Partial-profile CBC is an alternative to ACBC when there are many attributes in conjoint studies. In this article, Fisher and co-authors compared partial-profile CBC to ACBC for a smartphone choice study involving 14 attributes. The utility estimates and sensitivity analyses from both methods were similar, but partial-profile seemed to neutralize the differences between attributes relative to ACBC. A benefit for partial profile is that it took less time for respondents to complete than ACBC (two-thirds as long). However, ACBC was able to detect and better measure an important interaction between RAM and ROM attributes, whereas partial-profile was not able to detect the interaction. That particular tradeoff (between RAM and ROM) was critical to the client’s design decision for a new product launch, so a second-wave survey was fielded which isolated just the RAM and ROM tradeoff. That follow-up study demonstrated that the first-wave ACBC model better predicted respondents’ tradeoffs between RAM & ROM than the partial-profile prediction. With that second-wave survey validation in hand, the client decided to rely on ACBC’s prediction regarding the optimal combination of RAM and ROM to offer. Later sales data showed that ACBC’s predictions very closely predicted the actual sales result. The client launched two products with different RAM & ROM specifications. Relative to the key competitor, the ACBC predictions for the three products were 33%, 21%, and 46%; and the real relative market shares after launch ended up being 31%, 20%, and 49%.

Adaptive Choice-Based Conjoint (ACBC) is a new approach for adaptive choice-based conjoint studies. The interview has three main phases: 1) BYO (configuration) phase, 2) Consideration phase, 3) Choice phase. This follows the common proposition that buyers develop consideration sets and then choose a final product from within the consideration set. The interview adapts to each respondent, giving a more relevant, interactive experience. ACBC is recommended for experienced conjoint analysts and for projects involving about 5 or more attributes. Traditional brand-package-price studies should continue to be conducted under the standard (non-adaptive) CBC software.

This article is from a peer-reviewed journal article on Sawtooth Software’s ACBC (Adaptive CBC) approach. Indeed, most ACBC research has been published at the Sawtooth Software and joint SKIM/Sawtooth Software conferences, which aren’t subject to a formal academic review process. We’re pleased to point you to an article recently published in a peer-reviewed healthcare economics journal entitled “The Patient: Patient-Centered Outcomes Research”.

The article is authored by Charles E. Cunningham, Ken Deal, and Yvonne Chen.

The authors review previous ACBC studies involving consumer electronics, home purchases, PCs, automobiles, fast food, mobile telephone, chemical products, and consumer durables. They conclude:

"A review of randomized trials and cross-over studies suggests that, although ACBC surveys require more time than conventional approaches to CA, informants find ACBC surveys more engaging. In most studies, ACBC surveys yield lower standard errors, improved prediction of hold-out task choices, and better estimates of real-world product decisions than conventional choice-based CA surveys."

In this article, the author (Orme) uses random split-sample experiments to test different ways of asking CBC and Adaptive CBC (ACBC) questionnaires. Specifically, he examines:

• Use of minimal overlap vs. modest overlap for CBC questionnaires (modest overlap seems to improve results)
• ACBC for small designs--just 4 attributes (ACBC is shown to work as well as CBC)
• Placing “Unacceptable” screening questions prior to “Must-Have” screening questions in ACBC (seems to work better)
• Individual-level (customized) utility constraints in ACBC (no benefit shown for this 4-attribute data set, though benefits should be greater for larger, more demanding designs)
• Whether giving respondents a “consistency game” will improve their data and their experience (minimal gains in fit observed, but with the risk of annoying about 1/5 of the respondents)

Validity studies that include data on actual sales are hard to obtain. In this article, Chris Chapman of Microsoft Corporation compares CBC and Adaptive CBC (ACBC) in terms of a number of measures, including ability to predict actual market shares for a consumer electronics device. ACBC produced slightly better estimates of market share. Also, Chapman cites evidence that ACBC may obtain more precise estimates than CBC, indicating need for lesser sample size (with reduction in fielding costs). Price sensitivity was also greater for ACBC than CBC.

Chapman conludes: "In short, the performance of ACBC for our CE product was similar to CBC and somewhat better in alignment with market data. We believe future research would be useful to determine whether this pattern of results (better prediction; higher price sensitivity; lower standard deviation) continues with other product categories."

Rich Johnson, a key person in the development of conjoint analysis methods over the last 30 years, gives his perspective on Adaptive CBC (ACBC). What role does it play in the evolution of conjoint methods? What about the respondent burden? What about respondent engagement? What strengths does ACBC have over other approaches? This short essay contains pearls of wisdom.

Bryan Orme and Rich Johnson report on another methodological test of their Adaptive CBC procedure (comparing it to standard non-adaptive CBC). This time, they study home purchases and employ shorter questionnaires, to see if similar results can be achieved with substantially less information. They use a slightly improved design algorithm as compared to their previous test reported in their 2007 article, "A New Approach to Adaptive CBC". Also, they test the use of a "most likelies" question rather than the standard BYO question.

Adaptive CBC again is shown to have greater internal predictive validity than standard CBC. Reducing the length of the questionnaires doesn't seem to reduce the predictive ability of Adaptive CBC for this test, though the authors caution that some information must have been lost. The "most likelies" option is shown to be a viable approach, though Orme and Johnson caution that the utilities are mis-informed by at least a small degree when using this approach.

In this paper, Rich Johnson and Bryan Orme from Sawtooth Software take an entirely new approach from their previous attempts at adaptive CBC. The new approach mimics the purchase process of formulating a consideration set using non-compensatory heuristics (such as “must have” or “must avoid” features), followed by a more careful tradeoff of alternatives within the consideration set using compensatory rules.

Adaptive CBC involves three core stages: 1) Build-Your-Own (BYO) Stage, 2) Screening Stage, and 3) Choice Tasks Stage. Rich and Bryan conducted a split-sample experiment comparing the new approach to traditional CBC. They found that respondents liked the adaptive survey more and felt it was more realistic--even though it took about double the time as traditional CBC. Furthermore, part-worths developed from ACBC were more predictive of holdout tasks than traditional CBC, despite the methods bias in favor of CBC for predicting the CBC-looking holdouts.