Standard rating scales fail to predict actual customer behavior
Most surveys don’t work. Businesses rely on traditional rating scales, thinking they reveal what customers will do next. They don’t. Asking people how likely they are to see a movie or buy a product leads to optimistic answers that don’t translate into action. There’s no cost to selecting “extremely likely,” so respondents answer based on intent, not reality.
A media company tried using one of these studies to allocate its advertising spend. The results were useless. The responses had no predictive value—marketing efforts based on this data performed no better than random chance. The issue wasn’t sample size or methodology. The problem was the question itself. Customers don’t decide in isolation. Their actions depend on multiple factors—time constraints, competing priorities, and habits. A five-point scale doesn’t reflect those complexities.
Relying on flawed data leads to poor forecasts, wasted budgets, and missed opportunities. If your survey doesn’t reflect real choices, it can’t predict customer behavior.
Forced choice surveys provide more accurate predictions
If you want real insights, you have to force a decision. When survey respondents are asked to make trade-offs—choosing between competing priorities—their answers align more closely with actual behavior. Unlike rating scales, which allow people to answer hypothetically, forced choice questions require consideration of real constraints.
A media company tested this by improving its survey design. Instead of asking people how likely they were to see a movie, they asked whether it was in their top three weekend plans. The number of affirmative responses dropped significantly—from 15% to just 1–3%—but this smaller figure was far more accurate. The movies that performed well in the survey also performed well at the box office. By requiring participants to weigh options rather than select an easy answer, the company gained real insight into consumer intent.
For executives making strategic decisions, this is critical. Forced choice reveals what customers will actually do, not what they say they might do. This leads to better forecasting, more effective marketing, and smarter resource allocation. If your surveys don’t demand real choices, they aren’t telling you what matters.
Forced choice techniques outperform standard ratings
Standard rating systems often fail to capture meaningful insights. Many businesses assume that asking customers to rate a product or experience on a scale will predict future behavior. In reality, these scores are often inflated, inconsistent, and lack real decision-making value. People tend to give high ratings out of habit, politeness, or lack of a better reference point. This makes it difficult to extract useful data from traditional surveys.
Forced choice surveys solve this problem by requiring clear, committed decisions. Instead of asking customers to rate a brand like Apple on a five-star scale, where extreme opinions cancel each other out—you ask them directly whether their next phone will be an iPhone. This approach removes ambiguity and provides a clearer signal about future purchasing behavior.
For business leaders, this method leads to better segmentation and more targeted strategies. Understanding what customers are actually willing to choose—not just what they claim to like—enables better demand forecasting and more effective product positioning. If you want to know where your customers are going, you need to ask the right questions.
Well-designed survey questions can segment users and uncover decision drivers
Asking the wrong questions leads to meaningless data. Many surveys fail because they allow respondents to rate everything as important, failing to reveal true priorities. When participants can give the highest rating to multiple factors—such as compassionate health care and advanced medical technology—the results provide little value. Forced choice surveys eliminate this issue by requiring respondents to make a decision between competing priorities.
This approach provides actionable insights. For example, when asked whether compassionate care or leading-edge technology matters more in choosing a hospital, respondents provide a clear indication of what drives their decision-making. Techniques like semantic differential questions and discrete choice models (including MaxDiff and conjoint analysis) make it possible to segment users based on real preferences rather than vague ratings.
For business leaders, this matters because real decisions require trade-offs. Understanding what customers will prioritize allows for better product development, service design, and marketing strategy. If your data doesn’t show what people will actually choose when faced with constraints, it isn’t guiding your business in the right direction.
Key takeaways for leaders
- Rating scales fail to predict customer behavior: Traditional surveys using rating scales produce misleading data because they allow respondents to provide hypothetical answers without trade-offs. Leaders should replace them with methods that reflect real-world decision-making to avoid misallocating resources.
- Forced choice surveys provide more accurate insights: When respondents must make trade-offs, their answers align more closely with actual behavior. Businesses should adopt forced choice survey techniques to improve forecasting, optimize marketing spend, and refine strategic planning.
- Forced choice methods are more reliable for market research and CX: Simple ratings often exaggerate satisfaction and fail to predict future actions. Asking customers to make definitive choices, such as selecting a preferred brand or product, provides clearer insights for product positioning and competitive strategy.
- Well-designed survey questions uncover real priorities: Customers often rate multiple factors as equally important, making data unreliable. Leaders should implement forced choice and structured trade-off questions to segment audiences more effectively and align business strategies with genuine consumer preferences.
