SlideShare ist ein Scribd-Unternehmen logo
1 von 32
CONJOINT ANALYSIS Prof Narayan Janakiraman
Customer Value Customer Value Assessment Procedures Inferential/Value-Based Behavior-Based Attitude-Based Indirect/(Decompositional Methods) ,[object Object],Direct Questions Unconstrained Constrained/Compositional Methods ,[object Object],[object Object]
Show one product concept and get overall “Purchase Intent” feedback
Also get product diagnostics
Conjoint Analysis
Show multiple concepts and ask for overall preference
Concepts differ on Attributes and levels within an attribute
Based on overall preference get “part-worths” for attributes and levels within an attribute,[object Object]
Survey Results
Conjoint Analysis in Product Design Should we offer our business travelers more room space or a fax machine in their room? Given a target cost for a product, should we enhance product reliability or its performance? Should we use a steel or aluminum casing to increase customer preference for the new equipment?
P&G and Disposable Diapers Question: What value do consumers associate with two improved features in disposable diapers: Improved absorbency Elastic waistband
Conjoint Analysis Assumption Products can be defined by their individual attributes and levels within the attribute Consumer responses to the overall preference can be then partitioned to attributes
Eg. Packaged Soup
Eg. Packaged Soup individual concept
Eg. Packaged Soup Conjoint INPUTCards and Ratings
Eg. Packaged Soup Conjoint OUPUT 1Part Worths
Eg. Packaged Soup Conjoint OUPUT 1Part Worths
Eg. Packaged Soup Conjoint OUPUT 2Importance Weights of Attributes Flavor45% Calories25% Salt Freeness22% Price8%
How were the part-worths calculated and how was the importance determined? How does one use Part Worths? Importance? The Black box
The Conjoint Model
Notebook computer example 1)Processingspeed:1.5GHzor2.5GHz 2)Harddrive:120GBor160GB 3)Memory:1GBor2GBRAM Thereare8differentcombinationsofnotebook-definedasproductprofiles:
One respondent’s preference
Input to computer system – dummy variable regression
Part Worth Estimation Regression of ranks vs the attributes U = a + b1*Processor + b2*Hard Drive + b3*Memory The intution
Forecast preferences to check accuracy
Weightage and Relative Importance of Each Attribute Processor Speed = 57% Hard Drive = 29% Memory = 14%
Segment consumers based on preferences Are there segments in terms of preferences? Here preference is the “basis” and “age” could be the descriptor
Eg. Packaged SoupWhich is the most important attribute & which is the best product to introduce?
Conjoint Simulation - The Motivation What share can the new brand obtain? Where does this share will come from?
Conjoint Simulation - The Principle Before introduction share: A=40%, B=60%.  After introduction share: A=20%,B=50%, and New=30%.
Other ways of getting responses
Conjoint Study Process Stage 1—Design the conjoint study: Step 1.1:Select attributes relevant to the product or service category, Step 1.2:Select levels for each attribute, and Step 1.3:Develop the product bundles to be evaluated. Stage 2—Obtain data from a sample of respondents: Step 2.1:Design a data-collection procedure, and Step 2.2:Select a computation method for obtaining part-worth functions. Stage 3—Evaluate product design options: Step 3.1:Segment customers based on their part-worth functions, Step 3.2:Design market simulations, and Step 3.3:Select choice rule.

Weitere ähnliche Inhalte

Was ist angesagt?

Conjoint analysis advance marketing research
Conjoint analysis advance marketing researchConjoint analysis advance marketing research
Conjoint analysis advance marketing research
Lal Sivaraj
 
Discriminant analysis group no. 4
Discriminant analysis  group no. 4Discriminant analysis  group no. 4
Discriminant analysis group no. 4
Advait Bhobe
 

Was ist angesagt? (20)

Guide: Conjoint Analysis
Guide: Conjoint AnalysisGuide: Conjoint Analysis
Guide: Conjoint Analysis
 
Conjoint Analysis Part 3/3 - Market Simulator
Conjoint Analysis Part 3/3 - Market SimulatorConjoint Analysis Part 3/3 - Market Simulator
Conjoint Analysis Part 3/3 - Market Simulator
 
Conjoint Analysis - Part 2/3
Conjoint Analysis - Part 2/3Conjoint Analysis - Part 2/3
Conjoint Analysis - Part 2/3
 
A Simple Tutorial on Conjoint and Cluster Analysis
A Simple Tutorial on Conjoint and Cluster AnalysisA Simple Tutorial on Conjoint and Cluster Analysis
A Simple Tutorial on Conjoint and Cluster Analysis
 
Conjoint analysis
Conjoint analysisConjoint analysis
Conjoint analysis
 
Conjoint analysis advance marketing research
Conjoint analysis advance marketing researchConjoint analysis advance marketing research
Conjoint analysis advance marketing research
 
Marketing research ch 6_malhotra
Marketing research ch 6_malhotraMarketing research ch 6_malhotra
Marketing research ch 6_malhotra
 
Methods for Pricing Research
Methods for Pricing ResearchMethods for Pricing Research
Methods for Pricing Research
 
Why Customers Buy | Conjoint Analysis: Unlocking the Secret to What Your Cu...
Why Customers Buy  |  Conjoint Analysis: Unlocking the Secret to What Your Cu...Why Customers Buy  |  Conjoint Analysis: Unlocking the Secret to What Your Cu...
Why Customers Buy | Conjoint Analysis: Unlocking the Secret to What Your Cu...
 
Consumer behavior all material Prepared by karventhan
Consumer behavior all material Prepared by karventhan Consumer behavior all material Prepared by karventhan
Consumer behavior all material Prepared by karventhan
 
Product Differentiation
Product DifferentiationProduct Differentiation
Product Differentiation
 
Converse Consumer Profile
Converse Consumer ProfileConverse Consumer Profile
Converse Consumer Profile
 
Chapter 8 by Malhotra
Chapter 8 by MalhotraChapter 8 by Malhotra
Chapter 8 by Malhotra
 
Malhotra03
Malhotra03Malhotra03
Malhotra03
 
Consumer’s choice & post purchase behavior
Consumer’s choice & post purchase behaviorConsumer’s choice & post purchase behavior
Consumer’s choice & post purchase behavior
 
How to Run Discrete Choice Conjoint Analysis
How to Run Discrete Choice Conjoint AnalysisHow to Run Discrete Choice Conjoint Analysis
How to Run Discrete Choice Conjoint Analysis
 
Discriminant analysis group no. 4
Discriminant analysis  group no. 4Discriminant analysis  group no. 4
Discriminant analysis group no. 4
 
Pricing Analytics: Estimating Demand Curves Without Price Elasticity
Pricing Analytics: Estimating Demand Curves Without Price ElasticityPricing Analytics: Estimating Demand Curves Without Price Elasticity
Pricing Analytics: Estimating Demand Curves Without Price Elasticity
 
Setting Product and Brand Strategy
Setting Product and Brand StrategySetting Product and Brand Strategy
Setting Product and Brand Strategy
 
Malhotra11
Malhotra11Malhotra11
Malhotra11
 

Ähnlich wie Lecture9 conjoint analysis

lecture9conjointanalysis-110607121417-phpapp01.pdf
lecture9conjointanalysis-110607121417-phpapp01.pdflecture9conjointanalysis-110607121417-phpapp01.pdf
lecture9conjointanalysis-110607121417-phpapp01.pdf
ssuser1ecf25
 
Market analysis tools in npd (final)
Market analysis tools in npd (final)Market analysis tools in npd (final)
Market analysis tools in npd (final)
Omid Aminzadeh Gohari
 
Product Recommendations Enhanced with Reviews
Product Recommendations Enhanced with ReviewsProduct Recommendations Enhanced with Reviews
Product Recommendations Enhanced with Reviews
maranlar
 
Multi criteria decision support system on mobile phone selection with ahp and...
Multi criteria decision support system on mobile phone selection with ahp and...Multi criteria decision support system on mobile phone selection with ahp and...
Multi criteria decision support system on mobile phone selection with ahp and...
Reza Ramezani
 

Ähnlich wie Lecture9 conjoint analysis (20)

lecture9conjointanalysis-110607121417-phpapp01.pdf
lecture9conjointanalysis-110607121417-phpapp01.pdflecture9conjointanalysis-110607121417-phpapp01.pdf
lecture9conjointanalysis-110607121417-phpapp01.pdf
 
Conjoint Analysis.pptx
Conjoint Analysis.pptxConjoint Analysis.pptx
Conjoint Analysis.pptx
 
IM426 3A G5.ppt
IM426 3A G5.pptIM426 3A G5.ppt
IM426 3A G5.ppt
 
Chapter 9
Chapter 9Chapter 9
Chapter 9
 
978 3-642-33980-6 56
978 3-642-33980-6 56978 3-642-33980-6 56
978 3-642-33980-6 56
 
Market analysis tools in npd (final)
Market analysis tools in npd (final)Market analysis tools in npd (final)
Market analysis tools in npd (final)
 
PRODUCT DEVELOPMENT
PRODUCT DEVELOPMENTPRODUCT DEVELOPMENT
PRODUCT DEVELOPMENT
 
Value in Use Analysis for New Product Introductions
Value in Use Analysis for New Product IntroductionsValue in Use Analysis for New Product Introductions
Value in Use Analysis for New Product Introductions
 
MINING COMPETITORS FROM LARGE UNSTRUCTURED DATASETS
MINING COMPETITORS FROM LARGE UNSTRUCTURED DATASETSMINING COMPETITORS FROM LARGE UNSTRUCTURED DATASETS
MINING COMPETITORS FROM LARGE UNSTRUCTURED DATASETS
 
Ab testing 101
Ab testing 101Ab testing 101
Ab testing 101
 
Supplier Classification and Selecetion With Artificial Neural Network
Supplier Classification and Selecetion With Artificial Neural NetworkSupplier Classification and Selecetion With Artificial Neural Network
Supplier Classification and Selecetion With Artificial Neural Network
 
About. Quality
About.                           QualityAbout.                           Quality
About. Quality
 
Basics of AB testing in online products
Basics of AB testing in online productsBasics of AB testing in online products
Basics of AB testing in online products
 
Pricing Ppt
Pricing PptPricing Ppt
Pricing Ppt
 
Assess quality level of the final product by using Demerit system: A case stu...
Assess quality level of the final product by using Demerit system: A case stu...Assess quality level of the final product by using Demerit system: A case stu...
Assess quality level of the final product by using Demerit system: A case stu...
 
Assess quality level of the final product by using Demerit system: A case stu...
Assess quality level of the final product by using Demerit system: A case stu...Assess quality level of the final product by using Demerit system: A case stu...
Assess quality level of the final product by using Demerit system: A case stu...
 
bp case study
bp case study bp case study
bp case study
 
Product Recommendations Enhanced with Reviews
Product Recommendations Enhanced with ReviewsProduct Recommendations Enhanced with Reviews
Product Recommendations Enhanced with Reviews
 
Multi criteria decision support system on mobile phone selection with ahp and...
Multi criteria decision support system on mobile phone selection with ahp and...Multi criteria decision support system on mobile phone selection with ahp and...
Multi criteria decision support system on mobile phone selection with ahp and...
 
IRJET- Product Aspect Ranking and its Application
IRJET-  	  Product Aspect Ranking and its ApplicationIRJET-  	  Product Aspect Ranking and its Application
IRJET- Product Aspect Ranking and its Application
 

Lecture9 conjoint analysis

  • 1. CONJOINT ANALYSIS Prof Narayan Janakiraman
  • 2.
  • 3. Show one product concept and get overall “Purchase Intent” feedback
  • 4. Also get product diagnostics
  • 6. Show multiple concepts and ask for overall preference
  • 7. Concepts differ on Attributes and levels within an attribute
  • 8.
  • 10. Conjoint Analysis in Product Design Should we offer our business travelers more room space or a fax machine in their room? Given a target cost for a product, should we enhance product reliability or its performance? Should we use a steel or aluminum casing to increase customer preference for the new equipment?
  • 11. P&G and Disposable Diapers Question: What value do consumers associate with two improved features in disposable diapers: Improved absorbency Elastic waistband
  • 12. Conjoint Analysis Assumption Products can be defined by their individual attributes and levels within the attribute Consumer responses to the overall preference can be then partitioned to attributes
  • 14. Eg. Packaged Soup individual concept
  • 15. Eg. Packaged Soup Conjoint INPUTCards and Ratings
  • 16. Eg. Packaged Soup Conjoint OUPUT 1Part Worths
  • 17. Eg. Packaged Soup Conjoint OUPUT 1Part Worths
  • 18. Eg. Packaged Soup Conjoint OUPUT 2Importance Weights of Attributes Flavor45% Calories25% Salt Freeness22% Price8%
  • 19. How were the part-worths calculated and how was the importance determined? How does one use Part Worths? Importance? The Black box
  • 21. Notebook computer example 1)Processingspeed:1.5GHzor2.5GHz 2)Harddrive:120GBor160GB 3)Memory:1GBor2GBRAM Thereare8differentcombinationsofnotebook-definedasproductprofiles:
  • 23. Input to computer system – dummy variable regression
  • 24. Part Worth Estimation Regression of ranks vs the attributes U = a + b1*Processor + b2*Hard Drive + b3*Memory The intution
  • 25. Forecast preferences to check accuracy
  • 26. Weightage and Relative Importance of Each Attribute Processor Speed = 57% Hard Drive = 29% Memory = 14%
  • 27. Segment consumers based on preferences Are there segments in terms of preferences? Here preference is the “basis” and “age” could be the descriptor
  • 28. Eg. Packaged SoupWhich is the most important attribute & which is the best product to introduce?
  • 29. Conjoint Simulation - The Motivation What share can the new brand obtain? Where does this share will come from?
  • 30. Conjoint Simulation - The Principle Before introduction share: A=40%, B=60%. After introduction share: A=20%,B=50%, and New=30%.
  • 31. Other ways of getting responses
  • 32. Conjoint Study Process Stage 1—Design the conjoint study: Step 1.1:Select attributes relevant to the product or service category, Step 1.2:Select levels for each attribute, and Step 1.3:Develop the product bundles to be evaluated. Stage 2—Obtain data from a sample of respondents: Step 2.1:Design a data-collection procedure, and Step 2.2:Select a computation method for obtaining part-worth functions. Stage 3—Evaluate product design options: Step 3.1:Segment customers based on their part-worth functions, Step 3.2:Design market simulations, and Step 3.3:Select choice rule.
  • 33. 29 Attributes Should Be… Determinant Easily measured and communicated Controllable by the company Realistic Such that there will be preferences for some levels over others Compensatory As a set, sufficient to define the choice situation Without built-in redundancies
  • 34.
  • 36.
  • 37. Designing a Frozen Pizza – Paired Comparison Approach 1. Crust2. Type of Cheese3. Price PanRomano$ 9.99 ThinMixed cheese$ 8.99 ThickMozzeralla$ 7.99 4. Topping5. Amount of Cheese Pineapple2 oz. Veggie4 oz. Sausage6 oz. Pepperoni A total of 324 (3 * 4 * 3 * 3 * 3) different pizzas can be developed from these options!