Every business leader should pay attention to behavioral economics feasibility, the fusion of psychology and economics seeking to understand how human behavior shapes financial decisions. Traditional economic models assume rational factors, but behavioural economics reveals how emotions and biases systematically sway judgment. These lessons provide invaluable guidance for feasibility studies services evaluating new initiatives.
Feasibility analysis projects consumer demand, competitive dynamics, and adoption rates to determine viability. Rather than relying on theoretical assumptions, behavioral economics moves beyond the “homo economicus” conception of human behavior to data-driven models that mirror real-world decision-making.
In this post, we will highlight three contributions of behavioral economics that can significantly strengthen the accuracy and usefulness of feasibility studies for new products, services, and business opportunities. We will identify specific cognitive biases and emotional factors that must be accounted for, along with practical steps to do so. While adding some complexity, applying behavioral principles allows for more nuanced projections, the surfacing of unseen risks, and ultimately smarter go-no-go decisions.
Correcting for Optimism Bias
One of the most robust findings from behavioral economics is the innate human tendency toward optimism bias. When evaluating plans or predictions, we unconsciously discount risks and challenges while inflating projections of success. This bias affects entrepreneurs and enterprise leaders across all industries and job functions.
Behavioral research quantifies the typical optimism bias in various settings. For example, when forecasting timelines, costs, or adoption rates, studies show projections tend to be skewed high by 10–30%. Effective feasibility studies must account for this bias by deliberately adjusting estimates downward and including conservative scenarios.
Let’s look at a few ways to correct for optimism bias:
- Conduct premortem analysis: Imagine it is one year in the future and the initiative has failed. Identify likely reasons for failure to balance optimistic assumptions.
- Benchmark against competitors: If projecting higher sales or market share gains than incumbents, incorporate historical data justifying projections.
- Model the best, worst, and base-case scenarios: It requires teams to defend both optimistic and pessimistic assumptions.
- Check for planning fallacies: Review past forecasts to compare projections versus actuals.
Accounting for Anchoring Bias
Closely related to optimism bias is the tendency to anchor on initial reference points when making judgments. First impressions, suggestions, and available examples sway our perspective. Anchoring leads us to overestimate certain data points, pulling estimates towards those anchors.
In feasibility testing, anchoring can introduce troubling distortions if not addressed intentionally. For example, if the first business model generates excitement, anchoring may cause teams to downplay subsequent options. Or if benchmarking reveals an impressive competitor, anchoring may skew the competitive analysis.
Steps to counteract anchoring include:
- Actively label potential anchors: When an exciting idea or impressive benchmark emerges, call out anchoring risk.
- Expand reference points: Introduce additional comparables and data to balance anchor influence.
- Ask “what if” questions: Challenge assumptions by considering opposite anchors, like “How would this change if a new disruptive competitor emerged?”
- Conduct blind testing: Remove identifying anchors when getting independent opinions on estimates or scenarios.
Segmenting for emotional drivers
Traditional feasibility testing often relies heavily on firmographic and demographic data for segmentation. Behavioral economics underscores the equal or greater importance of psychographic factors—understanding emotional decision drivers. This allows for more nuanced segmentation.
For example, behavioral research shows loss aversion is a dominant motivation, with people weighing potential losses far more than gains. Segmenting target customers based on emotional risk profiles enables more accurate modeling of likely adoption rates. Expect risk-avoidant segments to be more sensitive to downside concerns.
Social influence is another key factor. Customers prone to trends or heavily swayed by peer reviews will demonstrate different purchase behaviours than pragmatic evaluators. Social-proof tendencies should inform adoption projections.
Additional psychographic factors to consider:
- Change aversion vs. novelty-seeking
- Self-image and identity motivations
- Variations in future time perspective (short-term vs. long-term focus)
In summary, traditional feasibility studies have blind spots. They often rely on assumed rationality rather than human psychology. Behavioral economics provides tools to correct optimism and anchoring biases, as well as emotional and social decision drivers.
While incorporating behavioral principles creates some added complexity, the payoff is well worth the effort. More accurate models mean better evaluation of risks, returns, and readiness for go-or-no-go decisions.
In your next feasibility study, be sure to apply lessons from behavioral economics. The factors in this article are just a subset of the valuable insights this evolving field provides. Don’t leave behavioral economics out of your feasibility study analysis toolkit. Let it strengthen your evaluation and reveal smarter pathways forward.
