1. Introduction to Adaptive Conjoint Analysis (ACA) Copyright Sawtooth Software, Inc.

2. Background: What is Conjoint?
A way to learn how people make decisions about products or services that are made up of different features.
A questioning method which mimics real world: we show people products and let them tell us how much they like them. By varying the features of the product and analyzing responses, we can quantify how aspects of the product drive preference.
We assume that the value (utility) of the product is equal to the sum of its parts (attributes):
Computer = (Brand) + (Processor Speed) + (RAM) + (Monitor) + (Price)

3. Background: What is Conjoint? (cont.)
The attributes we measure must be “levelable”
Brand: Dell, Compaq, IBM
Processor Speed: 200 MHz, 300 MHz
RAM: 32 Mbytes, 64 Mbytes, 128 Mbytes
Price: $1,500, $2,000, $3,000
If we learn how much value (utility) people have for each of these levels, we can add them up and predict how much they would like potential PCs we could present: Hypothetical Utilities for Individual i
Computer = (Dell) + (300 MHz) + (64 Mbytes RAM) + ($1,500) (45) = (5) (10) (5) (25)

4. Background: Card-Sort Conjoint
Conjoint method used in industry since the 1970s.
Full-profile approach: describe products using one level from each attribute.
Carefully choose a set of products and print each on separate card. Combinations of attributes are chosen so we can estimate the impact of each feature independent of the others. Ask respondents to sort cards from best to worst (or rate cards based on preference).
Use Ordinary Least Squares (OLS) regression for ratings or non-parametric techniques (LINMAP, Linear programming, or Monotone Regression) for rankings to analyze results.

5. How likely are you to purchase… Automatic transmission
Traditional Conjoint: Card-Sort Method (CVA) (Three Attributes in Full-Profile)
Using a 100-pt scale where 0 means definitely
would NOT and 100 means definitely WOULD…
How likely are you to purchase…
1997 Honda Accord
Automatic transmission
$18,900
Your Answer:___________

6. Full-Profile Conjoint
Works very well as long as the number of attributes and number of questions (cards) is kept to a reasonable number.
What is a reasonable number?

7. Three Attribute Full-Profile Conjoint
Easy to process three attributes per card
Assuming 3 levels per attribute, 9 total levels:
(#Levels - #Attributes + 1) is minimum number of cards
3x minimum is recommended number to achieve stable estimates for each individual
( ) = 7 cards minimum, 21 cards recommended
21 cards described on 3 attributes each is relatively easy task for respondents

8. Traditional Conjoint: Card-Sort Method (CVA) (Six Attributes)
Using a 100-pt scale where 0 means definitely
would NOT and 100 means definitely WOULD…
How likely are you to purchase…
1997 Honda Accord
Automatic transmission
No antilock brakes
Driver and passenger airbag
Blue exterior/Black interior
$18,900
Your Answer:___________

9. Six Attribute Full-Profile Conjoint
Challenging, but typically manageable for most respondents to process six attributes per card, as long as attribute text is concise
Assuming 3 levels per attribute, 18 total levels, ( ) = 13 cards minimum, 39 cards recommended
39 cards described on 6 attributes each is relatively difficult, but doable for many respondents

10. Traditional Conjoint: Card-Sort Method (CVA) (Fifteen Attributes)
Using a 100-pt scale where 0 means definitely
would NOT and 100 means definitely WOULD
How likely are you to purchase…
1997 Honda Accord
Automatic transmission
No antilock brakes
Driver and passenger airbag
Blue exterior/Black interior
50,000 mile warranty
Leather seats
optional trim package
3-year loan
5.9% APR financing
CD-player
No cruise control
Power windows/locks
Remote alarm system
$18,900
Your Answer:___________

11. Fifteen Attribute Full-Profile Conjoint
Very challenging to process fifteen attributes per card
Assuming 3 levels per attribute, 45 total levels, ( ) = 31 cards minimum, 93 cards recommended
93 cards described on 15 attributes each is nearly impossible for most respondents to do without adopting simplification strategies

12. Possible Solutions to the Problem of Too Many Attributes
Respondents only evaluate a subset of cards, and analyst computes utilities across individuals (block or randomized design)
Assumes respondent homogeneity, lose richness of individual-level data
Respondents still need to evaluate cards described on many attributes
Show only a subset of attributes (partial profiles) in a block or randomized design and compute utilities across individuals

13. Possible Solutions to the Problem of Too Many Attributes
Self-explicated Approach
Respondents rank-order (or rate) levels within the attributes
Respondents state how important each attribute is
Multiply level preferences by attribute importances to compute utilities
Self-explicated data are easy for the respondent to provide and very informative (a lot of information per respondent effort)
But this sort of task is not very realistic
Possible double counting of attributes which are not independent and low discrimination between attribute importances are common problems
Hybrid Conjoint
Collect both self-explicated data and additional conjoint judgments
Combine data from both sources to compute utilities

14. Possible Solutions to the Problem of Too Many Attributes
ACA (Adaptive Conjoint Analysis), Richard M. Johnson, 1985
Computerized, adaptive interview
Respondents rank-order (or rate) levels within the attributes
Respondents state how important each attribute is
Pairwise conjoint questions show only a subset of attributes at a time (usually 2 or 3)
Pairwise conjoint questions focus on attributes of most importance to respondents, and are customized to be relevant and informative

15. Sample ACA Conjoint Interview
ACA interview demonstration

16. ACA Pros
Good choice for measuring many attributes (more than about six)
Very efficient at capturing a lot of information in short time
Respondents typically find ACA more enjoyable and perceive it to take less time to complete than traditional conjoint
ACA utilities are robust and have fewer reversals (out of order part-worths) than traditional conjoint methods
Has advantages for predicting preferences for high-involvement categories
Attribute prohibitions can be specified, and part-worth estimation is more stable in the face of attribute prohibitions than traditional conjoint or choice methods

17. ACA Cons Must be a computerized survey.
Potential double-counting of attributes that are not truly independent.
Respondents may have difficulty keeping in mind that all other attributes not involved in the current question are assumed to be equal.
May “flatten” importances (particularly for low-involvement categories) due to spreading respondents’ attention across individual attributes--but the jury is still out.
Can underestimate the importance of price (especially if many attributes included). CBC, Adaptive CBC (ACBC), and CVA considered better for pricing research.

18. ACA Best Practices
Show only 2 or 3 attributes at a time in the pairs section. More than that causes respondent fatigue, which outweighs the modest amount of additional information.
ACA can measure up to 30 attributes, but users should streamline studies to have as few attributes as necessary for the business decision.
Pretest the questionnaire to make sure it is not too long. If too long, reduce number of attributes, levels, number of pairs questions, or complexity of pairs questions.
Examine pretest data to make sure results are logical and conform to general expectations.
Make sure respondents are engaged in the task: understanding the attributes and levels and being in the market/having an interest in the category.

19. ACA Use Today Since about 2000, the use of ACA is declining. Why?
Many researchers have shifted to CBC, as choices are viewed as more realistic and accurate than concept ratings.
Sawtooth Software’s newest Adaptive form of CBC (ACBC) has many of the benefits of ACA, but without the weakness in pricing research.