Test Hypothesized Pain - Maya Patel

Testing commuter pain hypotheses using validation, ouch factor, and willingness-to-pay

Following the toolkit naming conventions, this file is named exp-10.a-pain-test-summary-2025-03-20.qmd.

Demo Overview

This demo continues the Halo Alert case.
We’ve already developed Maya Patel’s persona, traced her experience map, and abductively generated a set of pain hypotheses.
Now we test the most plausible hypothesis:

Hypothesis: Maya experiences significant anxiety when commuting at night because she feels unsafe walking alone, especially in unpredictable or poorly lit environments.

Our goal is to gather evidence using three complementary tests:
1. Pain Validation
2. Ouch Factor
3. Willingness to Pay for Relief (WTP)

1. Pain Validation

Purpose: Confirm the pain occurs in real life, not just in theory.

Method: Short intercept survey of 20 late-evening subway commuters in Queens.

Sample Questions

  • “In the past 30 days, have you felt unsafe walking alone at night after exiting the subway?”
  • “When was the last time this happened?”
  • “How often has this come up in the past month?”
  • “Rank these pains from most to least disruptive: [dark streets], [crowded train exits], [unexpected detours].”

Results

Pain % Reporting Avg Frequency (30d) Avg Rank (1 = most disruptive)
Dark/unlit blocks 85% 3.2 times 1.4
Crowded exits 70% 4.5 times 2.2
Unexpected detours 60% 2.1 times 2.8

A small contrast group is shown for context but not used in decision-making.
Contrast (n=2): Lower recognition; avg ouch 5.5/10; minimal substitute spend.

“I’ll walk an extra 10 minutes just to stay under the lights.” — R1, grad student
“The worst is when the train is delayed and then I miss the busy sidewalks — it feels sketchy walking alone.” — R2, office manager

Interpretation
  • In-segment: High recognition (88%), strong recency, pain ranks consistently #1.1
  • Contrast: Recognition and severity lower, but still present.
  • Pain ranks consistently near the top, above other commute issues.

2. Ouch Factor

Purpose: Gauge the relative severity of pains within the same group.

Method: Same intercept survey, asking for 1–10 severity ratings + follow-up on behaviors.

Sample Questions

  • “On a scale of 1–10, how severe is the stress of walking home in dark/unpredictable blocks?”
  • “What do you do about it? (time, money, routines, substitutes)”

Results

Pain Avg Ouch (1–10) Common Workarounds
Dark/unlit blocks 8.1 Longer route, keys in hand, check-in text
Crowded exits 6.4 Plans exit early, avoids rush-hour trains
Unexpected detours 5.9 Adds buffer time, downloads transit app

In-segment (n=8): 88% reported dark/unlit blocks in past 30 days; avg ouch 8.3/10; 45% substitute spend.
Contrast (n=2): Lower recognition; avg ouch 5.5/10; minimal substitute spend.

“It feels dangerous, even if nothing actually happens.” — R1, grad student
“It’s not every night, but when I do take rideshare, it’s worth the money.” — R3, office assistant

Interpretation
  • In-segment: Ouch scores high (8.3/10) and anchored by clear behaviors (time lost, money spent).
  • Contrast: Severity scores lower (avg ≈ 5.5/10), with fewer behaviors.
  • Reminder: Severity without behavior ≠ urgency.

3. Willingness to Pay for Relief

Purpose: Understand what commuters already spend to mitigate the pain.

Method: Prompt about current substitute spending, not hypothetical “what would you pay.”

Sample Questions

  • “What do you currently spend to reduce this pain? (rideshare, late-night bus, other fees)”
  • “If you had to choose, which pain would you be most willing to pay to solve first?”

Results

Pain % Paying for Substitutes Avg Monthly Spend Substitute Types
Dark/unlit blocks 40% $28 Rideshare, longer routes
Crowded exits 10% $8 Off-peak rides
Unexpected detours 15% $12 Transit apps, buffers

In-segment (n=8): 88% reported dark/unlit blocks in past 30 days; avg ouch 8.3/10; 45% substitute spend.
Contrast (n=2): Lower recognition; avg ouch 5.5/10; minimal substitute spend.

“I already pay for peace of mind — but I’d pay more if something really worked.” — R3, office assistant
“Not worth money — I just deal with it.” — R4, delivery driver

Interpretation
  • In-segment: Nearly half already spend (avg ~$32/mo), mainly on rideshares or longer routes.
  • Contrast: Minimal substitute spending; some explicitly reject paying.
  • Reminder: Look at actual behaviors and spending rather than hypothetical WTP numbers.

4. Triangulation & Conclusion

We combine results across the three tests.
Headline estimates reflect the in-segment sample (professional women with ≥10-minute night walks). A small contrast group is noted separately for context.
For transparency, each column links back to the transcript/data files where raw responses are stored.

Pain Validation2 Ouch Factor3 WTP4 Confidence
Dark/unlit blocks High (8/10 reported in past 30d) High (avg 7.2/10, strong workarounds) Medium–High (40% already paying, time costs common) Green
Crowded exits Medium (6/10 reported, lower rank) Medium (avg 6.0/10, mild behaviors) Low (10% paying, limited costs) Yellow
Unexpected detours Medium (6/10 reported, secondary pain) Medium (avg 5.9/10, buffer time) Low (15% paying for apps/buffers) Yellow
Scope of Claims

Headline estimates reflect the in-segment sample (professional women with ≥10-minute night walks).
We include a small contrast group for context but don’t base decisions on it.

Takeaway

  • Dark/unlit blocks emerge as a validated, urgent, and economically salient pain.
  • This pain is both frequent (validation), severe with behaviors (ouch factor), and already costly (WTP).
  • Confidence: Green light to carry this pain into Diamond 3 (solution exploration).

Traceability

  1. Headline estimates reflect the in-segment sample (professional women with ≥10-minute night walks).
    Contrast group responses provide context only. ↩︎

  2. See Pain Validation transcript↩︎

  3. See Ouch Factor transcript↩︎

  4. See WTP transcript↩︎