Elena Verna
Head of Growth at Lovable
Where to hear and read about Elena Verna4 episodes and tests
Lenny's PodcastThe new AI growth playbook for 2026 | How Lovable hit $200M ARR in one year
Lenny's Podcast10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey)
Lenny's PodcastThe ultimate guide to product-led sales | Elena Verna
Lenny's PodcastWhy product-led growth is the future | Elena Verna (Amplitude, Miro, Surveymonkey)Elena Verna is a growth leader who started as a data analyst, built growth at SurveyMonkey, held interim leadership roles at Miro, Netlify and Amplitude, and now runs growth at Lovable. The thing most worth learning from her is that growth comes from patterns, earned channels and layered motions built on real product-market fit, never from copied shortcuts, and that in AI even those patterns must be reinvented every few months.
copying competition is like the fastest way to mediocrity because you'll never be a leader if you copied somebody else.
10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey)
Elena Verna: the story and the lessons
1Elena Verna's path from data analyst to Lovable's head of growth
Verna started in data and chased a company that matched her pace. She studied statistics at UC Berkeley, skipped the actuary path, and left a slow-moving analyst job at Safeway. She pursued SurveyMonkey for over eight months until it hired her as its first data hire.
She rose from senior analyst to manager to director, then kept asking her bosses for more to learn because no chief data officer role existed yet.
She doubted growth teams, then became SurveyMonkey's first growth PM. She first expected growth to fizz out as Silicon Valley hype. She became the first growth product manager and later ran growth marketing, growth product and analytics together.
She tried product marketing first, then hired a marketer who was clearly better at it. She calls her almost seven and a half years at SurveyMonkey her most fulfilling role, her "teenage years and graduation".
Interim roles let her try leadership before committing. After running the consumer business unit at Malwarebytes, she was interim CMO at Miro for 11 months through covid, then interim head of growth at Netlify and Amplitude.
She calls Miro the most demanding and gratifying job she has had, because covid forced full repositioning mid-tenure. She takes six to eight months off between interim roles and advises companies such as MongoDB, Sanity and Clockwise.
She now leads growth at Lovable, a company rewriting her own playbook. Lovable reached $200M ARR in under a year after launch with fewer than 100 people and raised a Series B at a $6B valuation.
Before Lovable she worked at Dropbox, where she used ChatGPT only here and there. She calls the Lovable job the best work of her life.
2How Elena Verna grows Lovable by giving the product away
Lovable's growth comes mostly from capturing demand in an exploding category. Revenue went from $100M at the end of July to $200M four months later, with over 8 million users and hundreds of thousands of paid subscribers.
Verna warns founders not to treat this as a benchmark, since part of the speed comes from the market itself. Reaching $1M ARR in a year is still strong.
Free AI usage is booked as marketing, not as a margin problem. Lovable gives free credits to every hackathon and event and tracks that LLM cost as marketing spend. With little paid marketing or sales, free usage costs less per eyeball than Google.
She says traditional companies paywall AI to protect fat margins, which invites a rival to give it away. She had never used free as a strategy before and now calls it non-negotiable. "So we give them away as candy."
The goal is a wow moment, not a full aha moment. Lovable's wow moment is the first preview after the first prompt, which proves an idea is possible even though the app is far from finished.
Usage is the north star and monetization gets tuned later. Teams debate how to give more away even at the cost of revenue growth rate, and engagement retention is prioritized over paid retention.
Paid retention sits level with Miro, Dropbox, SurveyMonkey and Amplitude rather than above them. Net dollar retention is strong because builders keep buying credits.
Lovable ships daily and lets engineers announce their own work. Founders and employees post on X and LinkedIn, and engineers announce what they ship instead of routing it through marketing. The constant stream replaces newsletters as re-engagement.
Tier one launches still rally the whole company as turbo boosts. Without engineers announcing, she says Lovable would need an enormous marketing team.
Verna says organic now means social, not SEO, even in B2B. Posts need real personality and vulnerability; copy pasted from ChatGPT fails. Influencer marketing is 10 times bigger than paid social at Lovable.
Short demo videos sell a product nobody can explain: "you watch 10 seconds of it and you go that's new." Influencers still drive only low double digits of growth.
The brand lives inside the product as a minimum lovable product. The rule is "If it's not lovable, we're not going to ship it," and calling a bug "not lovable" gets it fixed at once. There is no brand marketing team yet.
As software gets cheap to build, emotional feel becomes the differentiator, so she says a designer should be one of a startup's first hires.
A Discord community multiplies every other growth lever. Lovable's Discord has hundreds of thousands of members, with community managers and an ambassador program.
If you cannot build a community, she says to plug into someone else's.
3Why Elena Verna says AI companies must keep reinventing
Verna now spends 95% of her time innovating, 5% optimizing. In past roles innovation was 5 to 10% of her time. Only 30 to 40% of her 15 to 20 years of growth experience transfers to Lovable.
Her growth team ships core features such as a Shopify integration and voice mode. Her frameworks are mostly for optimization; innovation has none.
Activation belongs to the team building the agent. The agent team obsesses over the first generation, which improves every later generation too. Growth writes agentic workflows and agent instructions instead.
"I've never been at the company where core team thinks so much about activation."
She believes AI product-market fit must be recaptured every three months. Each model release resets what is possible and consumer expectations shift as fast. Lovable throttles scaling, reinvents, then scales again in short blitzes.
She notes even OpenAI lost share when Gemini 3 launched. Ten years ago she would have called it crazy that a $200M company growing 10% a month could lose fit in three months.
She worries pioneers crowd out the latent majority. Using Bangaly Kaba's adjacent user theory, she says chasing early adopters leaves no time for users just outside the core.
The risk is a brand known only for techies. She admits she doesn't know the answer.
Every task at Lovable starts with what AI can do first. Specs come with a clickable Lovable prototype; for a pricing change she screenshots the page and asks Lovable to recreate it with her edits.
Vibe coding an idea tests it before it is pitched. She used to kill ideas too early or push them too far through design queues; now she calibrates quickly.
She runs women-only hackathons to close the AI adoption gap. Third-party data suggests at most 20% of Lovable users are women. She Builds runs 48-hour women-only hackathons with unlimited access.
Participants built tools for elderly parents, kids and church groups, software never worth building when it was expensive.
4Growth tactics Elena Verna says never work
Founders, not a growth hire, must find product-market fit. Founders should reach the first $1M to $10M ARR themselves; some companies wait until $100M to $200M to build a growth team.
"If you have 10 users or 10 customers, that's not data, that's a G-sheet." If sales collects the money, hire sales first; with self-serve monetization, hire growth first.
A growth team cannot rescue a declining business. At best it lifts a declining business by 10 to 15%. Core product and marketing must first plateau the decline, then growth amplifies pockets of life.
Hired during a decline, a growth team disappoints within about a year.
She has never seen a rebrand or redesign improve performance. Redesigns often cost $1M or more and 8 to 10 months, start below the optimized baseline, and at best come out net neutral.
Forecast a dip and budget two to three months of optimization at minimum, ideally six. Do it only for a real shift such as a new category.
Copying a competitor's flow fails about 95% of the time. You can't see their segments, test cells or channel. Someone once wanted to copy a Dropbox page untouched for 10 years that performed badly.
She signs up to products with many Gmail accounts, studies their flows in Miro boards, and keeps only common elements that fit, then runs her own design and testing.
Benchmarks are inputs, not targets. Even "visitor" can mean all traffic, new traffic, a new IP or a new persistent ID, and tools define it differently.
Your growth problem is almost certainly not unique. Patterns get you about 60% of the way, so ask people who solved it. At Miro an Atlassian community lead told her to start with agency and partner communities, saving six to eight months.
She was interim CMO then, and admitting she didn't know earned her more credit than she expected.
Testing everything paralyzes a growth team. If the sample can't be collected within a month, she ships and compares pre versus post, reading out at 24 hours, 7 days and 28 days.
Rigorous tests are for big strategic bets or spots where 0.1% is worth millions. She treats 95% significance as only directional.
Button colors, social logins and single emails don't move results. "A blue is a blue is a blue." Google or Slack auth is customer experience, except GitHub for developer products. Email open rates average about 25%, so test series, not one email.
She also crosses "simplify onboarding" off roadmaps: simplifying is a solution, so name the real problem, such as users getting lost.
5How Elena Verna builds growth engines that last
A growth team's first job is an earned channel nobody can buy. Over 50% of Dropbox acquisition comes from file sharing, with a dedicated pod optimizing both sender and recipient experience.
Rented channels like SEO and paid only get costlier. "An algorithm can giveth, but algorithm can also taketh away at any point."
Every growth model fades, so she layers new loops constantly. She puts 20 to 25% of growth team time each year into new loops and aims for something new every 18 months. Most loops run out in five to seven years.
The Dropbox sharing loop, still firing after 17 years, is the exception.
New loops need room before they carry metric goals. Miroverse, a library of user-created Miro boards, ran about 18 months before metric expectations, then became both an engagement and acquisition engine.
User-generated templates, case studies and reviews are the loops she tries first, and matter more as AI reshapes search.
Her three core frameworks are loops, the race car and adjacent users. Loops beat funnels. The race car sorts work into engines, fuel, turbo boosts and oil. Adjacent user theory grows by serving users just outside the core.
She uses frameworks so each new problem becomes a local application, which cuts context switching and spreads growth culture.
6Why Elena Verna layers product-led growth on top of sales
In B2B, users now make the buying decision. "Not a single enterprise buyer I know has ever chosen Slack." Employees brought it in and buyers paid once they saw usage.
So she redefines the customer as the user: onboarding, time to value, habit loops, and affinity measures like NPS instead of feature checklists.
Product-led retention comes before product-led acquisition. Users who don't engage habitually can't buy, refer or create content, so there is nothing for an acquisition engine to hook into.
She treats acquire, retain and monetize as three separate choices, each product-led, marketing-led or sales-led.
Go-to-market is a layering game, never a switch. Sales-led companies keep sales and overlay product-led; product-led companies add sales on top of usage.
"The game is a layering game, it's a sequential game." Staying purely sales-led risks disruption from below.
Moving upmarket kills PLG when sales replaces it. She estimates about 80% of companies drop product-led work after big deals, and the buyer sits in the user base only about 30% of the time or less.
She saw versions of it at SurveyMonkey, Netlify and Amplitude. An enterprise-only roadmap is the warning sign. "They've forgotten the hand that feeds them."
Product-led deals are an expansion game, not a big land. First deals are smaller because the account was entered earlier, so incentives must match.
7How Elena Verna thinks about freemium
She prefers freemium bounded by usage over time-limited trials. MongoDB offers a free shared cluster with no time limit; nobody runs production on it, so real use moves to paid.
A seven-day trial suits a startup but shuts out an enterprise that needs a month. "Don't time bound me in this stressful environment."
Free needs a strategic reason beyond conversion. About half of SurveyMonkey's users were free and drove its acquisition, and LinkedIn's free base made Sales Navigator possible.
"The biggest mistake is to think of freemium as just conversion rate optimization for paid."
Anything that feeds the growth model goes in free. Virality, the aha moment, habit loops like notifications and features every user needs belong in free; whatever adds friction to growth gets gated.
Most free users don't know what is being sold. She guarantees about 75% of freemium users are unaware of paid features. At SurveyMonkey a consistent gold paid-trigger UI, seen at least three times, lifted conversion.
She measures awareness by surveys and by the share of activated accounts that viewed pricing at least once, and changes the plan offer last.
8Elena Verna's playbook for product-led sales
Product must own the pipeline it creates for sales. Product leadership owns the target and PMs own KPIs like free-to-paid conversion and qualified accounts. Run from marketing alone, she says it fails within six months.
At Miro the head of growth carried a self-serve revenue target and shared a pipeline goal with marketing.
Add sales only if you are ready to go upmarket. Self-serve tops out around $10,000; sales deals start near $15,000 and aim at $100,000 to $200,000.
Products for freelancers or startups should stay self-serve, since those buyers are price sensitive.
Seven users in one account is her usual sales trigger. Qualified accounts combine user count, volume and velocity. Ranking accounts by user count alone gives about 80% of the value.
Admin changes and visits to terms of use pages are strong buyer signals at Miro and MongoDB; webinar views are not.
Ninety percent of the work is finding the buyer. Most qualified accounts have no decision maker inside, so marketing must find one and connect them to the usage.
She tracks usage with a lead, usage with no lead and leads with no usage separately, since each converts differently.
Never send every signup to sales. A new signup is solving a personal job, and low close rates make quota-driven reps write off product leads.
She profiles every signup with four questions: company size, department, seniority and use case.
Sell the first deals yourself before hiring salespeople. Hire only after users ask to buy; Miro hired its first salesperson around $5 to 7 million ARR and Notion past $10 million.
She wants ROI for a hire before opening a req and has been on chat herself trying to close deals. Start with spreadsheets and the existing CRM, Wizard of Oz style.
Expect about a year of usage before an enterprise contract. At Netlify, Miro and Amplitude it took 12 months or more. Conversion stays flat, around 5% for freemium and 10 to 15% for trials, while contract values rise.
9How Elena Verna hires and runs her career
Promote the first growth lead from inside. An outsider needs three to six months to learn the model and tends to paste an old playbook, which she says fails near 99% of the time.
Founders must build the first growth model: "It's like hiring a product person and saying I don't know what I want to build."
Every growth team needs a paid advisor. She hires advisors herself in every operator role, vetting each with a paid workshop before a monthly retainer.
"Any advisor that is cagey about what they know is not the type of advisor that you're going to want."
At Lovable she hires for agency and runs paid trials. Headcount went from 30 to about 100 in six months. Candidates do paid work trials of a couple of days, and Lovable favors new graduates and ex-founders.
"We prioritize people that don't look for clarity but can create clarity out of chaos."
She treats leadership roles like freemium. Leadership hiring fails because both sides hide problems, so she advises or works interim before converting to full time.
She optimizes for career optionality, not titles. She judges each job by whether it increases, keeps or reduces her options, after earning depth in full-time roles.
She says people management is a terrible job if you don't enjoy it.
She chooses by what she won't regret rather than chasing balance. With two kids, she says no to her CEO Anton when family needs her and protects sleep, workouts and downtime.
"Part of doing my best work is to take time off." Her motto is progress over perfection.
The new AI growth playbook for 2026 | How Lovable hit $200M ARR in one year
Lovable's head of growth explains how the growth playbook changes for fast-moving AI companies: spend most effort on innovation, ship constantly, give the product away, and rebuild product-market fit every three months. Lovable passed $200M ARR in under a year with about 100 people, mostly through word of mouth, founder socials, influencers and free credits rather than paid ads or sales.
She now leads growth at Lovable, a company rewriting her own playbook. Lovable reached $200M ARR in under a year after launch with fewer than 100 people and raised a Series B at a $6B valuation.
Before Lovable she worked at Dropbox, where she used ChatGPT only here and there. She calls the Lovable job the best work of her life.
Lovable's growth comes mostly from capturing demand in an exploding category. Revenue went from $100M at the end of July to $200M four months later, with over 8 million users and hundreds of thousands of paid subscribers.
Verna warns founders not to treat this as a benchmark, since part of the speed comes from the market itself. Reaching $1M ARR in a year is still strong.
Free AI usage is booked as marketing, not as a margin problem. Lovable gives free credits to every hackathon and event and tracks that LLM cost as marketing spend. With little paid marketing or sales, free usage costs less per eyeball than Google.
She says traditional companies paywall AI to protect fat margins, which invites a rival to give it away. She had never used free as a strategy before and now calls it non-negotiable. "So we give them away as candy."
The goal is a wow moment, not a full aha moment. Lovable's wow moment is the first preview after the first prompt, which proves an idea is possible even though the app is far from finished.
Usage is the north star and monetization gets tuned later. Teams debate how to give more away even at the cost of revenue growth rate, and engagement retention is prioritized over paid retention.
Paid retention sits level with Miro, Dropbox, SurveyMonkey and Amplitude rather than above them. Net dollar retention is strong because builders keep buying credits.
Lovable ships daily and lets engineers announce their own work. Founders and employees post on X and LinkedIn, and engineers announce what they ship instead of routing it through marketing. The constant stream replaces newsletters as re-engagement.
Tier one launches still rally the whole company as turbo boosts. Without engineers announcing, she says Lovable would need an enormous marketing team.
Verna says organic now means social, not SEO, even in B2B. Posts need real personality and vulnerability; copy pasted from ChatGPT fails. Influencer marketing is 10 times bigger than paid social at Lovable.
Short demo videos sell a product nobody can explain: "you watch 10 seconds of it and you go that's new." Influencers still drive only low double digits of growth.
The brand lives inside the product as a minimum lovable product. The rule is "If it's not lovable, we're not going to ship it," and calling a bug "not lovable" gets it fixed at once. There is no brand marketing team yet.
As software gets cheap to build, emotional feel becomes the differentiator, so she says a designer should be one of a startup's first hires.
A Discord community multiplies every other growth lever. Lovable's Discord has hundreds of thousands of members, with community managers and an ambassador program.
If you cannot build a community, she says to plug into someone else's.
Verna now spends 95% of her time innovating, 5% optimizing. In past roles innovation was 5 to 10% of her time. Only 30 to 40% of her 15 to 20 years of growth experience transfers to Lovable.
Her growth team ships core features such as a Shopify integration and voice mode. Her frameworks are mostly for optimization; innovation has none.
Activation belongs to the team building the agent. The agent team obsesses over the first generation, which improves every later generation too. Growth writes agentic workflows and agent instructions instead.
"I've never been at the company where core team thinks so much about activation."
She believes AI product-market fit must be recaptured every three months. Each model release resets what is possible and consumer expectations shift as fast. Lovable throttles scaling, reinvents, then scales again in short blitzes.
She notes even OpenAI lost share when Gemini 3 launched. Ten years ago she would have called it crazy that a $200M company growing 10% a month could lose fit in three months.
She worries pioneers crowd out the latent majority. Using Bangaly Kaba's adjacent user theory, she says chasing early adopters leaves no time for users just outside the core.
The risk is a brand known only for techies. She admits she doesn't know the answer.
Every task at Lovable starts with what AI can do first. Specs come with a clickable Lovable prototype; for a pricing change she screenshots the page and asks Lovable to recreate it with her edits.
Vibe coding an idea tests it before it is pitched. She used to kill ideas too early or push them too far through design queues; now she calibrates quickly.
She runs women-only hackathons to close the AI adoption gap. Third-party data suggests at most 20% of Lovable users are women. She Builds runs 48-hour women-only hackathons with unlimited access.
Participants built tools for elderly parents, kids and church groups, software never worth building when it was expensive.
At Lovable she hires for agency and runs paid trials. Headcount went from 30 to about 100 in six months. Candidates do paid work trials of a couple of days, and Lovable favors new graduates and ex-founders.
"We prioritize people that don't look for clarity but can create clarity out of chaos."
She chooses by what she won't regret rather than chasing balance. With two kids, she says no to her CEO Anton when family needs her and protects sleep, workouts and downtime.
"Part of doing my best work is to take time off." Her motto is progress over perfection.
10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey)
Ten growth tactics that repeatedly fail, from hiring a growth team too early to rebrands, copying competitors, and testing everything, plus three frameworks to use instead. Backed by Elena Verna's 15+ years leading growth at Dropbox, Miro, Amplitude and SurveyMonkey and advising dozens of companies.
Founders, not a growth hire, must find product-market fit. Founders should reach the first $1M to $10M ARR themselves; some companies wait until $100M to $200M to build a growth team.
"If you have 10 users or 10 customers, that's not data, that's a G-sheet." If sales collects the money, hire sales first; with self-serve monetization, hire growth first.
A growth team cannot rescue a declining business. At best it lifts a declining business by 10 to 15%. Core product and marketing must first plateau the decline, then growth amplifies pockets of life.
Hired during a decline, a growth team disappoints within about a year.
She has never seen a rebrand or redesign improve performance. Redesigns often cost $1M or more and 8 to 10 months, start below the optimized baseline, and at best come out net neutral.
Forecast a dip and budget two to three months of optimization at minimum, ideally six. Do it only for a real shift such as a new category.
Copying a competitor's flow fails about 95% of the time. You can't see their segments, test cells or channel. Someone once wanted to copy a Dropbox page untouched for 10 years that performed badly.
She signs up to products with many Gmail accounts, studies their flows in Miro boards, and keeps only common elements that fit, then runs her own design and testing.
Benchmarks are inputs, not targets. Even "visitor" can mean all traffic, new traffic, a new IP or a new persistent ID, and tools define it differently.
Your growth problem is almost certainly not unique. Patterns get you about 60% of the way, so ask people who solved it. At Miro an Atlassian community lead told her to start with agency and partner communities, saving six to eight months.
She was interim CMO then, and admitting she didn't know earned her more credit than she expected.
Testing everything paralyzes a growth team. If the sample can't be collected within a month, she ships and compares pre versus post, reading out at 24 hours, 7 days and 28 days.
Rigorous tests are for big strategic bets or spots where 0.1% is worth millions. She treats 95% significance as only directional.
Button colors, social logins and single emails don't move results. "A blue is a blue is a blue." Google or Slack auth is customer experience, except GitHub for developer products. Email open rates average about 25%, so test series, not one email.
She also crosses "simplify onboarding" off roadmaps: simplifying is a solution, so name the real problem, such as users getting lost.
A growth team's first job is an earned channel nobody can buy. Over 50% of Dropbox acquisition comes from file sharing, with a dedicated pod optimizing both sender and recipient experience.
Rented channels like SEO and paid only get costlier. "An algorithm can giveth, but algorithm can also taketh away at any point."
Every growth model fades, so she layers new loops constantly. She puts 20 to 25% of growth team time each year into new loops and aims for something new every 18 months. Most loops run out in five to seven years.
The Dropbox sharing loop, still firing after 17 years, is the exception.
New loops need room before they carry metric goals. Miroverse, a library of user-created Miro boards, ran about 18 months before metric expectations, then became both an engagement and acquisition engine.
User-generated templates, case studies and reviews are the loops she tries first, and matter more as AI reshapes search.
Her three core frameworks are loops, the race car and adjacent users. Loops beat funnels. The race car sorts work into engines, fuel, turbo boosts and oil. Adjacent user theory grows by serving users just outside the core.
She uses frameworks so each new problem becomes a local application, which cuts context switching and spreads growth culture.
Every growth team needs a paid advisor. She hires advisors herself in every operator role, vetting each with a paid workshop before a monthly retainer.
"Any advisor that is cagey about what they know is not the type of advisor that you're going to want."
She optimizes for career optionality, not titles. She judges each job by whether it increases, keeps or reduces her options, after earning depth in full-time roles.
She says people management is a terrible job if you don't enjoy it.
She chooses by what she won't regret rather than chasing balance. With two kids, she says no to her CEO Anton when family needs her and protects sleep, workouts and downtime.
"Part of doing my best work is to take time off." Her motto is progress over perfection.
The ultimate guide to product-led sales | Elena Verna
Product-led sales (PLS) turns self-serve usage into enterprise pipeline by flagging product qualified accounts, finding the buyer outside the user base, and making product accountable for monetization. Elena shares the signals, team setup, and benchmarks she used at Miro, Amplitude, Netlify and MongoDB, including seven or more users per account as a common sales trigger.
Interim roles let her try leadership before committing. After running the consumer business unit at Malwarebytes, she was interim CMO at Miro for 11 months through covid, then interim head of growth at Netlify and Amplitude.
She calls Miro the most demanding and gratifying job she has had, because covid forced full repositioning mid-tenure. She takes six to eight months off between interim roles and advises companies such as MongoDB, Sanity and Clockwise.
Most free users don't know what is being sold. She guarantees about 75% of freemium users are unaware of paid features. At SurveyMonkey a consistent gold paid-trigger UI, seen at least three times, lifted conversion.
She measures awareness by surveys and by the share of activated accounts that viewed pricing at least once, and changes the plan offer last.
Product must own the pipeline it creates for sales. Product leadership owns the target and PMs own KPIs like free-to-paid conversion and qualified accounts. Run from marketing alone, she says it fails within six months.
At Miro the head of growth carried a self-serve revenue target and shared a pipeline goal with marketing.
Add sales only if you are ready to go upmarket. Self-serve tops out around $10,000; sales deals start near $15,000 and aim at $100,000 to $200,000.
Products for freelancers or startups should stay self-serve, since those buyers are price sensitive.
Seven users in one account is her usual sales trigger. Qualified accounts combine user count, volume and velocity. Ranking accounts by user count alone gives about 80% of the value.
Admin changes and visits to terms of use pages are strong buyer signals at Miro and MongoDB; webinar views are not.
Ninety percent of the work is finding the buyer. Most qualified accounts have no decision maker inside, so marketing must find one and connect them to the usage.
She tracks usage with a lead, usage with no lead and leads with no usage separately, since each converts differently.
Never send every signup to sales. A new signup is solving a personal job, and low close rates make quota-driven reps write off product leads.
She profiles every signup with four questions: company size, department, seniority and use case.
Sell the first deals yourself before hiring salespeople. Hire only after users ask to buy; Miro hired its first salesperson around $5 to 7 million ARR and Notion past $10 million.
She wants ROI for a hire before opening a req and has been on chat herself trying to close deals. Start with spreadsheets and the existing CRM, Wizard of Oz style.
Expect about a year of usage before an enterprise contract. At Netlify, Miro and Amplitude it took 12 months or more. Conversion stays flat, around 5% for freemium and 10 to 15% for trials, while contract values rise.
Why product-led growth is the future | Elena Verna (Amplitude, Miro, Surveymonkey)
Elena Verna explains how B2B companies should layer product-led growth and sales rather than pick one, why freemium beats time-boxed trials, and how to hire a first growth lead. Her pattern matching comes from running growth at SurveyMonkey, Miro, Netlify and Amplitude and advising companies like MongoDB.
Verna started in data and chased a company that matched her pace. She studied statistics at UC Berkeley, skipped the actuary path, and left a slow-moving analyst job at Safeway. She pursued SurveyMonkey for over eight months until it hired her as its first data hire.
She rose from senior analyst to manager to director, then kept asking her bosses for more to learn because no chief data officer role existed yet.
She doubted growth teams, then became SurveyMonkey's first growth PM. She first expected growth to fizz out as Silicon Valley hype. She became the first growth product manager and later ran growth marketing, growth product and analytics together.
She tried product marketing first, then hired a marketer who was clearly better at it. She calls her almost seven and a half years at SurveyMonkey her most fulfilling role, her "teenage years and graduation".
Interim roles let her try leadership before committing. After running the consumer business unit at Malwarebytes, she was interim CMO at Miro for 11 months through covid, then interim head of growth at Netlify and Amplitude.
She calls Miro the most demanding and gratifying job she has had, because covid forced full repositioning mid-tenure. She takes six to eight months off between interim roles and advises companies such as MongoDB, Sanity and Clockwise.
Her three core frameworks are loops, the race car and adjacent users. Loops beat funnels. The race car sorts work into engines, fuel, turbo boosts and oil. Adjacent user theory grows by serving users just outside the core.
She uses frameworks so each new problem becomes a local application, which cuts context switching and spreads growth culture.
In B2B, users now make the buying decision. "Not a single enterprise buyer I know has ever chosen Slack." Employees brought it in and buyers paid once they saw usage.
So she redefines the customer as the user: onboarding, time to value, habit loops, and affinity measures like NPS instead of feature checklists.
Product-led retention comes before product-led acquisition. Users who don't engage habitually can't buy, refer or create content, so there is nothing for an acquisition engine to hook into.
She treats acquire, retain and monetize as three separate choices, each product-led, marketing-led or sales-led.
Go-to-market is a layering game, never a switch. Sales-led companies keep sales and overlay product-led; product-led companies add sales on top of usage.
"The game is a layering game, it's a sequential game." Staying purely sales-led risks disruption from below.
Moving upmarket kills PLG when sales replaces it. She estimates about 80% of companies drop product-led work after big deals, and the buyer sits in the user base only about 30% of the time or less.
She saw versions of it at SurveyMonkey, Netlify and Amplitude. An enterprise-only roadmap is the warning sign. "They've forgotten the hand that feeds them."
Product-led deals are an expansion game, not a big land. First deals are smaller because the account was entered earlier, so incentives must match.
She prefers freemium bounded by usage over time-limited trials. MongoDB offers a free shared cluster with no time limit; nobody runs production on it, so real use moves to paid.
A seven-day trial suits a startup but shuts out an enterprise that needs a month. "Don't time bound me in this stressful environment."
Free needs a strategic reason beyond conversion. About half of SurveyMonkey's users were free and drove its acquisition, and LinkedIn's free base made Sales Navigator possible.
"The biggest mistake is to think of freemium as just conversion rate optimization for paid."
Anything that feeds the growth model goes in free. Virality, the aha moment, habit loops like notifications and features every user needs belong in free; whatever adds friction to growth gets gated.
Promote the first growth lead from inside. An outsider needs three to six months to learn the model and tends to paste an old playbook, which she says fails near 99% of the time.
Founders must build the first growth model: "It's like hiring a product person and saying I don't know what I want to build."
She treats leadership roles like freemium. Leadership hiring fails because both sides hide problems, so she advises or works interim before converting to full time.
Lovable