Contents
Conjoint Analysis - Lecture 1 ......................................................................................................................... 2
Conjoint Analysis - Lecture 2 ......................................................................................................................... 7
Conjoint Analysis – Lecture 3 ...................................................................................................................... 13
Tutorial 1: Conjoint Design ......................................................................................................................... 20
Conjoint Analysis – Lecture 4 ...................................................................................................................... 28
Counting Analysis .................................................................................................................................... 29
Logit Analysis – Estimate utilities ............................................................................................................ 32
Conjoint Analysis – Lecture 5 ...................................................................................................................... 37
LCA .......................................................................................................................................................... 37
Tutorial 2: Conjoint Analysis ....................................................................................................................... 42
Conjoint Analysis – Lecture 6 ...................................................................................................................... 45
Hierarchical Bayes ................................................................................................................................... 45
Conjoint Analysis – Lecture 7 ...................................................................................................................... 47
Market Simulator .................................................................................................................................... 47
Q&A ............................................................................................................................................................. 55
1
Eline van de Ven – Conjoint 2018-2019
,Conjoint Analysis - Lecture 1
What is Conjoint Analysis? (=joint together)
Products are represented as bundles of attributes.
Levels of each attribute define the product.
Trade-off between product attributes we can see what the trade-offs are with conjoint
analysis.
A product is sometimes called a stimulus in conjoint analysis.
A product has multiple attributes. These attributes can take on multiple levels Each product
has one level per attribute.
Conjoint analysis is a survey-based technique that allows the analyst to understand people’s
preferences for a [product / service / brand / medical treatment / job / course] and especially
the trade-offs they make in making choices.
By choosing “none-of-them” you can further define the market. When you show all options and
people do not want any of them, you can rule those people out (they will not buy any cola at all).
Example of differences between Pepsi and Coca Cola: price / brand / taste / packaging
None of them
Why Conjoint Analysis?
In direct surveys, respondents might say they consider all attributes important
o Not informative
Conjoint enforces tradeoffs between attributes
o All attributes evaluated at once
o Respondents evaluate “complete” products with both strong and weak attributes
Conjoint reduces problem of socially desirable answers
Conjoint adds realism
o In real-life consumers evaluate products, not isolated attributes (do they consciously
know which attributes matter?)
2
Eline van de Ven – Conjoint 2018-2019
, Conjoint analysis is straightforward
Suitable software is available (Sawtooth)
How to respond to customer complaints?
Should we react at all?
Yes, research has shown
Should we be personal or not? Yes
Should we be fast? Yes
Course objectives:
- Which new product to launch?
- How to position existing products better?
- How to price the products?
- How to manage product portfolios/product
line designs?
Part 1: Conjoint Design
Example
sugar content (0%, 50%, ...), price brand, package, slogan on package
Ranking-based conjoint: Choose the most-preferred product, then the second most-preferred product,
until the least-preferred product
Rating-based conjoint: Give a score to each product in turn
- Problems of Rating-Based Conjoint:
o Not realistic: In real-life, we buy products rather than rating them
o Not clear whether spread in ratings is due to real preferences or due to response style
(E.g., small spread in example above, weak preferences or cautious answers?)
o Implications for sales levels and market shares are not clear. Sales and shares result from
consumer choices, not ratings. What would be the rating threshold?
Choice-based conjoint: Choice between different variants choose the most preferred product only
We record the choices made by every customer during the n tasks (i.e. choice sets). Because, in every
choice set, a different combination of attribute levels is used, we can derive the effect of different
combinations of attribute levels on choice.
Preferences = attribute part-worths
3
Eline van de Ven – Conjoint 2018-2019
Conjoint Analysis - Lecture 1 ......................................................................................................................... 2
Conjoint Analysis - Lecture 2 ......................................................................................................................... 7
Conjoint Analysis – Lecture 3 ...................................................................................................................... 13
Tutorial 1: Conjoint Design ......................................................................................................................... 20
Conjoint Analysis – Lecture 4 ...................................................................................................................... 28
Counting Analysis .................................................................................................................................... 29
Logit Analysis – Estimate utilities ............................................................................................................ 32
Conjoint Analysis – Lecture 5 ...................................................................................................................... 37
LCA .......................................................................................................................................................... 37
Tutorial 2: Conjoint Analysis ....................................................................................................................... 42
Conjoint Analysis – Lecture 6 ...................................................................................................................... 45
Hierarchical Bayes ................................................................................................................................... 45
Conjoint Analysis – Lecture 7 ...................................................................................................................... 47
Market Simulator .................................................................................................................................... 47
Q&A ............................................................................................................................................................. 55
1
Eline van de Ven – Conjoint 2018-2019
,Conjoint Analysis - Lecture 1
What is Conjoint Analysis? (=joint together)
Products are represented as bundles of attributes.
Levels of each attribute define the product.
Trade-off between product attributes we can see what the trade-offs are with conjoint
analysis.
A product is sometimes called a stimulus in conjoint analysis.
A product has multiple attributes. These attributes can take on multiple levels Each product
has one level per attribute.
Conjoint analysis is a survey-based technique that allows the analyst to understand people’s
preferences for a [product / service / brand / medical treatment / job / course] and especially
the trade-offs they make in making choices.
By choosing “none-of-them” you can further define the market. When you show all options and
people do not want any of them, you can rule those people out (they will not buy any cola at all).
Example of differences between Pepsi and Coca Cola: price / brand / taste / packaging
None of them
Why Conjoint Analysis?
In direct surveys, respondents might say they consider all attributes important
o Not informative
Conjoint enforces tradeoffs between attributes
o All attributes evaluated at once
o Respondents evaluate “complete” products with both strong and weak attributes
Conjoint reduces problem of socially desirable answers
Conjoint adds realism
o In real-life consumers evaluate products, not isolated attributes (do they consciously
know which attributes matter?)
2
Eline van de Ven – Conjoint 2018-2019
, Conjoint analysis is straightforward
Suitable software is available (Sawtooth)
How to respond to customer complaints?
Should we react at all?
Yes, research has shown
Should we be personal or not? Yes
Should we be fast? Yes
Course objectives:
- Which new product to launch?
- How to position existing products better?
- How to price the products?
- How to manage product portfolios/product
line designs?
Part 1: Conjoint Design
Example
sugar content (0%, 50%, ...), price brand, package, slogan on package
Ranking-based conjoint: Choose the most-preferred product, then the second most-preferred product,
until the least-preferred product
Rating-based conjoint: Give a score to each product in turn
- Problems of Rating-Based Conjoint:
o Not realistic: In real-life, we buy products rather than rating them
o Not clear whether spread in ratings is due to real preferences or due to response style
(E.g., small spread in example above, weak preferences or cautious answers?)
o Implications for sales levels and market shares are not clear. Sales and shares result from
consumer choices, not ratings. What would be the rating threshold?
Choice-based conjoint: Choice between different variants choose the most preferred product only
We record the choices made by every customer during the n tasks (i.e. choice sets). Because, in every
choice set, a different combination of attribute levels is used, we can derive the effect of different
combinations of attribute levels on choice.
Preferences = attribute part-worths
3
Eline van de Ven – Conjoint 2018-2019
