One of the key challenges in any advertising setting is to know which messages are the most important. Naturally, you want the messages to resonate with the clients, convincing them that your product is worth buying. But you also want to select those messages that align with who you are as a brand. It’s common to pre-test an advertisement to check the effectiveness of one message or direction, but you don’t get any indication from these tests if a completely different message would have worked even better.
With the goal of tackling this tricky issue, one of our automotive clients approached us as they prepared for the launch of a new model. They had come up with many different directions they could take their advertising efforts, and they wanted to discover which messages would incite purchases, but that would also inspire customers to think of the client as a well-defined brand. In addition to identifying key messages, they also wanted more qualitative background about why people appreciate those particular messages and which elements of messages are sticking with audiences.
To do this, we chose to use a multi MaxDiff setup. Like a normal MaxDiff, a multi MaxDiff allows us to rank the different messages in terms of both purchase intent and for brand fit. Even better, this approach allows us to measure the difference between the messages to evaluate how important each is at an individual level – for a detailed subgroup analysis.
The setup was even further augmented by the addition of another query. MaxDiff enables us to determine on-the-fly which ones are key messages for each respondent in terms of purchase intent and brand fit. This information is then channelled into numerous qualitative questions and typical market research grids. In taking this multifaceted approach, it’s possible to boost respondent engagement and the overall quality of the data.
To enable the client to fully understand what parts of the messages are being picked up and why people prefer each part, we decided to perform text analytics on the unstructured data. By creating word clouds of the key messages, we can offer summaries of the data without reading each text separately.
Digging even deeper, we also ran several TURF analyses, identifying combinations of messages that allowed the client to reach as many respondents as possible at one time. These analyses also demonstrate the uptake gained with the addition of each message – i.e. the value of each message.
In the end, our client gained a good understanding of which messages were the ideal combinations in terms of maximising purchase intent and brand fit. This, in addition to the insights gained through qualitative data, gives creative teams the precision directions they need to create powerfully focused and well-designed campaigns.