A brief primer for Adaptive Choice-Based Conjoint
Throughout the history of commerce – essentially since the invention of the wheel – product manufacturers have asked, “what makes my product valuable and sets it apart from the competition?” Manufacturers know all too well that the right features, or combination of features, can make the difference between a huge success and an unmitigated failure.
But how can manufacturers determine what features are most valued? Simply asking customers what they value is helpful, but it often doesn’t provide enough insight to place value on those features. Sure, I would love an iPhone with a five-day battery life, but am I will to pay $1,500 for it? Probably not.
In the past, manufacturers and their research suppliers often have utilized discrete choice modeling (DCM) or choice-based conjoint (CBC) methodologies to help answer these questions and understand the trade-offs made by consumers among a specified set of product features and attributes. These methodologies work well, but both have limitations.
- DCM is only useful for a limited number of attributes and price is generally a linear function of the product as a whole.
- CBC on the other hand is able to handle a larger number of attributes, but the process is often cumbersome and can lead to respondent fatigue and inconsistent results.
While traditional conjoint and discrete choice modeling still have their place in the research toolbox, now there is a more robust modeling methodology available. Within the past few years, the Sawtooth Software company developed and launched a new trade-off software called Adaptive Choice-Based Conjoint (ACBC). This software has been successfully deployed when clients need a deeper understanding of customers’ decision-making and product configuration processes. ACBC leverages many of the best aspects of both discrete choice modeling and choice-based conjoint and is more respondent-friendly to use.
So, what is Adaptive Choice-Based Conjoint and how does it work? ACBC projects provide deeper insights into how consumers make decisions and trade-offs with regard to complex products or services and involves a multi-step process to quantify those decisions.
The approach begins with a “build-your-own” exercise, which allows respondents to configure products and services to their specific needs/wants while considering the price premium or discount associated with the pre-defined attributes and levels. This exercise is similar to buying a product online. Want a bigger hard drive? – there is an upcharge; don’t want or need a keyboard? – the price is lower. We often refer to this as the “Mr. Potato Head” phase of the research. Add a piece here, remove a piece there… essentially, customers build the product they would most like to purchase.
Once respondents have “designed” the product or service to suit their needs within their budget parameters, the task is taken to the next level. At this next stage, respondents are asked to evaluate several product configurations with similar features to determine “must have” and “never have” features. Essentially, the ACBC software tracks whether respondents are drawn to particular features or repeatedly find any features unacceptable in order to determine the final “consideration set.”
With the consideration set finalized, product configurations are then presented in a choice-based forum. This forum is essentially a process of elimination for the product features in the consideration set, requiring respondents to choose the most desirable product configuration and ultimately, their preferred product configuration, inclusive of price.
When the ACBC data is analyzed, researchers can provide a deeper understanding of the value of individual product features. Going back to my iPhone example, we may find that a majority of consumers are willing to pay $1,500 for a five-day battery life and are willing to “trade-off” thinness to get that battery life.
How can ACBC help market researchers like me? In addition to keeping respondents more engaged and connected to the research process (through a more dynamic Q&A structure) and allowing for a better understanding of the specific value of individual features; ACBC projects are able to include a greater number of attributes, and it works well among the small populations and sample sizes common to B2B research. Additionally, it provides insight into both solid behavioral theory (consideration first, then choice) and statistical theory (experiment and choice data).
Gone are the days of guessing what customers want or asking sales people what they “think” customers want to buy. Manufacturers can now understand, early in the product development cycle, what customers value and what they are willing to pay for specific features—without investing millions in a potential flop. Personally, I hope that Apple did their research before they eliminated the headphone jack on the iPhone… What if you want to listen to music while you’re charging your phone and you aren’t willing to pay $159 for wireless AirPods? Guess those customers are out of luck.
Do you have experience with adaptive choice-based conjoint? Contact us and we can talk ACBC.
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