A manufacturer was in the process of updating its marketing strategy. In the initial steps of this process, they developed nearly 40 potential messages (across a variety of themes). And, before moving forward with next steps and aligning on a final strategy, they wanted to gather reactions to the potential messages among consumers already in the category, as well as consumers who they are hoping to attract to the category.
Because there was a need to understand a hierarchy of the messaging statements, a max diff exercise was determined to be the optimal approach, as it would identify the relative ranking of the messages. In addition, there was a desire to understand gut reactions regarding the uniqueness of the statements, so a fast-explicit exercise was included in the study.
The research team developed an online survey to address both of these components to determine the best messaging strategy for the manufacturer to move forward with.
A manufacturer needed to prioritize among nearly 40 potential messages to understand which would be best for its strategy moving forward – in terms of not only motivating trial and usage of its products, but also in terms of which would stand out and be most unique vs. other products like it.
While the max-diff exercise identified which messages were motivating, the client team wanted to pursue an alternative method to better understand underlying feelings about the potential messages – specifically, to see which are unique to consumers.
The research identified the types of messages that were most motivating and unique, as well as which ones rose to the top of the list for both measures. Because the messages were categorized by theme, we were able to identify which themes the manufacturer should focus on as it develops its final strategy.
We found that messages related to the quality of the product were both motivating and unique. Messages related to pricing/being cheaper than an alternative ended up being motivating; however, they were not that unique. On the flip side, messages related to the brand being a leader in the category were seen as unique, but they weren’t that motivating.
The fast-explicit component of the exercise was also used as a tool to measure assuredness vs. hesitancy in determining whether a message was unique (based on how quickly respondents selected their answer). But, in this case, the range of how long it took respondents to answer was small enough across the messages that it did not alter findings for uniqueness (that is, it did not factor into determining whether a message was seen as unique or not).
With the results of this research, our client was able to determine its strategy moving forward and used the strongest messaging themes as it developed both appropriate advertising and taglines.
An online survey with max diff choice and fast-explicit exercises was employed to determine which messages were strongest in terms of motivation and uniqueness – including consumers who were already in the category, as well as those who were on the periphery of the category.
For the max diff exercise, respondents were shown several screens with four different messages on each screen, and on each screen, they were asked to select the one that made them the most interested in using the brand, as well as the one that made them least interested in using the brand. The results of this exercise were processed (using a regression analysis) to obtain “utilities” of the messages, which enabled us to determine the relative ranking of the messages.
And, for the fast-explicit portion of the survey, we partnered with the neuro-testing agency, Cloud Army, to assess uniqueness. After going through a “priming” exercise (to calibrate their speed of answering), respondents were shown each message one at a time and asked to indicate whether they felt it was unique to the brand. In addition to understanding their answer (unique or not), the exercise also measured how much time it took them to answer, which enabled us to assess assuredness vs. hesitancy (based on how quickly respondents selected their answer).
Data for both of the exercises were plotted on a quadrant so we could assess which messages were strong in both areas (or strong in one area but not the other); data were analyzed separately among category users vs. non-users, enabling us to understand if/how messaging strategies should be different for the two groups.