How I'd build Userflow's
lifecycle engine from zero
Demo-to-pipeline already works. I'm here to turn signups into activated users, and activated users into expansion, with the speed this team deserves.
I'm not coming in to learn PLG lifecycle on the job. I spent 7 years inside the product adoption category, AI-native in how I operate, ready to build the activation, conversion, and expansion motion Userflow needs next.
The signal from the role description.
A lifecycle motion to build from zero, not a backlog to triage. That's exactly the kind of zero-to-one I ran at Pendo and Tipalti.
Diagnose where qualified users drop off, instrument the fix, iterate fast. This is funnel surgery, and it's the work I already think about every day.
Help architect the AI-native marketing stack, not just adopt tools. I rely on AI systematically for hypothesis generation, segmentation, and execution velocity.
I've spent 7 years inside the product adoption space.
Pendo and Userflow target the same customer-obsessed teams, solve the same activation and engagement problems, and live inside the same buyer's stack. I already know how this category sells, how it gets bought, and where the lifecycle leverage hides.
In-app flows, behavioral triggers, segmentation, event tracking. I don't need a ramp on what Userflow does or who buys it. I've lived next to it for years.
Small and mid-sized SaaS teams are exactly who I've been marketing to. I know how PMs and CS leaders at PLG companies evaluate adoption tools and what makes them say yes.
I can speak Pendo, Appcues, WalkMe, Chameleon fluently. That shortens content cycles, sharpens positioning, and tightens the messaging in every lifecycle touchpoint.
Every requirement, mapped to something I've done.
Build the lifecycle engine from the ground up: activation sequences, behavioral triggers, in-app messaging strategy, retention and expansion plays.
Designed activation and retention sequences at Tipalti and Pendo from scratch. Behavioral triggers tied to product usage, not generic drip schedules.
Drive PQL volume and conversion. Identify drop-offs, develop hypotheses, run experiments that move the number, explain why it moved.
Built PQL scoring and conversion experiments at Tipalti (a PLG conversion product itself). Funnel diagnosis is the daily muscle, not a quarterly project.
Run growth experiments with the squad. Bring hypotheses and customer intuition, let the GTM Engineer handle instrumentation.
I bring sharp hypotheses framed in customer language and partner closely with GTM Eng. I write the test, they ship the wiring, we both read the results.
Own the in-product experience alongside CS. Develop hands-on expertise in Userflow's own product.
7 years adjacent to in-app onboarding tools. I'd be a power user of Userflow inside a week, dogfooding it on our own funnel.
Architect an AI-native marketing stack: behavioral segmentation, predictive modeling, personalization at scale.
AI is how I diagnose funnels, generate hypotheses, automate messaging, and increase execution velocity. Not a side project, my default workflow.
Contribute to top-of-funnel acquisition mix as the role matures.
Brought ABM and demand gen at Tipalti from 120 to 1,000 employees and built the program at Pendo from zero. Top-of-funnel is in the toolkit.
5+ years lifecycle, growth, demand gen, ideally at a PLG SaaS company.
7 years across all three, with PLG depth at Tipalti and product adoption depth at Pendo. The breadth this role asks for is exactly my shape.
High autonomy, low overhead. Move fast, create structure where it doesn't exist.
Built ABM at Tipalti and the program at Pendo with no inherited playbook, no team beneath me. Structure-from-zero is the work I'm best at.
My proof, in three numbers.
Quarterly targets at Pendo, hit consistently. Built from zero, no inherited playbook, no list. Same operating mode I'd bring to building Userflow's lifecycle engine.
Hypotheses, experiments, and AI leverage on every loop.
The lifecycle motion isn't a calendar of emails. It's a system of diagnoses, experiments, and instrumented learnings. Here's the loop I'd run with the squad.
Diagnose with the data, not a deck.
Pull the funnel cold, find the highest-leverage drop-off, frame a falsifiable hypothesis. AI is how I sift behavior data fast: clustering activation paths, surfacing the signups that look like activators but stalled, isolating the moment of friction.
Ship the experiment with the GTM Engineer.
I write the hypothesis, success metric, and customer-facing copy. The GTM Engineer handles instrumentation and automation. We agree on read-out criteria before the test ships, not after.
Build the lifecycle touchpoint, in-app first.
Onboarding checklists, behavioral triggers, in-product nudges (built in Userflow itself) before fallback to email. Email and in-product flows belong to the same orchestration, segmented by behavior, not by list.
Explain the why, not just the what.
Every shipped test gets a narrative: hypothesis, mechanism, outcome, what we learned, what's next. The growth squad compounds because learnings get documented, not just dashboards.
What I'd build across the PLG funnel.
Each stage gets its own hypothesis, its own behavioral trigger, its own measurement. The signups → activated → paying → expanding flywheel, instrumented end to end.
Activation sequences
Behaviorally triggered, in-product first. The signup who completes the first flow gets a different next step than the signup who stalls at install. AI segments and personalizes the path.
Behavioral triggers
Usage milestones, drop-off signals, and intent moments wired to in-app and email touchpoints. The trigger is the event, not the day on the calendar.
In-app messaging
Userflow itself becomes the primary lifecycle surface. Email is the fallback for re-engagement, not the default. Dogfood the product, learn it deeper than any blog post can teach.
Retention + expansion plays
Usage-based plays partnered with CS. Power users get the expansion path before they ask. At-risk accounts get an intervention before the renewal conversation.
AI-native martech
Predictive PQL scoring, behavioral cohorting, generative personalization at scale. Help architect the stack, not just buy into it. This is where I want my fingerprints.
Squad partnership
Daily loop with the GTM Engineer and PM. Weekly read-out with VP Marketing. Insights surfaced into Product and CS conversations, not stuck in a marketing silo.
Funnel diagnosis to first instrumented experiment in five days.
What success looks like in my first quarter.
The JD names it: funnel mapped, drop-offs identified, first lifecycle sequences live and instrumented. Here's how I get there, plus the structural work that compounds.
What I'd build
- 1Map the PLG funnel end to end with the GTM Engineer. Find the two or three drop-offs with the most pipeline leverage.
- 2Ship the first activation sequence, in-product first, instrumented day one. Read-out criteria defined before launch.
- 3Stand up behavioral segmentation in the AI-native stack: power users, stalled signups, expansion-ready accounts.
- 4Run the first PQL conversion experiment with the squad. Document the hypothesis, mechanism, outcome, next test.
- 5Build the lifecycle read-out the VP, PM, and CS leadership all use to see what's moving and why.
Squad partnership
A VP who co-architects strategy, a GTM Engineer who ships the wiring, a PM and CS team inside the loop. That's the structure I've spent my career trying to build into demand gen orgs. With it already in place at Userflow, I spend my time on hypotheses and customer intuition, not on negotiating cross-functional alignment.
Built for your speed
Lean team, high conviction, ideas-to-market in days not quarters. That's the environment I want. AI-native workflows are how I keep one operator's throughput on par with a team of three.
Not a buzzword. The actual workflow.
- • Funnel diagnosis: cluster signup behaviors, surface the cohorts that should be activating but aren't
- • Hypothesis generation: turn behavior patterns into testable, prioritized experiment briefs
- • Personalization at scale: generate and adapt in-product copy per segment without losing voice
- • Operational leverage: replace meetings and slides with structured prompts and read-outs