Conjoint Analysis
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Conjoint analysis is a technique used to understand how consumers develop preferences for products and services. It involves presenting respondents with profiles consisting of different attribute levels and measuring their preferences. The preference data is then analyzed, usually via regression, to estimate the part-worth or utility that respondents associate with each attribute level. These utilities can then be used to simulate market shares of potential new products and identify key drivers of preferences. Conjoint analysis sees wide commercial use across various industries to guide new product design and selection.
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Conjoint Analysis
It is on Conjoint Analysis presented by Radhika Gupta, Shivi Agarwal, Neha Arya, Neha Kasturia, Mudita Maheshwari, Dhruval Dholakia, Chinmay Jaggan Anmol Sahani and Madhusudan Partani of FMG-18A, FORE School of Management
conjoint analysis
This document discusses conjoint analysis and provides an example using SPSS. It defines conjoint analysis as a technique used to understand how consumers develop preferences for product attributes. The key steps are identified as identifying the problem, attributes and levels, methodology, collecting responses, analysis, interpretation and application. Types include traditional, adaptive choice-based conjoint analysis. An example uses attributes of cars to identify preferred combinations through partial profile surveys and estimating utilities in SPSS. The results show price, fuel type and model have most importance in driving sales.
Conjoint analysis
This covers the following
WHAT IS CONJOINT ANALYSIS?
WHAT DOES IT DO AND WHAT IT IS USED FOR?
SITUATION WHERE CONJOINT ANALYSIS IS APPLICABLE?
KEY CONJOINT ANALYSIS TERMS
TYPES OF CONJOINT ANALYSIS
APPLICATION OF CONJOINT ANALYSIS IN MARKETING
STEPS IN CONJOINT ANALYSIS
Conjoint analysis
Conjoint analysis is a technique used to understand how people value different attributes or features of a product or service. It involves varying product attributes across many concepts that respondents then rate or rank. The ratings are analyzed to determine the relative importance or "part-worth utility" of each attribute level. Choice-based conjoint analysis, where respondents choose between different product profiles in choice sets, has become preferred as it can better model price elasticity and tradeoffs. Conjoint analysis techniques like adaptive conjoint analysis and choice-based conjoint analysis allow for analysis of more attributes and levels than traditional conjoint analysis.
Conjoint analysis
The document provides definitions and explanations of key terms and concepts related to conjoint analysis. It can be summarized as follows:
1. Conjoint analysis is a multivariate tool used to understand how individuals derive utility from the different attributes of a product or brand. It breaks down the overall utility or preference into partial utilities for each attribute level.
2. Traditional conjoint analysis methods include full profile, partial profile, paired comparison, and self-explicated. Adaptive conjoint analysis and choice-based conjoint are also mentioned.
3. The process involves identifying attributes and levels, collecting responses, analyzing data to estimate part-worths for each level, and validating the results. Part-worths represent
Conjoint analysis advance marketing research
The document summarizes a group project on conjoint analysis. It discusses the objectives to determine the most preferred product combination of car type, fuel type, and price where customers find maximum utility. A conjoint analysis was conducted with 20 respondents rating combinations of sedan, SUV, and MUV vehicles with CNG, petrol, or diesel fuel priced at 6, 8, or 9 lakhs. The analysis found that customers derived the most utility from a sedan powered by CNG priced at 6 lakhs.
Lecture9 conjoint analysis
This document discusses conjoint analysis, a technique used to understand how consumers make decisions based on the attributes of a product or service. It describes how conjoint analysis works, provides examples of companies that have used it, and outlines the typical steps involved in conducting a conjoint analysis study. These include selecting attributes and levels, developing product profiles for respondents to evaluate, analyzing the data to estimate part-worth utilities and attribute importance, and using the results to evaluate new product or pricing concepts.
Conjoint analysis
- Conjoint analysis is a statistical technique used in market research to understand how consumers value different product attributes and trade-offs between attributes.
- It involves identifying important product features, defining different levels within each feature, designing surveys that combine these into profiles representing potential products, collecting consumer preferences, and analyzing the data to determine the relative importance or "part-worth" of each feature level.
- An example application shows a conjoint analysis survey for the telecom industry to understand which features like carrier, speed, price, and contract duration most influence sales ratings. The data is then analyzed using a regression model to calculate the relative importance of each attribute level.
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Conjoint Analysis
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conjoint analysis
This document discusses conjoint analysis and provides an example using SPSS. It defines conjoint analysis as a technique used to understand how consumers develop preferences for product attributes. The key steps are identified as identifying the problem, attributes and levels, methodology, collecting responses, analysis, interpretation and application. Types include traditional, adaptive choice-based conjoint analysis. An example uses attributes of cars to identify preferred combinations through partial profile surveys and estimating utilities in SPSS. The results show price, fuel type and model have most importance in driving sales.
Conjoint analysis
This covers the following
WHAT IS CONJOINT ANALYSIS?
WHAT DOES IT DO AND WHAT IT IS USED FOR?
SITUATION WHERE CONJOINT ANALYSIS IS APPLICABLE?
KEY CONJOINT ANALYSIS TERMS
TYPES OF CONJOINT ANALYSIS
APPLICATION OF CONJOINT ANALYSIS IN MARKETING
STEPS IN CONJOINT ANALYSIS
Conjoint analysis
Conjoint analysis is a technique used to understand how people value different attributes or features of a product or service. It involves varying product attributes across many concepts that respondents then rate or rank. The ratings are analyzed to determine the relative importance or "part-worth utility" of each attribute level. Choice-based conjoint analysis, where respondents choose between different product profiles in choice sets, has become preferred as it can better model price elasticity and tradeoffs. Conjoint analysis techniques like adaptive conjoint analysis and choice-based conjoint analysis allow for analysis of more attributes and levels than traditional conjoint analysis.
Conjoint analysis
The document provides definitions and explanations of key terms and concepts related to conjoint analysis. It can be summarized as follows:
1. Conjoint analysis is a multivariate tool used to understand how individuals derive utility from the different attributes of a product or brand. It breaks down the overall utility or preference into partial utilities for each attribute level.
2. Traditional conjoint analysis methods include full profile, partial profile, paired comparison, and self-explicated. Adaptive conjoint analysis and choice-based conjoint are also mentioned.
3. The process involves identifying attributes and levels, collecting responses, analyzing data to estimate part-worths for each level, and validating the results. Part-worths represent
Conjoint analysis advance marketing research
The document summarizes a group project on conjoint analysis. It discusses the objectives to determine the most preferred product combination of car type, fuel type, and price where customers find maximum utility. A conjoint analysis was conducted with 20 respondents rating combinations of sedan, SUV, and MUV vehicles with CNG, petrol, or diesel fuel priced at 6, 8, or 9 lakhs. The analysis found that customers derived the most utility from a sedan powered by CNG priced at 6 lakhs.
Lecture9 conjoint analysis
This document discusses conjoint analysis, a technique used to understand how consumers make decisions based on the attributes of a product or service. It describes how conjoint analysis works, provides examples of companies that have used it, and outlines the typical steps involved in conducting a conjoint analysis study. These include selecting attributes and levels, developing product profiles for respondents to evaluate, analyzing the data to estimate part-worth utilities and attribute importance, and using the results to evaluate new product or pricing concepts.
Conjoint analysis
- Conjoint analysis is a statistical technique used in market research to understand how consumers value different product attributes and trade-offs between attributes.
- It involves identifying important product features, defining different levels within each feature, designing surveys that combine these into profiles representing potential products, collecting consumer preferences, and analyzing the data to determine the relative importance or "part-worth" of each feature level.
- An example application shows a conjoint analysis survey for the telecom industry to understand which features like carrier, speed, price, and contract duration most influence sales ratings. The data is then analyzed using a regression model to calculate the relative importance of each attribute level.
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Week 06Conjoint Analysishttpswww.smh.com.au.docx
Week 06
Conjoint Analysis
https://www.smh.com.au/business/companies/david‐jones‐and‐bp‐ink‐deal‐to‐bring‐fancy‐food‐to‐petrol‐ stations‐20190827‐p52l2z.html
Customer Value
Customer Value is the total amount of money that the customer is willing to pay for the benefits received from the product.
For pricing, each customer benefit should be equated to dollars and cents that customers are willing to pay (WTP) for it.
Benefit 1 + Benefit 2 + ….. = WTP 1 + WTP 2 + … = Total WTP
Customer value sets the ceiling or the highest possible price that can be charged for the product.
Understanding customer value requires an understanding of the types and number of benefits customers receive from the product and the product/ service features that contribute.
Source: Dholakia, How to price effectively, 2017
Attributes define a product
What are attributes that define a mobile phone?
What is Conjoint Analysis?
Which car should I get?
Conjoint Analysis: The Underlying Model
A Product is a “bundle” of attributes
Consumers evaluate the alternatives in the marketplace by examining how much they offer on the various attributes and how critical each attribute is to them
Total Value of product = sum of sub‐values (partworths) of its attribute levels to the individual
A consumer prefers the product that delivers the greatest Total Value to him/her
Decompose the product into the value of each sub‐part in order to determine preference for the composed product/service
Example: A Consumer’s Value System for a car
) = v(brand) + v(engine type) + v(body type) + v(price)
V(
Conjoint analysis model
Consumer’s overall judgment about a set of complex alternatives
Rank a set of alternatives; State their preferences
Decompose overall judgment into
separate utilities for individual attributes
Statistical analysis to recover individual attribute weights, w
Preference
=
=
∑ (w x µ)
w1 µ1 + w2µ2 + w3 µ3 + …
Given attribute levels for the item (0 or 1)
If you choose left, you prefer Power. If you choose right, you prefer Fuel Economy.
Rather than ask directly whether you prefer Power over Fuel Economy, we present realistic tradeoff scenarios and infer preference from your product choices.
Simple example of Conjoint Analysis
Would you prefer…
or
210 Horsepower
17 MPG
140 Horsepower
28 MPG
Another simple choice‐based conjoint
More elegant ranking‐based conjoint
Far more complicated examples
Discrete Choice Experiment
Identify a set of relevant product attributes (based on discussions with a car company)
Define reasonable levels for these attributes (based on carsales.com)
Stages in Conjoint Analysis
A real example: Buying a car (ratings task)
Source: Havard Business School
3. Create product profiles
4. Obtain consumer preferences for profiles via survey
Concrete Conjoint Example
Source: Havard Business School
Q: With 4 attributes and 3 levels e ...
Conjoint Analysis.pptx
Conjoint analysis involves showing respondents multiple product or service concepts that vary across key attributes and levels, and analyzing their overall preferences to determine the relative importance or "part-worths" of each attribute and level in driving customer choice. It allows companies to understand customer trade-offs and preferences to help design optimal product offerings. Various methods are used to collect conjoint data and estimate importance weights and part-worth utilities for application in product design, pricing, and market simulations.
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Survey analytics conjointanalysis_1
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.
IM426 3A G5.ppt
This document describes how to use the Analytic Hierarchy Process (AHP) to make a multi-criteria decision about purchasing an inventory management system. It involves defining the goal, criteria, and alternatives in a hierarchy. Pairwise comparisons are made between criteria and alternatives to assign weights. The weighted scores are calculated and the alternative with the highest score is selected. In this example, the goal is to purchase a system, the criteria are cost, functionality, supplier reputation, and user services, and the alternatives are Systems A, B, and C. System A is determined to have the highest total weighted score, making it the best choice.
Market analysis tools in npd (final)
This document discusses various market analysis tools used in new product development (NPD). It describes tools for idea generation, product optimization, marketing mix optimization, and market prediction. Specific tools covered include brainstorming, the Delphi technique, focus groups, user observation, conjoint analysis, concept testing, prototyping, and diffusion models. The document provides details on how each tool is used, its process, advantages, and disadvantages to help product developers select the right tools for analyzing markets and predicting new product success.
Conjoint by idrees iugc
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.
IRJET-Survey on Identification of Top-K Competitors using Data Mining
This document presents a method for identifying the top-K competitors of a given product using customer reviews and sentiment analysis. It defines competitiveness between two products based on how much their target markets overlap. The method uses natural language processing to analyze sentiment in customer reviews as positive, negative or neutral. It then calculates competitiveness scores between all products to identify the top competitors for a given product. The proposed approach is tested on multiple datasets from different domains like cameras, mobiles, TVs and laptops. It provides an efficient and scalable method for identifying competitors from large datasets.
Pricing Optimization using Machine Learning
This document discusses using machine learning algorithms to optimize pricing. Specifically:
1. It reviews previous research applying machine learning to price prediction and optimization in various industries like e-commerce, real estate, and insurance. Methods discussed include linear regression, clustering, random forests, and integer linear programming.
2. It then introduces using machine learning like regression trees and random forests to forecast demand and maximize revenue by setting optimal prices. Variables like holidays, promotions, and inventory are considered.
3. The goal of the paper is to develop a pricing algorithm that can predict and optimize daily prices in response to changing demand using machine learning techniques. Outcomes will demonstrate machine learning's ability to optimize pricing.
Competition Analysis, for RETAILERS
CAS/R enables marketing users to browser and analyze competition in terms of pricing on specific products. Beyond competition insight, CAS/R offers powerful tools for actionable decisions on your pricing policies, loyalty programs, personalized offers and more.
Intelligent Online marketing
CAS/R can be defined as an analytics framework for modern online retailers offering a wide range of marketing applications including product management, market analysis, Business Intelligence and predictive modeling. It is based on a powerful pricing database against your product catalogue and the ‘market’.
CAS/R includes several additional components enabling real-time, activity-based proposal generation for existing customers, dynamic product pricing schemes, personalized discount models, interactive market analysis and the intelligent alerting suite.
Artificial Intelligence in Action
Third episode ! let's learn more about the AI revolution and create your own machine learning model in Azure ML Studio !
Constraint and Qualitative Preference Specification in Multi-Attribute Revers...
This document describes a multi-attribute reverse auction (MARA) system that allows buyers to specify both constraints and qualitative preferences for product attributes. The key aspects are:
1) The MARA system allows buyers to specify constraints and qualitative preferences for product attributes in an interactive way using simple syntax.
2) The system transforms the qualitative preferences into quantitative weights and utility values to evaluate bids using multi-attribute utility theory (MAUT).
3) The MARA system automatically calculates the weights and utility functions, eliminating the difficult task for buyers of determining these values themselves.
Marketing Analytics.pptx
This document discusses marketing analytics and conjoint analysis. It defines marketing analytics as combining concepts of marketing and business analytics to solve broader marketing problems by breaking them into smaller questions. Conjoint analysis is introduced as a way to identify what attributes of a product or service consumers value most by having them evaluate combinations of attributes. The document provides examples of how conjoint analysis could be applied to new phone development by having consumers choose between combinations of phone brand, storage, RAM, etc. It outlines the steps of defining attributes and levels, creating combinations for consumers to evaluate, collecting their data, and interpreting the results.
F1033541
This document summarizes a research paper on developing a software tool called "Smart Sim Selector" to help users select simulation software. It describes the development of the tool, including:
1) Designing a database containing information on various simulation software packages based on over 200 evaluation criteria.
2) Creating an interface in Visual Basic that allows users to specify their requirements and priorities, then queries the database to recommend suitable software.
3) Implementing different techniques (AHP, weighted scoring, TOPSIS) to analyze users' inputs and software attributes to determine the best recommendation.
The tool aims to provide an unbiased approach to simulation software selection and reduce problems companies face in choosing inappropriate packages.
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IRJET-Survey on Identification of Top-K Competitors using Data Mining
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Conjoint Analysis
- 13. Interpreting the Coefficients or PART WORTHS 18K 17K 16K Sun Manual Auto No ABS ABS PRICE CONVERTIBLE BRAKING UTILITIES UTILITIES UTILITIES 30 40 10 40 20
- 15. Using CA for segmentation Two-Stage Approaches A priori Researcher selects specific attributes Post hoc Full set of attributes used Clustering (K-means) Relate clusters to background variables such as demographics using techniques like discriminant analysis One-Stage Approach Concomitant variable Latent Class Conjoint Simultaneous clustering and profiling using background characteristics