior models. It reviews four partial models: the economic model, which views consumers as rational actors seeking to maximize utility within a budget constraint; the linear experimental model, which sees consumers progressing through stages of awareness, interest, evaluation, trial, and adoption; the psychoanalytic model, which examines unconscious motivations; and the sociological model, which considers external social and cultural influences. The document then proposes integrating aspects of these partial models into a more comprehensive consumer behavior model.
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
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.
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/
A study on after sales and services in tvsProjects Kart
A comprehensive study on providing the after sales and services with one of dealers of TVS motors in Hassan city. This project report covers all the aspects of after sales and services on increasing the brand visibility by providing the services. Visit http://www.projectskart.com/p/contact-us.html for more information
STOCK MARKET PREDICTION USING MACHINE LEARNING METHODSIAEME Publication
Stock price forecasting is a popular and important topic in financial and academic
studies. Share market is an volatile place for predicting since there are no significant
rules to estimate or predict the price of a share in the share market. Many methods
like technical analysis, fundamental analysis, time series analysis and statistical
analysis etc. are used to predict the price in tie share market but none of these
methods are proved as a consistently acceptable prediction tool. In this paper, we
implemented a Random Forest approach to predict stock market prices. Random
Forests are very effectively implemented in forecasting stock prices, returns, and stock
modeling. We outline the design of the Random Forest with its salient features and
customizable parameters. We focus on a certain group of parameters with a relatively
significant impact on the share price of a company. With the help of sentiment
analysis, we found the polarity score of the new article and that helped in forecasting
accurate result. Although share market can never be predicted with hundred per-cent
accuracy due to its vague domain, this paper aims at proving the efficiency of Random
forest for forecasting the stock prices
This document discusses marketing mix strategies in the Indian cement sector. It begins by introducing the 4Ps (product, price, promotion, place) and 7Ps marketing mix models, as well as two versions of the 4Cs (customer, cost, communication, convenience) model. It then analyzes the cement industry in India using these frameworks. For the cement sector, the document examines factors relating to product mix, price strategies, promotional activities targeting contractors and retailers, and the distribution channels used. Statistical tools for data analysis in the industry are also mentioned.
Study on after sales and service in tvsProjects Kart
The document provides an overview of TVS Motor Company including:
- TVS Motor Company is one of India's leading two-wheeler manufacturers based in Hosur, Tamil Nadu.
- It started as a moped division in 1979 and later had a joint venture with Suzuki, becoming a leader in 100cc motorcycles.
- TVS Motor Company is part of the larger TVS Group, a diversified conglomerate with presence in automotive, electronics, and other industries.
A Review Of The Effect Of Pricing Strategies On The Purchase Of Consumer GoodsDereck Downing
This document summarizes a research paper that examined the effects of pricing strategies on the purchase of consumer goods. It reviewed literature on different pricing objectives, strategies, and frameworks. The research utilized secondary data and found that consumers perceive value in prices and competitors' prices affect purchase decisions. It also found that online pricing informs and influences consumer purchase decisions. The study contributes to knowledge around pricing strategies and the consumer purchase process. It recommends firms focus on communicating value through prices while also monitoring competitors' prices and how they impact sales.
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.
The document discusses consumer research methods. It outlines the objectives, scope, techniques and sources used in consumer research. Both quantitative and qualitative research methods are covered. Quantitative methods include observation, experiments and surveys. Qualitative methods include depth interviews, focus groups, projective techniques and metaphor analysis. Secondary data sources include published reports, census data, and company annual reports. Primary data is collected directly from consumers.
1) The document provides an overview of a webinar on conducting discrete choice conjoint analysis projects using SurveyAnalytics software.
2) It discusses key aspects of setting up a conjoint analysis study such as defining attributes and levels, sample size considerations, and best practices for survey design.
3) The webinar demonstrates how to interpret conjoint analysis outputs including relative importance scores and market simulations.
Modelling the Human Values Scale in Recommender Systems using Sales Pitch Mod...inventionjournals
This is a novel attempt to anticipate the reasons for key purchase decisions of individual customers and use them in recommender systems. Modern techniques are available to do this, such as data mining, user models, direct marketing and recommender systems. The most common, state of the art approach to recommender systems is to find out what is the right product for the right customer at the right time. Although our approach is diferent, it shares the same goal of increasing sales: how to convince any given customer that this is the perfect product for him and that he should buy it now! This is done with sales pitch modulation, a method that highlights the key benefits of a product according to what is important for a customer, according to what he thinks it is worth. The human values scale (HVS) model is an approach from modern psychology, normally applied to the human resource selection process in companies, that reveals which key values rule the decisions made by people across all domains of their life. This paper presents a method to calculate the HVS through existing user models, and shows how to apply it to a real case, a campaign to sell banking products, where the recommender system chooses the right message for every single customer, with good, solid results
Developing Relationships; consumers as a source for sustainable competitive a...Kevin Rommen
The world is changing thus business units should also be changing. The influences of social media and internet can no longer be neglected, case in point “Nestlé’s epic social media #fail”1. These changes are giving consumers more and more power in their relationship with business units. Furthermore the enormous amount of products available give consumers more and more possibilities to choose from. For example, at supermarkets in the USA you’ll find in the average week about 110 cereal brands in stock (Shum, 2004). The availability of that amount of different products/product-ranges within an industry raises the question to how business units can create competitive advantage within this enormous amount of competition, especially when the consumer is gaining power?
A Hypothesis is Placed to Justify the Extendibility of Recommender System/ Re...Dr. Amarjeet Singh
Researchers still believe that the information filtering system/ collaborating system is a recommender system or a recommendation system. It is used to predict the "rating" or "preference" of a user to an item. In other words, both predict rating or preference for an item or product on a specific platform. The aim of the paper is to extend the areas of the recommender system/recommendation systems. The basic task of the recommender system mainly is to predict or analyze items/product. If it is possible to include more products in the system, then obviously the system may be extended for other areas also. For example, Medicine is a product and doctors filter the particular medicine for the particular disease. In the medical diagnosis doctors prescribed a medicine and it a product. It depends on the disease of the user/patient so here doctor predicts a medicine or product just like an item is recommended in a recommender system. The main objective of the paper is to extend the Recommender System/Recommendation system in other fields so that the research works can be extended Social Science, Bio-medical Science and many other areas.
Similar to Analysis of consumer preferences for new smartphone - Xiomi India (20)
The document discusses digital transformation in the IT industry. It makes the following key points:
1. Customers now expect personalized, accessible content from organizations anytime and anywhere.
2. Top drivers of digital transformation are profitability, customer satisfaction, and speed to market.
3. Improving customer experience and growing revenues are top strategic priorities for organizations.
4. Focusing on digital experiences and establishing digital governance will be important areas of focus in the coming year.
Digital business transformation- IT StrategyTushar Sharma
A Study of Digital Business Transformation which is the need of the hour in the IT industry. It also showcases the need for B2B platform for a business transformation.
The Fall of Kodak- A tale of disruptive technology and bad businessTushar Sharma
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- It was slow to recognize the shift to digital and the threat this posed to its traditional film business.
- While Kodak invested in digital, it struggled to compete against fast moving competitors and saw its market share erode significantly.
- Kodak's history shows how a firm's core competencies can become rigidities that inhibit innovation when markets change radically.
This document provides an overview of KFC's supply chain operations in India. It discusses KFC's strategic goals and segmentation, targeting, and positioning strategies. It then describes key aspects of KFC's supply chain, including suppliers, logistics, facilities, warehousing, inventory management, and sourcing. The document analyzes several of KFC's performance metrics like return on equity and inventory turnover ratio. It aims to understand how KFC's supply chain aligns with and supports its strategic objectives.
Marketing plan -Xiomi New Product DevelopmentTushar Sharma
This is in conjunction with "Analysis of Consumer Preferences for New Smartphone" uploaded earlier. It showcases a detailed marketing plan for a new product in smartphone category.
An analysis of factors effecting rice production in indiaTushar Sharma
In this paper a broad study is completed to appraise the rice production in India in light of present and historical information. The critical components concentrated on are, land utilized, fertilizer, rainfall and production separately. To study the strength of interdependence between the factors and estimation of production multivariate correlation analysis and regression analysis have been applied.
Detailed Analysis of Tata Motors Ltd. by calculating its cost of capital usin...Tushar Sharma
The purpose of the study is to do a detailed analysis of a manufacturing company, Tata Motors Ltd. In this report, we have calculated the beta, cost of equity and the cost of capital for Tata Motors Ltd., one of the largest two-wheeler manufacturing organizations in India. Along with this, we have also studied the capital structure and calculated the degree of financial and operating leverages of Tata Motors Ltd. for the years 2014-15. Data for calculating the beta and risk free rate has been obtained from the Ace Equity. To find out the capital structure and the degree of financial and operating leverage, data has been taken from the annual report of the company
Did you know that while 50% of content on the internet is in English, English only makes up 26% of the world’s spoken language? And yet 87% of customers won’t buy from an English only website.
Uncover the immense potential of communicating with customers in their own language and learn how translation holds the key to unlocking global growth. Join Smartling CEO, Bryan Murphy, as he reveals how translation software can streamline the translation process and seamlessly integrate into your martech stack for optimal efficiency. And that's not all – he’ll also share some inspiring success stories and practical tips that will turbocharge your multilingual marketing efforts!
Key takeaways:
1. The growth potential of reaching customers in their native language
2. Tips to streamline translation with software and integrations to your tech stack
3. Success stories from companies that have increased lead generation, doubled revenue, and more with translation
Embark on style journeys Indian clothing store denver guide.pptxOmnama Fashions
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Breaking Silos To Break Bank: Shattering The Divide Between Search And SocialNavah Hopkins
At Mozcon 2024 I shared this deck on bridging the divide between search and social. We began by acknowledging that search-first marketers are used to different rules of engagement than social marketers. We also looked at how both channels treat creative, audiences, bidding/budgeting, and AI. We finished by going through how they can win together including UTM audits, harvesting comments from both to inform creative, and allowing for non-login forums to be part of your marketing strategy.
I themed this deck using Baldur's Gate 3 characters: Gale as Search and Astarion as Social
Build marketing products across the customer journey to grow your business and build a relationship with your customer. For example you can build graders, calculators, quizzes, recommendations, chatbots or AR apps. Things like Hubspot's free marketing grader, Moz's site analyzer, VenturePact's mobile app cost calculator, new york times's dialect quiz, Ikea's AR app, L'Oreal's AR app and Nike's fitness apps. All of these examples are free tools that help drive engagement with your brand, build an audience and generate leads for your core business by adding value to a customer during a micro-moment.
Key Takeaways:
Learn how to use specific GPTs to help you Learn how to build your own marketing tools
Generate marketing ideas for your business How to think through and use AI in marketing
How AI changes the marketing game
From Subreddits To Search: Maximizing Your Brand's Impact On RedditSearch Engine Journal
The search landscape is undergoing a seismic shift, and Reddit is at the epicenter. Google's Helpful Content Update and its $60 million deal with Reddit, coupled with OpenAI's partnership, have catapulted Reddit's real-time content to unprecedented heights.
Check out this insightful webinar exploring the newfound importance of Reddit in the digital marketing landscape. Learn how these changes make Reddit an essential platform for getting your brand and content in front of evolving search audiences.
You’ll hear:
- The evolution of Reddit as a major influencer on SERPS over the years.
- The impact of recent changes and partnerships on Reddit’s place in search.
- A comprehensive look at Reddit, how it works, and how to approach it.
- Unique engagement opportunities presented by Reddit.
With Brent Csutoras, a Reddit expert with over 18 years of experience on the platform, we’ll delve into the intricacies of Reddit's communities, known as Subreddits, and how to leverage their power without compromising authenticity or violating community guidelines in the age of AI-driven search experiences.
Don't miss this opportunity to stay ahead of the curve and leverage Reddit for your brand's success.
INTRODUCTION TO SEARCH ENGINE OPTIMIZATION (SEO).pptxGiorgio Chiesa
This presentation is recommended for those who want to know more about SEO. It explains the main theoretical and practical aspects that influence the positioning of websites in search engines.
Mindfulness Techniques Cultivating Calm in a Chaotic World.pptxelizabethella096
In today’s fast-paced world, stress and anxiety have become common companions for many. With constant connectivity and an unending stream of information, finding moments of peace can seem like an insurmountable challenge. However, mindfulness techniques offer a beacon of calm amidst the chaos, helping individuals to center themselves and find balance. These practices, rooted in ancient traditions and supported by modern science, are accessible to everyone and can profoundly impact mental and emotional well-being.
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Conferences like DigiMarCon provide ample opportunities to improve our own marketing programs by learning from others. But just because everyone is jumping on board with the latest idea/tool/metric doesn’t mean it works – or does it? This session will examine the value of today’s hottest digital marketing topics – including AI, paid ads, and social metrics – and the truth about what these shiny objects might be distracting you from.
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- How NOT to shoot your digital program in the foot by using flashy but ineffective resources
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In the face of the news of Google beginning to remove cookies from Chrome (30m users at the time of writing), there’s no longer time for marketers to throw their hands up and say “I didn’t know” or “They won’t go through with it”. Reality check - it has already begun - the time to take action is now. The good news is that there are solutions available and ready for adoption… but for many the race to catch up to the modern internet risks being a messy, confusing scramble to get back to "normal"
Basic Management Concepts., “Management is the art of getting things done thr...DilanThennakoon
The managers achieve organizational objectives by getting work from
others and not performing in the tasks themselves.
Management is an art and science of getting work done through people.
It is the process of giving direction and controlling the various activities
of the people to achieve the objectives of an organization Management is a universal process in all organized, social and economic activities. Wherever
there is human activity there is management.
Management is a vital aspect of the economic life of man, which is an organized group activity. A
central directing and controlling agency is indispensable for a business concern. The productive
resources –material, labour, capital etc. are entrusted to the organizing skill, administrative ability
and enterprising initiative of the management. Thus, management provides leadership to a
business enterprise. Without able managers and effective managerial leadership the resources of
production remain merely resources and never become production. Management occupies such an
important place in the modern world that the welfare of the people and the destiny of the country
are very much influenced by it.
1.2 MEANING OF MANAGEMENT
Management is a technique of extracting work from others in an integrated and co-ordinated
manner for realizing the specific objectives through productive use of material resources.
Mobilising the physical, human and financial resources and planning their utilization for business
operations in such a manner as to reach the defined goals can be benefited to as management.
1.3 DEFINITION OF MANAGEMENT
Management may be defined in many different ways. Many eminent authors on the subject have
defined the term "management". Some of these definitions are reproduced below:
In the words of George R Terry - "Management is a distinct process consisting of planning,
organising, actuating and controlling performed to determine and accomplish the objectives by the
use of people and resources".
According to James L Lundy - "Management is principally the task of planning, co¬ordinating,
motivating and controlling the efforts of others towards a specific objective",
In the words of Henry Fayol - "To manage is to forecast and to plan, to organise, to command, to
co-ordinate and to control".
According to Peter F Drucker - "Management is a multipurpose organ that manages a business and
manages managers and manages worker and work".
In the words of J.N. Schulze - "Management is the force which leads, guides and directs an
organisation in the accomplishment of a pre-determined object".
In the words of Koontz and O'Donnel - "Management is defined as the creation and maintenance
of an internal environment in an enterprise where individuals working together in groups can
perform efficiently and effectively towards the attainment of group goals".
According to Ordway Tead - "Management is the process and agency which directs and guides the
operations of an organisation in realising of established aim
Evaluating the Effectiveness of Women-Focused MarketingHighViz PR
Women centric marketing is a vital part in reaching one of the most influential groups of consumers. Here is a guide to know and measure the impact of women-centric marketing efforts-
Top Strategies for Building High-Quality Backlinks in 2024 PPT.pdf1Solutions Pvt. Ltd.
As we move into 2024, the methods for building high-quality backlinks continue to evolve, demanding more sophisticated and strategic approaches. This presentation aims to explore the latest trends and proven strategies for acquiring high-quality backlinks that can elevate your SEO efforts.
Visit:- https://www.1solutions.biz/link-building-packages/
Boost Your Instagram Views Instantly Proven Free Strategies.pptxInstBlast Marketing
Join Performance Car Exclusive to drive the finest supercars, engineered with advanced materials and cutting-edge technology for peak performance.
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Mastering Local SEO for Service Businesses in the AI Era"" is tailored specifically for local service providers like plumbers, dentists, and others seeking to dominate their local search landscape. This session delves into leveraging AI advancements to enhance your online visibility and search rankings through the Content Factory model, designed for creating high-impact, SEO-driven content. Discover the Dollar-a-Day advertising strategy, a cost-effective approach to boost your local SEO efforts and attract more customers with minimal investment. Gain practical insights on optimizing your online presence to meet the specific needs of local service seekers, ensuring your business not only appears but stands out in local searches. This concise, action-oriented workshop is your roadmap to navigating the complexities of digital marketing in the AI age, driving more leads, conversions, and ultimately, success for your local service business.
Key Takeaways:
Embrace AI for Local SEO: Learn to harness the power of AI technologies to optimize your website and content for local search. Understand the pivotal role AI plays in analyzing search trends and consumer behavior, enabling you to tailor your SEO strategies to meet the specific demands of your target local audience. Leverage the Content Factory Model: Discover the step-by-step process of creating SEO-optimized content at scale. This approach ensures a steady stream of high-quality content that engages local customers and boosts your search rankings. Get an action guide on implementing this model, complete with templates and scheduling strategies to maintain a consistent online presence. Maximize ROI with Dollar-a-Day Advertising: Dive into the cost-effective Dollar-a-Day advertising strategy that amplifies your visibility in local searches without breaking the bank. Learn how to strategically allocate your budget across platforms to target potential local customers effectively. The session includes an action guide on setting up, monitoring, and optimizing your ad campaigns to ensure maximum impact with minimal investment.
Capstone Project: Luxury Handloom Saree Brand
As part of my college project, I applied my learning in brand strategy to create a comprehensive project for a luxury handloom saree brand. Key aspects of this project included:
- *Competitor Analysis:* Conducted in-depth competitor analysis to identify market position and differentiation opportunities.
- *Target Audience:* Defined and segmented the target audience to tailor brand messages effectively.
- *Brand Strategy:* Developed a detailed brand strategy to enhance market presence and appeal.
- *Brand Perception:* Analyzed and shaped the brand perception to align with luxury and heritage values.
- *Brand Ladder:* Created a brand ladder to outline the brand's core values, benefits, and attributes.
- *Brand Architecture:* Established a cohesive brand architecture to ensure consistency across all brand touchpoints.
This project helped me gain practical experience in brand strategy, from research and analysis to strategic planning and implementation.
3. Introduction
Technology has been a constant force of change in human lives. In today's world, the development of
the smartphone has risen to prominence has one of the technologies to have radically transformed
people's lifestyles by allowing them to digitally connect with their lived environments. With the
development of mobile internet services, more and more consumers are adopting smartphones as
their primary communication device. A smartphone offers more advanced computing ability and
connectivity than a feature phone, and typically includes a high‐resolution touch screen and offers
wireless‐internet access to web pages through a built‐in web browser. Telecommunication companies
have recently begun to promote smartphone products in hopes of promoting mobile internet services
as a way to increase revenues. While Android mobile devices account for over half of the market for
smartphones, Android users only accounted for a 16 percent share of the mobile internet market in
2011. Recently, several studies have explored motivation for adopting smartphones and mobile
internet from a variety of perspectives, such as technology acceptance model (TAM), aesthetic design,
and perceived value. However, few of these studies have specifically investigated Android
smartphones in mobile internet context, which are relatively new to the market. Android is an open
source system which allows manufacturers to customize their devices, including hardware and
software. Moreover, perceived value is the main determinant of payment intention
LITERATURE REVIEW:
THE NATURE OF CONJOINT ANALYSIS
Description Attempts to construct consumer typologies are an enduring feature of retailing research
and frequently centre on economic and demographic characteristics. Such research highlights the
relatively poor understanding of real-life consumer behaviour and, in particular, the need to develop
more appropriate methods of examining the behaviour of consumers in real-life retail settings. By
using a conjoint study researchers could gain a better understanding of the real value consumers
attach to certain attributes when making purchasing decisions in a retail situation. The concept
conjoint analysis is described by Hair et al (1998:392) as follows: “Conjoint analysis is a multivariate
technique used specifically to understand how respondents develop preferences for products or
services. It is based on the simple premise that consumers evaluate the value of a product or service
by combining the separate amounts of value provided by each attribute.” Sudman and Blair
(1998:229-230) warn that it is not a data analysis procedure like factor analysis or cluster analysis. It
must be regarded as a type of “thought experiment” designed to show how various elements of
products or services (price, brand, style) predict customer preferences for a product or service. Kotler
(2000:339) defines conjoint analysis as”…a method for deriving the utility values that consumers
attach to varying levels of a product’s attributes.” Churchill and Iacobucci (2002:748) refer to conjoint
analysis as “…conjoint measurement, which relies on the ability of respondents to make judgments
about stimuli.” These stimuli represent some predetermined combinations of attributes, and during a
laboratory experiment, respondents are asked to make judgments about their preferences for various
attribute combinations. The basic aim, therefore, is to determine the features they most prefer. From
the definitions given above it is clear that conjoint studies centre on certain attributes of products or
services and also various levels within each attribute.
4. In conjoint analysis respondents indicate their preference for a series of hypothetical multi-attribute
alternatives, which are typically displayed as profiles of attributes. The responses to these profiles are
analysed to yield estimates of the relative importance of the attributes and to build predictive models
of consumer choice for new alternatives (Oppewal & Vriens, 2000). Conjoint analysis is a dependence
technique that has brought new sophistication to the evaluation of objects, such as new products,
services or ideas (Hair et al, 1998:15). The theory and methods of conjoint analysis deal with complex
decision-making, or the process of assessment, comparison, and/or evaluation. In this process
consumers decide which aspects of products or services are important, compare the products or
services on each of the important aspects, and decide which one to choose (Louviere, 1988:9). Schutte
(1999:90-92) lists the following to indicate the value of conjoint analysis in assisting marketers to
provide answers when strategic marketing and selling decisions have to be made: Understanding
market preferences When a product has, say five key attributes: price, quality, style, brand and
packaging, these attributes and their associated levels represent the factors that materially affect
consumer preferences. Predicting market choices conjoint analysis offers the researcher opportunities
to apply certain simulations. The simulation capability of conjoint analysis enables the analyst to
explore alternative market scenarios. The impact on market share or changes in the product can be
assessed and the impact of competitive moves can then be anticipated (Wyner, 1995). Developing
market strategies it can aid marketers to identify product concepts that are extremely attractive from
the consumer's perspective. Concepts that are not technically or financially feasible can be eliminated.
The best of the remaining products must be selected, and then the attributes of this product must be
fine-tuned to achieve the stated objective. A series of simulation tests must be run to identify the
point at which the product performs best (Wyner, 1995). Segmenting the market conjoint results are
very useful for segmentation purposes. Consumers may be segmented on the basis of utility values or
attribute important scores. Thus simulations can be viewed as segmentation analyses that group
people together according to their most preferred product among other substitutes or competitive
products (Wyner, 1995).
Approaches to conjoint analysis
There are two general approaches to data collection for conjoint – the two‐factor‐at‐a‐time trade‐off
method and the multiple factor full‐concept method. The two‐factor‐at‐a‐time trade‐off method is
now seldomly used. The full‐concept is more realistic as all factors are considered and evaluated at
the same time.
In the full‐concept (or full‐profile), the respondents are asked to rank or score a set of profiles
according to their preference. On each profile, all factors of interest are represented and a different
combination of factor levels (i.e. features) appears. The factors are the general attribute categories of
the product/service such as colour, size, or price. The factor levels (i.e. product/service features) are
the specific values of the factors, such as red, small, and expensive. The possible combination of all
factor levels can become too large for respondents to rank or score in a meaningful way. The full‐
concept approach in SPSS categories conjoint uses fractional factorial designs, which uses a smaller
fraction of all possible alternatives. This reduced size subset (orthogonal array) considers only the
main effects and the interactions are assumed to be negligible.
The factor levels can be specified as DISCRETE (when factor levels are categorical), LINEAR (when data
are expected to be linearly related to the factor), IDEAL, or ANTI‐IDEAL (for quadratic function models).
The SPSS conjoint procedure can calculate utility scores (or part‐worths) for each individual
respondent and for the whole sample. These utility scores, analogous to regression coefficients, can
5. be used to find the relative importance of each factor. SPSS permits the use of simulation profiles to
represent actual or prospective products to estimate or predict market share of preference.
Research Design
In order to generate an orthogonal design for the appropriate smartphone factors and factor levels,
some rounds of focus group, survey and discussions were held. The focus group consisted of six
persons from different occupational backgrounds and survey had 150 respondents. The focus group
members were selected on the basis that they had smartphone use experiences. The focus group
members discussed in detail their experience, either good or bad, from many different perspectives
and occasions.
S No. Attribute
1 Screen Size
2 RAM
3 Operating System
4 Internal Memory
5 Network Type
6 Battery Capacity
7 Sim Type
8 Primary Camera
9 Selfie Camera
10 Display Type
11 Processors (Cores)
12 Fingerprint Sensor
13 Waterproof Body
14
Network Type
(2G/3G etc.)
15
Resolution (HD, full
HD etc.)
16
Body Type (metal,
fibre etc.)
17 LED Flash
18 Quick Charging
19
USB OTG (to
connect USB)
20
Display Type (LCD,
OLED etc.)
Despite a careful selection of factors, there were still too many possible profiles for the respondents
to choose from. The SPSS generated a parsimonious orthogonal array of 16 profiles.
It was decided it would be useful to study the following attributes:-
1. Price
2. Battery Capacity
3. Operating System
4. Internal Memory
6. 5. Primary Camera
6. Processor
7. Resolution
8. Quick Charging
The Following cards were created using the orthogonal design which were later sent in for
ranking from the respondents:
Price Battery
Capacity
Operating
System
Internal
Memory
Primary
Camera
Processo
r Cores
Resolutio
n
Quick Charging
10000-
12000
2001-3000
mAh
Android 16 GB
12 MP &
above
Octa HD Yes
12001-
15000
4001-5000
mAh
Windows 16 GB 8-12 MP Hexa Full HD Yes
12001-
15000
4001-5000
mAh
Android 32 GB
12 MP &
above
Octa HD Yes
10000-
12000
4001-5000
mAh
Android 64 GB
12 MP &
above
Hexa 4K No
10000-
12000
4001-5000
mAh
Windows 16 GB 8-12 MP Octa HD No
12001-
15000
2001-3000
mAh
Android 16 GB
12 MP &
above
Hexa Full HD No
12001-
15000
3001-4000
mAh
Android 64 GB 8-12 MP Hexa HD Yes
12001-
15000
2001-3000
mAh
Android 16 GB 8-12 MP Octa 4K No
10000-
12000
2001-3000
mAh
Windows 32 GB 8-12 MP Hexa 4K Yes
12001-
15000
2001-3000
mAh
Windows 64 GB 8-12 MP Octa HD No
10000-
12000
2001-3000
mAh
Windows 64 GB
12 MP &
above
Octa Full HD Yes
10000-
12000
2001-3000
mAh
Android 16 GB 8-12 MP Hexa HD Yes
12001-
15000
3001-4000
mAh
Windows 16 GB
12 MP &
above
Octa 4K Yes
10000-
12000
3001-4000
mAh
Android 32 GB 8-12 MP Octa Full HD No
10000-
12000
3001-4000
mAh
Windows 16 GB
12 MP &
above
Hexa HD No
12001-
15000
2001-3000
mAh
Windows 32 GB
12 MP &
above
Hexa HD No
7. To examine consumer preference for mobile devices, this study carried out a survey asking
respondents to rank a set of alternatives. Each respondent was asked to rank the 16 profiles describing
the form of mobile devices attributes as a tool according to their own usage intention on a scale from
1 to 16. The respondents were selected in an “A” class university in Ghaziabad, India as they form the
main target group. This study assumed that all students have mobile devices or they have used one
before. Therefore, respondents could evaluate the profiles considering the price of the mobile device.
In addition, most undergraduate students have more knowledge about mobile devices including smart
phones and tablet PCs. Thus, this work could obtain more meaningful results from the respondents
who were the main users of mobile devices. A total of 200 respondents were interviewed via a survey.
38 respondents were excluded because they failed to respond to some of the values. Thus, the analysis
is based on the data from the final 162 respondents, consisting of 124 males (77.2%) and 38 females
(22.8%).
Results of Conjoint Analysis
Model Description
N of Levels Relation to
Ranks or
Scores
Price 2 Discrete
Battery_Capacity 3 Discrete
Operating_System 2 Discrete
Internal_Memory 3 Discrete
Primary_Camera 2 Discrete
Processor_Cores 2 Discrete
Resolution 3 Discrete
Quick_Charging 2 Discrete
All factors are orthogonal.
8. Utilities
Utility Estimate Std. Error
Price
10000-12000 .023 .384
12001-15000 -.023 .384
Battery_Capacity
2001-3000 mAh .487 .512
3001-4000 mAh 1.029 .600
4001-5000 mAh -1.516 .600
Operating_System
Android 1.559 .384
Windows -1.559 .384
Internal_Memory
16 GB .563 .512
32 GB .279 .600
64 GB -.842 .600
Primary_Camera
8-12 MP .312 .384
12 MP & above -.312 .384
Processor_Cores
Hexa .240 .384
Octa -.240 .384
Resolution
HD -.003 .512
Full HD -.075 .600
4K .078 .600
Quick_Charging
Yes .467 .384
No -.467 .384
(Constant) 8.211 .443
Importance Values
Price 6.302
Battery_Capacity 21.419
Operating_System 19.525
Internal_Memory 16.258
Primary_Camera 8.760
Processor_Cores 6.424
Resolution 12.986
Quick_Charging 8.326
Averaged Importance Score
Correlationsa
Value Sig.
Pearson's R .935 .000
Kendall's tau .817 .000
a. Correlations between observed and
estimated preferences
9. Implications of the Empirical Findings
The empirical results have a number of interesting and meaningful implications for policy makers and
business players to understand the essential characteristics of mobile devices.
The correlation is significant which represents that the amount of correlation between the observed
preference scores and the conjoint model estimated preference score. The model does a good job of
predicting the respondent’s preference for different attributes towards the smartphone.
1. The model description table displays the No of levels of each attribute and relation to rank or
scores.
2. The utilities table showcases that the following attributes with described values are significant :-
S No. Attribute
1 Price 10000-12000
2 Battery Capacity 3001-4000 mAh
3 Operating System Android
4 Internal Memory 16 GB & 32 GB
5 Primary Camera 8-12 MP
6 Processors (Cores) Hexa
7
Resolution (HD, full
HD etc.) 4K
8 Quick Charging Yes
3. According to the importance values table Battery Capacity is the most important attribute for
the respondents followed by OS, Internal Memory, Resolution and Quick Charging.
As per the exploratory studies conducted it was observed that the price sensitive consumer favours
some other attributes such as metal body, finger print sensor, light UI etc. These attributes clubbed
with the above mentioned attributes which showcases resolution as 4K and quick charging as a unique
feature in this price segment of 10000-12000 INR can create a unique and appealing product.