Telehealth Analytics: Attribution, LTV, and the Metrics That Actually Matter

Here's a scenario we see regularly: a telehealth operator spending $50,000-$100,000 per month on paid acquisition across Google, Meta, and TikTok with no reliable way to answer two basic questions: Which channel produces patients who stay the longest? And what is the actual revenue per patient by source?

Without attribution (the ability to trace a paying, retained patient back to the ad, campaign, and channel that acquired them), every dollar spent on marketing is a guess. You might be pouring money into a channel that produces high checkout conversion but terrible 90-day retention, while starving a channel that produces lower volume but significantly better LTV.

Why Telehealth Attribution Is Harder Than E-Commerce

In standard e-commerce, the conversion event happens at checkout and the analytics story is simple: click → purchase → revenue. In telehealth, the journey is longer and the value is distributed across months or years of subscription payments.

• The 'conversion' is a subscription start, not a one-time purchase, and the true value isn't known until the patient churns
• The checkout flow may span multiple sessions (ad click on Monday, intake on Wednesday, payment on Friday)
• HIPAA constrains which patient data can be passed to advertising platforms for optimization
• The most important metric (LTV by source) takes months to calculate, but ad spend decisions happen weekly

The 6 Metrics Telehealth Operators Should Track

1. Patient Acquisition Cost (CAC) by Channel

Total spend on a channel divided by the number of patients acquired from that channel. This is the most basic metric, but it's meaningless without LTV context. A $300 CAC from Meta might be better than a $150 CAC from Google if Meta patients retain twice as long.

Industry benchmarks: GLP-1 programs see CAC ranging from $200-$500+ depending on competition and geography. Non-GLP-1 verticals (TRT, dermatology, mental health) typically see $100-$250. The overall target CAC for a sustainable telehealth business is whatever produces a 3:1 or better LTV:CAC ratio.

2. LTV:CAC Ratio by Channel

This is the north star metric. It tells you whether each channel produces profitable patients. A ratio of 3:1 or higher indicates healthy unit economics. Below 2:1, you're likely losing money on that channel after accounting for cost of care, platform fees, and operations.

Calculate LTV as: (average monthly revenue per patient) × (average subscription lifetime in months) × (gross margin percentage). Then divide by the CAC for each channel. This requires clean attribution from ad click all the way through months of subscription payments.

3. Checkout Conversion Rate by Device and Source

What percentage of patients who start your checkout complete payment? Break this down by device type (mobile vs. desktop) and traffic source. You'll typically find significant variation. Mobile checkout conversion is often 10-20% lower than desktop, and organic traffic converts better than paid.

4. Time to First Fill

For medication-based programs, the time between checkout completion and the patient receiving their first shipment is a critical experience metric. Every day of delay is a churn risk. Benchmark: under 5 days from checkout to delivery for medication programs.

5. 30/60/90-Day Retention by Cohort

Cohort-based retention tells you whether your patient experience is improving over time. If your February cohort retains better at 90 days than your November cohort, the changes you made between those months are working. Track by acquisition channel, plan type, and whether the patient added any upsells.

6. Revenue Per Patient (RPP)

Total revenue generated per patient across their lifetime, including subscription payments, add-ons, lab panels, and upsells. This metric combined with CAC tells you the complete unit economics story. Track it by channel to understand which sources produce the most valuable patients.

Building the Attribution Pipeline

Clean attribution requires data to flow from the first ad click all the way through months of subscription payments. Here's the technical pipeline:

1. Capture UTM parameters and click IDs at landing: Store UTM source, medium, campaign, and platform click IDs (gclid, fbclid) in a first-party cookie or session storage.
2. Pass attribution data through checkout: When the patient completes payment, attach the stored attribution data to the patient record in your CRM and billing system.
3. Fire server-side conversion events: Send conversion data back to ad platforms via their Conversions APIs (Google, Meta CAPI). Server-side events are more reliable than browser-based pixels, especially with cookie restrictions.
4. Connect billing events to attribution: Each subscription payment, plan change, and churn event should be traceable back to the original acquisition source.
5. Build LTV reports by source: Aggregate revenue by acquisition source at 30, 60, 90, and 180-day intervals to understand true channel performance.

HIPAA and Analytics: What You Can and Can't Track

HIPAA constrains telehealth analytics in ways that standard e-commerce doesn't face. You cannot send protected health information (PHI) to advertising platforms or most analytics tools without a BAA and proper de-identification.

• Never send patient names, email addresses, or health conditions to ad platforms directly
• Use hashed identifiers for server-side conversion events
• Analytics tools that process PHI (like a CRM that stores health data alongside analytics) need BAAs
• Google Analytics and similar tools should only receive de-identified, non-PHI events
• Consider HIPAA-compliant analytics platforms (PostHog, Segment with proper configuration) for patient-level data

The Bottom Line

Analytics infrastructure isn't glamorous, but it's the difference between scaling profitably and scaling into a loss. The operators who know their LTV by channel, their retention by cohort, and their true CAC after accounting for churn are the ones making smart growth decisions. Everyone else is guessing.

Build attribution into your checkout and portal from day one. Invest in server-side tracking. Calculate LTV by source, not just overall averages. And make sure your analytics infrastructure respects HIPAA while still giving you the data you need to grow.