Ranking messages for purchase intent and brand fit
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.
Picking the perfect sales message with multi MaxDiff
Demystifying data with word clouds
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.
Pointing creative efforts in the right direction
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 powerful, focussed, well-designed campaigns.