MedMinder Pro - Medication Adherence Coach

Model: z-ai/glm-4.7
Status: Completed
Cost: $0.231
Tokens: 153,860
Started: 2026-01-05 14:38

Section 06: Validation Experiments & Hypotheses

Transforming assumptions into actionable data for MedMinder Pro.

1. Hypothesis Framework

Hypothesis #1: The "Why" Gap

🔴 Critical Risk
We believe that adults 50+ managing 3+ chronic medications will actively seek to understand the root causes of their missed doses (cost, side effects, complexity) if we provide an intervention engine that identifies these patterns automatically.

We will know this is true when we see 60%+ of surveyed users identify "forgetting" as a secondary issue to "side effects" or "logistics" AND 40%+ click on a "Find out why you missed this" button in a prototype.
Current Evidence:
✅ High industry churn (80%) suggests current reminder-only solutions fail.
⚠️ Anecdotal evidence suggests patients prefer "forgetting" over admitting side effects.
Experiment Design:
Method: Problem Discovery Interviews + "Fake Door" Prototype
Sample: 30 interviews, 500 prototype clicks
Duration: 2 weeks
Metric Fail Minimum Success
Root Cause Interest <20% 20-40% >40%
"Why" Feature CTR <10% 10-25% >25%

Hypothesis #2: Caregiver Peace of Mind

🔴 Critical Risk
We believe that adult children managing aging parents' medications will adopt a monitoring dashboard if we reduce the emotional burden of "nagging" through objective, non-invasive alerts.

We will know this is true when we see 50%+ of surveyed caregivers express willingness to pay $5/mo specifically for "silence/peace of mind" (knowing meds were taken without calling).
Current Evidence:
✅ "Sandwich generation" is highly stressed.
❓ Privacy concerns between parent/child are high.
Experiment Design:
Method: Caregiver-specific Landing Page Test
Sample: 1,000 visitors via FB/Google Ads
Duration: 2 weeks

Hypothesis #3: Willingness to Pay (B2C)

🟡 High Risk
We believe that price-sensitive retirees will pay $4.99/month for premium features if we demonstrate a direct cost saving (e.g., finding a generic coupon that covers the subscription cost for 3 months).

We will know this is true when we see 10%+ of free users convert to a paid trial after receiving a "Money Saved" report.

Hypothesis #4: Pharmacy Data Trust

🟡 High Risk
We believe that patients will grant access to their pharmacy history if we offer a "One-Tap Refill" feature that compares prices across local pharmacies.

We will know this is true when we see 30%+ of users complete a pharmacy connection flow in a prototype environment.

2. Experiment Catalog

Prioritized tests designed to validate the critical path assumptions.

Exp 1: "Mom Test" Interviews

Tests: H1 (Problem)

Method: 30-minute interviews with 20 adults 50+ on 3+ meds. Ask about last time they missed a dose. Do not mention the solution.


Cost: $200 (Gift cards)
Time: 2 Weeks
Success: 60% cite non-forgetfulness reasons (cost/side effects) as primary barrier.

Exp 2: Landing Page Smoke Test

Tests: H1, H2 (Interest)

Method: Run Google Ads (Search: "medication reminder app") to a landing page highlighting "Find out WHY you miss doses" vs standard "Never forget a pill".


Cost: $750 (Ad Spend)
Time: 2 Weeks
Success: >3.5% conversion rate on "Why" variant vs baseline.

Exp 3: Wizard of Oz Coupon Finder

Tests: H3 (Value/Pricing)

Method: Users enter medication name. Backend (human/manual search) returns 1-2 cost-saving options (generic/coupon) after 24 hours.


Cost: $0 (Scraping/Manual labor)
Time: 3 Weeks
Success: 80% report savings as "High Value"; 40% ask for "more features like this".

Exp 4: Concierge SMS "Snooze"

Tests: H1 (Behavior)

Method: Manually send SMS reminders to 15 users for 1 week. Include "Snooze" buttons with tags: "Side Effects", "Cost", "Busy".


Cost: $50 (SMS API)
Time: 2 Weeks
Success: >50% of snoozes utilize a specific reason tag (not just ignore).

3. Experiment Prioritization Matrix

Experiment Hypothesis Impact Effort Priority
Problem Interviews H1 🔴 Critical Medium #1
Concierge SMS Snooze H1 🔴 Critical Low #2
Landing Page Test H1, H2 🟡 High Low #3
Wizard of Oz Coupons H3 🟡 High Medium #4
Pharmacy Connection Prototype H4 🟢 Medium High #5

4. 8-Week Validation Sprint

Phase 1: Problem & Behavior (Wk 1-3)

Week 1
Recruitment & Setup: Recruit 20 interview participants (LinkedIn, local senior centers). Set up SMS Twilio number.
Week 2
Execution: Conduct 20 interviews. Launch SMS "Snooze" test with first 5 users.
Week 3
Analysis: Transcribe interviews. Identify top 3 barriers. Analyze SMS interaction data.

Phase 2: Solution & Value (Wk 4-6)

Week 4
Build: Create "Coupon Finder" input form. Setup manual backend process.
Week 5
Run Ads: Launch Google Ads to Landing Page (Budget: $500). Direct traffic to Coupon Finder.
Week 6
Deliver Value: Manually fulfill coupon requests. Survey users on "Perceived Value" of savings found.

5. Minimum Success Criteria (Go/No-Go)

The Decision Framework

Problem Validation
60%+ of interviews identify non-forgetfulness barriers (Cost, Side Effects).
If <40%, pivot to pure reminder app.
Engagement Signal
50%+ of SMS users use "Reason" tags for snoozing.
If <20%, users ignore complexity; simplify.
Value Perception
$10+ average perceived monthly value of coupon feature.
Must exceed $4.99 subscription cost target.

✅ GO Condition: 2 out of 3 criteria met OR 1 critical met with clear pivot path.
❌ NO-GO Condition: All criteria missed by significant margin (>30% gap).

6. Pivot Triggers & Contingencies

Trigger Signal Action Pivot Path
"It's too much work"
>70% say they don't want to input reasons.
Analyze friction points. Is it the UI or the concept? Passive Coach: Move from active input to passive AI inference (detect missed refill patterns instead of asking).
"I trust my doctor"
Users reject AI suggestions for alternatives.
Reframe AI as "Assistant for Doctor" not "Replacement". Provider Tool: Pivot B2B. Sell dashboard to doctors/pharmacists to manage patients, removing patient-side AI burden.
Low CTR on Ads
<1.5% on search terms.
Test different channels (Facebook groups, offline flyers in pharmacies). Demographic Shift: Target younger chronic patients (30-40s) or caregivers exclusively if seniors unreachable via web.