SaaS Business Growth

The shift from seats to agents pressures SaaS margins. At the same time, the longstanding practice of getting enterprise customers to pre-commit and also prepay for functionality they may never deploy will get harder as CIOs look to free budget for their own LLM costs. To weather the storm, some SaaS companies have increased prices. This boosts revenue and margins in the short-term but can't be done repeatedly and creates even greater scrutiny over shelfware as procurement teams right-size and shift contracts to "pay as you go." To achieve sustainable growth, SaaS companies need to become hyperefficient at sales and marketing. Here are common ways to do so and who's doing it well: 1. PLG. Shopify and Atlassian exemplify efficient go-to-market based on product-led growth with free trials, low-friction upgrades and upsells. Their sales teams only need to get involved in the biggest opportunities at the largest accounts; every other step in acquisition, commercial transaction, activation, onboarding, and growth is self-service and automated. 2. Vertical SaaS. Guidewire Software and Veeva Systems are laser-focused on insurance and life sciences, respectively. Rather than casting a wide net, they spear-fish with deep domain knowledge and purpose-built solutions for that industry's specific workflows and regulatory requirements. Guidewire doesn't need to buy Super Bowl ads– their annual customer conference is the Super Bowl for property & casualty insurance executives. Nearly zero GTM effort is wasted– unsurprisingly they're the two most efficient on the list. We modeled Hearsay Systems after both these companies, and this focus allowed us to win incredible market share among Fortune 500 banks & insurers despite only raising $60M in totality. 3. Relocate operations to lower-cost regions and AI. This is private equity's favorite playbook to take costs out of companies they buy. Field sales continues to shift more to Zoom, which means you can hire AEs anywhere. Inside sales contributes a greater % of revenue as PLG motions are established. AI handles top-of-funnel leads qualification and generating marketing content and campaigns. 4. Focus on gross revenue retention. Because of high customer acquisition costs in #SaaS, leaky buckets are margin killers. Use LLMs to help customer success teams analyze product usage, segment cohorts, and identify opportunities to increase value realization. Put in guardrails to prevent sales reps from overselling an account, as doing so only creates churn in the next renewal cycle. 5. Introduce another product line. This only works if your new product has the same buyer as your existing products. Many SaaS acquisition pro formas fail to actualize for this reason, as it's not actually feasible to have the same AE sell both old and new products. Every SaaS company right now needs to double down on one or more of these levers in the AI era.

When I started my first company in 2011, there were two paths: 1. Bootstrap everything. 2. Raise VC money and chase hyper-growth. I took a third path. Here’s how: ~~ I call it Seed-Strapping: • Raise a small seed round to gain social proof, investor connections, and initial runway. • Build a profitable, capital-efficient company. • Never raise again. It’s sustainable growth without the pressure to “grow at all costs.” == When I built StackCommerce, I raised $800K. That was it. We scaled to $100M+ annual revs without raising another dime. Here’s exactly how Seed-Strapping works (and how you can do it too): == 1. Raise a small seed round—but think like a bootstrapper. Why raise? Social proof, connections, and initial runway. How much? Just enough to get to profitability ($500K–$2M can do it). VCs are helpful at this stage, but don’t let them push you to over-raise or over-spend. == 2. Make profitability your North Star. Seed-Strapping works because it’s about financial independence. From day one: • Focus on recurring revenue. • Cut unnecessary costs ruthlessly. • Reinvent how you grow: organic > paid, efficiency > speed. At Stack, we tracked cash flow weekly and avoided any “growth at all costs” decisions. == 3. Build the right business model. Seed-Strapping doesn’t work for every company. Focus on business models that: • Are high-margin (SaaS, marketplaces, DTC brands with pricing power). • Have good cash cycles and low fixed costs. • Monetize quickly (avoid years of R&D or delayed revenue). If your model requires huge capital to work, this isn’t the path for you. == 4. Spend where it matters. Seed-Strapping is about prioritization. Here’s where I spent money: • Sales: Hired founder-level talent and focused on enterprise deals. • Tech: Built fast, but avoided overbuilding. • Customer acquisition: Invested in organic channels like affiliates and partnerships. Where I didn’t spend: • Fancy offices, big PR firms, or massive brand awareness paid campaigns. == 5. Think like a bootstrapped founder. Even after raising: • Test ideas fast before over-investing. • Push team accountability—every dollar has to prove ROI. • Focus on profitability milestones, not vanity metrics. == 6. Leverage your investors strategically. With Seed-Strapping, you’re not raising follow-ons, so your investors should do more than write checks: • Use their connections to unlock partnerships and deals. • Ask them to make customer introductions. • Treat them as advisors, not just financial backers. == 7. Avoid the “raise or die” trap. In the traditional VC model, companies are pressured to chase their next round constantly. Seed-Strapping frees you from this treadmill. Instead, you can: • Operate on your terms. • Grow sustainably. • Build a company you can be proud of (without sacrificing ownership). == Is Seed-Strapping right for you? If you’re starting a SaaS, marketplace, or DTC brand, it’s worth considering. Follow Josh Payne for more!

Salesforce just fired the starting gun on a seismic shift in how we pay for software. At Salesforce #Agentforce, they announced they’re moving away from the traditional per-seat SaaS model to a consumption-based pricing for their AI agents. This is huge. Why? Because it signals the end of paying just to have access to technology. Instead, we’re moving toward paying for outcomes—the actual value delivered. Think about it. In a world where AI agents can perform the job functions of entire departments, does it make sense to charge per seat? Probably not. Here’s what’s changing: - From access to outcomes: Companies will pay for what the AI actually accomplishes. - From subscriptions to value: Pricing adjusts based on usage and results. - From Software-as-a-Service to Agent-as-a-Service: Technology that collaborates with you as a partner This isn’t just a tweak in pricing—it’s a radical upending of commercial models for large SaaS companies. What does this mean for businesses? - Budgeting will evolve: Costs align directly with value received. - ROI becomes clearer: Easier to measure the direct impact of technology investments. - Greater flexibility: Scale usage up or down based on needs without worrying about seat counts. It’s an exciting time, but also a challenging one. Is every SaaS company ready to embrace a model where companies pay directly for the value they receive? At Uniti AI, we’ve been thinking along these lines. We price our AI agents based on the amount of work they do, not on how many seats a company has. I believe this is the future. What do you think? Is the per-seat model on its way out?

A few weeks ago I shared data that most SaaS startups never make it. Just 3.5% reach $20 million in ARR within ten years of monetizing, worse odds than getting into Harvard. What makes those outliers different ⤵️ I investigated new data from ChartMogul, the SaaS metrics & growth platform where I'm an Analyst-in-Residence. Their dataset covers 6,525 software companies with historical data going back 10+ years. What I found surprised me. The winners didn’t necessarily *start* better. They *became* better, reinventing their startups from $1M to $20M ARR. The four key takeaways you need to know: 1. Interestingly, starting metrics at $1M ARR were pretty similar for the companies that made it to $20M & those who stalled out. The main difference was MoM growth rate. Outliers were growing 16.7% MoM on average; others were growing 8.7%. But even this wasn’t as pronounced as it appears. The top quartile of those who *didn't* make it outpaced more than half of the outliers. Clearly, starting momentum isn't the only factor at play. 2. The outliers were meaningfully better at improving their growth metrics compared to everyone else. Few managed to accelerate growth rates. But the majority got better at everything else including: - Average revenue per account: 72% improved by more than 10% - Share of MRR on annual plans: 71% improved by more than 10% - Gross revenue retention: 51% improved by more than 10% - Net revenue retention: 45% improved by more than 10% 3. Outliers raised prices, increasing avg revenue per account by 82% (!) from $1 to $20M. This was 20 pts better than others. There are many paths to a higher revenue per account. The common denominator is to increase perceived value over time while simultaneously monetizing that extra value. 4. Outliers got better at expanding their customers with NRR increasing by about 10 percentage points. This was 6 pts better than others, which compounds year after year. A major driver of this is shifting from single product to multi-product, allowing for more surface area to expand customers. --- See the full report in Growth Unhinged here: https://lnkd.in/eva67mqr A special thank you to founders Alina Vandenberghe 🌶️, Daniel Lang, Archie Hollingsworth, Amjad Masad, Zeb Evans, and Varun Anand for sharing their 🔥 learnings on the $1 to $20M ARR journey. #startups #saas #retention #pricing

Microsoft's CEO says AI agents will transform SaaS. Here’s what that means: • 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝗮𝗮𝗦 𝗮𝗽𝗽𝘀 𝗮𝗿𝗲 𝗷𝘂𝘀𝘁 𝗖𝗥𝗨𝗗 (𝗰𝗿𝗲𝗮𝘁𝗲, 𝗿𝗲𝗮𝗱, 𝘂𝗽𝗱𝗮𝘁𝗲, 𝗱𝗲𝗹𝗲𝘁𝗲) 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗿𝘂𝗹𝗲𝘀: essentially fancy (and not so fancy) interfaces (like Salesforce, Asana, or Notion) sitting on top of databases where users input and manage data with some extra features (business logic). • 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝘄𝗶𝗹𝗹 𝘁𝗮𝗸𝗲 𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 “𝗿𝘂𝗹𝗲𝘀” 𝗽𝗮𝗿𝘁 (𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗹𝗼𝗴𝗶𝗰): Instead of rules being hardcoded into each app (e.g., Salesforce automating workflows or permission settings), AI will dynamically manage those rules across multiple apps or databases. For example: An AI agent could pull data from Salesforce, update a Notion page, and send a Slack notification—all at once. • 𝗔𝗜 𝘄𝗶𝗹𝗹 𝘀𝘁𝗼𝗽 𝗰𝗮𝗿𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Right now, each SaaS app works with its own database. In the future, AI agents will work across many databases without worrying about the specifics of their backends (e.g., it won’t matter if one uses SQL and another uses MongoDB). • 𝗕𝗮𝗰𝗸𝗲𝗻𝗱𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲𝗰𝗼𝗺𝗲 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗮𝗯𝗹𝗲: If all the “smart” stuff happens at the AI layer, the underlying SaaS apps and databases become less important. Companies might switch backends (or replace apps entirely) because the AI can adapt seamlessly. • 𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝘁𝗼𝘄𝗮𝗿𝗱 𝗔𝗜-𝗻𝗮𝘁𝗶𝘃𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝗽𝗽𝘀: Businesses will demand apps built from the ground up to work with AI agents, rather than retrofitting AI onto old CRUD-based systems. My 2 cents: While this vision is compelling, the timeline may be longer than anticipated. True "AI-native" systems require both seamless integration across fragmented software ecosystems and significant advancements in AI's ability to understand nuanced business logic. Legacy systems won't disappear overnight, and adoption in enterprises—where SaaS is deeply entrenched—could take years. The opportunity? Founders who build modular, AI-first apps today are positioning themselves to lead when the shift happens. What do you think? 📌 Source: Podcast episode from the great BG2 podcast by Bill Gurley and Brad Gerstner on Youtube. #ai #llms #startups #founders #saas

A bootstrapped SaaS just sold for $200M. No VC funding. No fancy marketing. No Silicon Valley office. Just pure product-led growth that turned into a $50M ARR business. Here's the untold story of Wingify's incredible journey: For the last 15 years, SaaS companies followed the same playbook: • Raise massive VC rounds • Burn cash for growth • Focus on expansion over profits But Wingify chose a different path. Here's what they did instead: It started in 2010 with a simple idea: Help businesses make better decisions with A/B testing. The twist? Instead of copying existing tools, they built the world's first visual editor for A/B testing. No code required. Just point and click. The results were immediate: • 1,000 beta users • 10 paid customers on day one • First enterprise deal within months • $1M revenue by 2011 All without a single dollar of outside funding. But they were just getting started: While competitors chased funding rounds, Wingify focused on innovation: • First to launch heatmaps (2010) • Pioneered visual editing (2011) • Integrated with Google Analytics (2012) • Built Bayesian statistics engine (2015) Each innovation drove organic growth. Think about what this means: When you're bootstrapped, you can't rely on fancy marketing. You can't outspend competitors. You can't hire hundreds of salespeople. You have to build something people actually want. The numbers tell the story: 2011: $1M ARR 2021: $25M ARR 2022: $30M ARR 2024: $50M ARR All while staying profitable. But there's an even bigger lesson here: You don't need: • Billions in funding • Hundreds of engineers • A Silicon Valley office Just: • A great product • Happy customers • Sustainable growth The playbook is about to change: Every SaaS founder studying Wingify will realize: • Product > Marketing • Profits > Growth • Sustainability > Scale The era of endless fundraising is ending. Here's what happens next: 1. More founders choose bootstrapping 2. Focus shifts to unit economics 3. Products win over marketing 4. Customers matter more than VCs But there's something even bigger happening: The acquisition shows that you can: • Build in India • Sell globally • Stay profitable • Exit big Without playing the VC game. The lesson? Sometimes having less means building more. Wingify proves you don't need: • Massive funding rounds • Fancy offices • Complex strategies Just a great product and the patience to grow sustainably.

B2B SaaS founders will hate me for saying this. But the traditional B2B SaaS playbook is dead. Here's why: The smartest founders I know have stopped building traditional SaaS. Instead of selling software for a mere $5/seat/month, they're selling AI-enabled services that solve real business problems. These services are generating millions in ARR with skeleton crews and 50%-70% margins that make traditional SaaS businesses look weaksauce. Their philosophy is: Stop forcing customers to learn your tool and start solving their actual problems. When you charge based on outcomes (instead of seats), customers happily pay multiples of traditional SaaS pricing, because you are directly impacting their bottom line. I'm see smart tech founders implement this exact model and quietly banking seven-figure revenues in months: • An AI-enabled financial services company hit $4MM ARR in 18 months with 15 people while maintaining 70% margins • An AI-enabled IT services company hit $20MM ARR with 15 FTE and 50% margins • Creme Digital is a 7 figure agency and made $170k/m building low-code services on Lovable with under 10 people • Hundreds of AI Automation Agencies have exceeded 6 figures in ARR by building chatbots and voicebots for SMB workflows These companies are an entirely new species that combine the scalability and margins of software with the precision of human expertise. The economics are insane compared to both traditional SaaS and services: - 50%+ margins - Higher ACVs than SaaS (customers pay for outcomes, not licenses) - Faster sales cycles (solving painful problems = urgent purchases) But picking the right niche is everything. After analyzing countless businesses, I have identified a pattern: Look for businesses where highly-paid people waste time on spreadsheets and manual processes: - The VP who manually pulls data from 5 systems every week to create the same dashboard  - The law firms where associates bill $400/hour to review documents  - The financial team making million-dollar decisions based on Excel formulas There’s a reason these industries haven't adopted software yet: their workflows are too nuanced and specialized for one-size-fits-all SaaS. AI changes this completely. You can now build custom workflows for niche industries with a fraction of the engineering that traditional SaaS required. The key is solving very specific, high-value problems with AI plus human expertise. While everyone else chases the same crowded markets selling CRUD SaaS tools for $5/month, the real money is in specialized workflows that very few are addressing. The new playbook is: 1) identify a niche process 2) apply AI to automate 80% 3) add human oversight for the 20% that matters 4) charge based on outcomes This is one of the fastest paths to building a wildly profitable AI-Native business in 2025. ------------ If you liked this, follow me, Henry Shi as I decode what actually works for founders building in the AI era.

If I were running a legacy SaaS company today, I wouldn’t be sleeping much. For legacy SaaS startups, pivoting to an AI-native company is an existential challenge, testing the core of the Innovator's Dilemma. To their credit and courage, most SaaS CEOs are taking action, yet far too incremental, taking an "AI 1.0" approach by adding a copilot to their existing product. Real transformation lies in "AI 2.0"—reimagining the fundamental user interaction from the ground up. Why the alarm bells are ringing? * AI 1.0 ≠ transformation. Most SaaS incumbents bolt on a “copilot”. Nice demo, small impact. * AI 2.0 re-imagines the interface and workflow. Think GitHub Copilot vs Cursor: autocomplete add-on vs. full-stack code co-author that rewrites files, reasons across repos, and adapts to any model — developers feel the difference instantly. *The system-of-record moat is eroding. SaaS data model-based moat that created stickiness for the last two decades—is being replaced by conversational, intent and agentic based systems. Example:  CRM goes from a database to completing RFPs and follow-up emails. Why Legacy SaaS default to AI 1.0? - SaaS CEOs overestimate stickiness of the current UX and data model.  Customers will migrate. - Underestimate CIO/CTO AI mandates (new AI budgets are cannibalizing legacy line items). - Culture favors incremental roadmaps over zero-to-one bets. How Legacy SaaS can build for AI 2.0? 1. Redesign the interface. Start with the work-to-be-done, not the existing SaaS interface. 2. Build an orchestration layer for agentic workflows, tool calling, and human in the loop. Your current middleware gives a head start; extend it. 3. Staff for 0→1. Put founder-type product & engineering leaders, perhaps in an autonomous pod. Protect them from quarterly roadmap gravity. 4. Incentivize Customer Migration.  Ensure incentives of GTM teams are aligned to upgrading and moving existing customers over to the new platform.  Leadership test Ultimately, this is a test of leadership.  The SaaS CEOs and Founders who win will be those with the conviction to build for a new reality, even if it means disrupting their own successful products.

Notes from the field ✍ Agents are taking two very different paths into the enterprise: ▪️ Horizontal Agent Platforms: Sell the “What” These companies position as: “Build a fleet of agents” or “Your enterprise AI layer.” It’s a noun-first pitch. You are buying “agents” - powerful, general and, in the abstract, quite compelling. The problem? The buyer now has homework: define the use case, find budget, justify ROI. In other words, the hardest part of the sale gets outsourced to the customer. The usual response: - “Cool… what should we use it for?” - “Who owns this?” - “Which budget does this come from?” - “Can you help us design a use case?” Sales turn into co-creation and roadmaps resemble consulting. Most didn't set out to become systems integrators, but that is where gravity pulls them when the use case must be invented alongside the sale. These platforms are technically powerful but commercially blunt because they lead with capability (agents) instead of pain (a specific broken workflow). ▪️ Vertical Agents: Sell the “Why” They start with: “Reduce support cost per ticket” or “Resolve 60% of IT tickets autonomously.” Now the nouns are irrelevant. Call it an agent, a bot, or magic. What matters is that it attaches to an existing metric and budget. There is an incumbent to displace - no category creation required. Think Decagon in B2C support, Pylon in B2B support, Serval in ITSM. They’re selling outcomes, not AI. The vertical starting point may looks narrower. Increasingly, operators and CTOs are telling a different story: the fastest way to go broad is to start specific and earn your way out. Traditional vertical SaaS gets boxed in by its workflow. AI-native agents don’t, because the core asset is not the workflow but the layer that observes, orchestrates, and accumulates context across systems. Imagine: - A company launches a customer support agent - automating refunds, order changes, subscription issues. Soon they realize most issues are symptoms of pricing and billing friction. Embedded across CRM and billing, it starts triggering fixes, not just answering complaints. Support automation → control layer for customer experience and revenue leakage. - Another launches in IT - password resets, access requests, provisioning. Soon they realize most tickets stem from identity drift. Sitting across HR and IAM, it expands into security (privilege risk, audit) and finance (license optimization). IT automation → control layer for access entropy. Most enterprise workflows are artifacts of how software was purchased, not how work actually happens. You can have different tools across IT, Support, and Security all compensating for the same upstream limitation. Fix the root constraint and you’re not improving a workflow, you’re collapsing artificial boundaries between them. That’s the opportunity. Start vertical to get distribution, trust, and data. Expand horizontally by following the problem, not by declaring a platform.

I’ve sent over 100,000 cold emails (and I learned the hard way). 45% failed because the copy isn’t good enough, or the email never reaches the inbox. That’s why you need both: 1. Copy that gets replies 2. A system that ensures delivery Here’s my 7-step framework to write cold emails that actually get responses: 1. Get crystal clear on your ICP “Founders” is not an ICP. “SaaS founders at $2–10M ARR, hiring SDRs” is. The narrower you go, the stronger your message. 2. Subject line = half the battle 47% of recipients open based on it alone. Examples that work: → “Scaling SDR hiring?” → “Quick note on your Series A round” Keep it under 60 characters. Curiosity-driven, not clickbait. 3. First line > small talk “Hope you’re doing well” kills momentum. Better: “Saw your team just crossed 50 employees—congrats. Curious how you’re managing outbound at that scale?” 4. Keep it under 120 words Data shows 50–125 words = highest replies. One email = one idea. If you need more space, the positioning isn’t sharp enough. 5. Write like a human Short sentences. Simple words. Conversational tone. If you wouldn’t say it in a coffee chat, don’t write it in an email. 6. Call-to-Value, not Call-to-Action “Can we hop on a quick call?” is about you. “Would it help if I showed you how [peer company] cut reply times in half?” is about them. People don’t buy calls. They buy outcomes. 7. Follow-ups make the difference 70% of replies to cold emails come from follow-ups. Most reps stop after 1–2 emails. Big mistake. Change the angle each time…new benefit, proof point, or case study. The framework gets you replies. But scaling it consistently? That’s where most teams fall short. → Staying out of spam filters. → Keeping sequences human. → Testing which subject line actually works. → Managing dozens of replies without losing track. That’s exactly where Saleshandy makes the difference: → Find what works faster with subject line + copy testing → Scale with reply-based sequences that feel personal → Stay out of spam with inbox placement tests → Manage replies in one AI-powered inbox Because at the end of the day: Good copy gets replies. Saleshandy gets it delivered. 👉 Try it out here: https://lnkd.in/dtGtKYUR What’s the most underrated cold email tip you’ve learned from experience?