Attributes are ‘dimensions’ of your product (such as price, colour, shape, size, brand, location). Include the attributes that you believe are most important to your customers when they make buying decisions, as well as any attribute whose importance you would like to check. For example, if you know that customers are driven by price and size, and want to investigate whether colour is important, include all three attributes (price, size, and colour). Try not to include more than seven attributes because it might confuse your respondents or may look too clunky, especially on mobile devices.
Levels are the ‘values’ that each attribute can take. For example, the attribute ‘colour’ can have levels ‘blue’, ‘red’, ‘transparent’. The attribute ‘features’ can be ‘no updates included’, ‘automatic update every month for one year’, ‘lifetime automatic updates’. Keep in mind that you need to have at least two levels per attribute. If you are looking only at one type of product, make the product description more specific.
When describing both attributes and levels, use the language that customers understand (or even pictures) because in their screen they will see attributes and levels exactly as you describe them. Think of the kind of language you would use in a customer-oriented brochure. If you have salespeople in your organisation, you can ask them to test your conjoint study for understandability before sending it out to customers or panel respondents.
Related to the previous tip, levels are like degrees of a characteristic and should be precise: e.g. the levels of the engine power of a car are 1.5L, 1.8L, 2.0L, not “less than 1.5L” or “more than 2L”.
Levels need to be realistic and feasible to achieve real-life scenarios and should include competitive offerings.
Price does not need to be an attribute: You can use Conjoint.ly to test for any attribute in any type of setting, whether for consumer goods, services or for charities trying to find out donor preferences.
Include an opt-out or “none of the above” option as the customer might not want to take any of the options in real-life. This is absolutely necessary for pricing. The only major exception is in medical circumstances where patients or doctors are often forced to make a choice.
More is not better: A moderate number of questions is best (around 10 but not more than 14). Having more than 14 questions causes responder fatigue and makes your results less useful. Conjoint.ly generates the optimal number of questions based on the setting you specify and also recommends a minimum number of respondents.
Include some profiling questions to learn more about the demographics of your customers: e.g., age, income, education level, but don’t overdo it. Conjoint.ly lets you analyse these as well.
A picture is worth a thousand words. Use images, especially if you have trouble describing your product features.
One of the great things about doing conjoint analysis is that it estimates market share based on customers’ preferences. Check out Conjoint.ly’s market share simulation functions. But remember: conjoint-based market share simulation should not be relied on in isolation as it is nearly impossible to include every competitor’s offering in the market, and factors such as shelf availability of products do come in the way.
And, finally, when you’re doing conjoint analysis, always pre-test your survey with a handful of respondents (your colleagues) to ensure the survey fulfills the objectives of your investigation.
Conjoint.ly allows you to prevent specific combinations of levels from showing on the same alternative (under “Advanced settings”). For example, a particular price (say, $5) might be incompatible with a particular size (“recycled”). For best results, we recommend using this option sparingly: not more than four restrictions.
Keep in mind that you only need to prohibit combinations that are unrealistic in the eyes of the consumer, rather than those that are not feasible for the company to make. For example, if consumers would not be surprised to see $5 recycled tissue paper, but it is not feasible for the company, you should not prohibit this pair in the design of your experiment. Instead, you should simply ignore product concepts with this combination when you look at the results of the analysis.
If you find that you need to have more than seven prohibitions, we strongly encourage you to think through these options: