Decker wanted to provide some details on how to interpret data from Sentient Prime, along with a few tips and a case study showing some past results. These details are meant to get you thinking about how to best put this data to use for your clients and/or your business.
As with any research project, Decker start with the design. The initial set up of a Sentient Prime project is straightforward. After selecting one of following:
You’ll be provided with simple prompts to build your study. This includes the selection of attribute or emotional associations you want to test with your brand, package, concept or ad; followed by instructions for deployment and fielding.
Once you have collected a minimum of n=200¹ (per segment) through your study, you are ready to export your data! Sentient’s proprietary implicit association testing platform, Sentient Prime, automatically integrates the multiple implicit response data inputs collected by the technology to derive the quantitative assessment of implicit attitudes. Sentient is talking response time, accuracy, and more.
This final output is provided as an “index score.” This value represents the non-conscious association between the tested brands/concepts/creative/packages and the tested emotions/attributes. Values range from 0 to 200 and are provided at the individual level, meaning there is a value for each association for each respondent. These values work just like data from a standard Likert scale–think a ‘1-to-7 scale’ or ‘0-to-10 scale,’ but with much more detail in-between.
Want to know what’s statistically significant? Statistical tests can be performed on these index values similarly as with any other quantitative data. A confidence interval is provided by the Sentient Prime Results control panel automatically (look for ‘current margin of error for this study’).
To better understand what a finalized Sentient Prime project looks like, here is some example data from a brand positioning study. The index values represent the distinct implicit associations between each of the brands above and the labeled emotions and attributes. In this example, “Your Brand” is scoring higher on pride and expert, moderate on excitement, and low (but not as low as competitors) on anxiety. Competitive comparisons are also valuable: “Major Competitor 1” is also scoring high on expert, and is scoring much higher on tasty and excitement. As true implicit “emotional associations,” these index values provide valuable differentiation of brand attitudes within the consumer subconscious. Their quantitative nature lends itself to further statistical techniques like t-tests or regression to identify statistically significant differences and/or relationships.
The other products available through Sentient Prime are equally simple to analyze; comparing index values across brands, concepts, or packages, potentially split by demographic segments. Here are some examples of the many ways to use implicit associations:
¹ n=400 is preferred; higher sample sizes reduce the margin of error, allowing you to more precisely detect smaller effects. The article is re-posted on Top Box Magazine. Original post is published on Sentient Decision Science.