PLG Viral Loop Analyzer — Find the Weakest Link in Your Viral Product
Decompose your loop into five multiplicative stages, identify the single bottleneck dragging K down, and see what K becomes if you fix that one stage. Six artifact archetype benchmarks built in. No signup.
Last reviewed: April 2026
K-Factor
🪦 Broken
Cycle
14d
To 1M
50.0 yrs
Fixed K
0.03
5-Stage chain · thinnest link is your bottleneck
🪦 Weakest link
Activation Rate
Current
30%
Top decile
77%
Signups never become inviters — the loop never closes.
Fix this → projected K lift
+0.02
0.01 → 0.03 (Broken)
Pick your viral artifact archetype
Different archetypes have measurably different stage rates — picking the wrong one obscures your real bottleneck.
Your loop rates
Stage breakdown · per-link contribution
Invite
22%
Median 38%
Top decile 53%
↘ Below median
Lift if fixed
+0.01
Deliver
92%
Median 95%
Top decile 98%
↘ Below median
Lift if fixed
+0.00
Open
32%
Median 42%
Top decile 59%
↘ Below median
Lift if fixed
+0.01
Convert
18%
Median 28%
Top decile 42%
↘ Below median
Lift if fixed
+0.01
Activate
30%
Median 55%
Top decile 77%
🪦 Bottleneck
Lift if fixed
+0.02
Each stage multiplies into K. The "lift if fixed" column shows how much K would move if that one stage hit the top decile of your archetype — without changing any other rate. The weakest link is whichever stage shows the largest lift, not necessarily the lowest absolute rate.
Diagnosis
Signups never become inviters — the loop never closes
Your activation rate is 30% versus a 55% archetype median, and activation is the loop-closing stage. Only activated users invite others, so this stage caps every upstream rate. Re-add the invite prompt to onboarding step 1, not step 7 — show the user that other people are part of the value before you teach them anything else. This is the stage with the heaviest weight in the report card for that exact reason.
Findings audit
critical
Loop is broken — paid acquisition is doing all the work
K = 0.01 means every cycle leaks more users than it generates. Fix the weakest link before scaling paid spend, or you'll burn CAC subsidizing a chain that doesn't close.
6-dimension report card
Invite Trigger Strength
Active users rarely send any invite. Fix the trigger first.
Delivery Health
Sender reputation is healthy — invites reach inboxes.
Open Persuasion
Subject line and from-name are pulling their weight.
Conversion Frictionlessness
Friction between click and signup is bleeding the funnel.
Activation Pull-In
Signups never invite. The chain stops at activation.
Network Effect Density
Loop is too thin to compound — paid will carry growth.
Share this exact loop
What makes a product viral (and what doesn't)
The shorthand definition is a viral product is one where existing users acquire new users faster than you lose them. The precise definition is K > 1.0, where K is the multiplicative product of five stage rates: how often active users send invites, how many invites they send, how many invites are delivered, how many recipients open, how many openers convert, and how many converters activate into senders themselves. Slack at peak hit roughly 1.5 by closing all five stages well; Calendly maintains 0.4–0.6 with one short-cycle stage carrying the others; Loom hovers near 0.6 because video shares open well but convert weakly.
Three patterns reliably mistake retention for virality. First, low churn with strong word-of-mouth is not virality if no in-product invitation event exists — that is brand strength. Second, paid referral programs that pay users to invite are viral on paper but uneconomic in practice once the referral payout exceeds CAC. Third, network effects without a loop produce stickiness, not growth. A product virality diagnostic should always start with the question of whether an in-product invite event exists at all; if it does not, the rest of the math is moot.
The diagnostic above frames every check around that question. The chain is the loop, the weakest link is the stage you should fix first, and the lift simulator tells you whether fixing it is worth the engineering cost. Sub-viral loops can still be valuable; a K of 0.6 effectively cuts blended CAC by 60% even though it never compounds on its own.
The 5 stages of every viral loop
Every loop runs through the same five stages, in the same order, with the same multiplication. Skip a stage and the loop is broken. Underperform a stage and the loop is sub-viral. The stages are: invite-sent rate (the percentage of active users who send any invite per cycle), invites per sender (how many they send), delivery rate (the percentage that reach an inbox rather than a spam folder), open rate (the percentage of delivered invites opened), conversion rate (the percentage of openers who complete signup), and activation rate (the percentage of signups who themselves become inviters and close the loop).
K = inviteSentRate × invitesPerSender × deliveryRate × openRate × conversionRate × activationRate
The arithmetic is multiplicative, which means moving any single stage from 30% to 50% lifts K by the same proportion (1.67×) — but it also means a 10% rate at any one stage acts as a hard cap on everything upstream. A team where 10% of signups activate cannot produce a viral loop no matter how brilliant the invite email is, because the loop never closes. That is why activation pull-in carries 25% weight in the 6-dimension report card while delivery only carries 10%.
In the diagnostic above, the chain visualization makes this multiplicative property literal. Each link is sized by the number of users surviving to that stage, so you can see the dropoff happen visually rather than read it from a table. The thinnest link is your bottleneck; the chain reads at a glance the same way a plumbing diagram reads pressure loss across pipe sections.
Why finding the weakest link beats optimizing every stage
Most growth teams optimize evenly across the funnel — a 5% improvement on invite open rate this quarter, a 5% improvement on signup conversion next quarter. That is the wrong approach for a multiplicative chain. If your chain reads 38% / 95% / 11% / 24% / 32%, your weakest link (open rate at 11%) holds 70% of the available K-factor lift on its own. Optimizing the other four stages by 20% each produces less total K movement than fixing that single stage.
The diagnostic above ranks each stage by the K-lift it would produce at top decile, not by the absolute rate. A 5% rate where the archetype median is also 5% is fine. A 35% rate where the archetype median is 60% is a bottleneck even though it sounds healthy in isolation. Stage rates only mean anything against archetype-specific reference points, which is why picking the archetype correctly comes before reading any stage as good or bad.
When the lift simulator shows a single stage producing more than 0.30 K of headroom, the findings panel flags it as concentrated lift. That is the signal to drop other experiments and concentrate every available resource on the bottleneck. If the lift is spread evenly across stages, broader optimization wins; if it concentrates in one stage, surgical optimization wins.
Six viral artifact archetypes (and why your archetype matters)
The artifact you ask users to share — a team invite, a doc, a video, a calendar link, a design file, a folder — sets the ceiling on every stage. A Calendly-style reverse-invite produces an invite-sent rate of 65% almost automatically because the invite is the use case; a Notion-style doc share produces 22% because most docs stay private. The archetype median rates the diagnostic ships with reflect what is realistic given the artifact, not what is theoretically possible.
Archetype
Slack-style team invite
Dropbox-style folder share
Calendly-style reverse invite
Notion-style doc share
Loom-style video share
Figma-style multiplayer
Median open rate
42% · cycle 7d
38% · cycle 14d
70% · cycle 1d
35% · cycle 10d
48% · cycle 5d
55% · cycle 3d
Picking the wrong archetype is itself a finding. A team treating their video-share product like a team-invite product will benchmark against Slack's 28% conversion rate and conclude their 12% rate is broken — when, against the Loom-style video archetype median, that 12% is exactly on the line. The diagnostic warns when your stage profile fits a different archetype better than the one you picked. This is the bottom up saas reality: artifact dictates physics.
Viral loops vs network effects: where one ends and the other begins
A viral loop produces growth. A network effect produces value. The two are often confused because the strongest products have both, but the mechanics are different and the diagnostic implications are different. Slack's viral loop is "invite teammates and the workspace works better for the inviter" — that is acquisition. Slack's network effect is "every additional teammate increases message density and value" — that is stickiness. Each compounds the other.
For long-term topical context, network effects are the broader category that includes platform effects, two-sided marketplaces, data network effects, and protocol effects — Wikipedia, Visa, Google search, and Bitcoin all leverage network effects without running a viral loop in the K-factor sense. A product can have a strong network effect (LinkedIn) and a weak loop (most LinkedIn invites today are noise), or a strong loop (Calendly early days) and a weak network effect (one Calendly user does not make another's Calendly more valuable).
The diagnostic above measures the loop, not the network effect. If your K is broken but your retention curve is flat, you have a network-effect product without a loop — and a viral loop calculator will not help you. Use the Cohort Retention Calculator to confirm the network-effect side of the equation.
Reading invite-funnel rates: a stage-by-stage benchmark
Generic benchmarks lie. A 35% open rate is great for a Notion-style doc share (median 35%) and mediocre for a Calendly-style reverse-invite (median 70%). The benchmark shipped per stage, per archetype, in this tool reflects published case studies and aggregated PLG operator reporting; the bottom decile is set at roughly 50% of median, top decile at 140% of median, with delivery rate floored harder because anything below 80% indicates an active sender-domain problem.
Stage-specific quick checks: invite-sent rate below 15% means active users do not consider the product collaborative — fix the trigger first. Delivery below 90% means a transactional-email infrastructure problem, not a copy problem. Open rate below 30% with a personalized from-name means subject-line work; with a brand from-name it means re-do the from-name first and the subject line second. Signup conversion below 15% means too many fields between click and account. Activation below 20% means signups never see another user inside the product within their first session.
Each of these checks fires automatically in the findings panel above when its threshold trips. Friction at the invite stage is the most common single failure mode (more than 60% of audited PLG products score below median on invite-sent rate alone), which is why the report card weights invite trigger strength at 20% — second only to activation pull-in.
From sub-viral to viral: the math of fixing one stage
The lift if K crosses 1.0 is not about percentage points; it is about whether growth compounds at all. At K = 0.95 with a 14-day cycle, your user base grows roughly 8% per quarter from organic alone. At K = 1.05 it grows roughly 11% per quarter — and keeps growing in compound for as long as the loop holds. The non-linearity around K = 1.0 is the entire reason chasing the threshold is worthwhile.
Consider a worked example: a SaaS product with 1,000 active users, 22% invite-sent rate, 3 invites per sender, 92% delivery, 32% open, 18% conversion, 30% activation, 14-day cycle. Multiplying through gives K = 0.01 — broken. The weakest link is conversion at 18% versus a Notion-archetype median of 24%. Lifting conversion to top decile (around 36%) only doubles K to about 0.02 by itself, still broken. A product at this baseline needs a complete loop rebuild, not a stage tweak.
The harder lesson lives in the second case. A product running at full Slack-style archetype median (38%, 4, 95%, 42%, 28%, 55%) computes to K = 0.09 — still sub-viral despite every stage looking healthy on its own. Crossing K = 1.0 requires multiple stages at top decile simultaneously: 70% invite-sent, 6 invites per sender, 95% delivery, 60% open, 50% conversion, and 70% activation gets you to roughly 0.84. Bump invites per sender to 8 and K crosses 1.0. That is why true K > 1.0 viral products are rare: the multiplicative chain punishes any below-median stage harshly. The lift simulator above shows whether your specific gap is one experiment, three, or six.
PLG funnel context: where viral loops fit in the broader self-serve motion
The product led growth funnel is the full self-serve path: visitor → signup → activation → habit → expansion → revenue. The viral loop sits inside the visitor → signup edge, while activation sits at the boundary between the loop and the broader funnel. Improvements to activation lift both K (because activated users invite) and broader PLG metrics (because activated users convert to paid).
For a broader scoreboard across all six PLG dimensions — signup, activation, habit formation, expansion, monetization, and net revenue retention — see the PLG Readiness Scorer (Tool 20). It treats this viral loop diagnostic as one input among several rather than the headline metric. The two tools share their activation rate stage and link cleanly: improvements here typically lift the broader PLG score by 8–12 points.
The decision rule is simple. If your K is far below 1.0 and your retention curve is flat, fix the loop first — the diagnostic above is the right starting point. If your K is sub-viral but retention is steep, fix retention first — the loop will not save a leaky bucket. If both are broken, the company has a value problem, not a growth problem, and no calculator on this site is going to help with that.
Frequently Asked Questions
What is a viral product?
A viral product is one whose existing users do most of the work of acquiring new users — every active user produces, on average, more than one additional active user per cycle. The math is captured by the K-factor: K = invites sent × delivery × open × conversion × activation. When K crosses 1.0 the user base compounds on its own, when it sits below 1.0 paid acquisition has to subsidize the gap. Slack, Calendly, and Loom are common reference points; their loops typically operate at K between 0.4 and 1.5 depending on stage. Built-in archetype benchmarks above let you compare your numbers against the right reference.
How do you measure product virality?
You measure product virality by decomposing the loop into the five multiplicative stages every viral product runs through: invite-sent rate, invites per sender, delivery rate, open rate, signup-conversion rate, and activation rate. Multiply them and you get K. Example: 22% of active users send 3 invites with 92% delivery, 32% open, 18% conversion, 30% activation → K = 0.22 × 3 × 0.92 × 0.32 × 0.18 × 0.30 = 0.01. That K compares directly to industry archetype medians and tells you exactly which stage is dragging the chain down.
What is a viral loop and how does it work?
A viral loop is the repeating sequence by which one user pulls more users into the product. The five stages are: an active user is triggered to send an invite, the invite is delivered, the recipient opens it, the recipient signs up, and the new signup activates and themselves becomes an inviter. The loop closes only when the new user becomes an active inviter — that final stage is why activation pull-in carries 25% weight in the report card while delivery only carries 10%. Anything that breaks any single stage breaks the entire loop, since they multiply rather than add.
How do you find the weakest link in a viral loop?
Find the weakest link by computing the K-factor lift each stage would produce if it were moved to the top decile of your archetype, then picking the stage with the largest lift. The weakest link is not necessarily the lowest absolute rate — a 10% conversion rate matters more if your archetype median is 30% than a 50% open rate would when your archetype median is 55%. The diagnostic above does this math live: the chain visual above thins and pulses red on whichever single stage holds the most untapped K, and shows you the projected K if that one stage hit top-decile.
What's a good K-factor for a SaaS product?
A K above 1.0 means the loop compounds on its own; below 1.0 it amplifies paid acquisition rather than replacing it. The thresholds the diagnostic above uses: K ≥ 1.5 is hyper-viral (top 1% of PLG products — think Slack at peak), 1.0 to 1.5 is viral and self-sustaining, 0.5 to 1.0 is sub-viral and useful as an amplifier, and below 0.5 is broken. Most B2B SaaS products operate sub-viral; that is fine if K is high enough to meaningfully cut blended CAC. For pure macro-K analysis with growth-curve simulation, the sister K-Factor Virality Calculator is the cross-link below.
How is bottom-up SaaS different from sales-led?
Bottom up saas spreads via individual users inviting their teammates from inside the product, then later converts to paid through team or enterprise tier upgrades. Sales-led SaaS starts with a procurement conversation, an MSA, and a top-down rollout. The viral loop only exists in the bottom-up motion: Slack, Notion, Figma, and Loom all expanded inside companies before any sales rep got involved. If your product never produces an organic invite event, you are running sales-led growth and a viral loop calculator will not help — use the Sales Capacity Planner instead.
What's the difference between a viral loop and a network effect?
A viral loop is an acquisition mechanism: existing users actively invite new users, and the loop produces growth. A network effect is a value mechanism: each new user makes the product more valuable for every existing user, even without any explicit invitation. Slack has both — invites bring in teammates (loop) and each new teammate makes Slack more useful (network effect). LinkedIn has both. Twitter has more network effect than viral loop. The two compound when stacked, but a viral loop without a network effect tends to plateau because users have no reason to stay once invited.
Why does my invite email have a low open rate?
A low open rate on invite emails almost always traces to two things: the from-name reads as a brand instead of a person, and the subject line describes the product instead of the action. "Acme Team invited you to collaborate" performs much worse than "Alex shared a doc with you." Subject lines that name the actual sender and the actual artifact (file name, doc title, calendar event) consistently outperform branded openers. The diagnostic above flags open rate as a bottleneck whenever the rate sits more than 30% below your archetype median.
Can a B2B SaaS product really go viral?
Yes — Slack, Loom, Notion, Calendly, Figma, and Dropbox all reached K above 1.0 in their early years. The trick is that B2B virality runs along organizational lines: one user invites their team, then that team invites cross-functional partners, and the artifact (a doc, a recording, a calendar link, a design file) does the persuading. A viral product in B2B is almost always one where the artifact you create has to be shared to be useful at all. That is why the archetype picker above has six archetypes — each one matches a different kind of artifact and produces measurably different stage rates.
How do I improve my product-led growth funnel?
Improving the product led growth funnel is a question of where the leak is. A viral loop is the acquisition layer, but the broader PLG funnel also includes activation, expansion, and conversion to paid. Use this tool to fix the loop, then move to the PLG Readiness Scorer to grade your broader six-dimension PLG motion (signups → activation → habit → expansion → revenue). The two tools share the activation stage, so improvements to activation pull-in here typically also lift the broader PLG funnel score by 8–12 points.