Retention: the growth work nobody wants
Long feedback loops, hard problems, and exponential returns- why companies chase vanity metrics instead of building lasting value
Growth feels good because it's visible - more users signing up, more logos on the homepage, more pipeline, more charts trending upward. But most PLG businesses are running on a treadmill - users leak out as quickly as they come in, so acquisition just papers over churn while the underlying machine runs harder to stay in the same place.
This is why retention is different from every other metric companies obsess over. Acquisition can be manufactured through Ads, content marketing, SEO optimization, and referral programs; expansion can be engineered with freemium upgrades, upselling/cross-selling, clever pricing; but retention asks a simple question: did you build something people actually want to keep using? It determines whether you are compounding or just backfilling losses. Acquisition can make you feel faster, but retention determines whether that speed has direction; whether each new user becomes a permanent part of your business or just a temporary visitor you'll need to replace next month.
The math is pretty obvious, but also makes this concrete - think of your user base as
where Ut is your current users, R is retention, A is acquisition, and V is virality.
At low retention rates, that first term collapses and you start from scratch every month. At high retention rates, the base compounds: each month builds on the last. E.g, consider two companies, both adding 1000 new users monthly. Company A has 50% monthly retention. Company B has 80% retention. After 12 months, Company A has 2000 users total - barely double its monthly acquisition despite a full year of growth. Company B has 8000 users. Same acquisition, 4x the result, entirely due to retention.
PLG makes this equation more brutal - the funnel is intentionally frictionless: no demos, no contracts, no sales reps filtering for intent. This is PLG's superpower - anyone can try the product in seconds, but it's also the harshest test. The same open doors that eliminate barriers to entry also eliminate barriers to exit. Students, hobbyists, competitors, and casual browsers all count in your denominator, making your retention curves look worse than sales-led businesses where reps pre-qualify commitment. But that harsh reality is actually useful - PLG shows you immediately whether people are getting value; if they're not, they disappear, and you see the truth in real time.
why teams abandon retention work
Retention optimization feels uniquely frustrating because it violates the fast feedback loops that make other growth work satisfying - launch a Facebook ad campaign and you'll see signups in no time, complete with click-through rates and cost per acquisition data; roll out a new pricing tier with annual discounts or clever bundling/pricing and expansion revenue appears in the next billing cycle. But ship an improved onboarding flow today and you won't know whether it actually improved 90-day retention until three months pass.
This delay creates a measurement problem that compounds over time - unlike acquisition, where you can run controlled experiments with statistical significance in days, retention optimization often requires making multiple simultaneous bets across different parts of the user journey. You might improve page load speeds from 4s. to 2s, simplify signup from eight fields to three, redesign onboarding with interactive tutorials, and launch behavioral email sequences all in the same quarter. When retention improves six months later, isolating which changes actually mattered becomes nearly impossible - not because the math is hard, but because the experiment data lives with different teams who tracked different metrics.
The systemic nature makes retention especially challenging organizationally - acquisition can be optimized channel by channel, e.g. Google ads has its own team optimizing keyword bids, facebook has specialists running creative tests, content marketing has dedicated writers tracking organic conversion rates. Each channel has clear ownership and measurable daily outcomes, btt retention emerges from the intersection of product design, backend engineering, customer success, content strategy, and email marketing. A user might churn because signup is confusing (design team), APIs are slow (backend engineering), they can't find help when stuck (content team), they never discovered key features (product marketing), or they hit javascript errors (frontend engineering). No single team can own retention because improving it requires coordinating across every touchpoint simultaneously.
To add to it, human behavior adds complexity that product optimization can't solve either - users change jobs, budgets get cut, teams consolidate tools, new management arrives with different vendor preferences. The 2022 economic downturn saw many PLG companies experience retention drops not because their products got worse, but because customers scrutinized every software expense more carefully. This creates false signal problems e.g. users might appear retained because they log in weekly to check notifications, but they're not creating projects or building anything meaningful.
Lastly, and this bit is fairly crucial, the organizational dynamics make retention work politically difficult - acquisition improvements generate visible dashboard spikes that executives celebrate - “Wow, our signups doubled Q/Q with the new campaign release”. Expansion ties directly to revenue growth that makes finance teams happy. Retention work is slower, less dramatic, and often requires saying no to exciting features so engineering can focus on mundane problems like reducing signup abandonment or fixing edge case bugs. These improvements don't generate press releases but quietly determine whether unit economics work.
Most retention problems are actually activation failures; users who never send their first message, build their first dashboard, or connect their first integration were never truly retained - they were just temporarily present. This is why successful PLG companies obsess over time-to-first-value, removing every unnecessary step from onboarding and personalizing flows so a hobbyist doesn't see the same experience as an enterprise buyer.
Expansion drives retention as much as revenue - when users invite teammates, connect integrations, or upgrade plans, they embed the product deeper into their workflow. Slack reported that teams with 2k+ messages sent had 93% retention rates, while teams with fewer than 2k messages had much lower retention. The difference isn't just usage but rather organizational embedding - once Slack becomes how a team communicates, switching away requires changing fundamental workflows for dozens of people.
measuring what matters
Companies track retention in different ways, but not all metrics tell the same story. The standard approach focuses on percentages - "our 30-day retention is 42%" - which captures whether users return but not whether they're getting value. Dollar-based retention sounds sophisticated but can be misleading for PLG businesses; a small startup might get massive value from your free tier while a large enterprise might pay thousands but barely use the product. Login-based retention is the most common metric but also the most deceptive; users might check in weekly out of habit without accomplishing anything meaningful. Feature usage retention gets closer to value but can optimize for the wrong behaviors - if you track users who create five documents per month, you might encourage busy work instead of solving real problems.
The most predictive retention metrics track completion of core workflows rather than raw activity - e.g. Figma measures users who share their first design with teammates, notion tracks users who create their first database. These behavioral milestones correlate strongly with long-term retention because they indicate that users have experienced the product's core value proposition.
the retention advantage
Retention buys you pricing power and competitive moats - when users depend on your product daily, they become less price-sensitive. Salesforce can raise prices because switching CRM systems is painful, slack can get to per member pricing for the same reasons; high retention creates switching costs that competitors can't easily overcome. Several pieces of research show that increasing retention rates by just 5% can increase profits by 25-95%. The math works because retained customers cost nothing to acquire and typically expand their spending over time. Companies with strong retention can afford to invest in longer-term bets because they're not constantly fighting to replace churned users. This is why retention ultimately determines market position - companies that solve retention can focus on product depth instead of marketing spend, build for power users instead of casual browsers, and create features that increase switching costs instead of just driving trial conversion.