Presentation on theme: "Background: What is Conjoint?"— Presentation transcript:
1Introduction to Adaptive Conjoint Analysis (ACA) Copyright Sawtooth Software, Inc.
2Background: 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)
3Background: What is Conjoint? (cont.) The attributes we measure must be “levelable”Brand: Dell, Compaq, IBMProcessor Speed: 200 MHz, 300 MHzRAM: 32 Mbytes, 64 Mbytes, 128 MbytesPrice: $1,500, $2,000, $3,000If 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 iComputer = (Dell) + (300 MHz) + (64 Mbytes RAM) + ($1,500) (45) = (5) (10) (5) (25)
4Background: 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.
5How 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 definitelywould NOT and 100 means definitely WOULD…How likely are you to purchase…1997 Honda AccordAutomatic transmission$18,900Your Answer:___________
6Full-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?
7Three Attribute Full-Profile Conjoint Easy to process three attributes per cardAssuming 3 levels per attribute, 9 total levels:(#Levels - #Attributes + 1) is minimum number of cards3x minimum is recommended number to achieve stable estimates for each individual( ) = 7 cards minimum, 21 cards recommended21 cards described on 3 attributes each is relatively easy task for respondents
8Traditional Conjoint: Card-Sort Method (CVA) (Six Attributes) Using a 100-pt scale where 0 means definitelywould NOT and 100 means definitely WOULD…How likely are you to purchase…1997 Honda AccordAutomatic transmissionNo antilock brakesDriver and passenger airbagBlue exterior/Black interior$18,900Your Answer:___________
9Six Attribute Full-Profile Conjoint Challenging, but typically manageable for most respondents to process six attributes per card, as long as attribute text is conciseAssuming 3 levels per attribute, 18 total levels, ( ) = 13 cards minimum, 39 cards recommended39 cards described on 6 attributes each is relatively difficult, but doable for many respondents
10Traditional Conjoint: Card-Sort Method (CVA) (Fifteen Attributes) Using a 100-pt scale where 0 means definitelywould NOT and 100 means definitely WOULDHow likely are you to purchase…1997 Honda AccordAutomatic transmissionNo antilock brakesDriver and passenger airbagBlue exterior/Black interior50,000 mile warrantyLeather seatsoptional trim package3-year loan5.9% APR financingCD-playerNo cruise controlPower windows/locksRemote alarm system$18,900Your Answer:___________
11Fifteen Attribute Full-Profile Conjoint Very challenging to process fifteen attributes per cardAssuming 3 levels per attribute, 45 total levels, ( ) = 31 cards minimum, 93 cards recommended93 cards described on 15 attributes each is nearly impossible for most respondents to do without adopting simplification strategies
12Possible 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 dataRespondents still need to evaluate cards described on many attributesShow only a subset of attributes (partial profiles) in a block or randomized design and compute utilities across individuals
13Possible Solutions to the Problem of Too Many Attributes Self-explicated ApproachRespondents rank-order (or rate) levels within the attributesRespondents state how important each attribute isMultiply level preferences by attribute importances to compute utilitiesSelf-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 realisticPossible double counting of attributes which are not independent and low discrimination between attribute importances are common problemsHybrid ConjointCollect both self-explicated data and additional conjoint judgmentsCombine data from both sources to compute utilities
14Possible Solutions to the Problem of Too Many Attributes ACA (Adaptive Conjoint Analysis), Richard M. Johnson, 1985Computerized, adaptive interviewRespondents rank-order (or rate) levels within the attributesRespondents state how important each attribute isPairwise 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
16ACA ProsGood choice for measuring many attributes (more than about six)Very efficient at capturing a lot of information in short timeRespondents typically find ACA more enjoyable and perceive it to take less time to complete than traditional conjointACA utilities are robust and have fewer reversals (out of order part-worths) than traditional conjoint methodsHas advantages for predicting preferences for high-involvement categoriesAttribute prohibitions can be specified, and part-worth estimation is more stable in the face of attribute prohibitions than traditional conjoint or choice methods
17ACA 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.
18ACA Best PracticesShow 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.
19ACA 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.