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|   Customer Experiences with ACBC
Emanuele's Experience with Adaptive Choice (ACBC) We have completed six ACBC studies in five months, all for clients in the automotive industry. We feel ACBC is, today, the best methodology to handle complex product (or complex competitive environment) studies. Its adaptive scheme conforms well to each individual's purchase decision funnel, and increases the relevance of the information at the individual level. Treatment of price is very realistic. In our view, ACBC is today the most powerful conjoint method for pricing studies. My clients like the dynamic structure of the Adaptive CBC survey, and the increased relevance of the questions and tasks. They understand the value of the adaptive approach of ACBC much better than the importance of orthogonality in traditional CBC. All else being equal, ACBC is an easy sell, as the client clearly sees how naturally it mimics the purchase decision-making process. Even though the interview is longer than CBC, we have seen very high completion rates and minimal drop-out rates (in some cases less than 5%) from the start task (BYO) to the finish (calibration). We feel respondents are really engaged by the variety of tasks, perceive the narrowing focus through the survey, and 'want to see where all this will lead.' The need for cleanup of cheaters and speedsters has dropped dramatically compared to standard CBC studies, reducing the need for make-up oversampling and overall time needed for fielding/data cleaning (estimated down by 20%). The information provided by BYO, Choice Winner, Must-haves and Unacceptables is of additional value to the client. Simulations based on HB analysis have reproduced market conditions very closely, even when working with subsamples of the order of 100-150 respondents. Given enough experience, we think we will be able to reduce sample size (currently we use a standard of 500 respondents for a 10-attribute study), passing through cost and time savings to the client. Clustering on the price utilities from ACBC is, in our experience, highly resolved, with stable clusters. Clients have explicitly recognized in these cluster patterns their qualitative understanding of customer response and 'price barriers.' We feel this makes ACBC the strongest methodology available for quantitative price positioning and 'willingness-to-pay' studies. The additional time required by the survey development is well worth the effort in terms of survey efficiency and quality. Sawtooth Software in our experience has an outstanding record in anticipating our needs for additional features. The software is reasonably – if not very – user-friendly.
Emanuele Leveroni Ph.D. Craig's Experience with Adaptive Choice (ACBC) As specialists in medical technology marketing research, The MarkeTech Group is often faced with the challenge that different stakeholders (e.g., physicians, administrators) generally focus on different features. For example, physicians tend toward clinical benefits, administrators may focus on cost, and Biomedical engineers and IT might emphasize maintenance and compatibility with other equipment/systems. Including all the attributes in a full or partial profile CBC design forces respondents with different interests to constantly review features that are not part of their decision set. Traditional CBC with irrelevant attributes to some respondents can result in unstable and inaccurate utility estimates. With ACBC we can tailor the attribute set to match respondents' decision process and retain the advantage of keeping them all in one study for preference simulations afterward. Respondents are able to focus only on the features that drive their selection process by pre-selecting those attributes that are most important to their decision process. The program adapts to individual choice and creates product sets that are of high interest to respondents.To date the MarkeTech Group (TMTG) has completed five studies using ACBC in eight different countries including China and Brazil (four languages plus English). Although ACBC is more difficult to program and survey lengths are longer, we are confident that ACBC achieves a higher degree of accuracy because respondents feel highly engaged with questions that are more realistic and relevant to them. We have feedback from respondents indicating how much they appreciate the "learning" that takes place and are impressed that it ".. knows what I do and don't want." TMTG's clients eagerly embrace ACBC and while a few view conjoint as a black box they love the BYO exercise and summed pricing in ACBC because it mimics real-world processes and has a "real" feel to it. Loading the utilities from ACBC into TMTG's online preference simulator makes it easy for clients to perform multiple "what if" scenarios and fully appreciate the value of looking at preferences across stakeholders and feature sets. ACBC is truly a big step towards improving the conjoint experience from everyone's perspective. Respondents are more engaged, clients begin to understand and embrace it, and researchers obtain reliable and stable utility estimates so it is possible to provide clients with better advice and direction about product configurations.
Craig V. King, Ph.D. Chris's Experience with Adaptive Choice (ACBC) At Microsoft Hardware, we have fielded several ACBC studies for consumer PC accessory product lines in the US and Japan, including two studies where we directly compared CBC and ACBC results given identical attributes and features. In our comparative studies, ACBC part worth estimates have been consistent with CBC estimates. At an aggregate level, we've seen improved stability of market simulation estimates across respondent samples using data from ACBC estimates, as compared to CBC. We feel that ACBC estimates are of high quality and are comparable to those we obtain from traditional CBC. The most important strength of ACBC is that it allows study designs that are difficult or impossible for CBC. ACBC allows direct examination of features that are "must haves" or "won't accepts", which are of immediate interest to product management. Because ACBC drills into preference within each subject's response pattern, it gives us confidence that the entire feature space of interest to respondents is investigated in depth. We are currently exploring the ability of ACBC to work with real-time changes in attribute lists, such as including or removing features from the tasks for certain respondents. This allows experimental manipulation of choice tasks based on prior information in the survey, a capability that is impossible in CBC designs. In respondent feedback, ACBC has been preferred over CBC even with longer survey times. Respondents rate ACBC trial blocks as less boring than CBC. They respond particularly well to the dialogue format when ACBC asks about their response patterns; verbatim comments have been that ACBC is more "interactive", "fun", and that it "learns" from their responses. We've even seen respondents in a lab setting inquire whether the ACBC adaptive system is controlled by an actual observer behind the scenes. The higher respondent engagement means that complex choice-based surveys are more tolerable to respondents. Overall, we are increasingly using ACBC alongside traditional CBC and ACA surveys. Each method has advantages for different kinds of product questions and study designs. We gain increased confidence in our models when we combine data from multiple methods, ensuring that we sample respondent preference in depth with a variety of study formats. ACBC makes this possible for a larger range of questions and experimental designs.
Chris Chapman, Ph.D. Bob's Experience with Adaptive Choice (ACBC) Lifetime Products is a vertically integrated manufacturer of consumer products constructed primarily of blow-molded polyethylene resin and powder-coated steel. Over the past three years, the company has begun to use conjoint analysis and other quantitative marketing tools internally to better inform product development and marketing decisions.
We needed the flexibility of adaptive conjoint, the realism of choice-based conjoint, and the task simplification of partial-profile CBC, all with "reasonable" sample sizes. We found that solution - and more - in Sawtooth Software's Adaptive Choice (ACBC). In late 2008, we completed our first ACBC beta-test study using the Lifetime outdoor storage shed product line. The internal client had a long "wish list" of shed features to study, which we whittled down to 16 attributes and a maximum of 8 levels (in the Brand attribute). Using constructed-list methodology in ACBC, we were able to administer a 10-attribute / 5-level maximum ACBC survey to a sample of 400 shed owners and prospects. The client and other top management were especially impressed that we could ask the entire market to consider all 16 attributes, but simplify the conjoint tasks so that each respondent focused only on the most relevant ones (his/her "top 10" attributes). (Management was even more delighted by my ability to cut the sample size - and resulting out-of-pocket research costs - almost in half, compared with our previous partial-profile CBC designs!)
Robert J. (Bob) Goodwin, M.B.A. |
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As Lifetime progressed (over a three-year period) from conventional conjoint to choice-based conjoint and then to partial-profile CBC, it became apparent that we needed another solution to accommodate the large attribute/level designs necessary to study our product lines effectively.
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