If you are developing new complex products, you should be thinking that conjoint analysis is a useful tool to evaluate your target market preferences. It is also well known that Choice Based Conjoint (CBC) analysis is very popular between the other conjoint techniques because it simulates quite well the real decision taking process.
However, when we are talking about complex products with many attributes (like computers, homes, cars or consumer electronics), choosing between options becomes a difficult task, especially when the process is repeated several times as it is done with the CBC interview. The decision taking process of complex products includes non-compensatory steps where some options are rejected taking into account attribute levels that should be or not present in the choices. Similarly, some options are accepted only if they have certain attribute level. For example, for one consumer a car must have high acceleration and shouldn’t expend too much gas. Therefore, all the options shown to this consumer within an interview should have high acceleration and low gas consumption and change the levels for the other attributes. The Adaptive Choice Based Conjoint (ACBC) Analysis takes into account the non-compensatory process to filter the options before applying the compensatory selection of traditional CBCs. It also centers the options to be evaluated around the ideal product of the participant, eliminating the time spend with options very different and therefore no relevant. Next, I describe briefly the steps of the ACBC. The first step of the ACBC allows the interviewee to tell the most important levels per attributes taking into account the incremental value that higher levels add to the total value. The second step of the ACBC identify the must have and un-acceptable attribute levels. To do this, choices close to the ideal product from the step one are generated and the participant is asked if each choice is acceptable or un-acceptable. From these answers, the must have and un-acceptable attribute levels are identified. After few screens, the participants are required to confirm the “must have” and un-acceptable attribute levels. These levels are used to create the product options that the participant will have to compare. The third step present the choices in a tournament style comparison. That means that three options are shown in the first question and the winner is shown in the second question with other two different options. The process continues until a final winner is found. The fourth step is optional and requires the participant to evaluate few models in a rating scale. The ideal product, the winner from the choice tournament and few more rejected and accepted concepts are shown. This information is used to estimate the “none” threshold and utilities of the concepts for further analysis. Evidence of success: the following results summarize examples were ACBC and CBC has been used. Electronic Consumer Products[1]. The study included product concepts with 8 attributes with 2 – 5 levels. 400 personal computer users completed CBC and ACBC surveys. Although the ACBC survey required more time to complete, its preferences share predictions of a product A (33.0%±4.8%) were closer to actual market data (34.6%) than those derived from CBC (43.5%±5.0%). Laptop Computers[2]. This study included options with 10 attributes. There were 277 interviews for the CBC and 282 for the ACBC. To validate the results, there were three holdout tasks per participant with four alternatives each and a fourth holdout task that included the winner of the first three. The ACBC survey had significant better hit rates (60.8%) than the CBC (50.0%) predicting the fourth holdout task. Homes[3]. Two ACBC surveys with 24 and 32 concepts were compared with a CBC survey with 18 choice tasks of 4 concepts per task. The two ACBC surveys had 299 and 303 interviewees and the CBC had 314 interviewees. Four holdout tasks with four choices were assigned to the respondents. A final holdout task showed the first four choices. The two ACBC experiments got higher hit rate (40.5 and 48.8) than the CBC experiment (36.9) predicting the fifth holdout task. When the share of preference were summarized for the first four hold out tasks of all the respondents, the mean absolute error (MAE) from the share of preferences calculated with the ACBC methods was smaller (3.47 and 3.15) than the MAE calculated with the CBC method (5.47). Summarizing, the ACBC is preferred when: The product concepts have 6 or more attributes The sample size is small (thanks to the additional information acquired during the interview) Individual level conclusion are required The drawbacks of the methodology are: Longer time to complete the survey Requires computers to administer More complex to analyze and program Sawtooth Software provides an useful software to execute ACBC studies and more information about the topic.
References: [1] Chapman, Alford JL, Johnson C, et al. CBC vs. ACBC: comparing results with real product selection [Sawtooth Software research paper series] Sequim, WA. Sawtooth Software, Inc. 2009 [2] Johnson RM, Orme BK. A new approach to adaptive CBC [Sawtooth Software research paper series]. Sequim (WA): Sawtooth Software, Inc., 2007 [3] Orme BK, Johnson RM. Testing adaptive CBC: shorter questionnaires and BYO vs. ‘most likelies’ [Sawtooth Software research paper series]. Sequim (WA): Sawtooth Software, Inc., 2008
