Behavioral Economics is a game-changing idea if you are in the game of predicting decisions. By now most of us know about Thinking, Fast and Slow. Kahneman and Tversky’s groundbreaking idea (supported by lots of creative and compelling social science) is that most decisions are made instinctually or based on habit. Kahneman calls this “fast thinking”, which is easy, automatic, and intuitive.
There is also “slow thinking”, which is hard, conscious, and deliberative. It is easy to think of fast and slow thinking as separate systems, that you tackle a problem using either fast or slow thinking. However, the most powerful implications of Behavioral Economics, particularly for marketers, stem from the interplay of these two systems. This is where behavior change starts and finishes. Specifically, to change ‘fast thinking’ in any way we must engage ‘slow thinking’.
Understanding the root causes to help define an initial step to disrupt behavior is definitively ‘slow thinking’. Any hope to change behavior requires this kind of conscious effort. The good news is sustained conscious effort (some say it takes 7 attempts) leads to a new habit, which lets you get back to ‘fast thinking.’
Behavioral Economics clearly shows us that we need to understand how both ‘fast’ and ‘slow’ thinking relate to any specific behavior. For example, to change a physician’s prescribing decision, we need to recognize the ‘fast’ and ‘slow’ thinking underpinning the physician’s original prescribing choice. With this insight, we can develop strategies to change this behavior.
To help this effort, we leverage data integration and advanced analytics to understand both fast and slow thinking, particularly with respect to primary and secondary data. In essence, primary data captures what customers say, which is our best view into ‘slow’ thinking. Secondary research, on the other hand, captures what customers do and is our best view into ‘fast’ thinking.
Therefore, the need to understand the relationship between ‘fast’ and ‘slow’ thinking prompts the need to combine primary and secondary data. We leverage state-of-the-art (but often underutilized) analytical tools that overcome these problems and provide a single set of cohesive insights from multiple different data sources. Our approach is analogous to combining results from clinical studies into meta-analyses.
One robust example of these kinds of tools is Bayesian Belief Networks (BBNs). With the computational resources that are now commercially available, BBNs can simultaneously evaluate literally thousands of interdependent probabilities to sort out the most important factors and relationships in individual or across multiple data sets. More specifically BBNs allow us to create links between networks, thereby allowing us to connect data that cannot be connected otherwise. In addition, a BBN can be directed to predict a particular decision or behavior. And in some cases, a BBN can also help parse out cause and effect.
To give you a concrete example, it is often helpful to evaluate the extent to which a ‘soft’ measure like quality-of-life (QOL) impacts a ‘hard’ measure like prescribing. Primary research is our only way to measure something like QOL, reflecting what HCPs ‘say’. And secondary data is our best measure of prescribing reflecting what HCPs ‘do’. Therefore, to best answer that question we must combine primary and secondary data.
Another example is trying to identify which HCPs have the greatest number of patients in need of treatment change or, ideally, have candidates for a new treatment. The common approach is to leverage either claims (i.e., secondary data) or chart data (i.e., primary data) to identify which HCPs have the biggest unmet need. The issue is these two data sources each provides a different perspective, where the best answer requires combining primary and secondary data. By creating a BBN for each data source and then finding connection points to evaluate specific relationships between the data sources. The result is an integrated solution that allows us to simultaneously evaluate data that reflects both what HCPs ‘say’ and what HCPs ‘do’.
Leveraging behavioral economics using BBNs to combine primary and secondary data is one distinctive way we help our clients establish better answer mission-critical business questions. And while we are committed to expanding the use of advanced analytics, in the end, it is not about using fancy tools. It’s about using the best tools available to generate better answers to our three critical business questions: What is our opportunity? Where do we focus? How do we win?
Want to drive better business decisions with advanced analytics and Behavioral Economics thinking? Please contact us and someone will be in touch with you right away.