At some stage during the Product Development cycle, you are likely to need to uncover consumer preferences of your products or services. This may be to fine-tune your offer to drive stronger purchase levels and/or differentiate from your competitors to a greater degree. This could happen prior to launch (usually after concept testing is done) and/or post-launch if a product/service isn’t selling as well as expected.
A well-known way of doing this is by using a choice-based conjoint / trade-off methodology. Within a survey, the target audience is asked to review different scenarios of features, prices, and benefits so we see how they choose and trade-off different options and deduce their optimal preference combination. This approach is a realistic proxy of what buyers actually do in the marketplace. It is a simple, engaging, and natural task for any respondent to answer, and it is adaptable for most categories/industry sectors.
Top Tips for Conducting Online Surveys with Choice Optimization
Choice-based surveys can be complex and take several weeks to achieve, putting a big hole into the product, marketing, or insight budgets. Today, if you choose a platform approach using automation and standardization, an online project can deliver results within a week at a fraction of the original spend.
Your target audience for this type of project could be varied, so look out for panel resources that provide flexibility and offer more than just a nat rep sample. You want to aim the survey at consumers who are likely to have potential interest in your product/service and in some cases at different segments to see if they have consistent or different choices/preferences. As a minimum, you probably want to exclude category rejecters.
Creating the right design is important and needs some thought and time. This simple cereal example shows how we might display different option combinations to a respondent within the survey. You can see that each option is packaged as a holistic product/service offer and this view would be one task scenario.
When choosing the options and combinations for the survey you need them to be as realistic as possible and tailored to your category, product/service, and overall objective. So in our example for cereals the options might be fruit, health message, ingredient message, price, pack size, flavor, etc. The levels within the options for fruit might be blueberry, coconut, mango, etc. and for pack size 250g, 375g, 500g, 720g, and so on. Yet for a mobile operator the options will be very different e.g. contract type, contract period, design features, device and hardware choices, and services/software options and for financial services they might focus on tariffs, interest rate levels, fees/charges, rewards and so on. If you are including competitive options then the brand will be included. You also need to consider if all option combinations are possible in reality, or whether some items need excluding, or whether to include fixed options that appear in all combinations.
In light of ensuring a realistic design, we suggest including both text and images where relevant to provide as much clarity as possible to the respondent about what the options and choices are. The text should be kept as short as possible and using consumer language so it is clearly understood/interpreted in the correct way.
Another key decision is how many options to display on-screen to a respondent at one time – 4 or 5 is generally acceptable – but the more detail you are showing the fewer this should be so you don’t overwhelm the respondent. You then need to decide how many task scenarios of different option combinations to show to each respondent and we suggest up to 10. Within the survey, there will be some level of randomization, so different respondents will view various combinations of the overall design to avoid bias. The design and randomization of the design are typically done using specific software.
Within the design, a respondent should only be able to choose one option per task scenario and we would recommend adding an ‘I wouldn’t choose any of these’ options so respondents who do not like any can express their lack of interest. Analysis of the ‘none’ category might reveal particular consumer groups who are more or less likely to purchase products/services.
Given the design already asks a lot of the respondents and delivers the main insight for this type of project, we suggest keeping additional survey questions to a minimum.
Because a choice-based conjoint survey is a bit more advanced than a basic survey and there is a lot of data collected, you’ll need it to be delivered in an easy way for you to digest it and act on it. Our methodologists have created an excel based simulator with different ‘what if’ scenarios that can be tested by switching on or off different combinations of options to see how this impacts consumer preference shares. At the top of the simulator is a simple visual showing how the preference share % changes when different options are selected. So using our simple cereal example from earlier we would ultimately identify that the combination of blueberry, chocolate, low in fat, high in fiber at a price of £2.99 drives the highest preference (likelihood to purchase). Within the simulator, filters can be applied to look at different demographic/segment groups.
If you are new to choice-based conjoint, then you may need some help with the design and interpretation of the insight and what it means for your business, so look for a partner with the relevant research and methodology expertise.