Conjoint Test Study Guide

How is the relative importance of different attributes determined?

Range of one attribute divided by sum of all attribute ranges.

Identify three different approaches (that we discussed in class) to conduct these simulations

-"Highest Utility": the alternative "j" with the highest Vj is selected (not a good predictor of market shares) -Linear Approach- V1/(V1+V2+V3) -Logit Approach- (e^v1)/(e^v1 + e^v2 + e^v3).

Typical assumptions of simulations?

-All attributes have been accounted for that affect the buyer choice -Each product has equal availability -Respondents are aware of all products -The products reach long range equilibrium -Equal effectiveness of the sales force -No out of stock conditions

While conducting a conjoint study, what is meant by "attributes" and "levels"?

-Attributes are characteristics of a product. Levels are the degrees of the characteristics -Attribute = Color, Level= Red, Purple, Orange.

What is the role of exploratory research— such as focus group interviews—in conducting a conjoint study (and what is meant by a focus group discussion guide)?

-Focus groups show their preferences and perceptions toward a set of products and that is analyzed and converted into value system that shows the importance and desired value of each attribute -Guide of questions for conducting a focus group discussion

How is a full-factorial conjoint study design different from a partial-factorial design?

-Full-Factorial: all combinations are shown to user/Sawtooth is different -Choice Based Conjoint: they make a choice on combinations Partial-Factorial

What is a good criterion to use in selecting among the approaches? Is there a preferred approach? ****

-Logit is more predictable (find partworth for attributes and add to constant) -Highest Utility (First choice approach) -Logit (More Accurate)

Identify and explain the differences among the outputs one can obtain from conjoint analysis (e.g., how are part worth utilities different from the relative importance of attributes, etc.).

-Part worths show desirability -Importance shows influence on choice

Are there conditions under which conjoint analysis may not be appropriate?

-Product cannot be a bundle of attributes -category is revolutionary or customers cannot reasonably rate the product -if a firm cannot change the attribute levels -trade-off is reasonable = yes conjoint, no tradeoffs available = no Conjoint

What is a dummy variable?

-Takes only the value 1 or 0 to indicate the absence or presence of some categorical effect that may change the outcome. Shows preference levels for each consumer, on different attributes and levels

How might conjoint analysis help the brand manager?

-what aspects of product drives customer's brand choice -Conjoint simulators predict respondents' interest in product - "what-if" games investigate the value of modifications to an existing product or alternative, and investigates product line extensions

What is Discover (from Sawtooth)?

A web-based survey platform that allows to build surveys easily and do CBC and MaxDiff Finds Part-Worth level

Do you see any advantage(s) to using Discover for conducting conjoint analysis?

Design Matrix, Partial Factorial, part-worth

What might be a weakness in such an approach that we discussed in class?

Doesnt mimic the real world or take into account trade-offs.

Which attributes and levels tend to be useful for a given study?

Attribute levels should cover the full range of possibilities for existing products as well as products that may not yet exist

Why is it important to consider competitors' products in conducting simulations?

Because we do market simulations to predict market shares

How is traditional conjoint analysis different from choice-based conjoint analysis (in particular, what type of "answers" do respondents provide in traditional conjoint, and what do they provide in choice based conjoint)?

Instead of being asked for rating or ranking respondents are shown a set of products on the screen side by side and asked which one they would purchase.

Why might some software provide no constant term in the conjoint analysis report (alternatively: how do we generate coefficients analogous to the commercial soft- ware from the Excel output for traditional conjoint analysis)?

It gives a value for each level instead

Given the regression output, how is the total value of a specific bundle of attributes (with specified levels) determined?

K+(Dummy1xUtility1)+(Dummy2xUtility2)

How are 'Linear' and 'Logit' based simulations different?

Linear contains dependent variable Logit has limited # of possible values

In the context of conjoint analysis, what are market simulations and how might they be useful?

Market simulations are competitive market scenarios to predict which products respondents would choose

What is meant by a design matrix—and if a study's attributes and levels are given, can you write the design matrix?—and what is its role in conducting conjoint analysis? *****

Shows preference levels for each consumer, on different attributes and levels

What are the steps to conduct traditional conjoint analysis using Excel?

Step 1: Design number of: attributes, levels for each attribute, and combinations shown to consumers Step 2: Design matrix using dummy variables. Rule: required dummy variables = no. of levels- 1 dummy variables can take on only values 0 or 1 Step 3: Use LINEST to Count number of dummy variables; add 1 to that number, write names in reverse order, Select as many cells as that total Step 4: Interpretation (and comparing answers)two decimal places sufficient

First be familiar with at least one alternative approach (i.e., other than conjoint analysis) that brand managers can employ to design new products and services.

Surveys that ask people about their preferences.

How should one interpret the Excel output from traditional conjoint analysis—in particular, how does one interpret the regression coefficient of a dummy variable, and what is the role for the constant term in the regression equation?

The coefficient reflects effect relative to the benchmark category so we have to add the constant to get the total effect

How many dummy variables do we need to represent the levels of an attribute?

dummy variables Rule: required dummy variables = no. of levels- 1 dummy variables can take on only values 0 or 1