As a product manager or UX researcher, you’ve probably faced this scenario before:
A new development cycle begins and there’s a pile of feature requests from all over the organization, from the Sales team to the C-suite—everyone wants everything. If you’re not careful, you end up with a bloated product that’s costly to build, hard to use, and fails to resonate with customers.
Too often, feature prioritization is based on internal voices or wish lists without enough grounding in actual customer needs. There is a smarter path to tackle this problem: using structured tradeoff research to reveal what users actually value.
The old way built the bloat
Often, surveys are used to help in determining features and prioritization. Potential end users are asked, “Which of the following features would you like to see in your next smartphone? (Select all that apply).” While the question seems straightforward, it’s flawed. That’s like asking “Which candy bars do you want from the candy store? (Select all that apply).” Who wouldn’t want all of the candy in the candy store?
These types of questions lack constraints—such as cost, feasibility, or need—so respondents are inclined to say “yes” to everything. It’s the research equivalent of handing someone a blank check. Without friction or tradeoffs, there’s no signal, just noise. And worse, it sets your team up to overbuild or inaccurately prioritize.
Making tradeoffs brings a better approach
Effective research reflects how real people make decisions. In the real world, customers weigh options, make sacrifices, and consider cost (whether in dollars, time, or complexity).
Unfortunately, you cannot just come out and ask people how much they will pay for some yet-to-be developed product or feature; in most cases, they will want it for as little as possible, regardless of its intrinsic complexity or cost to develop.
By designing studies that introduce constraints, participants are forced to think more critically. Instead of “What would you like?” they’re answering a better question: “If you really want this feature, how much are you willing to give up for it?” This insight is far more powerful for prioritization.
Seasoned researchers employ alternative research methods that lead to meaningful data:
Limit choices
Giving respondents a set number they’re allowed to select from a feature list helps cull down the list, but does not tell us a whole lot about the relative value of the features selected. To learn the relative value, give respondents a budget, whether it’s points, tokens, or fake dollars, and ask them to “spend” it on features. This approach captures preferences through the lens of tradeoffs, forcing prioritization.
Attribute mapping
Not all features serve the same purpose. Some are “must-haves,” others are “nice-to-haves,” and some won’t move the needle at all. Attribute mapping helps teams understand which is which. Respondents are asked how they’d feel if a feature were missing, as well as to rate each feature in terms of its perceived importance. By combining the categorization and rating information, researchers can generate a single prioritized classification “map” that separates table stakes from differentiators.
Conjoint analysis
This is one of the most reliable ways to quantify feature value. Instead of asking respondents to evaluate features in isolation, they are shown bundles of features in hypothetical products and are asked to choose between the options. The virtue of this method is that respondents are never asked to rate any individual feature in isolation. From their choices, the implicit value of each feature can be modeled used advanced statistical techniques. What drives selection? What’s a dealbreaker? Conjoint analysis surfaces those insights, both quietly and powerfully. Simulating real-world constraints helps users clarify what matters most.
Why this matters for product teams
In a time when budgets are tighter and timelines are compressed, making the right decisions early is everything. Tradeoff research methods do more than gather opinions, they clarify real priorities.
These approaches help you:
Build with confidence, knowing you’re aligning with real user value
Say no with data, pushing back on feature creep with insights, not opinion
Deliver simpler, smarter products that users will value (and actually use)
Great products aren’t built by checking every box. They’re built by focusing on the right boxes. Want to know what features your users will pay for and not just wish for? Sentier helps teams design research that cuts through the complexity and delivers clarity.