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# Conjoint Analysis

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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

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### Conjoint Analysis

1. 1. CONJOINT ANALYSIS By: GROUP -10 Anmol Sahni Chinmay Jagga Dhruval Dholakia Mayank Sharma Madhusudan Partani Mudita Maheshwari Neha Arya Neha Kasturia Radhika Gupta Shivi Aggarwal
2. 2. What is ConjointAnalysis? • Definition : Conjoint Analysis is a multivariate technique developed specifically to understand how respondents develop preferences for any type of object. • Based on the premise that consumers evaluate the value of an object by combining the separate amounts of value provided by each attribute. • Also known as : o Multi-attribute Compositional Modelling o Discrete Choice Modelling o Stated Preference Research
3. 3. Important Terminology • Factor: Independent variable the researcher manipulates that represents a specific attribute • Level: Specific non-metric value describing a factor. Each factor must be represented by two or more levels. • Profile : Combination of all possible levels of factors. For ex: 3 factors with 2 levels each will create (2x2x2) i.e. 8 profiles. • Utility: An individual’s subjective preference judgment representing the holistic value or worth of a specific object. • Part-worth: Estimate from conjoint analysis of the overall preference or utility associated with each level of each factor used to define the product or service
4. 4. Why use Conjoint Analysis ? Different Perspectives, Different Goals • Buyers want all of the most desirable features at lowest possible price • Sellers want to maximize profits by: 1) minimizing costs of providing features 2) providing products that offer greater overall value than the competition Conjoint Analysis is concerned with understanding how people make choices between products or services or a combination of product and service, so that businesses can design new products or services that better meet customers’ underlying needs.
5. 5. Attributes andLevels • Attribute: An attribute is a general feature of a product or service – say size, colour, speed, delivery time. • Level: Each attribute is then made up of specific levels. So for the attribute colour, levels might be red, green, blue and so on.
6. 6. Understanding Conjoint Analysis • Conjoint analysis takes these attribute and level descriptions of product/services by asking people to make a number of choices between different products. Example : Would you choose phone A or phone B? Phone A Phone B Weight 200g 120g Battery Life 21 hours 10 hours Price Rs 5000 Rs 8000
7. 7. • Researcher can work out numerically (from the responses) how valuable each of the levels is relative to the others around it – this value is known as the utility of the level. • We can also compare across attributes to see which attributes make have the greatest impact in making a choice. Utility value for each level of Weight 40 50 35 25 15 40g 80g 120g 160g 200g Relative importance of attributes 45% 35% 20% Weight Price Battery
8. 8. Company’sObjective How our product/service compares to our competitors and how we can best optimise the value we give to the customer? By Conjoint analysis : We can total up the utility value our product is giving the customer and compare it to the value for the competition
9. 9. Example (cont.) : Note : Utility values are in brackets. A lower price has a higher utility. Phone A (Our Product) Phone B (Competitor’s Product) Weight 200g (15) 120g (35) Battery Life 21 hours (15) 10 hours (10) Price Rs 5000 (25) Rs 8000 (15) Total Utility 55 60
10. 10. Estimating Part-worth • Part-worths are calculated by using: – Multiple Regression with dummy variables – ANOVA – Multinomial logit Models • Calculations are done for each respondent separately
11. 11. DeterminingAttribute Importance • Importance of a factor is represented by the range of its levels (difference between highest and lowest values) divided by the sum of the ranges across all the factors. • This provides relative importance of each attribute based on the range of its part-worth estimates.
12. 12. Assessing PredictiveAccuracy To examine the ability of the model to predict the actual choices of respondents: • We predict the preference order by summing the part-worths for the profiles and then rank- ordering the resulting scores • Comparing the predictive preference order to the respondent’s actual preference order assesses predictive accuracy
13. 13. Designing profiles Selecting and defining factors and levels Its important as it determines -> effectiveness of the profiles in the task -> accuracy of results -> managerial relevance
14. 14. Generalcharacteristicsof factors 1) Communicable -> sensory and multimedia effects included 2) Actionable -> should be capable of being put into practice
15. 15. Issues in defining factors • Number of factors • Inter attribute correlation • Unique role of price
16. 16. Specification issues regarding levels 1) Number and balance of levels 2) Range of levels
17. 17. • Two major key decisions involved in specifying the model are as follows:- 1) Specifying the composition rule used:- a) Additive rule b) Interactive rule 2) Selecting the type of part worth relation ships a) Linear model b) Quadratic model c) Separate part worth model
18. 18. 0 1 2 3 4 Level Prefernce 0 1 2 3 4 0 2 4 Preferenc e 0 1 2 3 4 5  Linear model Quadratic model   Separate part worth model
19. 19. Designing a conjoint analysis experiment
20. 20. Stage 1:Objectivesof the conjoint analysis • To determine the contributions of predictor variables and their levels in determination of consumer preference • To establish a valid model of consumer judgments
21. 21. Requirementsfor a successful conjointanalysis • Defining the total utility of the object • all attributes that potentially create or detract from the overall utility of the product or service should be included • Specifying the determinant factors • include the factors that best differentiate between the objects
22. 22. Stage 3:Assumptions Of ConjointAnalysis  The product is a bundle of attributes.  Utility of a product is a simple function of the utilities of the attributes.  Utility predicts behaviour (i.e., purchases). Least restrictive set of statistical assumptions but highly critical conceptual assumptions.
23. 23. Stage 4: Estimating the ConjointModel • Traditional Estimation Approaches: – For non metric preference measures: » MONANOVA, LINMAP – For metric preference measures: » Multiple regression with dummy variables – For more complex consumer preference measures: » Logit model and its extensions
24. 24. • Extension of Basic Estimation Process – Bayesian Estimation – Incorporation of additional variables reflecting characteristics of the individuals
25. 25. Stage 5: Interpretingthe results • Generally done at disaggregate level • For aggregate behavior, such as market share, aggregate analysis more accurate. • Two main things considered in interpretation – Examining the estimated part-worth • Magnitude and pattern of part-worth for each factor • Higher the part-worth, higher the impact on overall utility – Relative importance of attributes • Factor with greatest range of part-woths has greatest contribution to overall utility.
26. 26. ManagerialImplications
27. 27. Define the object with the optimal combination of features Predict market shares of different objects with different sets of features Isolate groups of customers who place differing importance’s on different features Identify marketing opportunities by exploring the market potential for feature combos not currently available Show the relative contributions of each attribute and each level to the overall evaluation of the object
28. 28. Types Of Analysis
29. 29. AggregateAnalysis • Used to generally determine – average part worth of each attribute level across the entire sample of respondents • This usually helps in determining the – The importance of each attribute – The desired level of each attribute
30. 30. Segment Analysis • In most marketing situations, strategies are based on customer segments • Cluster analysis used to produce “benefit segments” • Clusters are formed to group respondent such that segments have similar within a segment and different across segments
31. 31. ScenarioSimulations • Conjoint Analysis also helps researchers to simulate various competitive scenarios • Scenarios are used to estimate how respondents react to various scenarios • It is used to understand how the set of respondents would choose among a specified set of profiles
32. 32. Comparingconjoint analysis with other multivariatemodels • Its decompositional nature • Specification of the variate • The fact that estimates can be made at individual level • Its flexibility in terms of relationships between dependent and independent variables
33. 33. Compositionalvsdecompositional techniques • In compositional models the researcher collects ratings from the respondent on many product characteristics and then relates theses ratings to the to some overall preference rating to develop a predictive model • Conjoint analysis a type of decompositional model, differs in that the researcher needs to know only respondents overall preference for a profile.
34. 34. Specifyingthe conjointvariate • Conjoint analysis employs a variate quite similar in form to what is used in other multivariate techniques. • The conjoint variate is a linear combination of effects of the independent variables (levels of each factor) on a dependent variable. • The important difference is that in the conjoint variate the researcher specifies both the independent variables (factors) and their values (levels).
35. 35. Separatemodels for eachindividual • Conjoint analysis can be carried out at the individual level • other multivariate methods use each respondent’s measures as a single observation and then perform the analysis using all respondents simultaneously.
36. 36. Flexibilityin types of relationships • Conjoint analysis not limited in types of relationships required between the dependent and independent variables • Most dependence methods assume that a linear relationship exists • Conjoint analysis can easily handle non linear relationships as well