Emotion-Weighted Choice Model
Emotional weightings will be captured at the individual respondent level, and combined with explicit choice data from the Conjoint experiment into a single predictive algorithm that integrates both conscious and non-conscious influences on preference and choice. By integrating both the conscious (Conjoint) and sub-conscious (IAT/Sentient Prime®) data sets using our proprietary and published (peer-refereed) mathematical model patterned after fundamental human decision-making processes, we will develop a forecasting model for the new Range concepts that combines both rational and emotional inputs to accurately predict marketplace performance.
How do we combine System 1 & Stytem 2 two data sets?
It’s not enough to simply measure both systems. We also need a theoretically-based and mathematically specified model for how System 1 and System 2 combine to form preferences and drive behavior. There are several models in the decision making literature that allow researchers to base their recommendations. The Proportion of Emotion model uses emotion as a mathematical weighting mechanism in the human mind. The format of the data that you will get from measuring System 1 will look and feel quite similar to what you get from measuring System 2. It is typically transformed into understandable, normative scales to ease interpretation. For example, millisecond response time and swipe velocity data following visual primes are commonly reported on an easy-to-understand 0 to 200 scale, where 100 represents neutral and the poles of the scale represent extreme positive or negative associations.
How is this better than just measuring System 2 alone?
When we use a true implicit research technique to measure emotional associations in the consumer mind and mathematically combine that with reason-based reflection we are universally more accurate in predicting consumer behaviors from product sales to online views of an ad.
Sentient consumer choice models are used by the world’s largest corporations to model product demand within a competitive environment. Our behavioral scientists are experts in the design and analysis of derived preference measures including:
- Discrete choice model
- Choice-based conjoint
- MaxDiff scaling
- Proportion of Emotion model
Derived preference measures, such as choice-based conjoint, are among our industry’s best assessment tools of the outcome of consumer System 2 deliberative reasoning. Sentient scientists use these tools to reveal insights into how consumers consciously trade-off product features and benefits.