v2.0.0 to v2.1.0

+ Freemium and free trial conversion optimization — model selection with

+ full-funnel math, activation design, paywall placement, PQL scoring, and

+ benchmark-anchored experiment planning. Use when optimizing freemium

+ conversion, designing free-to-paid upgrade paths, or choosing between

+ freemium and free trial models. Triggers on: "freemium optimization",

+ "free trial conversion", "PQL scoring", "activation flow", "paywall design",

+ "freemium to paid".

+ version: "2.1.0"

+ - "Kyle Poyar (Growth Unhinged) + ChartMogul + ProductLed — 2026 Free-to-Paid Conversion Report"

+ - "ProductLed — State of B2B SaaS 2025 (446 companies)"

+ - "OpenView Partners — 2023 Product Benchmarks (with Pendo)"

+ - "Wes Bush — Product-Led Growth / Product-Led Onboarding"

+ - "Dharmesh Shah (HubSpot) — Freemium as Flywheel Attract"

+ is where most PLG companies die — and the common mistake is optimizing

+ conversion rate in isolation rather than full-funnel math. Kyle Poyar's 2026

+ Free-to-Paid Conversion Report (200 B2B products, with ChartMogul and

+ ProductLed) shows that freemium drives roughly 90 signups per 1,000 visits vs

+ 45 for free trials, meaning a lower per-signup conversion rate (8-12% GREAT for

+ freemium vs 10-15% for no-CC trial) can still produce more customers

+ end-to-end. Optimizing conversion rate in isolation causes teams to abandon

+ freemium prematurely.

+ The second mistake: skipping activation instrumentation and jumping straight

+ to conversion optimization. ProductLed's State of B2B SaaS 2025 (446

+ companies) found only 34% of companies track activation — but you cannot

+ improve conversion from an experience users have not completed. OpenView's

+ 2023 Product Benchmarks (with Pendo) found that tracking PQLs/PQAs increased

+ the likelihood of fast growth by 61% — the single most influential lever in

+ their study.

+ ## When to Use

+ - "Optimize freemium conversion"

+ - "Design free-to-paid upgrade path"

+ - "Set up PQL scoring"

+ - "Improve activation flow"

+ - "Choose between freemium and free trial"

+ - "Reduce time-to-value"

+ - Triggers on: "freemium optimization", "free trial conversion", "PQL scoring",

+ "activation flow", "paywall design", "freemium to paid"

+ ## Authoritative Foundations

+ - **Kyle Poyar (Growth Unhinged) + ChartMogul + ProductLed — 2026

+ Free-to-Paid Conversion Report (200 B2B products).** Primary benchmark

+ source for this skill. Provides median and GOOD/GREAT conversion rates by

+ model type and credit-card-gate status, and the full-funnel signup-rate data

+ that makes model choice a visits-to-customers calculation rather than a

+ conversion-rate comparison. Key finding: 57% of products lead with free

+ trial vs 26% freemium. See `references/framework-notes.md` for the full

+ benchmark table by model and ACV bracket.

+ - **ProductLed — State of B2B SaaS 2025 (446 companies).** Source for PQL

+ adoption and activation tracking data. Only 24-25% of PLG companies use

+ PQLs, but PQL users see roughly 3× higher free-to-paid conversion. Only 34%

+ track activation. Companies with "highly intentional" free models (8+/10)

+ report 57% better free-to-paid conversion than unintentional ones (3 or

+ below). Intentional design means the free tier showcases core value, creates

+ natural upgrade paths, has deliberate value limits, and makes upgrade

+ benefits visible inside the free experience.

+ - **OpenView Partners — 2023 Product Benchmarks (with Pendo, ~1,000

+ participants).** Tracking PQLs/PQAs increased likelihood of fast growth by

+ 61% — the single most influential lever measured. Outreach to free signups

+ adds 28%; a dedicated growth team adds 17%; over-relying on paid acquisition

+ is inversely correlated with fast growth.

+ - **Wes Bush — Product-Led Growth / Product-Led Onboarding.** Provides the

+ bowling-alley onboarding model — guide rails (in-app prompts) and bumpers

+ (email nudges) keep users on the path to their activation moment. The

+ principle that the free tier must deliver the core value experience, and that

+ time-to-value reduction is the primary activation lever. Used in Phase 2 to

+ design the activation flow.

+ - **Dharmesh Shah (HubSpot) — Freemium as Flywheel Attract.** Free CRM/tier as

+ top-of-flywheel acquisition; upgrade on seats, automation, integrations. Pair

+ with `inbound-triage` for PQL→SQL handoff. `references/dharmesh-shah-hubspot-inbound.md`.

+ ### Phase 1: Model Decision with Full-Funnel Math

+ Before optimizing, select (or audit) the right model using full-funnel math —

+ not conversion rate alone.

+ | Model | Signups / 1,000 visits | GOOD conv rate | GREAT conv rate | When to use |

+ |---|---|---|---|---|

+ | Freemium (forever free) | ~90 | 3-5% | 8-12% | Core value deliverable in free; large TAM; viral/network effects |

+ | Free trial (no CC) | ~45 | 4-6% | 10-15% | Complex product; hands-on trial needed; mid-market motion |

+ | Free trial (CC required) | ~15-20 | 25-35% | 50-60% | High-intent buyers; self-serve checkout; lower volume acceptable |

+ Source: Poyar/ChartMogul/ProductLed 2026. The CC-gated model shows the highest

+ conversion rate but suppresses signup volume 4-6×. Run the full-funnel math

+ (signups × conversion rate = customers per 1,000 visits) before choosing it.

+ See `references/framework-notes.md` for the benchmark table by ACV bracket.

+

+ **Model intentionality test (ProductLed 2025):** Score your free model 1-10 on

+ intentionality. Companies scoring 8+ report 57% better free-to-paid conversion

+ than those scoring 3 or below. The test: does the free tier showcase core

+ value? Are value limits deliberate? Are upgrade benefits visible inside the

+ free experience?

+

+ **Define the activation moment before measuring anything else.** Activation

+ is the point where a free user experiences your product's core value for the

+ first time. Without a named definition, there is no baseline and no conversion

+ target.

+ 1. Name the activation event as a specific product event — for example:

+ "User completes first export," "Two team integrations connected,"

+ "Live-data report viewed."

+ 2. Measure current time-to-activation (median and p90). Target: median under

+ 7 days.

+ 3. Apply Wes Bush's bowling-alley model: in-app guide rails for the critical

+ path, email bumpers for users who stall between steps.

+ 4. Fill empty states with sample data showing what the user will see at

+ activation — empty states are the leading cause of early drop-off.

+ 5. Reduce steps-to-activation: every screen added between signup and the

+ activation moment reduces completion rate.

+ Track activation before running conversion experiments. Only 34% of PLG

+ companies currently instrument activation (ProductLed 2025). If you are not

+ in that 34%, instrument first.

+ ### Phase 3: Paywall Design and Placement

+ **Paywall timing:** After activation, before habit. A user who has experienced

+ core value but is not yet a daily user is at peak upgrade motivation. Show the

+ paywall before activation and they leave; after habit is formed, urgency

+ diminishes.

+ **Paywall triggers — map to product events, not just calendar time:**

+ - Usage limit reached (volume-based gate)

+ - Feature in paid tier requested (feature gate)

+ - Team invite sent (collaboration requires paid tier)

+ - Time-based trial expiration (fallback trigger only)

+ - Export/share/publish (output-based gate)

+ **Free tier design principle:** Enough to love, not enough to stay forever.

+ Free must deliver the core value experience so users activate and form intent

+ to buy, but leave a natural job undone that the paid tier completes.

+ ### Phase 4: PQL Scoring and Routing

+ OpenView 2023: tracking PQLs/PQAs increased likelihood of fast growth by 61% —

+ the highest-impact lever in the benchmark. Despite this, only 24-25% of PLG

+ companies currently use PQLs (ProductLed 2025).

+ **Default scoring skeleton — calibrate weights to your product:**

+ | Signal | Weight |

+ |---|---|

+ | Activation event completed | 30 |

+ | Usage depth (key feature engagement) | 25 |

+ | Usage frequency (days active / last 14 days) | 20 |

+ | ICP fit (firmographic match) | 15 |

+ | Expansion signals (team invites, integrations connected) | 10 |

+ **Routing thresholds:**

+ - Score ≥ 70: immediate sales or high-touch outreach (PQL)

+ - Score 50–69: automated nurture sequence + SDR monitoring

+ - Score < 50: focus on activation, not conversion

+ A PQL model that scores users but does not trigger a defined action within an

+ SLA delivers no value. Define routing and response SLAs before launch. See

+ `references/framework-notes.md` for PQL trigger examples by product type.

+ ### Phase 5: Experiment Backlog

+ Structure experiments against full-funnel stages, ordered from top of funnel

+ to bottom:

+ 1. **Activation experiments:** Reduce time-to-activation; test guided vs

+ self-directed onboarding; test sample-data pre-fill; test step reduction.

+ 2. **Paywall experiments:** Test trigger placement (usage limit vs feature gate

+ vs time); test paywall copy and upgrade-value framing; test pricing page

+ layout.

+ 3. **PQL routing experiments:** Test outreach timing (immediate vs 24-hour

+ delay post-score); test channel (email vs in-app vs sales call).

+ 4. **Model experiments:** If evidence supports it, test CC-gate vs no-CC gate

+ on a traffic split — measure customers-per-1,000-visits as the primary

+ metric, not conversion rate.

+ ## Output Format

+ Freemium optimization plan containing: model decision with full-funnel math

+ (signups × conversion rate = customers per 1,000 visits) benchmarked against

+ Poyar/ChartMogul data; activation definition with named product event,

+ instrumentation plan, and time-to-activation baseline; paywall trigger map with

+ product-event-to-gate assignments and timing rationale; PQL scoring model with

+ signal weights, routing thresholds, and response SLAs; benchmark-anchored

+ experiment backlog ordered by funnel stage (activation first, then conversion,

+ then routing).

+

+ - [ ] Model choice is justified with full-funnel math (visits → signups → conversions → customers), not conversion rate alone

+ - [ ] Activation event is named as a specific product event, not "user engages with the product"

+ - [ ] Paywall placement is after the defined activation event, not at signup

+ - [ ] PQL model has at least 5 signal dimensions with documented weights and routing thresholds

+ - [ ] Every benchmark cited names the model type (freemium vs trial vs CC-gated) and the source report

+ - [ ] Experiment backlog is ordered by funnel stage with activation experiments first

+ ## Common Pitfalls

+

+ 1. **Optimizing conversion rate instead of full-funnel customers-per-visit.**

+ A CC-gated trial shows 50%+ conversion but suppresses signup volume 4-6×;

+ end-to-end customer count can be lower than freemium. Fix: always run the

+ full-funnel math before declaring a model "better."

+ 2. **Skipping activation instrumentation.** You cannot improve conversion from

+ an experience users have not completed. Fix: define and instrument the

+ activation event before running any conversion experiment — only 34% of PLG

+ companies currently do this (ProductLed 2025).

+ 3. **Free tier that replaces paid.** If free delivers 90% of what paid delivers,

+ users do not upgrade. Fix: apply the intentionality test — free showcases

+ core value, paid completes the job; deliberate value limits and visible

+ upgrade benefits inside the free experience are required.

+ 4. **PQL scoring without routing.** A model that scores users but triggers no

+ action within a defined time window delivers no revenue lift. Fix: define

+ routing thresholds and response SLAs before launch; OpenView confirms

+ outreach to free signups adds 28% to fast-growth likelihood.

+

+ ## Execution Artifacts

+

+ - `references/framework-notes.md` — Conversion benchmarks, PQL triggers, activation template

+ - `templates/output-template.md` — Deliverable shell for agent output

+ - `scripts/check-output.py` — Lightweight deliverable validator

+

+ **Lifecycle (Acquisition → Activation):** `references/activation-playbook.md` · `references/gtm-lifecycle-stages.md` · Pattern 18 in `using-gtm-skills`

+

+ **Canonical lifecycle (repo root):** `references/gtm-lifecycle-stages.md` (Acquisition, Activation) · `references/activation-playbook.md` · `references/lifecycle-metrics-by-stage.md`

+

+ - plg-strategy, growth-experimentation, onboarding-flow, pricing-psychology, a-b-testing