A 20-26 week installation of Wes Bush's Product-Led Growth methodology (MOAT + UCD + Triple-A + Bowling Alley) for $3-30M ARR B2B SaaS evaluating PLG or stuck on a revenue plateau. Decides motion (free trial / freemium / hybrid / sales-led), redesigns onboarding to first-value, ships a value-based pricing page, installs paywalls + in-product upgrade triggers + self-serve billing, stands up a 35-event PLG taxonomy with PQL scoring, and operates the Triple-A monthly sprint with a Tiger Team and SDR/AE hybrid comp. Reference methodology: https://productled.com/book/product-led-growth. Total scope: 8 modules, 36 sections, 150 tasks, 1058 hours canonical (560-680h typical-customer after gates). 8 feature gates fork the playbook by GTM motion, billing-vendor presence, trust-center status, analytics-stack presence, and sales-team configuration.
Decide the right motion. Output is a written, signed-off MOAT decision doc covering the four Bush axes: Market strategy (dominant/disruptive/differentiated), Ocean conditions (red/blue), Audience (top-down/bottom-up), Time-to-Value archetype (Mission Impossible/Rookie/Veteran/Spoiled). STOP-frame: this is the Standardize step that anchors every downstream module. If MOAT recommends sales-led, the playbook's go/no-go gate routes to the sales-methodology playbook. Source: https://productled.com/book/product-led-growth.
Pull the last 12 months of revenue, funnel, and CAC data; run aha-moment customer interviews; confirm the four PLG triggers (CAC payback >18mo, free-trial conversion <15%, demo-request bottleneck, competitor with free product); map stakeholders for rollout comms. Establishes the baseline scorecard the entire playbook is measured against.
Pull last 12-mo revenue snapshot (ARR, MRR, churn, ARPU)
Pull the last 12-month revenue snapshot covering ARR, MRR, churn, and ARPU as the baseline scorecard the entire playbook is measured against. Reference Bessemer's State of the Cloud benchmark for CAC payback bands, Rule of 40, and Rule of X (https://www.bvp.com/atlas/state-of-the-cloud-2024). Without a documented baseline, the MOAT decision tree has no anchor for the Market-Strategy axis and the Triple-A monthly sprint has nothing to analyze in month one.
Pull current funnel: visitor -> signup -> activation -> paid
Pull the current top-of-funnel through bottom-of-funnel: visitor -> signup -> activation -> paid. This is the second baseline the MOAT decision tree needs (the Time-to-Value axis depends on it) and is also the input to M5 (Bowling Alley) onboarding redesign. Reference Lenny's free-to-paid benchmark survey (https://www.lennysnewsletter.com/p/what-is-a-good-free-to-paid-conversion) for industry-grade comparison points by trial type and customer segment.
Calculate blended + self-serve CAC payback
Compute blended CAC payback and self-serve CAC payback as separate numbers. The MOAT decision tree weighs heavily on this — if blended CAC payback is >18 months, that's one of the four PLG triggers. Reference Bessemer's Rule of X and CAC payback bands (https://www.bvp.com/atlas/the-rule-of-x). Self-serve payback should be <=6 months at scale; blended <=14 months.
Run 8 customer aha-moment interviews
Run 8 customer aha-moment interviews to surface the first-value event candidates that M5 (Bowling Alley) will rigorously test with logistic regression. Use the aha-moment interview guide (5 questions x 8 customers) referenced in Amplitude's TTV / retention research (https://amplitude.com/blog/time-to-value-drives-user-retention). The qualitative output from these 8 interviews seeds the 3 candidate aha events that M5 narrows to one strike.
Confirm PLG triggers (CAC, conv, demo bottleneck, competitor)
Confirm at least one of the four PLG triggers is firing: rising CAC payback >18 months, free-trial conversion <15%, demo-request bottleneck (sales saturated), or competitor offering free product. Reference Bush's trigger framework (https://productled.com/book/product-led-growth) — without at least one trigger present, PLG transformation is premature and the playbook should be deferred until a trigger fires.
Stakeholder map + rollout comms plan
Build the stakeholder influence/impact matrix for the PLG transformation and author per-stakeholder communication SLAs. PLG transformations fail when team misalignment results in 'everyone has a slightly different take on what PLG is supposed to achieve' (per https://growthuntold.com/9-hard-truths-about-product-led-growth-you-should-know/). Single MOAT decision + single rollout comms plan defends against that drift.
Bush's M axis. Classify market posture (dominant / disruptive / differentiated), validate TAM (50M-user threshold for dominant freemium), map competitor pricing + free-tier strategies, and write the 1-page Market-Strategy axis output that feeds the MOAT decision tree.
Classify market posture: dominant / disruptive / differentiated
Classify the company's market posture using Bush's Market-Strategy axis: dominant (best product at lowest price, needs ~50M-user TAM — freemium or trial both work), differentiated (premium for an underserved niche — trial + demos), or disruptive (simplified product for an over-served market — freemium is best). This is the M of the MOAT decision tree. Source: https://productled.com/book/product-led-growth.
Validate TAM (50M users threshold for dominant freemium)
Validate the addressable user TAM. Bush's M-axis treats 50M users as the threshold for dominant freemium to work — below that, freemium economics break (you can't trade off free-tier cost against blended margin at scale). Reference OpenView's 2022 PLG benchmarks (https://openviewpartners.com/blog/your-guide-to-product-led-growth-benchmarks/). If TAM is below 50M for freemium-suitable products, route to trial or hybrid in MOAT synthesis.
Map competitor pricing + free-tier strategies
Scan the top 8 competitors and map each on pricing model, free-tier shape, value metric, and tier limits. This feeds both the MOAT Market-Strategy axis (Bush's M) and the M2 model-decision matrix. Reference the OpenView benchmark series and competitor public pricing pages — for canonical examples see Slack's case study (https://www.getmonetizely.com/articles/plg-monetization-case-study-lessons-from-slacks-bottom-up-pricing-strategy).
Bush's O / A / T axes. Classify ocean color (red/blue) for competitive density, map buyer vs end-user (top-down vs bottom-up), measure current time-to-value, classify TTV archetype (Mission Impossible / Rookie / Veteran / Spoiled). All three axis writeups feed the MOAT decision tree.
Classify ocean color (red / blue)
Classify the competitive ocean: red (dense competition — PLG is the cheapest path to share) or blue (greenfield — sales-led for complex products, PLG for simple ones). Bush's O axis. Source: https://productled.com/book/product-led-growth. The output feeds the MOAT decision tree's O slot.
Write Ocean axis output
Write the 1-page Ocean axis output for the MOAT decision tree. Synthesize the red/blue classification with the product-complexity dimension and recommend a motion on the O axis ('red+complex blocks sales-led; red+simple unlocks PLG; blue+complex needs sales-led; blue+simple unlocks PLG'). Source: https://productled.com/book/product-led-growth.
Map buyer vs end-user (top-down vs bottom-up)
Map the buyer vs end-user gap. Bush's A axis: top-down (CIO/CFO buyer) pairs with sales-led and high ACV; bottom-up (frontline user / team buyer) pairs with PLG and lower CAC. Reference Elena Verna on growth loops vs funnels (https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company). Where buyer == user (a single decision-maker) PLG is unblocked; where buyer != user (procurement gap) PLG needs an enterprise sales-assist motion.
Decision-gate section. Fill the weighted MOAT decision tree (1-5 each axis), build the 10-slide readout deck, run the 60-min exec readout to capture decisions and dissent, capture the signed MOAT decision doc (CEO + CPO + Head of Growth), record the STOP framework Standardize-step checkpoint, draft Pain-Claim-Gain narrative for board / company-wide comms, and execute the go/no-go gate (route to MEDDPICC if MOAT says sales-led).
Fill MOAT decision tree (weighted 1-5 each axis)
Fill the proprietary T2D3 MOAT decision tree: weight each Bush axis 1-5 and run the weighted-sum logic to derive the recommended motion (free trial / freemium / hybrid / sales-led). Source: https://productled.com/book/product-led-growth. This is one of the T2D3 IP gap-fills — Bush's book defines the axes but does not give a quantified decision tree, so customers default to gut-feel synthesis. The weighted tree forces a rigorous synthesis.
Build MOAT readout deck (10 slides)
Build the 10-slide MOAT readout deck for the executive readout. Slides cover: 1 cover, 1 PLG triggers + baseline, 4 axis writeups (M / O / A / T), 1 weighted decision tree, 1 recommended motion + rationale, 1 risks/dissent, 1 next-step ask. Reference Bush's framework (https://productled.com/book/product-led-growth) and the standard T2D3 readout template. The deck must surface dissent — leadership decisions that paper over disagreement do not survive sprint pressure.
Pick free trial vs freemium vs hybrid; design the value metric the customer pays on; draft the pricing thesis. Includes Van Westendorp willingness-to-pay survey (n>=150), economic-value analysis, anti-per-seat check (only viable with Slack-class network effects), and a grandfathering plan for existing accounts. Source: https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company.
Build the trial-vs-freemium-vs-hybrid decision matrix on 8 inputs, decide credit-card policy (opt-in vs opt-out — Bessemer's State of the Cloud shows opt-out trial conversion at 48-50%), pick trial length if trial (7/14/30), choose tier-limit shape if freemium (usage / feature / collab), and capture the signed decision doc.
Build trial-vs-freemium-vs-hybrid decision matrix (8 inputs)
Build the trial-vs-freemium-vs-hybrid decision matrix on 8 inputs (TAM, TTV archetype, competitor free-tier, willingness-to-pay distribution, support cost per free user, network effects present, ACV target, sales-cycle length). T2D3 IP — Bush's MOAT decides motion but does not give a quantified comparator across the three PLG options. Reference Bessemer's State of the Cloud benchmark on opt-out trial conversion at 48-50% (https://www.bvp.com/atlas/state-of-the-cloud-2024) for the trial-vs-freemium scoring rationale.
Decide credit-card policy (opt-in vs opt-out)
Decide the credit-card policy: opt-in (no card to start trial) vs opt-out (card required, trial auto-converts unless cancelled). Bessemer's State of the Cloud reports opt-out trial conversion at 48-50% vs opt-in at ~25% (https://www.bvp.com/atlas/state-of-the-cloud-2024). Opt-out trades signup volume for higher conversion; opt-in trades the inverse. The decision is reversible but anchors the funnel design for M4 signup-flow redesign.
Decide trial length (7 / 14 / 30 days) if trial
If model = trial or hybrid, decide the trial length. The TTV archetype from M1 is the primary input: Spoiled (minutes to value) -> 7-day trial; Veteran (days) -> 14-day trial; Rookie (weeks) -> 30-day trial. Reference 2026 free-trial conversion stats (https://www.amraandelma.com/free-trial-conversion-statistics/) — longer trials skew toward higher conversion at the cost of slower payback.
If freemium: decide tier-limit shape (usage / feature / collab)
If model = freemium or hybrid, decide tier-limit shape: usage-limited (cap on the value metric — Loom's video count, Notion's block count), feature-limited (some features paywalled), or collab-limited (cap on team size — Slack's 10K message archive cap). Reference Notion's PLG strategy (https://www.thegrowthelements.com/p/notion-growth-strategy-product-led-growth-plg) for the canonical 'personal free + 14-day team trial' hybrid shape. Per Bush, avoid pure feature-limited for new-tech categories — users need to use the feature to discover the value.
Capture signed model-selection decision doc
Capture the signed model-selection decision doc covering: model (trial/freemium/hybrid), credit-card policy, trial length (if applicable), tier-limit shape (if applicable). Three named signers: CPO, Head of Growth, Head of Sales. Reference Bush's framing that model selection is downstream of MOAT but must be its own signed doc (https://productled.com/book/product-led-growth) — otherwise downstream M3-M7 work re-litigates the model under sprint pressure.
Brainstorm 5-8 value-metric candidates, score each on comprehension / value alignment / scaling, customer-test the top 2 with 6 customers, pick the metric with documented rationale, and run the anti-per-seat check (per-seat is only viable with Slack-class network effects per Kyle Poyar).
Brainstorm 5-8 value-metric candidates
Brainstorm 5-8 candidate value metrics — the unit the customer is charged for. Examples (per Kyle Poyar https://amplitude.com/blog/benchmark-product-led-growth): meetings booked (Calendly), videos recorded (Loom), GB transferred (Dropbox), API calls (Twilio), seats (only with Slack-class network effects), messages (Intercom). Pull from m1-baseline-customer-interviews to seed candidates from how customers describe value.
Score each candidate on 3 criteria (comprehension / value alignment / scaling)
Score each candidate on Bush's 3 criteria: comprehension (does the customer understand the metric without explanation?), value alignment (does the metric scale 1:1 with realized customer value?), scaling (does the metric naturally drive expansion as the customer grows?). Source: https://productled.com/book/product-led-growth. Score 1-5 each; weighted-sum to rank.
Customer-test top 2 metrics with 6 customers
Customer-test the top 2 value metrics with 6 customers (3 per metric). Test format: explain the metric in 1 sentence, ask the customer to predict their cost at three usage levels, ask which metric feels fairer. Reference Pocus's PLG monetization research (https://www.pocus.com/blog/the-definitive-pql-guide-part-1) — the metric must pass comprehension before it scales. The output picks the metric for the pricing thesis.
Run Van Westendorp WTP survey (n>=150), build economic-value analysis (alternatives x differentiated value), draft 3-tier shape (free/pro/business) with feature splits, write the 8-10 page pricing thesis doc, draft the grandfathering plan + customer-comms script for existing accounts, and lock OKR baselines for O1/O2 KRs.
Run Van Westendorp willingness-to-pay survey (n>=150)
Run a Van Westendorp price-sensitivity survey with n>=150 respondents segmented by ICP. The Van Westendorp method asks 4 prices (too cheap, cheap, expensive, too expensive) and extracts the acceptable price range, optimal price point, and indifference price point. Reference OpenView's PLG benchmark methodology (https://openviewpartners.com/blog/your-guide-to-product-led-growth-benchmarks/). The survey output anchors the pricing thesis tier prices.
Build economic-value analysis (alternatives x differentiated value)
Build an economic-value analysis: identify the customer's next-best alternative, quantify the differentiated value (time saved, revenue gained, cost avoided), and compute the economic-value-to-customer ceiling. Per Bush (https://productled.com/book/product-led-growth), pricing should be a fraction (10-30%) of the differentiated value the customer captures. EVA + WTP triangulate the optimal tier prices. T2D3 IP gap-fill — Bush gives the framework but not the spreadsheet.
UCD step 1. Run JTBD interviews across power users, mid-tier, and churned; tag and code snippets; synthesize 5 canonical job stories; build the canonical PLG persona (job, day-in-life, anti-persona); lock the value statement (CEO-signed). The output is the foundation every downstream module references for messaging, paywall copy, onboarding bumpers, and PQL scoring. Source: https://www.producttalk.org/.
Bush's three outcome layers. List 5-8 functional outcomes (task completion), 3-5 emotional outcomes (how the user feels), 3-5 social outcomes (how the user wants to be perceived), and rank via 12 customer card-sorts. The ranked output drives the value statement and onboarding bumpers.
List 5-8 functional outcomes (task-completion)
List 5-8 functional outcomes the customer hires the product to deliver: the task-completion outcomes ('schedule a meeting without back-and-forth', 'record a 5-minute video for an async stakeholder'). Bush's Understand-Value step 1. Source: https://productled.com/book/product-led-growth. Functional outcomes are the load-bearing input to the value statement (M3.S4) and to the persona JTBD interviews (M3.S2).
List 3-5 emotional outcomes
List 3-5 emotional outcomes — how the customer wants to feel after using the product (in control, confident, unblocked, calm). Bush's Understand-Value step 2: emotional layer (https://productled.com/book/product-led-growth). Emotional outcomes are the load-bearing input to the lifecycle email cadence (M5.S5) and the Pain-Claim-Gain triplets (M4.S3).
List 3-5 social outcomes
List 3-5 social outcomes — how the customer wants to be perceived by others when using the product (professional, technical, on top of things, modern). Bush's Understand-Value step 3 (https://productled.com/book/product-led-growth). Social outcomes are an undervalued input to the message house (M4.S3) and the trust surface positioning (M6.S5).
Rank outcomes via 12 customer card-sorts
Run 12 customer card-sorts to rank outcomes by relative importance. Card-sort method: print each outcome on a card, ask customer to order from most-to-least important to them. Reference Product Talk on customer research method (https://www.producttalk.org/). The ranked output is the prioritization input to the value statement (the top-3 outcomes drive the headline framing).
Write the 16-question JTBD interview guide, recruit 12 customers (4 power, 4 mid, 4 churned), run 45-min interviews recorded, tag and code snippets in the research repo, and synthesize 5 canonical job stories ('when / I want / so I can').
Write JTBD interview guide (16 questions)
Write the 16-question Jobs-to-be-Done interview guide. Structure: 4 trigger questions (when did the customer first realize they needed our category?), 4 first-thought questions (what alternatives did they consider?), 4 push/pull questions (forces of progress), 4 anxiety/habit questions (forces of inertia). Reference https://www.producttalk.org/ for the canonical interview-guide pattern. The guide is reused across all 12 JTBD interviews in M3.S2 and is the core artifact that the persona doc references in M3.S3.
Recruit 12 JTBD interview customers (4 power, 4 mid, 4 churned)
Recruit 12 JTBD interview customers across three cohorts: 4 power users (heavy product use, long tenure), 4 mid-tier (moderate use, medium tenure), 4 churned (cancelled in last 90 days). Reference Product Talk's continuous-discovery recruitment patterns (https://www.producttalk.org/) — the churned cohort is the highest-leverage signal for value-statement gaps and is also the hardest to recruit (offer the strongest incentive).
Run 12 JTBD interviews (45 min each, recorded)
Draft the canonical PLG persona (job, day-in-life, anti-persona), validate with sales / CS / product (3 meetings), and lock a versioned persona doc that downstream Pain-Claim-Gain triplets, paywall copy, lifecycle emails, and onboarding bumpers all reference.
Draft canonical PLG persona (job, day-in-life, anti-persona)
Draft the canonical PLG persona. Sections: job title and core responsibility, day-in-life narrative, anti-persona (who is explicitly NOT the target — the seat that converts but doesn't expand). Reference Bush's persona discipline (https://productled.com/book/product-led-growth) and the T2D3 anti-persona pattern (the anti-persona is what stops PLG-funnel optimization from optimizing for the wrong audience and tanking ARPU). The persona doc is the single source of truth for downstream messaging.
Validate persona with sales / CS / product (3 meetings)
Validate the persona draft with sales, CS, and product in three 45-min meetings. Each function will challenge a different angle — sales on the buyer signals, CS on retention patterns, product on use-case fidelity. Reference Bush (https://productled.com/book/product-led-growth) on cross-functional persona validation. Without this validation, downstream PCG triplets and paywall copy land flat with the internal teams that have to ship them.
Lock persona doc (versioned)
Draft 3 value statement versions, A/B test with 8 customers, lock the CEO-signed statement, and execute STOP-Templatize step (templatize the value-statement + persona kit for org-wide use). Closes UCD step 1.
Draft value statement (3 versions)
Draft 3 value-statement versions anchored to the top-3 ranked outcomes and the 5 job stories. Each version: 1 sentence, 12-20 words, leads with the outcome (not the feature). Reference Bush's Understand-Value step (https://productled.com/book/product-led-growth) on the canonical value-statement format. The 3 versions are A/B-tested in m3-value-statement-customer-test before locking.
A/B test value statements with 8 customers
A/B test the 3 value-statement versions with 8 customers in 20-min sessions. Test format: show all 3 versions; ask which most accurately describes the value they get; ask which would make them most likely to share with a peer. Reference Bush's customer-testing discipline (https://productled.com/book/product-led-growth). The winner gets locked as v1 of the value statement.
Lock value statement (CEO-signed)
Lock the v1 value statement with CEO signature. The statement is the load-bearing copy for the pricing page (M4.S1), the message house (M4.S3), the lifecycle email cadence (M5.S5), and the trust-center hero (M6.S5). Without CEO sign-off, downstream stakeholders argue the statement and the messaging library fragments. Reference Bush (https://productled.com/book/product-led-growth) on the standardize-then-templatize discipline.
UCD step 2. Pricing page redesign that passes the five-second test, feature-flag tier matrix shipped in product, value-metric usage meter and customer-facing quota dashboard, Pain-Claim-Gain triplets per persona, message house with promise + 3 pillars, signup-flow redesign (<=3 fields, social auth, async email verify), and prebuttals for the top 5 upgrade objections. Closes with a monthly pricing-page A/B test cadence. Source: https://productled.com/bowling.
Audit 8 competitor pricing pages (annotated), wireframe new page (3 tiers + feature comparison + FAQ), build high-fidelity design (mobile + desktop), run the five-second test (n>=30) per Bush's pricing-page test, and ship via web release. Targets KR1.1 (free-to-paid conversion >=18%).
Audit 8 competitor pricing pages (annotated screenshots)
Audit the top 8 competitor pricing pages with annotated screenshots covering: above-the-fold framing, tier count, feature comparison table density, FAQ depth, social proof placement, signup CTA design. Reference Slack's PLG monetization case study (https://www.getmonetizely.com/articles/plg-monetization-case-study-lessons-from-slacks-bottom-up-pricing-strategy) for the canonical pattern. The audit is the design input to the new pricing page wireframe (m4-pricing-page-wireframe).
Wireframe new pricing page (3 tiers + feature comparison + FAQ)
Wireframe the new pricing page with the 3-tier shape (free / pro / business), feature comparison table, FAQ (with prebuttals from m4-objection-prebuttals-write), and social proof zone. Reference Bush's five-second test framing (https://productled.com/book/product-led-growth) — the wireframe must communicate tier differentiation in 5 seconds. The wireframe is the input to the high-fidelity design (m4-pricing-page-design).
High-fidelity pricing-page design (mobile + desktop)
Build high-fidelity pricing-page designs (mobile + desktop) on the design system. Apply the locked value statement (M3.S4), the canonical PCG triplet (M4.S3), and the message house (M4.S3). Reference Bush's five-second test discipline (https://productled.com/book/product-led-growth) for the design quality bar. Hand off to engineering for build.
Run five-second test (n>=30)
Run Bush's five-second pricing-page test with n>=30 respondents (UsabilityHub or in-person). Show the page for 5 seconds, then ask: 'What does this product do?', 'Who is it for?', 'How does pricing work?'. Reference https://productled.com/book/product-led-growth on the test rationale — if the page can't communicate the basics in 5 seconds, no downstream signup-flow optimization will recover the lost intent. Targets KR1.1 (free-to-paid >=18%).
Ship new pricing page (web release)
Ship the new pricing page via web release. Wire analytics (the M8 PLG event taxonomy is concurrent — register page-view + tier-CTA-click events). Reference https://www.lennysnewsletter.com/p/what-is-a-good-free-to-paid-conversion for the post-launch monitoring plan. The new page replaces the old one with a soft-redirect; legacy URLs go through 301.
Implement the feature-flag matrix (free / pro / business), implement the usage meter for the value metric, and build the customer-facing quota / usage dashboard so customers can see how close they are to upgrade triggers.
Implement feature-flag matrix (free / pro / business)
Implement the feature-flag matrix wiring tier-to-feature gating across the codebase. Use a flag-management platform (LaunchDarkly / Statsig / GrowthBook) or a simple in-house table. Reference Pocus's product-led sales playbook (https://www.pocus.com/product-led-sales-playbook-volume-1) on the feature-flag-as-monetization pattern. The matrix is the load-bearing layer for paywall placement (M6.S1) and in-product upgrade triggers (M6.S2).
Implement usage meter for value metric
Implement the usage meter for the picked value metric. The meter must (a) record every value-metric event, (b) maintain a per-account aggregate, (c) compute % of tier limit, (d) emit an event when the customer crosses 80% / 90% / 100% of limit (these are the expansion-trigger events from M6.S4). Reference Pocus's PQL guide (https://www.pocus.com/blog/the-definitive-pql-guide-part-1) on usage-signal instrumentation. The meter is the most important single piece of M4 instrumentation.
Build customer-facing quota / usage dashboard
Write Pain-Claim-Gain triplets (3 versions x top-3 personas) and build the message house (1 promise + 3 pillars + proof points). Provides the canonical messaging library that paywall copy, lifecycle emails, and signup-flow microcopy all reference.
Write Pain-Claim-Gain triplet (3 versions x top-3 personas)
Write 9 Pain-Claim-Gain triplets (3 versions x top-3 personas). Each triplet: Pain (the specific friction the persona feels), Claim (the specific promise our product makes), Gain (the specific outcome the persona realizes). T2D3 IP — referenced from Loom's challenger-brand teardown (https://nrich.io/challenger-brand-gtm-library/loom). The 9 triplets are reused across paywall copy (M6.S1), lifecycle emails (M5.S5), the message house (m4-positioning-message-house), and sales enablement.
Build message house (1 promise + 3 pillars + proof points)
Build the message house: 1 promise (the value statement), 3 pillars (the three primary outcome categories from M3.S1), proof points per pillar (specific customer outcomes, metrics, case studies). Reference https://productled.com/book/product-led-growth on the communicate-value step. The message house is the canonical map every cross-functional team uses when writing customer-facing copy — it prevents the Pain-Claim-Gain triplets from drifting persona-by-persona into inconsistent positioning.
Audit current signup flow step-by-step (screenshot every screen), redesign to <=3 fields with social auth + async email verify, ship the new flow + A/B test against control. Single biggest TTV swing in M4.
Audit current signup flow step-by-step (screenshot every screen)
Audit the current signup flow step by step with annotated screenshots: every screen, every field, every CTA, every error state. Reference https://amplitude.com/blog/time-to-value-drives-user-retention on signup-flow friction as a TTV multiplier. The audit is the input to the redesign (m4-signup-flow-redesign).
Redesign signup flow (<=3 fields, social auth, async email verify)
Redesign the signup flow to <=3 fields with social auth (Google / Microsoft / Apple) and async email verification (post-signup, not pre-product-access). Reference Calendly's frictionless signup pattern (https://startupgtm.substack.com/p/calendly-growth-story-a-viral-product) — a 3-field signup with delayed verification typically lifts signup-to-activation by 15-30 percentage points. Targets KR1.2 (TTV <=7 days).
Ship new signup flow + A/B test against control
Ship the redesigned signup flow + run a 4-week A/B test against the control. Wire the flow into the M8 event taxonomy (signup_started, signup_completed, email_verified). Reference https://amplitude.com/blog/time-to-value-drives-user-retention on the typical signup-flow lift band. Target: signup-to-activation +20% vs control. Targets KR1.1 (free-to-paid >=18%).
Pull top 10 upgrade objections from sales / CS / churn surveys, write FAQ + in-flow prebuttals for the top 5, ship on the pricing page + signup flow, and execute STOP-Optimize (monthly pricing-page A/B test cadence scheduled).
Pull top 10 upgrade objections from sales / CS / churn surveys
Pull the top 10 upgrade objections from three sources: (1) sales call recordings + CRM notes for the last 90 days, (2) CS escalations + churn-survey free-text responses, (3) in-product feedback. Reference https://growthuntold.com/9-hard-truths-about-product-led-growth-you-should-know/ on the canonical PLG objections (price, integration cost, lock-in fear, security/compliance, missing features). The output is the objection backlog the prebuttals address (m4-objection-prebuttals-write).
Write FAQ + in-flow prebuttals for top 5 objections
Write FAQ entries + in-flow prebuttals for the top 5 objections. Each prebuttal: the objection (in the customer's voice), our response (Pain-Claim-Gain framed using the M4.S3 message house), and the proof point. Reference Bush (https://productled.com/book/product-led-growth) on prebuttals as the load-bearing trust mechanism for the pricing page and signup flow. Without prebuttals at the friction point, objections take customers out of the funnel.
Ship prebuttals on pricing page + signup flow
UCD step 3 and the biggest activation lift in the playbook. Map current onboarding step-by-step with funnel data; pull retention curves per signup cohort; hypothesize 3 candidate aha events and run a logistic regression to pick the strike; lock the PQL threshold; color-code every step red/yellow/green; cut red, delay yellow, ship the new straight line; build product bumpers (empty-state checklist, tour, progress bar, explainer video); ship lifecycle email cadence (8 emails) plus in-app nudges; A/B test 3 onboarding variants over 4 weeks. Source: https://www.productled.org/blog/bowling-alley-framework.
Screenshot every onboarding screen (annotated), list every step with owner / event / drop-off, pull funnel data per step (last 90 days), and plot retention curves by signup cohort. Establishes the baseline before the straight-line redesign.
Screenshot every onboarding screen (annotated)
Walk the current onboarding flow as a new user and screenshot every screen with annotations: field counts, copy length, errors, drop-off candidates. Reference Bush on the Bowling Alley framing (https://www.productled.org/blog/bowling-alley-framework) — you cannot color the lane red/yellow/green until every step is visible. The screenshot walk is the input to m5-onboarding-step-list.
List every onboarding step with owner / event / drop-off
List every onboarding step with the owner team, the analytics event fired, and the current drop-off rate. Reference https://www.productled.org/blog/bowling-alley-framework on the discipline of step-level visibility — without it, the red/yellow/green audit collapses to subjective judgment. The list is the canonical input to the funnel-data pull (m5-onboarding-funnel-data) and the red/yellow/green audit (m5-straightline-redyellowgreen).
Pull funnel data per step (last 90 days)
Pull funnel data per onboarding step over the last 90 days from the M8 event taxonomy. Per https://amplitude.com/blog/time-to-value-drives-user-retention, the per-step drop-off data is what separates real activation analysis from anecdote. The output is the input to retention-curve plotting (m5-onboarding-cohort-curves) and the strike hypothesis tests (m5-aha-hypothesize-3).
Plot retention curves by signup cohort
Plot retention curves by signup cohort (D1, D7, D30, M3 retention) per cohort week. Reference https://amplitude.com/blog/time-to-value-drives-user-retention on cohort retention as the canonical PLG diagnostic — flat curves mean the strike isn't reliably reachable, decaying curves mean the bumpers are missing. The curves are the input to the strike hypothesis tests (m5-aha-hypothesize-3).
Bush's strike step. Hypothesize 3 candidate aha events (event x magnitude x time x role), pull retention curves for each, run logistic regression (activation candidate -> 90-day paid), pick the winning event, define the PQL threshold, and lock the strike doc (CPO + Head of Growth signed).
Hypothesize 3 candidate aha events (event x magnitude x time x role)
Hypothesize 3 candidate aha events using the T2D3 IP framework: event (the action), magnitude (how many times — Slack used 2,000 messages, Loom used 5+ shared videos), time (within how long), role (which user role within the account). Reference Slack's case study (https://www.getmonetizely.com/articles/plg-monetization-case-study-lessons-from-slacks-bottom-up-pricing-strategy) — their strike was 2,000 messages exchanged by a team. The 3 hypotheses are tested via logistic regression in m5-aha-logistic-regression.
Pull retention curves for each candidate aha event
Pull D7/D30/M3 retention curves for users who hit each candidate aha event vs users who didn't. The aha event whose hitters retain at materially higher rates (typically 2-3x) is the strike. Reference https://amplitude.com/blog/time-to-value-drives-user-retention. The output is the input to the logistic-regression analysis (m5-aha-logistic-regression).
Run logistic regression: activation candidate -> 90-day paid
The Bowling Alley straight-line audit. Color-code every onboarding step red / yellow / green, cut red steps with rationale per removal, move yellow steps post-activation, ship the new straight-line onboarding. Largest TTV reduction in the playbook.
Color-code every onboarding step red / yellow / green
Apply Bush's straight-line audit: red (does not contribute to reaching the strike — must be cut), yellow (contributes but slows time-to-value — move post-activation), green (directly drives the strike — keep). Reference https://www.productled.org/blog/bowling-alley-framework. T2D3 IP — Bush's bowling-alley framework gives the principle but no per-step audit template; the red/yellow/green spreadsheet is one of the proprietary IP artifacts.
Cut red steps; document rationale for each removed step
Cut every red step from the new straight-line onboarding. For each cut, document the rationale (which strike-input it failed to advance) and the alternative path (move post-activation, gate behind a feature flag, or eliminate entirely). Reference https://www.productled.org/blog/bowling-alley-framework on Bush's discipline that 'every red step you don't cut is one more chance the user drops before the strike'. Targets KR1.2 (TTV <=7 days).
Move yellow steps post-activation
Bush's bumpers: empty-state checklist (3-5 actions), product tour for first session (<=4 tooltips), progress bar / activation checklist sticky, and 60-second explainer video (Loom or Wistia). Bumpers prevent gutterballs while the user walks the straight line.
Design empty-state checklist (3-5 actions)
Design the empty-state checklist (3-5 actions). Each action is a step toward the strike, communicated as a clear in-product CTA. Reference Bush (https://www.productled.org/blog/bowling-alley-framework) on bumpers — the empty state is the most-visited bumper because every new user lands there. The checklist must be visible without scroll, dismissable but persistent across sessions, and tied to the M8 event taxonomy.
Build product tour for first session (<=4 tooltips)
Build the product tour for the first session: <=4 tooltips, each pointing at one straight-line step. Reference Bush (https://www.productled.org/blog/bowling-alley-framework) on tour discipline — more than 4 tooltips and the tour becomes a wall of friction; fewer than 4 and key affordances are missed. Use a tour platform (Pendo / Appcues / Userflow) or in-house implementation if existing instrumentation supports it.
Add progress bar / activation checklist sticky
Add the progress bar / activation checklist sticky on the main product surface. Reference Bush (https://www.productled.org/blog/bowling-alley-framework) on the persistent-bumper pattern — the sticky checklist is what brings the user back to the next strike step on every product visit until activation. Targets KR1.5 (Day-7 activation rate >=40%).
Write the 8-email lifecycle cadence (welcome -> onboard -> activation -> upgrade), ship via Customer.io / HubSpot, and ship in-app inactivity nudges (day 2, day 7). Conversational bumpers run in parallel with product bumpers and reach users who have already left the product.
Write 8-email lifecycle cadence (welcome -> onboard -> activation -> upgrade)
Write the 8-email lifecycle cadence: (1) welcome, (2) onboard step 1, (3) onboard step 2, (4) onboard step 3, (5) activation reminder (if not yet at strike), (6) upgrade trigger, (7) win-back (if churned), (8) expansion (post-strike). Reference T2D3 IP — Bush's PLG book (https://productled.com/book/product-led-growth) frames lifecycle emails but does not give the canonical 8-email cadence template. Each email applies a Pain-Claim-Gain triplet from M4.S3.
Ship lifecycle emails in marketing automation (Customer.io / HubSpot)
Ship the 8-email lifecycle cadence in the marketing automation platform (Customer.io / HubSpot / Iterable). Wire the firing triggers from M8 events. Reference Bush (https://productled.com/book/product-led-growth) on the lifecycle-email shipment quality bar (every email must respect unsubscribe + transactional/marketing classification + consent flags). Targets KR1.1 (free-to-paid >=18%).
Ship in-app nudges for inactivity (day 2 / day 7)
Write the A/B test plan for 3 onboarding variants, run for 4 weeks, declare a winner. Closes UCD step 3 by validating the new straight-line outperforms the old.
Write A/B test plan for 3 onboarding variants
Write the A/B test plan for 3 onboarding variants: control (old), straight-line v1 (new from m5-straightline-ship), straight-line v2 (new + bumpers). Reference https://amplitude.com/blog/benchmark-product-led-growth on the onboarding-A/B test discipline. The plan covers hypothesis, MDE (minimum detectable effect), power calculation, sample size, duration, success metric (Day-7 activation rate). Targets KR1.5.
Run A/B test 4 weeks; declare winner
Run the 3-variant A/B test for 4 weeks via the M8 A/B platform. At conclusion, run the M8 A/B readout: per-variant metrics, statistical significance check, decision. Reference https://amplitude.com/blog/benchmark-product-led-growth. Ship the winner at 100%; archive the losers. Targets KR1.5 (Day-7 activation rate >=40%).
Paywall placement, in-product upgrade triggers, expansion mechanics, self-serve billing platform, and the trust surface (SOC 2 / DPA / trust center). Includes the Stripe vs Paddle vs Chargebee 12-input decision framework, tax handling (Stripe Tax / Paddle MoR / Avalara), dunning + recovery flows, self-serve customer portal (upgrade / downgrade / cancel), price-grandfathering customer comms, expansion events (seat add / overage / tier-up), and self-serve DPA click-through. Heaviest gate concentration: billing-vendor track, tax sub-track, trust+DPA stack are all gated. Source: https://www.churnmate.com/blog/stripe-vs-paddle-vs-chargebee-choosing-the-right-foundation-for-your-saas.
Fill the paywall placement rubric (convert friction vs value gating per Bush — paywall = 'deeper-value unlock', not 'value-gate'), pick paywall trigger points (feature x usage threshold x user role), write paywall copy with Pain-Claim-Gain triplet per trigger, and ship paywall components (modal + inline upsell + lock-icon).
Fill paywall placement rubric (convert friction vs value gating)
Fill the T2D3 paywall placement rubric: per candidate paywall location, score on 'convert friction' (does the paywall block users approaching the value moment?) vs 'value gating' (does the paywall require the user to have already realized value before being asked to pay?). Reference https://growthuntold.com/9-hard-truths-about-product-led-growth-you-should-know/ — wrong paywall placement is the #1 PLG failure mode. The rubric is T2D3 IP gap-fill: Bush gives the principle but no quantified rubric.
Pick paywall trigger points (by feature x usage threshold x user role)
Pick the paywall trigger points using the rubric: each trigger is feature x usage threshold x user role. Reference Bush's framing that 'paywall = deeper-value unlock, not value-gate' (https://productled.com/book/product-led-growth) and Slack's case study on paywall placement at the 10K-message archive (https://www.getmonetizely.com/articles/plg-monetization-case-study-lessons-from-slacks-bottom-up-pricing-strategy). Targets KR1.1 (free-to-paid >=18%).
Write paywall copy with Pain-Claim-Gain triplet per trigger
Write paywall copy per trigger using one of the 9 Pain-Claim-Gain triplets from M4.S3. Each paywall copy block: 1-sentence Pain (the friction the user just hit), 1-sentence Claim (the upgrade unlock), 1-sentence Gain (the outcome they unlock). Reference https://nrich.io/challenger-brand-gtm-library/loom on PCG-driven paywall copy. The copy must avoid friction language ('upgrade required') and lead with value ('unlock the outcome you came for').
Ship paywall components (modal + inline upsell + lock-icon)
Ship the three paywall components: modal (full-screen at deep-value gates), inline upsell (in-context within product flows), lock-icon (passive indicator on tier-gated features). Reference Bush (https://productled.com/book/product-led-growth) on paywall component discipline — each form has a different friction profile and is reserved for a specific trigger type. Wire all three into M8 events for paywall-view + paywall-CTR tracking. Targets KR1.1 (free-to-paid >=18%).
Define 5 in-product upgrade trigger events (e.g., quota approached, premium feature attempted) and ship in-product upgrade prompts at trigger events. Distinct from paywalls — these are nudges at the moment of value realization.
Define 5 in-product upgrade trigger events
Define 5 in-product upgrade trigger events distinct from paywalls. Triggers fire on moments of value realization (not value blocking): hitting 80% of tier limit, attempting a premium feature, sharing with a teammate, completing 10 strike-events in a week, etc. Reference Pocus's product-led sales playbook (https://www.pocus.com/product-led-sales-playbook-volume-1) on the upgrade-trigger-event canonical list. Triggers wire into the M8 event taxonomy.
Ship in-product upgrade prompts at trigger events
Ship in-product upgrade prompts at the 5 trigger events. Prompts fire as in-product modals or inline banners with PCG copy from m6-paywall-copy-write (or new PCG variants matched to the value-realization moment). Reference Bush (https://productled.com/book/product-led-growth) on prompt placement — the moment of value realization is the highest-conversion upgrade moment in the funnel. Targets KR1.1 (free-to-paid >=18%).
Score Stripe / Paddle / Chargebee on the 12-input framework, pick a vendor, integrate (subscriptions / invoicing / webhooks), wire tax (Stripe Tax / Paddle MoR / Avalara), configure dunning + recovery flows (target +8% recovered revenue), ship self-serve portal (upgrade / downgrade / cancel), and send price-grandfathering customer comms. Heaviest single feature gate in the playbook — full track suppressed when customer already has a billing vendor.
Score Stripe / Paddle / Chargebee on 12-input framework
Score Stripe / Paddle / Chargebee on the T2D3 12-input framework: tax handling, dunning, merchant of record, EU/UK VAT, US sales tax, proration, multi-currency, invoice-customer support, webhooks, partner ecosystem, pricing, time-to-integrate. Reference https://www.churnmate.com/blog/stripe-vs-paddle-vs-chargebee-choosing-the-right-foundation-for-your-saas — the 12-input framework is T2D3 proprietary IP gap-fill (the public comparison gives the principles but not the rubric).
Pick billing vendor; document tax / dunning / proration trade-offs
Pick the billing vendor and document the trade-off decisions: tax handling (Stripe Tax vs Paddle MoR vs Avalara), dunning sophistication (Stripe Smart Retries vs Chargebee Recurly), proration model (per-second vs per-day). Reference https://www.churnmate.com/blog/stripe-vs-paddle-vs-chargebee-choosing-the-right-foundation-for-your-saas. The trade-off decisions are the input to m6-billing-tax-handling and m6-billing-dunning.
Define expansion events (seat add / usage overage / tier-up), ship expansion prompts (admin notify on overage, auto-upgrade), and ship sharing / invite mechanics (Calendly / Loom-style recipient virality where every artifact is an unpaid ad).
Define expansion events (seat add / usage overage / tier-up)
Define the expansion events: seat add (new user invited), usage overage (above the tier limit), tier-up (customer upgrades to a higher tier). Reference Pocus's PQL guide (https://www.pocus.com/blog/the-definitive-pql-guide-part-1) on expansion-event instrumentation. The events wire into M8 and feed the expansion dashboard (M8.S4).
Ship expansion prompts (admin notify on overage; auto-upgrade)
Ship expansion prompts: admin notification on usage overage with one-click upgrade CTA, auto-upgrade for accounts that exceed the tier limit by >2x for 30 days. Reference Bush (https://productled.com/book/product-led-growth) on expansion mechanics — the auto-upgrade pattern (with explicit consent gate) is the load-bearing self-serve expansion lever. Targets KR1.3 (self-serve >=30% of new ARR).
Ship sharing / invite mechanics (Calendly / Loom-style recipient virality)
Ship sharing / invite mechanics following the Calendly / Loom recipient-virality pattern: every artifact the user creates has a shareable link, and every share creates a low-friction signup path for the recipient. Reference Calendly's case study (https://startupgtm.substack.com/p/calendly-growth-story-a-viral-product) on viral-coefficient design — recipient virality is what kept their CAC at zero. The shipped mechanics include: shareable artifact links, branded signup landing per shared artifact, branded watermark per shared artifact, recipient-aware product onboarding (the recipient lands in a context-aware empty state).
Design trust-center IA (security / SOC2 / privacy / DPA / subprocessors), write trust-center content (8 pages), ship SOC 2 / security page with downloadable summary, build self-serve DPA click-through (Salesforce / SafeBase / Wolfia), and execute STOP-Productize (paywall + billing + trust ship as a repeatable kit). Without this surface enterprise PQLs convert <=1%.
Design trust-center IA (security / SOC2 / privacy / DPA / subprocessors)
Design the trust-center information architecture covering: security overview, SOC 2 status, privacy policy, DPA (Data Processing Agreement), subprocessors list, sub-processor change log, vulnerability disclosure, business-continuity, audit logs availability. Reference Wolfia's SOC 2 self-serve guide (https://wolfia.com/blog/soc-2-compliance-requirements-complete-guide). The trust center is the load-bearing surface for self-serve enterprise PQLs — without it, enterprise PQL conversion is <=1%.
Write trust-center content (8 pages)
Write the 8 trust-center pages. Each page: 600-1000 words, plain English with enterprise-procurement signposting. Reference https://wolfia.com/blog/soc-2-compliance-requirements-complete-guide. Pages: (1) Security overview, (2) SOC 2 + audit, (3) Privacy policy, (4) DPA + subprocessors, (5) Encryption + key management, (6) Incident response + vulnerability disclosure, (7) Business continuity, (8) Compliance certifications. Pages wire into the main pricing page from M4.S1.
Stand up the Triple-A monthly sprint, the PQL -> AE round-robin SLA (4-hour response), and the SDR/AE hybrid comp plan (60/40 base/var, 50% meetings + 35% pipeline + 15% PQL conv) so every function pulls the same rope. Builds the Tiger Team RACI, runs three sprints to declare cadence steady-state, configures PQL routing (Pocus / Correlated / homegrown), maps PQL fields into Salesforce / HubSpot, and stands up monthly OKR review + quarterly business review. Source: https://cxl.com/blog/saas-growth/.
Cadence section. Pick Tiger Team roster (5-7 cross-functional), write charter + RACI, set rituals (weekly stand-up + monthly sprint + quarterly review). Three tasks - exempt from the 8-task floor per the live-build rules.
Pick Tiger Team roster (5-7 cross-functional)
Pick the Tiger Team roster: 5-7 cross-functional members covering product, growth, marketing, sales, CS, engineering, finance. Reference https://productled.com/book/product-led-growth on Tiger Team formation discipline — the team must be small enough to move fast (<=7) but cross-functional enough to ship without external dependencies. Each member commits >=20% of working time.
Write Tiger Team charter + RACI
Write the Tiger Team charter + RACI. Charter covers: mission (PLG transformation steady-state), scope (Triple-A monthly sprint, PQL routing, paywall A/B test cadence), decision rights (what the team decides without escalation, what escalates), success metrics (the OKR scorecard from M2). Reference https://productled.com/book/product-led-growth on Tiger Team charter discipline. T2D3 IP — the charter + RACI template is one of the proprietary artifacts.
Set rituals (weekly stand-up + monthly sprint + quarterly review)
Set the Tiger Team operating rituals: 30-min weekly stand-up, full Triple-A monthly sprint (M7.S2), quarterly business review (m7-quarterly-business-review). Reference https://cxl.com/blog/saas-growth/ on Bush's Triple-A sprint cadence. The ritual cadence is the operating mechanism for the playbook's steady state.
Cadence section. Build Triple-A template (Analyze worksheet + Ask brainstorm + Act 1-pager), build monthly inputs/outputs dashboard (signups / activation / ARPU / churn / ARR), run sprints 1, 2, 3 to declare steady-state. Five tasks - exempt from the 8-task floor per the live-build rules.
Build Triple-A template (Analyze worksheet + Ask brainstorm + Act 1-pager)
Build the Triple-A sprint template: Analyze worksheet (last month's outputs vs OKR), Ask brainstorm (one of three growth levers — churn down / ARPU up / more customers), Act 1-pager (chosen experiment with hypothesis + MDE + ship plan). Reference https://cxl.com/blog/saas-growth/. T2D3 IP gap-fill — Bush gives the principle but no canonical template; the worksheet + brainstorm + 1-pager set is one of the proprietary IP artifacts.
Build monthly inputs/outputs dashboard (signups, activation, ARPU, churn, ARR)
Build the monthly inputs/outputs dashboard for the Triple-A sprint Analyze step. Pulls signups, activation, ARPU, churn, ARR from M8 events and warehouse. Reference https://cxl.com/blog/saas-growth/ on the Analyze-step inputs. The dashboard is the load-bearing data surface for the monthly sprint — without it, the team analyzes from spreadsheets and the cadence collapses by month 3.
Run first Triple-A sprint (analyze -> ask -> act -> ship)
Write PQL routing rules (Sales-Ready / Sales-Assist / Uncertain / Deprioritize), the 4-hour-response SLA template with disposition feedback, map PQL fields into Salesforce / HubSpot, and build PQL routing automation (Pocus / Correlated / homegrown). Industry SLA is 4 hours — past 24h conversion drops 70%+.
Write PQL routing rules (Sales-Ready / Sales-Assist / Uncertain / Deprioritize)
Write the PQL routing rules with 4 disposition tiers: Sales-Ready (PQL score >= high threshold + ICP fit), Sales-Assist (mid-score, ICP fit, manual review), Uncertain (low PQL score but ICP fit, nurture path), Deprioritize (no ICP fit). Reference Pocus's PQL guide (https://www.pocus.com/blog/the-definitive-pql-guide-part-1) and Hightouch's implementation guide (https://hightouch.com/blog/the-definitive-guide-to-product-qualified-leads). The rules are the load-bearing input to the PQL routing automation (m7-pql-routing-automation).
Write PQL -> AE round-robin SLA template (4-hr response, disposition feedback)
Write the PQL -> AE round-robin SLA template: 4-hour first-response SLA, mandatory disposition feedback per PQL, weekly accept/reject rate review. Reference https://www.factors.ai/blog/product-qualified-lead and Pocus's PQL guide (https://www.pocus.com/blog/the-definitive-pql-guide-part-1). T2D3 IP — Bush leaves the PQL handoff open; the SLA template is one of the proprietary IP artifacts. Without a 4-hour SLA, conversion drops 70%+ at the 24-hour mark.
Design the hybrid comp plan (60/40 base/var; 50% meetings + 35% pipeline + 15% PQL conv), model 12-month earnings + ramp + pressure-test outliers, roll out with 1:1 sessions + FAQ, and stand up monthly OKR review + quarterly business review cadence.
Design SDR/AE hybrid comp plan (60/40 base/var; 50% meetings + 35% pipeline + 15% PQL conv)
Design the SDR/AE hybrid comp plan: 60/40 base/variable, with variable split as 50% meetings + 35% pipeline + 15% PQL conversion. Reference Everstage SDR comp benchmarks (https://www.everstage.com/sales-compensation/sdr-variable-compensation). T2D3 IP — Bush leaves the SDR comp plan open; the hybrid 50/35/15 split is one of the proprietary IP artifacts. Without compensating for PQL conversion, AEs deprioritize PLG signals and the playbook KR1.3 collapses.
Model 12-month earnings + ramp; pressure-test outliers
Model the 12-month earnings + ramp scenarios for each SDR/AE under the new comp plan. Pressure-test outliers (top performer earnings, bottom performer earnings, ramp earnings). Reference Everstage's SDR comp modeling guide (https://www.everstage.com/sales-compensation/sdr-variable-compensation). The model is what makes the plan defensible to the CFO + sales leadership.
Roll out comp plan with 1:1 sessions + FAQ doc
Roll out the new comp plan with mandatory 1:1 sessions per rep + a comprehensive FAQ doc. Reference Everstage on comp-plan rollout discipline (https://www.everstage.com/sales-compensation/sdr-variable-compensation) — comp-plan changes that don't include 1:1 sessions per rep generate 30%+ rep churn within 90 days. The FAQ doc anticipates the top 15 questions; the 1:1 walks through the rep's specific model.
Ships the canonical 35-event PLG taxonomy across Account/Activation/Engagement/Monetization/Virality categories, the analytics stack (Segment + Mixpanel or Amplitude + Snowflake/BigQuery + Hightouch/Census reverse-ETL), the PQL scoring model (fit + usage + intent with weights, threshold, decay), OKR + funnel + cohort + monetization dashboards, and the A/B testing platform (Statsig / Optimizely / GrowthBook). Concurrent with M3-M7. Source: https://amplitude.com/explore/data/event-taxonomy.
Design the canonical 35-event PLG taxonomy across 5 categories (Account / Activation / Engagement / Monetization / Virality), spec event properties (user_id, account_id, plan, role, utm_*, device), publish the tracking plan + naming-convention guide, and implement events in client + server (Segment SDK or direct).
Design 35-event PLG taxonomy across 5 categories
Design the canonical 35-event PLG taxonomy across 5 categories: Account/Auth (signup, login, role change), Activation (first-value, strike, key-event hit), Engagement (daily active, weekly active, feature use), Monetization (paywall view, upgrade click, plan change, payment success/fail), Virality (artifact create, share, recipient signup). Reference Amplitude's event-taxonomy framework (https://amplitude.com/explore/data/event-taxonomy) and Avo's tracking-plan templates (https://www.avo.app/blog/9-free-tracking-plan-templates-from-mixpanel-amplitude-segment-and-more). T2D3 IP — the 35-event canonical taxonomy is one of the proprietary artifacts.
Spec event properties (user_id, account_id, plan, role, utm_*, device)
Spec the canonical event properties shared across all 35 events: user_id, account_id, plan_tier, user_role, utm_source/medium/campaign/term/content, device_type, browser, app_version, signup_cohort_week. Reference Avo's tracking-plan templates (https://www.avo.app/blog/9-free-tracking-plan-templates-from-mixpanel-amplitude-segment-and-more). The shared property schema is what lets every event participate in cohort + funnel + retention analysis.
Publish tracking plan doc + naming-convention guide
Publish the tracking plan doc + naming-convention guide. The tracking plan covers each event with its trigger, owner, properties, expected volume; the naming-convention guide covers snake_case, verb-first event names, and the change-management process for adding events. Reference Avo's tracking-plan templates (https://www.avo.app/blog/9-free-tracking-plan-templates-from-mixpanel-amplitude-segment-and-more). Required artifact for any new engineering team member shipping events.
Implement events in client + server (Segment SDK or direct)
Implement the 35 events across client + server. Use Segment SDK for unified delivery to downstream tools, or direct integration with Mixpanel/Amplitude if vendor-specific. Reference Avo's tracking-plan templates (https://www.avo.app/blog/9-free-tracking-plan-templates-from-mixpanel-amplitude-segment-and-more). The 24-hour estimate is for initial implementation; ongoing event ownership transitions to the M7 Tiger Team via the change-management process.
Set up Segment workspace + sources/destinations, set up Mixpanel or Amplitude + permissions, stand up warehouse (Snowflake / BigQuery) + Segment sync, and reverse-ETL warehouse -> CRM (Hightouch / Census). Greenfield-only gate — fully suppressed when customer already has an event taxonomy.
Set up Segment workspace + sources / destinations
Set up the Segment workspace, sources (web SDK, server SDK, mobile SDK if applicable), and destinations (Mixpanel/Amplitude, the warehouse, the marketing automation platform). Reference Avo's tracking-plan templates (https://www.avo.app/blog/9-free-tracking-plan-templates-from-mixpanel-amplitude-segment-and-more). Segment is the canonical hub for PLG instrumentation — it lets the team change downstream tools without re-instrumenting events.
Set up Mixpanel or Amplitude project + permissions
Set up the Mixpanel or Amplitude project + permissions. Reference Amplitude's PLG benchmark (https://amplitude.com/blog/benchmark-product-led-growth) for canonical use. Both are viable; pick based on team familiarity and pricing. The chosen tool is where the Triple-A monthly sprint Analyze step pulls retention curves + cohort analysis from.
Stand up warehouse (Snowflake / BigQuery) + Segment sync
Stand up the warehouse (Snowflake or BigQuery) + Segment sync to load every event + user/account update into the warehouse. Reference Hightouch's PQL guide on warehouse-as-single-source-of-truth (https://hightouch.com/blog/the-definitive-guide-to-product-qualified-leads). The warehouse is the load-bearing layer for PQL scoring (M8.S3) and for any cross-tool analysis the Tiger Team runs.
Spec the PQL scoring model (fit + usage + intent — weights, threshold, decay), train the initial model on 6-mo historical wins, and ship the PQL score field to the CRM via reverse-ETL.
Spec PQL scoring model (fit + usage + intent; weights + threshold + decay)
Spec the PQL scoring model with three components: fit (ICP match — company size, industry, geo), usage (strike-event hits, frequency, depth), intent (high-value page views, paywall hits, support contact). Each component scored 0-100; weighted sum is the PQL score. Define the threshold (typical: PQL = score >= 70) and the decay (PQL score halves every 30 days of inactivity). Reference Pocus's PQL guide (https://www.pocus.com/blog/the-definitive-pql-guide-part-1) and the Factors.ai PQL definition (https://www.factors.ai/blog/product-qualified-lead). T2D3 IP — the scoring model spec is one of the proprietary artifacts.
Train initial scoring model on 6-mo historical wins
Train the initial PQL scoring model on 6 months of historical wins vs losses. Tune the component weights using logistic regression with paid-conversion as the outcome. Reference https://hightouch.com/blog/the-definitive-guide-to-product-qualified-leads on training discipline. The trained model is what makes PQL scoring quantitative rather than gut-feel. Targets KR2.3 (PQL -> paid conversion >=30%).
Build the OKR dashboard (KR1.1-1.5, KR2.1-2.3), funnel dashboard (visitor -> signup -> activation -> paid), cohort retention dashboard (D1/D7/D30/M3), and monetization dashboard (paywall hits, upgrade trigger conversion, expansion ARR). The OKR dashboard is the load-bearing artifact for the Triple-A monthly sprint.
Build OKR dashboard (KR1.1-1.5, KR2.1-2.3)
Build the OKR dashboard covering all 8 KRs from M2: KR1.1 free-to-paid, KR1.2 TTV, KR1.3 self-serve %, KR1.4 PQL volume, KR1.5 Day-7 activation, KR2.1 blended payback, KR2.2 self-serve payback, KR2.3 PQL -> paid. Reference https://amplitude.com/blog/benchmark-product-led-growth on the canonical PLG dashboard structure. The OKR dashboard is the load-bearing artifact for the M7 monthly OKR review.
Build funnel dashboard (visitor -> signup -> activation -> paid)
Build the funnel dashboard: visitor -> signup -> activation -> paid. Per-stage conversion rates + per-cohort comparisons. Reference https://amplitude.com/blog/benchmark-product-led-growth. The funnel dashboard surfaces the leakiest stage at any given time, which is the canonical input to the Triple-A sprint Analyze step (M7.S2).
Build cohort retention dashboard (D1/D7/D30/M3)
Build the cohort retention dashboard: D1, D7, D30, M3 retention per signup cohort week. Reference https://amplitude.com/blog/time-to-value-drives-user-retention. The dashboard is the canonical surface for tracking the impact of M5 (Bowling Alley) onboarding redesign. Targets KR1.5 (Day-7 activation rate >=40%).
Pick A/B platform (Statsig / Optimizely / GrowthBook), integrate with the event taxonomy, and write the A/B test runbook (hypothesis -> MDE -> power -> ship -> readout). Required for the monthly pricing-page A/B test cadence (M4) and the onboarding A/B (M5).
Pick A/B platform (Statsig / Optimizely / GrowthBook)
Pick the A/B testing platform: Statsig (modern + free tier + feature flags), Optimizely (legacy + sophisticated targeting), GrowthBook (open-source + lightweight). Reference Avo's tracking-plan templates (https://www.avo.app/blog/9-free-tracking-plan-templates-from-mixpanel-amplitude-segment-and-more). The platform is the load-bearing layer for the monthly pricing-page A/B test cadence (M4.S5) and the onboarding A/B (M5.S6).
Integrate A/B platform with event taxonomy
Integrate the A/B platform with the event taxonomy. Each experiment exposure fires an M8 'experiment_assigned' event; downstream conversion events automatically attribute via the shared user_id property. Reference Avo's tracking-plan templates (https://www.avo.app/blog/9-free-tracking-plan-templates-from-mixpanel-amplitude-segment-and-more). Without the integration, experiment results require manual user-id-list joins and the cadence collapses by month 2.
Write A/B test runbook (hypothesis -> MDE -> power -> ship -> readout)
Write Market-Strategy axis output (1 page)
Write the 1-page Market-Strategy axis output for the MOAT decision tree. Synthesize the classification (dominant/disruptive/differentiated), the TAM validation, and the competitor scan into a single Bush-axis recommendation: 'on the M axis, the recommended motion is X because Y'. Source: https://productled.com/book/product-led-growth. This 1-pager is one of four feeders into the weighted MOAT decision tree (m1-moat-decision-tree-fill).
Write Audience axis output
Write the 1-page Audience axis output for the MOAT decision tree. Synthesize the top-down vs bottom-up classification and recommend a motion on the A axis. Source: https://productled.com/book/product-led-growth.
Measure current time-to-value (signup -> first value event)
Measure the current time-to-value: median minutes/days from signup to first value event (whatever the team currently treats as activation). This is Bush's T-axis input — PLG only works if the customer can be delivered at Veteran (days) or Spoiled (minutes) TTV for the activation event. Reference Amplitude's TTV/retention research (https://amplitude.com/blog/time-to-value-drives-user-retention) for the canonical curve showing TTV correlation with 90-day retention.
Classify TTV archetype: Mission Impossible / Rookie / Veteran / Spoiled
Classify the user TTV archetype using Bush's four-tier framework: Mission Impossible (months — PLG blocked), Rookie (weeks — PLG fragile), Veteran (days — PLG works), Spoiled (minutes — PLG dominant). Source: https://productled.com/book/product-led-growth. The archetype determines whether the playbook can deliver on KR1.2 (TTV <=7 days) without an extreme onboarding rebuild.
Write Time-to-Value axis output
Write the 1-page TTV axis output for the MOAT decision tree. Synthesize the archetype (Mission Impossible / Rookie / Veteran / Spoiled) with the strike-event reachability and recommend a motion on the T axis: PLG only works at Veteran (days) or Spoiled (minutes) TTV. Source: https://productled.com/book/product-led-growth. The 1-pager is the fourth and final feeder into the weighted MOAT decision tree (m1-moat-decision-tree-fill).
Run 60-min exec readout, capture decisions + dissent
Run the 60-minute executive readout. The session structure: 10 min context, 25 min decision-tree walk, 15 min dissent surfacing, 10 min vote on the recommended motion. Per Bush, the MOAT decision must be a documented executive consensus or it will not survive the cross-functional friction of M2-M7 (https://productled.com/book/product-led-growth). If dissent is unresolved, defer the playbook by 2 weeks rather than ship a soft consensus.
Capture signed MOAT decision doc (CEO + CPO + Head of Growth)
Capture the signed MOAT decision doc with three named signers (CEO, CPO, Head of Growth). Without a signed doc, downstream module owners can re-litigate the MOAT decision under sprint pressure and the playbook collapses. Reference https://productled.com/book/product-led-growth on the standardize-step framing. The doc must include: chosen motion, axis-by-axis rationale, the dissent that was overruled, and the explicit commitment to defer the sales-methodology playbook (or the M2-M7 PLG track) based on the result.
STOP framework checkpoint: Standardize step recorded
Record the STOP framework Standardize-step checkpoint. The MOAT decision is the canonical Standardize step for the PLG transformation — it pins the motion, the value metric direction (M2), and the foundation order (M3-M5). Reference https://productled.com/book/product-led-growth on Bush's step-by-step framing. T2D3's STOP framework (Standardize -> Templatize -> Optimize -> Productize) treats this checkpoint as a hard gate before any M2 work begins.
Draft Pain-Claim-Gain narrative for board / company-wide comms
Draft the Pain-Claim-Gain narrative that will be reused across board updates, company all-hands, and customer-facing positioning. The PCG triplet for the PLG transformation: Pain (rising CAC, plateau, demo bottleneck), Claim (PLG transformation as the primary growth engine), Gain (specific KR targets). T2D3 IP — Pain-Claim-Gain is the load-bearing narrative format for the Triple-A monthly sprint readouts in M7. Reference Loom's challenger-brand teardown (https://nrich.io/challenger-brand-gtm-library/loom) for the canonical PCG-driven positioning shift.
Go/no-go gate: if MOAT says sales-led, route to MEDDPICC and STOP playbook
Execute the playbook's go/no-go gate. If the MOAT decision was sales-led (PLG is structurally wrong for this customer), route to the sales-methodology playbook (MEDDPICC + Command of the Message) and STOP this playbook. Per Bush (https://productled.com/book/product-led-growth), shipping PLG as a secondary motion to a sales-led-fit customer is one of the most common ways PLG transformations fail. Documented in the live-build rules as the canonical fork point — gate is always-on, but downstream M2-M7 effort is conditional on the decision.
Pick value metric; document rationale
Pick the value metric and document the rationale. The metric drives M4 packaging (the usage meter), M6 paywall trigger placement, and M8 PQL scoring (the usage signal). Source: https://productled.com/book/product-led-growth. The decision is the second-most-important in the playbook after MOAT — changing the value metric mid-stream invalidates downstream billing integration, paywall copy, and PQL scoring.
Anti-per-seat check (network effect required)
Run the anti-per-seat check: if the picked metric is per-seat or seat-adjacent, verify Slack-class network effects exist (the value of the product to user N is materially higher if user N+1 also uses it). Per Kyle Poyar / OpenView (https://amplitude.com/blog/benchmark-product-led-growth), per-seat without network effects leads to 'add-and-then-not-use' patterns where seat counts inflate without value realization and customers churn at renewal.
Draft 3-tier shape (free / pro / business) with feature splits
Draft the 3-tier shape (free / pro / business) with feature splits per tier. Use the value-metric tier-limit shape from M2.S1 to set the natural quotas. Reference Slack's tier shape for the canonical pattern (https://www.getmonetizely.com/articles/plg-monetization-case-study-lessons-from-slacks-bottom-up-pricing-strategy). The 3-tier shape is the input to M4 packaging (feature flags) and M6 paywall placement.
Write pricing thesis doc (8-10 pages)
Write the 8-10 page pricing thesis doc. Sections: (1) MOAT decision recap, (2) value metric + rationale, (3) WTP analysis, (4) EVA analysis, (5) 3-tier shape, (6) upgrade triggers, (7) competitive comparison, (8) grandfathering decisions, (9) risks/mitigations. Reference Bush's pricing-thesis structure (https://productled.com/book/product-led-growth). The doc is the single artifact the M6 billing-vendor decision references for the requirements.
Draft grandfathering plan + customer-comms script for existing accounts
Draft the grandfathering plan for existing customers. Decisions: who is grandfathered (date cutoff), how long they stay grandfathered (12 months, 24 months, perpetual), and the comms script (clear, single email, specific dates). Per https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company, mismanaged grandfathering is one of the top 3 ways PLG-transformation churn explodes — customers who feel ambushed by a price change cancel even when the new price is reasonable.
Lock OKR baselines for O1 / O2 KRs
Lock the OKR baselines for the playbook's two objectives. O1 (Establish PLG as primary growth engine): KR1.1 free-to-paid >=18%, KR1.2 TTV <=7 days, KR1.3 self-serve >=30% of new ARR, KR1.4 PQL volume +200%, KR1.5 Day-7 activation >=40%. O2 (Reduce CAC): KR2.1 blended payback <=14mo, KR2.2 self-serve payback <=6mo, KR2.3 PQL->paid >=30%. Reference https://amplitude.com/blog/benchmark-product-led-growth for industry comparison points. Baselines are pulled from M1 baseline tasks; targets are the locked KR thresholds.
Run the 12 JTBD interviews per the guide. Each session: 45 min, recorded, transcribed via Otter / Grain / Fathom. Reference https://www.producttalk.org/ on Torres's interview craft rules (story-based, not summary-based; 'tell me about the last time you...'). Capture the specific moment-of-decision the customer recalls — that's the JTBD trigger that the value statement aligns to.
Tag and code interview snippets in research repo
Tag and code interview snippets in the research repo (Dovetail / Notion / EnjoyHQ). Apply the JTBD tag schema: trigger, alternatives considered, push, pull, anxiety, habit, hire/fire moment. Reference https://www.producttalk.org/ on snippet tagging discipline. Tagged snippets become the load-bearing input to the 5 canonical job stories (m3-jtbd-job-stories).
Synthesize 5 canonical job stories ("when / I want / so I can")
Synthesize 5 canonical job stories using the 'when / I want / so I can' format. Each story represents a recurring JTBD pattern from the 12 interviews and is anchored to specific tagged snippets. Reference https://www.producttalk.org/ on Torres's job-story synthesis discipline. The 5 job stories are the core input to the canonical PLG persona (M3.S3) and the value statement (M3.S4).
Lock the persona doc as v1 with explicit version control. The persona is referenced from M4 (Pain-Claim-Gain triplets, message house, signup-flow microcopy), M5 (lifecycle emails, onboarding bumpers), and M6 (paywall copy, expansion prompts). Without versioned locking, downstream copy drifts off-persona and the playbook's KRs collapse. Source: https://productled.com/book/product-led-growth.
STOP: Templatize value-statement + persona kit for org-wide use
Execute the STOP-Templatize step: package the value statement + canonical persona + Pain-Claim-Gain triplets into a re-usable org-wide kit. Reference https://productled.com/book/product-led-growth on the post-Understand-Value standardize-to-templatize transition. The kit lives in the design system / brand-asset library and is the single source for anyone authoring customer-facing copy in the company.
Build the customer-facing quota / usage dashboard so customers can see how close they are to their tier limit and to upgrade triggers. Reference Notion's PLG strategy (https://www.thegrowthelements.com/p/notion-growth-strategy-product-led-growth-plg) — transparent in-product usage display drives organic upgrade intent because customers see the friction approach before they hit it. The dashboard is the customer-facing complement to m4-packaging-usage-meter.
Ship the prebuttals: FAQ entries on the pricing page (m4-pricing-page-ship), in-flow embedded copy on the signup flow at known friction points (m4-signup-flow-ship). Reference Bush (https://productled.com/book/product-led-growth) on prebuttal placement — the prebuttal must appear AT the friction point, not in a separate FAQ tab.
STOP: Optimize cycle scheduled (monthly pricing-page A/B test cadence)
Schedule the monthly pricing-page A/B test cadence. Each month: hypothesis -> MDE -> power calculation -> ship one A/B test -> readout. Reference https://productled.com/book/product-led-growth on Optimize-step rhythm. The Tiger Team (M7.S1) inherits ownership of this cadence at the steady-state hand-off. Schedule review covers conversion-rate, revenue-per-visitor, and tier-mix metrics.
Run a logistic regression with the activation candidate as the predictor and 90-day-paid as the outcome. The candidate with the highest odds ratio + smallest p-value wins as the strike. Reference https://amplitude.com/blog/benchmark-product-led-growth on the canonical method. Without rigorous statistical analysis, the team picks the aha event by gut feel and the 90-day-paid metric will not validate the choice.
Pick winning aha event; define PQL threshold
Pick the winning aha event and define the PQL threshold: at what magnitude / time does the user become a Product-Qualified Lead? Reference Pocus's PQL guide (https://www.pocus.com/blog/the-definitive-pql-guide-part-1) and Hightouch's PQL implementation guide (https://hightouch.com/blog/the-definitive-guide-to-product-qualified-leads). The PQL threshold is the load-bearing input to the M7 PQL handoff SLA and the M8 PQL scoring model.
Lock strike + PQL doc (CPO + Head of Growth signed)
Lock the strike + PQL doc with CPO + Head of Growth signatures. The doc gates downstream M5 (cut red steps, build bumpers), M6 (paywall placement, expansion mechanics), M7 (PQL routing, SDR comp), and M8 (PQL scoring). Reference https://productled.com/bowling on the Bowling Alley framework — without a locked strike the team paints lanes red/yellow/green based on opinion and the Bowling Alley collapses.
Move yellow steps post-activation: integrations, settings deep-dives, advanced features. Per Bush (https://www.productled.org/blog/bowling-alley-framework), yellow steps that must be done at some point but not before the strike should be sequenced post-activation via lifecycle email or in-app prompts. The new straight-line gets the user to the strike first, then the yellow steps unlock progressively.
Ship the new straight-line onboarding
Ship the new straight-line onboarding. Wire the M8 event taxonomy (onboarding_started, onboarding_step_completed, strike_event, activation_completed). Reference https://www.productled.org/blog/bowling-alley-framework on the canonical bowling-alley shape. Ship behind a feature flag at 50% traffic for the first week to monitor for regressions, then ramp to 100%. Targets KR1.5 (Day-7 activation rate >=40%).
Embed 60-second explainer video (Loom or Wistia)
Record and embed a 60-second explainer video on the empty state. Reference Loom's product-led GTM strategy (https://foundationinc.co/lab/loom-product-led-gtm) on video as a load-bearing onboarding bumper — Loom-style 60-second explainers reduce time-to-aha more than text walkthroughs because users do not have to read at the moment of highest uncertainty. Hosting via Loom (cheap, embeddable) or Wistia (analytics-rich).
Ship in-app nudges for inactivity at day 2 and day 7. The day-2 nudge surfaces the next straight-line step; the day-7 nudge surfaces the explainer video + the support channel. Reference Bush (https://www.productled.org/blog/bowling-alley-framework) on the conversational-bumper-as-recovery pattern — without inactivity nudges, users who bounce after signup do not return. Targets KR1.5 (Day-7 activation rate >=40%).
Integrate billing vendor (subscriptions / invoicing / webhooks)
Integrate the chosen billing vendor: subscription create/update/cancel, invoicing flow, webhook handlers (subscription state changes, payment failures, refunds). Reference https://www.churnmate.com/blog/stripe-vs-paddle-vs-chargebee-choosing-the-right-foundation-for-your-saas. The 32-hour estimate is for a single tier-shape integration with 3 plans, monthly + annual billing, and standard webhook coverage. More complex multi-currency or multi-MoR setups extend the estimate.
Wire tax (Stripe Tax / Paddle MoR / Avalara)
Wire tax handling. If billing vendor = Stripe, enable Stripe Tax + Tax registrations per jurisdiction. If billing vendor = Paddle, leverage Paddle MoR (Paddle handles tax). If Chargebee/Recurly, integrate Avalara. Reference https://www.churnmate.com/blog/stripe-vs-paddle-vs-chargebee-choosing-the-right-foundation-for-your-saas. Without tax handling, EU/UK VAT compliance breaks and US sales-tax exposure compounds.
Configure dunning + recovery flows (target +8% recovered revenue)
Configure dunning + recovery flows. Stripe Smart Retries / Chargebee Recurly / Paddle built-in dunning. Reference https://www.churnmate.com/blog/stripe-vs-paddle-vs-chargebee-choosing-the-right-foundation-for-your-saas — the underbuilt-billing failure mode loses 8-12% revenue to involuntary churn (failed payments). Target: dunning recovers 8% of would-be-failed payments. Targets KR1.3 (self-serve >=30% of new ARR).
Ship self-serve customer portal (upgrade / downgrade / cancel)
Ship the self-serve customer portal: upgrade, downgrade, cancel, payment-method update, billing-history view. Reference Bush (https://productled.com/book/product-led-growth) on self-serve portal as the load-bearing artifact for self-serve ARR — without it, every upgrade or cancel goes through a sales/support ticket and the self-serve KR1.3 collapses. Targets KR1.3 (self-serve >=30% of new ARR).
Send price-grandfathering customer comms (per M2 plan)
Execute the grandfathering comms plan from M2. Send the grandfathering email + in-app banner to existing customers per the cutoff date. Reference https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company on grandfathering execution discipline. The execution must be a single, clear comms event — drip-feed grandfathering comms confuse customers and inflate support volume.
Ship SOC 2 / security page with downloadable summary
Ship the SOC 2 / security page with downloadable SOC 2 Type II summary (gated behind an email-capture form). Reference https://wolfia.com/blog/soc-2-compliance-requirements-complete-guide on the canonical SOC 2 page pattern. The downloadable summary is the most-requested asset by enterprise procurement and is the load-bearing self-serve trust artifact.
Build self-serve DPA click-through (Salesforce / SafeBase / Wolfia)
Build the self-serve DPA click-through flow. Customer signs DPA in 2-3 clicks via SafeBase / Wolfia / DocuSign + Salesforce. Reference https://wolfia.com/blog/soc-2-compliance-requirements-complete-guide on self-serve DPA execution — without click-through, every enterprise procurement DPA goes through legal review and adds 2-4 weeks to the sales cycle. Targets KR1.3 (self-serve >=30% of new ARR).
STOP: Productize - paywall + billing + trust ship as repeatable kit
Execute the STOP-Productize step: package paywall components + billing integration + trust center as a repeatable monetization kit. Reference https://productled.com/book/product-led-growth on the post-Communicate-Value productize transition. The kit is the deliverable that lets a future product team replicate the monetization layer for a new product or geo without reinventing the wheel.
Run the first Triple-A sprint following Bush's structure: 1 day Analyze (review last month + identify the leakiest funnel stage), 1 day Ask (brainstorm experiments tied to that gap), 28 days Act (ship 1 experiment). Reference https://cxl.com/blog/saas-growth/. The first sprint sets the rhythm — its readout is what the rest of the company uses to validate the cadence is working. Targets KR1.4 (PQL volume +200%).
Run second Triple-A sprint
Run the second Triple-A sprint. Effort drops vs sprint 1 because the template + dashboard are reusable. Reference https://cxl.com/blog/saas-growth/. The second sprint is the moment the cadence is either internalized (the team uses the template without external facilitation) or stalled (the team needs another round of facilitation).
Run third Triple-A sprint (declare cadence steady-state)
Run the third Triple-A sprint and declare cadence steady-state. Reference https://cxl.com/blog/saas-growth/ on the typical 3-sprint internalization curve. After sprint 3, the team owns the cadence end-to-end and the playbook installation can hand off to BAU.
Map PQL fields into Salesforce / HubSpot (lead source, score, signal)
Map PQL fields into Salesforce / HubSpot: lead source = 'PLG product signal', PQL score, specific signal that triggered the score (which strike-event hits, which feature usage, which engagement). Reference Hightouch's PQL guide (https://hightouch.com/blog/the-definitive-guide-to-product-qualified-leads). The fields are the canonical input to the AE workflow — without them, AEs can't tell a high-PQL lead from a generic marketing lead and treat them identically.
Build PQL routing automation (Pocus / Correlated / homegrown)
Build the PQL routing automation. Options: Pocus / Correlated (out-of-the-box PQL routing with pre-built playbooks), or homegrown (Salesforce flows + reverse-ETL). Reference Pocus's product-led-sales playbook (https://www.pocus.com/product-led-sales-playbook-volume-1). The automation routes PQLs to AEs via the round-robin in m7-pql-sla-doc, fires the 4-hour SLA timer, and triggers escalation on breach. Targets KR2.3 (PQL -> paid conversion >=30%).
Stand up monthly OKR review + quarterly business review cadence
Stand up the monthly OKR review + quarterly business review cadence. Monthly OKR review (60 min): all 8 KRs from M2 + leading indicators + Triple-A sprint readout. Quarterly business review (3-hour off-site): full OKR retrospective + next-quarter targets + annual roadmap. Reference https://cxl.com/blog/saas-growth/ on the steady-state cadence discipline. The cadence is the operating mechanism that survives long after this playbook ships.
Reverse-ETL warehouse -> CRM (Hightouch / Census)
Set up reverse-ETL from the warehouse to CRM (Salesforce / HubSpot) using Hightouch or Census. Reference https://hightouch.com/blog/the-definitive-guide-to-product-qualified-leads. Reverse-ETL is the load-bearing layer for the M7 PQL handoff — without it, the AE view in CRM is decoupled from the product-side PQL scoring and the handoff collapses. Targets KR1.4 (PQL volume +200%).
Ship PQL score field to CRM via reverse-ETL
Ship the PQL score field to the CRM via reverse-ETL. The score is computed in the warehouse on a 15-min cadence and synced into CRM via Hightouch / Census. Reference https://www.factors.ai/blog/product-qualified-lead. Once the score is in CRM, AE views, lead-routing automation (M7), and dashboards (M8.S4) all read from a single canonical source. Targets KR1.4 (PQL volume +200%).
Build monetization dashboard (paywall hits, upgrade trigger conversion, expansion ARR)
Build the monetization dashboard: paywall views by location, paywall CTR, upgrade-trigger conversion rate, expansion ARR by event type. Reference Pocus's product-led-sales playbook (https://www.pocus.com/product-led-sales-playbook-volume-1). The dashboard is the load-bearing surface for the monthly pricing-page A/B test cadence (M4.S5) and the Triple-A monthly sprint.
Write the A/B test runbook covering: hypothesis statement, MDE (minimum detectable effect), power calculation, sample size, traffic allocation, ship plan, readout template. Reference Avo's tracking-plan templates (https://www.avo.app/blog/9-free-tracking-plan-templates-from-mixpanel-amplitude-segment-and-more). T2D3 IP — the runbook is one of the proprietary artifacts. The runbook ensures every experiment ships with the same statistical rigor and the same readout structure for the Tiger Team to consume.
