Feeling First Design

Feeling-First Design

Treat the feeling around a product task as a hypothesis to test, not a mood to decorate onto the interface. Start with evidence about a specific user moment, define one useful emotional shift, make the smallest change that could cause it, and measure both behavior and experience.

Operating rules

• Start from an observed moment, not a generic aspiration such as “make it delightful.”
• Choose one primary emotional shift. Add another only when the evidence requires it.
• Fix basic usability, reliability, or value problems before applying behavioral mechanisms.
• Prefer the smallest intervention that can isolate the hypothesis.
• Pair a behavioral metric with a direct user-reported measure. Behavior alone does not reveal how the user felt.
• Preserve informed choice. Never manufacture urgency, progress, scarcity, social proof, or consequences.

1. Frame the moment

Write down:

• Actor: the specific user or segment.
• Goal: what they are trying to accomplish in their own terms.
• Moment: the exact decision, action, or transition under review.
• Evidence: observed behavior, research, support signals, or product data.
• Stakes: what could be lost through error, delay, disclosure, payment, or commitment.

Separate observations from interpretations. “42% leave on the connection screen” is an observation; “they do not trust us” is a hypothesis. If evidence is missing, label the assumptions and recommend the cheapest way to investigate them.

2. Form a state-shift hypothesis

Describe one transition:

At [moment], the user may feel [current state] because [evidence-backed cause]. Help them feel [desired state] so they can [user-benefiting behavior].

Make the states concrete and moment-specific. Prefer “uncertain about what will be shared → informed and in control” over “worried → delighted.” The desired state should help the user pursue their goal, not merely make the product more persuasive.

Assign low, medium, or high confidence and name the evidence behind that rating. Do not present an inferred emotion as a user fact.

3. Diagnose what could create the shift

Choose one working mechanism from the signal in front of you. These are hypotheses, not universal laws.

If none fits, do not force a psychological explanation. The stronger recommendation may be clearer value, fewer steps, better performance, or a product defect fix.

4. Design the smallest intervention

Turn the mechanism into one concrete product change. Specify:

• what changes in the interface, interaction, timing, or copy;
• why that change could produce the state shift;
• what stays unchanged so the test remains interpretable; and
• how the treatment could backfire.

For example, consider a first-run budgeting flow with a sharp drop before account connection. Do not assume users need more motivation. A testable treatment might explain the exact data requested, offer a read-only preview, and show how to disconnect later. The hypothesis is “uncertain and exposed → informed and in control,” not “add reassurance.”

5. Define the test before recommending launch

Write the hypothesis in this form:

For [segment] at [moment], [intervention] will help shift [current state] toward [desired state], increasing [behavioral outcome], because [working mechanism].

Define all of the following:

• Primary behavior: the one observable action expected to change.
• Experience measure: a short, neutral question that checks the proposed state, such as confidence or perceived control.
• Guardrail: a measure that would reveal harm elsewhere, such as regret, reversal, complaints, accidental consent, or downstream abandonment.
• Stop condition: the result that should end or roll back the test even if the primary behavior improves.
• Method and window: an A/B test where volume and risk permit it; otherwise a prototype study, usability session, or staged release with a defined observation period.

Avoid metric bundles that make any result look successful. Choose the primary measure before seeing the outcome.

6. Run the agency review

Reject or revise the intervention if any answer is no:

1. Truthful: Are every claim, status, consequence, and progress indicator real?
2. Understandable: Can the user tell what is happening and why?
3. Voluntary: Can they decline without a hidden penalty or misleading obstruction?
4. Reversible: Can they recover when the stakes justify an undo or confirmation?
5. Proportionate: Does the emotional intensity match the importance of the action?
6. Safe: Have risks to vulnerable or high-stakes users been considered explicitly?

Do not use a survey score to excuse coercive behavior. A treatment must pass the agency review as well as the experiment.

Output format

Moment and evidence: [actor, goal, exact moment, observations, stakes]

State-shift hypothesis: [current state → desired state → user-benefiting behavior]

Confidence: [low / medium / high, with evidence and assumptions]

Working mechanism: [one mechanism and why it fits]

Smallest intervention: [concrete change, what remains fixed, possible backfire]

Test: [hypothesis, method, segment, and window]

Measures: [primary behavior, experience measure, guardrail, stop condition]

Agency review: [truth, understanding, choice, reversibility, proportionality, safety]

Open questions: [evidence still needed]

Scale the response to the ask. For a narrow critique, identify the weakest assumption and one test. For a full flow, repeat the method only for the moments that materially affect the user's goal.

Common failure modes

• Inventing the emotion. Treating an analytics drop as proof of anxiety, distrust, or boredom without research.
• Choosing a brand feeling. Optimizing for “premium” or “exciting” when the user first needs clarity, safety, or competence.
• Stacking interventions. Changing copy, layout, incentives, and timing together, then learning nothing about causality.
• Measuring behavior only. Calling a conversion lift successful while regret, confusion, or accidental consent rises.
• Adding psychology to a product defect. Using persuasion to compensate for weak value, broken reliability, or needless friction.
• Confusing honesty with softness. Clear consequences and protective friction can be the most respectful design at a high-stakes moment.

Research basis

The workflow is independently organized around evidence, a state-shift hypothesis, a minimal intervention, a test, and an agency review. Individual candidate mechanisms are informed by public research, including Buell and Norton's operational-transparency research, Fogg's behavior model, Loewenstein's information-gap account of curiosity, and Nunes and Drèze's endowed-progress research. These sources support mechanism selection; they do not prove that a particular treatment will work for a particular product or user.