Choice-based conjoint analysis is a member of a family of choice modeling approaches. In choice-based conjoint analysis (CBC), a product is dissected into its constituents or “attributes”, such as brand, price, features and specifications, each with their specific versions or “levels”. By systematically varying the attribute levels across profiles – a profile is a product description with a specific set of attribute levels to be tested – and repeatedly asking consumers to make choices among profiles, one can study consumer tradeoffs and attribute sensitivities. This helps to identify optimal product configurations at price points.
Conjoint exercises can be tedious, with many choice tasks and many options per task. The tediousness is a function of the number of attributes and levels, along with other requirements in the research design. It was a manageable issue when respondents were sitting at their computers. However, in a mobile world, taking surveys competes with more exciting activities that one can do on a mobile device. Consumers have more demands on their time and attention, reducing their interest in completing time-consuming conjoint tasks. This resistance may negatively impact response rates and data quality.
How to go mobileThe resistance to tedious or complex tasks on mobile devices is a significant challenge for researchers. One remedy is to simplify the number of choice tasks and the number of options per task. Today, the common number of choice tasks is 10 to 16, with five product profiles per task to choose from, in other words, a 16-by-5 research design. To accommodate mobile users, we have been able to reduce this to a 3-by-3 research design in simple studies: three choice tasks with three product profiles to choose from (Fig. 1).
The 3-by-3 research design is consistent with a trend in mobile surveys making surveys modular, spreading questions across respondents, and allowing for sample-level analysis by pooling data across respondents. “Simple studies” means a limited number of attributes and levels, no alternative-specific research designs and no prohibitions. Because the information collected from every respondent is limited, we recommend taking additional precautions, such as:
1. Increase the sample size. Early results of research on sample sizes needed to obtain valid results if the number of data points collected per respondent is low (as would be the case with these simplified designs for mobile users), show that we should increase the sample size by a factor of up to ten to deliver stable results.
2. Use prior information. Traditionally, one only looks at the orthogonally and efficiency of a research design. However, if one collects fewer data per respondent, one has to be more cautious to use all information one can get, also basing the research design on prior information, for example market shares of existing brands and products and preference ladders known beforehand.
The screen real estateScreen real estate is an important challenge for mobile surveys because of the potential for information overload on small screens. Information presented should be as focused on the task at hand as possible, balancing between product profiles, questions and answers, and survey progress information. We suggest using HTML5-based interaction designs that render effectively in common browsers such as iOS and Android. In addition, we recommend applying these formats across desktops and laptops, as well as tablets and smartphones.
One way to address information overload is to reduce attribute level descriptions to a minimum: ideally to a single word or an iconized visual. However, this simplification should be done carefully as it may result in unacceptable task simplification, limiting the ability to represent actual market situations. One suggestion for how to reduce the likelihood of problems is to conduct an interaction design exercise; having text say or a visual show what it is supposed to convey – nothing more – and what to put on a main screen and what to push to a deeper link. Another tool is to use virtual shelves as the vehicle for choice tasks; keeping exercises and attribute level information as close as possible to the way it would be in market, for example in online retail or in a store.
Shorter study lead timesMobile conjoint can deliver new benefits, not really possible before mobile users were everywhere. For example, study lead times may become shorter because consumers use their phones everywhere and all the time, so they can take surveys more easily. Furthermore, online surveys may replicate other mobile applications, for example online retail environments. The experimental nature of conjoint may help us to understand what happens if we change the way products are displayed in an online retail environment (Fig. 2).
Context and location specific surveysAlso, one can make surveys context and location specific by only asking questions to whom it would be relevant. For example those who just passed an aisle, are in front of a shelf, just passed an outdoor poster or were exposed to a promotion. Of course, respondents have to give permission to use location data. The goal is to assess the effect of context variations on the choices consumers make. For example, we may use conjoint analysis to study the effect of a sales conversation on the likelihood of brand churn or the impact of aisle end caps on brand perception.
Mobile conjoint analysis can already be used to do simpler studies. Mobile can also open up new opportunities for context and location-specific research. Future research needs to show how we can extend mobile conjoint to address more complex study objectives.