The Views-to-Signups Model
A consumer growth funnel is a chain of conversions. Each stage passes a fraction of people to the next. Reach is loud and free at the top; revenue is quiet and real at the bottom. The job of this model is to make every step visible, so you can forecast how X views become Y signups become Z revenue: and find the one stage quietly draining the whole machine.
This is the consumer equivalent of a paid CAC model. The difference is structural: paid resets to zero the day you stop spending. Organic compounds, because content keeps earning reach after you publish it. So your goal is not just to fill the funnel today, but to model a funnel that keeps filling itself.
A note on the algorithm before you start: distribution platforms optimize for what the viewer feels, not for what you intended. Watch time, replays, saves, and shares are votes the viewer casts. That is why the top of this funnel is won or lost in the first three seconds, the moment attention is either captured or abandoned.
The Five Stages
| Stage | What it measures | Typical conversion (to next stage) | What drives it | How to measure it |
|---|---|---|---|---|
| 1. Reach | Impressions / views | , (top of funnel) | Hook strength, first 3 seconds, retention curve, share rate | Native analytics: views, average watch time, share/save counts |
| 2. Profile / Link | Profile visits or link taps | 1 to 5% of views | A reason to learn more; a clear, single bio link or pinned CTA | Profile-visit count (native); link clicks (Linktree, UTM, app store referrer) |
| 3. Install / Signup | Installs or account creations | 20 to 40% of link taps | Store page or landing page clarity; promise matches the content | App store console (impressions to installs); landing page analytics |
| 4. Activation | Reached the "aha" moment | 20 to 40% of installs | First-run experience; time-to-value; one obvious next action | Product analytics event (e.g., first key action completed) |
| 5. Paying | First payment / subscription | 2 to 5% of activated users | Pricing clarity, paywall timing, demonstrated value before the ask | Billing system; checkout funnel events |
Ranges are typical industry bands, not promises. Your own numbers will differ by category, price point, and platform. Measure yours, then replace these.
How to Read Each Stage
Reach → Profile. Views are not the prize; they are the raw material. A high view count with near-zero profile visits means the content entertained but gave no reason to go further. The fix is a content-to-offer bridge: the video earns attention, the last frame or caption earns the click.
Profile → Install/Signup. This is where most consumer funnels leak hardest. The viewer was curious enough to tap, then hit a store page or landing page that did not match the promise. Make the first thing they see repeat the promise that brought them.
Install → Activation. Activation is the real top of your retention curve. An install that never reaches the "aha" moment is a vanity number. Define one concrete event that means "this person felt the value," and measure to it.
Activation → Paying. Conversion to revenue depends on showing value *before* the ask. Premature paywalls convert the few and lose the many.
The Fillable Worksheet
Fill the rate column with your own measured numbers. Then the model forecasts downward.
| Stage | Your input | Your conversion rate | Result |
|---|---|---|---|
| Monthly views | _______ | , | _______ |
| Profile visits / link taps | (views × rate) | ____% | _______ |
| Installs / signups | (taps × rate) | ____% | _______ |
| Activated users | (installs × rate) | ____% | _______ |
| Paying users | (activated × rate) | ____% | _______ |
| Revenue | (paying × ARPU) | $____ ARPU | $_______ |
Forecasting rule: multiply straight down. Views × R1 × R2 × R3 × R4 × ARPU = revenue. Change one rate and watch the bottom line move, that tells you which stage is worth fixing first.
Worked Example (EXAMPLE, placeholder numbers, not a result)
| Stage | Input | Rate | Result |
|---|---|---|---|
| Monthly views | 1,000,000 | , | 1,000,000 |
| Profile visits / link taps | , | 3% | 30,000 |
| Installs / signups | , | 30% | 9,000 |
| Activated users | , | 35% | 3,150 |
| Paying users | , | 4% | 126 |
| Revenue | $40 ARPU | , | $5,040 / mo |
Read it as a sentence: one million views, modeled at these rates, returns roughly 9,000 signups and about $5,040 in monthly revenue. Double any single rate and re-run it. Doubling the profile-tap rate from 3% to 6% doubles everything below it, 252 payers, ~$10,080. That is the leverage of fixing the right stage.
The One Metric to Watch
Watch the Reach → Profile rate first. In most consumer funnels this is the biggest leak, because it is where attention dies before felt value is delivered. Millions of views with a sub-1% tap-through rate is the most common failure pattern there is, the content got watched but never asked anyone to move.
Diagnose it by stage:
- If **Reach → Profile** is low, your content entertains but does not bridge to an offer.
- If **Profile → Install** is low, your store or landing page breaks the promise that earned the tap.
- If **Install → Activation** is low, people arrive but never feel the value, fix first-run experience.
- If **Activation → Paying** is low, you are either asking too early or not showing enough value before the ask.
Find the stage with the steepest unexpected drop. That single number, multiplied through everything beneath it, is where your next month of revenue is hiding. For reference, an organic engine running well can compound to real scale, one Series A gaming company reached 50M organic views in five months at $0 in ad spend, but only because every stage below the views was instrumented and fixed.
*You can run this model yourself today, pull your numbers, fill the worksheet, and find your steepest drop. If you would rather see it built on your actual product, send us a link and we will send back a teardown in three days.*