Depending on whether you have many designs, which you'd like to narrow down until you select the best packaging for a new product, or simply want to make sure that a new design will perform well, there are a few steers to help you gain insight.
The Simple Choice Steer provides users with a choice between two or three designs, presented side-by-side.
Split by Image, on the other hand, is a blind test, which allows you to show different images to different parts of the consumer panel. Each user only sees one image, similarly to a Yes/No Steer.
If you have several designs you wish to test, you can start with a Multi Option steer. This allows you to add up to six options. Once completed you can funnel the top results using a Split by Image or Simple Choice to find the top concepts.
Example:
Simple Skincare launched a range of travel sized pouches as an alternative to their less environmentally friendly travel sized bottles. But would it be much more successful than the original line? We ran a Simple Choice steer to check:
Whilst it looks like the new packaging is a win, in reality, people often make a stated choice of the more socially desirable option, which doesn't necessarily translate into purchase decisions. To better adjust our judgement for the new packaging success, we perform a blind test, i.e. a Split by Image steer.
Both options performed well meaning that whilst the pouch hasn't been the clear winner, it's a great opportunity for Simple to continue to promote the environmental benefits.
What about tweaks to the design? Simple are known for their simplistic packaging. Will adding a funky design score higher than a traditional look? We ran a Split by Image steer to see:
Adding the new design doesn't make that much of a difference for purchase intent. However, differentiating the new line from older versions that are still in retail makes sense, especially for attracting Gen Z consumers.
When looking at packaging design, it is important to remember that sometimes the results may not be as clear cut as you'd like but hopefully this example will provide you with some insight as to how to interpret these results.
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