Semaglutide Patient Lifecycle & Plan Profitability
OBJECTIVE ANALYSIS
Map and analyze the full lifecycle, profitability,
and churn of semaglutide plan patients
Key Metrics
Active patient count, Plan-specific revenue &
COGS, Dose pick-up rate, Doc fees, nCAC,
LTV
Active Patients Definition
Define Active Patients as those who
purchased any semaglutide injectable product
in the past 6 months
Includes: 1, 3, 6, 12-month injectable plans
(with or without B6/B12 additives)
- Patient ID, All order dates, types, and refill history, Plan type (duration + additives), Upfront vs. installment payment status Excludes: Oral sema, gummies, non-injectables Patient Lifecycle Mapping (dim_patients) For each patient, map their complete sema journey:
- Initial plan type purchased
- Refill/pickup behavior (e.g. 4 of 6 doses completed)
- Conversion from commercial to personalized (if applicable)
- Inactive periods and any reactivations Plan-Level Unit Economics Revenue per plan (1M, 3M, 6M, 12M) COGS per dose (including additive cost) Output: Margin waterfall chart by plan Filter by promo/discount/test Plan-Level Ad Spend Attribution (repurpose fact_ad_spent_by_transactions) INSIGHTS RECOMMENDATIONS
High churn between dose 1 and dose 2
- Possible first-dose side effect or expectations gap Many 12-month plan patients only pick up 50% of doses
- Upfront revenue is good, but engagement drops across longer term plan 6-month plans show higher refill rates than 12-month
- Mid-tier commitment may balance cost + adherence better April promo on B6+B12 bundle/kit increased conversions, but lower repeat purchase
- bundles/kits seen as experimental and not something consumers are quick to adopt Build margin model for each plan, factoring in unused doses and churn-adjusted actual delivery Shift spend to channels that produce higher LTV:CAC per plan Test pre-commitment upsells (buy 3M upfront vs. month-to-month) Add system flag when dose 3 is approaching to prompt personalized transition Flag any plan type with <60% dose pickup rate as needing follow-up or eligibility review
Client Questions
- How should we define an “inactive” sema patient in the data? Is it missing a refill window by a certain number of days?
- Where do we currently capture reasons for plan dropout… is this just in support tickets? Have not had to look into churn data to date.
- Can we clarify what exactly “conversion to personalized dosing” means from a data standpoint? Is this a mid plan SKU adjustment, custom refill cadence or something else? If so, what are the triggers?
- When do installment payments happen instead of upfront? Is this tied to plan duration or other internal rules?
- For multi-month plans (e.g. 12-month) that are paid upfront but only a few doses get processed, how are we handling that financially? I haven’t looked into partial credits and penalties before.
- Are there any historical plan pricing changes or promos (e.g., April-only tests) we should isolate in the data? If so, can we get a summary or a list of these?
Payback Optimization (LTV:CAC)
OBJECTIVE ANALYSIS
Goal:
Quantify customer acquisition cost and
lifetime value across paid and organic
channels to inform marketing efficiency and
budget allocation.
Key Metrics:
CAC, 30/90/180-day LTV, Payback Period,
LTV:CAC Ratio
Segmentation By:
Channel, product line, buyer persona
LTV by Channel/Product/Category/Plan
Model LTV by product line (e.g., GLP-1 vs.
hair vs. sexual wellness) and acquisition
channel (Meta, Google, Affiliates).
LTV:CAC Payback Period
- Compare CAC vs. 30/90/180-day LTV to define payback periods.
- LTV:CAC by weekly/monthly/quarterly cohort Competitor Benchmarking Benchmark against Ro/Hims payback targets (~6–12 months for high-retention verticals). INSIGHTS RECOMMENDATIONS GLP-1 patients acquired via Meta have higher CAC but better LTV if they refill ≥ times
- Longer-term medical users yield higher payback only if retention is secured after dose 2 CAC has remained constant, but median payback has slipped from 82 → 109 days over last 3 months
- Caused by shifting mix toward longer-term GLP-1 plans and slower refill cadence 6-month plans outperform 12-month plans in LTV:CAC ratio
- Likely due to better adherence and lower upfront drop-off; 12M plans often carry margin dilution from unused doses Dynamic Plan Offers Based on Predicted LTV
- Use machine learning to predict LTV at checkout based on quiz answers, device, and referrer
- Offer flexible billing or longer-term discounts only to predicted high-LTV users Refactor 12-Month Plans into Modular Upsells
- Instead of defaulting to 12M prepaid plans, convert through milestone upsells (e.g., “After your 3rd refill, unlock 20% off your next 3 doses”)
- Reduces upfront sticker shock and improves refill adherence without hurting margin Rebuild LTV model quarterly
- Capture true LTV by product over time, factoring in churn, operational costs (e.g., doc time), and COGS volatility
- Track per-plan profitability not just revenue
Client Questions
1. Which predictive elements in the LTV model beyond realized revenue? The current model is realized-only. We can recommend next steps like incorporating refill rate, plan type, or churn modeling. 2. Should we begin modeling LTV on a net margin basis (revenue – COGS – doc fees)? COGS is currently excluded from LTV. We need alignment on whether to factor in margin durability. 3. Do we have a centralized price change tracker by SKU or plan type? There are frequent pricing/discount shifts, but no log of when or why changes occurred. 4. How should we treat unused doses in prepaid plans? If someone pays for 12 months but only uses 4 doses, should we recognize partial LTV or keep all revenue in the model? 5. How do we think about an acceptable payback period right now? We need clarity on what’s considered a healthy LTV:CAC timeframe. For some products we recoup immediately, but what’s the trade-off for bringing in revenue up front? 6. What retention tactics have been used in the past that likely improved LTV? We want to capture a list of past interventions (e.g., SMS, onboarding, provider outreach) and whether any were effective. 7. What are all the growth tests we’ve tried and their results? Helps avoid repeating tests and provides context for interpreting payback curve shifts. 8. Have buyer personas ever been explicitly validated? We currently infer personas based on product type, but haven’t confirmed via survey data or CRM fields. 9. What are there any known attribution gaps we should account for? Currently no clear red flags, but we may be missing view-through conversions or stronger iOS attribution.
CRO Funnel Analysis
OBJECTIVE ANALYSIS
Diagnose where users are dropping off in the signup and first-purchase funnel—from landing page to completed checkout—to optimize the user journey, increase conversion rate, and reduce friction in high-intent acquisition paths. This analysis will support tactical website and onboarding changes across content, UX, pricing presentation, and provider workflows. Key Metrics Active patient count, Plan-specific revenue & COGS, Dose pick-up rate, Doc fees, nCAC, LTV Funnel Mapping & Event Instrumentation Define the full conversion funnel: ● ad_clicked → landing_page_viewed → quiz_started → quiz_completed → checkout_started → checkout_completed → provider_submitted → rx_fulfilled For each funnel stage, capture: ● Timestamp ● Referrer source / UTM parameters ● Device type, browser, and OS ● Product and plan selected (e.g., 1M vs. 3M plan) ● Page load time, quiz length, time-on-page metrics Drop-Off Rate Calculation Quantify conversion rates between each step Compare baseline conversion % by: ● Acquisition channel (Meta, Google, Affiliate, Direct) ● Product vertical ● Device type (iOS vs. Android vs. Desktop) ● Plan type (1M vs. 3M vs. 6M+) ● Tag and cluster friction points (e.g., timeouts, long quiz completions, price-related exits) INSIGHTS RECOMMENDATIONS High drop-off between quiz start and quiz completion
- Indicates friction from quiz length, question complexity, or unclear value proposition Mobile users abandon checkout at higher rates than desktop
- Suggests friction in mobile payment UX, Build Funnel Drop-Off Monitoring Dashboard - Weekly report showing drop-off rate by funnel stage, device, and product - Set alert thresholds when quiz-completion or checkout rates fall below defined benchmarks
form field behavior, or last-minute hesitation
Average quiz completion time exceeds 3.
minutes
- Long form fatigue or perceived effort without immediate incentive to continue Significant drop-off between checkout start and provider submission
- Patients may fear medical disqualification or
lack clarity on what happens post-payment
- Integrate feedback loop with creative/landing page testing team Shorten and Segment the Quiz Experience
- Move from one long flow to 2–3 short progressive step
- Add progress indicators and save-state functionality
- Insert soft CTAs earlier: “You may qualify” after first 3 questions to increase perceived momentum Build a Conversion Testing Roadmap Prioritize experiments across: ● Quiz flow (length, logic, CTA wording) ● Landing page headlines and plan previews ● Checkout trust elements (testimonials, provider credentials)
Client Questions
- Do we have a clear owner for each stage of the funnel? Helps clarify whether quiz, checkout, and provider approval fall under CRO or other teams 2. Have we defined which funnel steps we prioritize for CRO vs. medical vs. operational optimization? Ensures we’re not attributing drop-offs to UX friction when they may be due to eligibility filtering or provider backlog 3. What are the top 3 hypotheses we believe are driving funnel friction today? Aligns team around shared beliefs and accelerates experimentation 4. Do we currently bucket users into intent tiers based on entry page, quiz answers, or behavioral cues? Could help differentiate between high-LTV prospects and low-intent traffic for targeted CRO
5. Have we A/B tested pricing visibility during quiz vs. post-quiz? Supports decision-making around when to show cost and how it impacts checkout rate 6. Would you like funnel diagnostics delivered weekly as a snapshot or integrated into a testing roadmap? Determines whether CRO reporting should be standalone or folded into a broader experimentation engine
SKU Portfolio Diversification
OBJECTIVE ANALYSIS
Broaden Eden Health's product portfolio
beyond isolated wellness launches to enable
a sustainable, year-round health eCommerce
ecosystem. Establish a repeatable strategy
for identifying, validating, and scaling new
SKUs across therapeutic, wellness, and
adjacent lifestyle categories
Key goals:
● Smooth revenue volatility through
cross-seasonal SKUs
● Launch products that leverage Eden’s
existing clinical, fulfillment, and
telehealth infrastructure
● Create internal frameworks to
evaluate SKU feasibility, market
demand, and clinical alignment
● Unlock adjacent verticals (e.g., sleep,
metabolic health, skin, stress) that
align with Eden’s wellness brand
Build a criteria-based matrix to assess
new SKU ideas:
- Clinical rationale (provider-driven or patient-requested
- Fulfillment feasibility (3PL, cold-chain, pharmacy compounding)
- Regulatory friction (compounded vs. supplement vs. FDA Rx)
- Brand alignment (whole-body wellness vs. niche performance) Leverage Existing Infrastructure Already Built for RX Therapies
- Telehealth consult workflows
- Informed consent model
- Compounding pharmacy partners
- Lab testing & onboarding pipelines (if applicable) Track and Validate Consumer Interest - Benchmark against Ro/Hims/Modern Age to track positioning, product bundles, onboarding journeys, and pricing models
- Search trends (Google/Amazon/TikTok)
- Drop-off reasons in existing product flows INSIGHTS RECOMMENDATIONS Eden has clinical and fulfillment infrastructure to launch a broader SKU portfolio, but no clear internal framework to qualify or reject new ideas
- Many teams operate on intuition or Create Testing & Launch Framework
- Signal Validation (waitlists, blog interest, pre-quiz flows)
- Operational Feasibility (supply chain, pharmacy onboarding, legal)
anecdotal demand rather than quantified,
repeatable launch logic
Certain categories (sleep, metabolic, aging,
skin) consistently appear in patient queries or
search trends
- These represent low-friction expansion paths with strong wellness overlap and retention potential Current product testing is too slow and ad hoc
- Eden could unlock faster SKU validation
through pre-launch waitlists, A/B tests on
PDPs, and upsell modules inside existing
product journeys
- Soft Launch (email campaigns, limited availability, upsell pilot) Develop Reusable Launch Playbooks Create standardized briefs for:
- Clinical review and risk assessment
- Provider scripting and onboarding
- Packaging, PDP copy, and email flows
- Pricing, bundling, and promo testing
Client Questions
1. Do we have a clear process for proposing and vetting new SKU ideas cross-functionally? 2. Which team owns the final go/no-go decision on SKU launches?
Product Development Time-to-Listing Optimization
OBJECTIVE ANALYSIS
Map and evaluate the full lifecycle of how
new SKUs are sourced, reviewed, built, and
launched on Eden's platform — from concept
to site live — in order to reduce
time-to-revenue, increase team alignment,
and operationalize a repeatable SKU launch
process.
Today, SKUs are launched through a mix of:
● Internal new product development
(clinical innovation, compounding)
● Bundles or extensions from existing
SKUs
Identify Sources of SKU Intake
● New compounded products from
Medical/Clinical
● Repositioned or bundled SKUs from
Product Ops
● Strategic partnerships (e.g.
white-labeled NAD+, partner labs)
● Reactivated SKUs or promos from
Growth or CX
Map SKU Launch Dependencies
● Clinical: Provider scripting, dosing
● Strategic partnerships with OEM or wholesale brands However, there is no standardized SKU intake process across departments. This leads to: ● Long lead times from concept to listing ● No visibility into owner handoffs ● Bottlenecks in asset creation, compliance, or pharmacy onboarding guidance, contraindications ● Legal/Compliance: Informed consent, disclaimers, state restrictions ● Pharmacy Ops: Fulfillment readiness, labeling, shelf stability ● Product Marketing: PDP copy, benefits messaging, SEO, pricing tiers ● Engineering: Shopify configuration, availability logic, analytics Audit Time-to-Launch for Recent SKUs ● From idea intake to first revenue: ○ Sermorelin ODT ○ GLP-1 refills and promos ○ Supplement SKUs and peptide stacks ● Identify friction points: where were delays logged? Which assets or teams stalled progress? Benchmark Ideal Time-to-Listing ● For recurring SKU types (e.g. NPD peptides, partner products), define expected SLA: ○ MVP soft launch = 2–3 weeks ○ Full launch (with email, SEO, upsells) = 4–6 weeks INSIGHTS RECOMMENDATIONS Time-to-listing varies wildly depending on SKU source and owner Some SKUs take 2 weeks; others take 2+ months due to unclear responsibilities or missing inputs Bottlenecks often occur at the compliance → marketing handoff Legal approvals delay final messaging, disclaimers, and launch-ready assets No centralized system tracks SKU launch progress Teams rely on async Slack threads, Notion pages, or offline PM ownership Centralize SKU Launch Workflow Create a shared SKU Launch Tracker with required fields: ● Source of intake (Clinical, Growth, Partnerships, etc.) ● Launch owner ● Target date / SLA ● Dependencies and blocking teams ● Status: Idea → Draft → Asset Prep → Legal → Fulfillment → Site Live Define Launch Tiers and SLAs ● Tier 1: Rebundles / simple PDP edits (1 week)
● Tier 2: New compound SKUs w/
existing ingredients (2–3 weeks)
● Tier 3: Fully net-new product (4–
weeks)
Create Product Status Visibility for
Stakeholders
● Weekly or bi-weekly SKU standup:
align on blockers, go/no-go decisions
● Dashboard view of upcoming
launches with red/yellow/green
statuses
Client Questions
-
Do we have a single source of truth for tracking SKU launches? If so, where does it live (Notion, Airtable, internal tools), and who maintains it?
-
What teams are typically involved in a new SKU launch, and who owns the final go-live? Is ownership always consistent (e.g., Medical for clinical SKUs, Ops for vendor SKUs), or does it vary case-by-case?
-
Have we defined service-level expectations (SLAs) for different types of launches? Example: Are Tier 1 edits expected to ship in under 1 week, while Tier 3 NPDs get a longer window?
-
What are the most common blockers or delays we’ve seen in recent launches? Is it legal approval, pharmacy coordination, asset creation, or unclear handoffs?
-
Is fulfillment readiness (e.g., pharmacy logistics, inventory levels) consistently validated before a launch is approved? Do we have SLAs or gating criteria from Pharmacy Ops?
-
Is there a standardized intake brief for launching a new SKU? If not, would it be helpful to formalize this across departments?
-
Do we capture post-launch performance or feedback in a structured way? Are there retros on what went well / what delayed go-live? Where are these documented?
-
What does leadership care about most in SKU velocity? Is the focus on faster experimentation (e.g., soft launches), maintaining compliance, or minimizing rework and re-dos?