Vignesh Mohankumar

Our Micro SaaS portfolio company just hit ARR / FTE productivity rivaling $50M SaaS firms. Here are 4 lessons we learned along the way: Focus on operating leverage, not just revenue growth. EBITDA/FTE is an excellent scoreboard. Lean teams move faster and punch harder. Return on effort matters more than chasing scale or deal size. Where have you most recently created operating leverage in your business? #ARR #MicroSaaS #BusinessEfficiency

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The average successful exit for a bootstrapped SaaS company happens around the $5–10M ARR mark. Contrast that with VC-backed companies, where anything less than a 10x return is often considered a "failure." And while slow and steady wins the race for some, let's be realistic here — how long will it actually take? Years of grinding, reinvesting every penny, and hoping for that shmillion dollar outcome. So, here's a thought: 𝗺𝗮𝘆𝗯𝗲 𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗯𝘂𝘆𝗼𝘂𝘁 𝗶𝘀 the outcome you should be optimizing far all along.... Here's why: First off, it makes sense. You get immediate value realization.   No need to wait years for a 𝘱𝘰𝘵𝘦𝘯𝘵𝘪𝘢𝘭 IPO.   There's a clear and high probability off-ramp. Unlike VC, which often dilutes your control, strategic buyers actually want your expertise and want to preserve your vision. You might even get to lead your "baby" into its next growth phase. This means lower stress, higher satisfaction.   No more sleepless nights worrying about making payroll or prepping for the next funding round. Skip the exhausting cycle of pitches, term sheets, board meetings, and no pressure for unrealistic growth either. You get to focus on what you love—building and improving your product. Plus, you get to tap into the buyer's resources, distribution channels, and customer base. Your product could scale faster than you could ever do on your own. But what about the unicorn dream?   For every 1x unicorn, 1000x startups never make it big. Strategic acquisition offers a bird in hand—tangible success you can bank on. The real question here is: 𝘄𝗵𝗮𝘁'𝘀 𝘆𝗼𝘂𝗿 𝗲𝗻𝗱 𝗴𝗮𝗺𝗲? Do you want to build an empire? Create something valuable and move on to your next adventure? Is your goal financial security? Or do you just like the constant hustle of startup life? The choice is yours to make. If you're a SaaS founder with $40k+ MRR and curious about what's possible, let's talk! For the love of the game 🏴‍☠️ ⚡ #Fundraising #Investing #VentureCapital #Startups 

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7. That's the median employee headcount for SaaS companies under $1m in ARR. Nearly half of those positions are in engineering. I'm often asked about headcount planning and it's so helpful to provide data to drive decisions. The High Alpha SaaS Benchmark Survey is live and it's full of data like this. Download the report now. (Link in the comments)

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Summary of conversations at conferences about AI in commercial functions: Person a: what are you using AI for in your business? Person b: 🤷‍♂️ we tried to make a super cool predictive model but our data was too dirty. Also our revops team was understaffed. I did ask ChatGPT4 to make me a list of restaurants to visit at this conference. Person c: that’s weird, I asked it to make me a picture this morning Person a: 🙋‍♂️ still wondering….what are you using it for in your business again? I believe that AI will ultimately be really helpful to many different business functions. We’re in the very early innings, though.

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We analyzed hundreds of SaaS companies and the landscape of social proof to see how it has evolved and where it’s heading. We found 3 core eras and we’re now entering a 4th. 1. Era I: Analyst-led Back in the day, firms like Forrester and Gartner were the gold standard for advice. Their deep industry knowledge and extensive research were invaluable. But as the pay-to-play model crept in, their credibility took a hit. Only vendors who could afford to subscribe had access to their insights, which diluted the trust factor. 2. Era II: Platform Review-led Enter the era of platform review sites like G2 and Capterra. They shook things up with crowd-sourced reviews and ratings, much like Yelp but for software. This user-generated content, often incentivized by rewards, became a goldmine of information. Other review platforms emerged, and many were eventually acquired by larger firms. 3. Era III: Community-led (in this era) Then came the community-led era, which we’re still in. Professional networking sites like LinkedIn and online communities on Slack have become the new hubs for software recommendations. Influencers play a big role, offering genuine advice at first, but soon monetizing their opinions, which is eroding trust. Companies lean heavily on customer advocacy and influencer programs, but overusage and compensation blur the lines between genuine and incentivized endorsements. 4. Era IV: Authentic Word of Mouth (entering this era) Now, we’re in the era of authentic word-of-mouth. Organic feedback from peers is more valuable than ever. Platforms like Noble facilitate honest conversations between prospects and existing customers, making uncurated, genuine opinions the new norm. This approach emphasizes transparency and trust. This fourth era changes the game. Vendors confident in their product-market fit will embrace this approach, even at the risk of occasional negative feedback. Transparency builds trust, and unlike G2 reviews or paid influencer posts, this model of facilitating authentic word of month ensures people hear directly from customers in a trusted way. Read the full post in The GTM Newsletter in the comments. For more of what we are seeing across go-to-market and the insights of 350+ of the best operators in the game, join 52,000+ other revenue leaders who read The GTM Newsletter weekly below. ✍️

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Another take-private in SaaS: Zuora got acquired by PE for $1.7B! 🤑 These are their latest figures (Q2/25): 📊 Revenue: $115.4M (+7% yoy) 🔃 Subscription revenue: $104.1M (+9% yoy) 📈 ARR of $412.3M (vs $384.2M in Q2/24, +7.3% yoy) 🤝 Customers with an ACV >$250K: 445 (vs 444 in Q2/24) 🧑‍🤝‍🧑 Net Revenue Retention: 104% (down from 107% in Q2/24) 💸 Free Cash Flow: $12.2M (vs $4M in Q2/24, ~11% margin) 🔭 FY25 Projections: - Total Revenue of $455.5M - $461.5M - Adjusted Free Cash Flow of $82.0M+ - ARR Growth of ~6% If you do the math, you will find that Silver Lake and GIC will pay about 4-4.2x ARR for this transaction. Not a huge multiple, but a reasonable one. As a reminder, 1% growth is currently worth twice as much in the public SaaS market as 1% more profits. Silver Lake has been invested in the company since 2022, when they injected $400 million as part of a strategic investment. So they know the business and apparently believe the public market undervalues it, seeing growth potential once it moves away from being subject to public scrutiny. Not the first take-private deal in SaaS and certainly not the last. Especially SaaS companies with low growth / positive cash flow that are trading at reasonable multiples represent good targets for PEs.

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SaaS Buying Behavior Has Shifted: What Software Founders Need to Know The landscape for software purchasing has changed dramatically since 2021. Companies are no longer buying software for every need—instead, they’re categorizing products as either "nice-to-have" or "must-have." Here's what that means: Scrutiny is Higher Than Ever: Every purchase is heavily examined, with CFOs, head of sales, and RevOps teams now playing key roles in the decision-making process. Must-Have or Bust: If your SaaS product isn’t essential, you’re at risk. Companies demand solutions that solve critical problems. The game has changed. Has your SaaS strategy?

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"Mr. Money lives on focus island"* I regularly talk to B2B SaaS founders about the best revenue channel to start with. They usually have 3 dreams: A) The “product led growth” dream 🧑‍💻 Tons of small customers who come by themselves through the website. Problem: high churn, no expansion potential. B) The “someone else is selling for me “ dream 🤝 Finding sales partners who have access to the market and sell my solution. Problem: no commitment, complicated contracts, no direct access to customers. C) The “Big fish” dream 🐠 Closing big enterprise deals with a lot revenue potential. Problem: long sales cycle, many stake holders, high customization effort. Dreams are good, but revenue is better for a startup! ➡️ The best target group up to 1M ARR revenue are mid market companies! Here you have the best correlation between sales cycle, sales potential and product feedback. Focus and learn to sell to this customer group in the beginning. Start selling "up-market" later and do partner business opportunistically. Happy selling 🚀 *The saying is from my friend Johannis Hatt he's right and a great business angel on top. ____________________________ Sharing my experience as an entrepreneur and angel investor. Investing in early stage B2B SaaS startups. Happy to connect 👋

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The software PE model of Vista, Thoma Bravo, etc. is fundamentally flawed. ​ Buying legacy market leaders in B2B SaaS categories, raising prices, offshoring a % of R&D, buying adjacent products you can cross-sell, etc. etc. - doesn't work that well anymore. ​ It might have worked from ~2000-2015. ​ But it doesn't work in the modern era of software and AI. Markets are too competitive and technology is changing too quickly. ​ --- ​ Vista founder Robert Smith's thesis- "Software contracts are better than first-lien debt. A company will not pay the interest on its first lien until after they pay its software maintenance or subscription fee. We get paid our money first. Who has the better credit?" ​ The problem with this premise is that software contracts have much shorter duration on average than debt. The typical loan duration in private credit is 5-7 years. ​ The avg. enterprise software contract is 1-3 years. But in SMB and mid-market, it's rare to see contracts much longer than one year. Many companies with month-to-month contracts. Not to mention usage based pricing. ​​ Additionally, there's a lot more liquidity in B2B software than their used to be. Software is easier to build. Markets are more competitive. Customers have more choices. And LLMs make migrations easier than they used to be. ​ The most under-discussed flaw in this thesis is that it treats the customer as a captive audience. Prisoners to your mission critical product that they can't live without. ​ Software companies usually win by building a great product that solves a meaningful problem. It's trite to say, but this only happens if you're customer obsessed. Once you lose that, you become extremely vulnerable to up-starts that will obsess over the customer. No one that views their customers as debtors is going to be customer obsessed. ​ This is less of an issue in commodity products / industries like CPG. Oreo's in 2024 are pretty damn similar to Oreos in 1987. But technology is constantly changing. State of the art software in 2010 is miles behind what people are doing in 2024. ​​ Distribution and brand moats can protect your legacy products for a while (esp in enterprise) but eventually you get lapped by competitors with better products, service, pricing, etc. ​ Buying other products doesn't fix this either. The answer to customer problems is not to buy other products and jam them together with your existing solution just so you can call yourself a platform company (rather than point solution). ​ Software is too competitive and changes too fast for this model to work in 2024. ​ Anyone competing with incumbents recently purchased by private equity (like we are) knows exactly what I'm talking about.

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SaaS companies under $1M ARR are growing 250% YoY. And they're doing it with smaller teams than ever before. This mirrors exactly what we're seeing in our MicroSaaS portfolio - extraordinary growth is not a function of headcount and overhead. The old playbook said mass hire SDRs, blast cold emails, push push push. Result? Bloated teams, 12-month sales cycles, and burning cash in hopes of the ol' hockey stick (that rarely came) Here's the proof it doesn't have to be this way:  One of our companies just hit $325k ARR per employee, matching the productivity metrics of $50M companies. How? By building leverage into the core: - Content and authority building vs burning cash on ads - AI-driven operations replacing manual processes - User-led growth to complement traditional sales-led motions - Automation first, hiring second You don't need massive teams or deep pockets to drive extraordinary growth anymore. You just need to find and create leverage. What a time to be in the game! Thanks Kyle Poyar for the benchmark data and showing us early-stage SaaS is back! 📈 P.S. Running a MicroSaaS with 40k+ MRR? DM me to book a private session analyzing your key metrics and building a roadmap to higher valuations. #startups #saas #growth #sales 

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Trying to get from $0 to $1M ARR? Find your PMF. Obsess over identifying a problem that a big enough market urgently needs solving. Talk to your customers about their most painful problems and validate your assumptions. Not all feedback will be valuable. Focus on insights from your early adopters—they become your ICP and shape your product. Stay true to your core customer profile. Chasing revenue or artificially widening the PMF dilutes your product's effectiveness. Your product can only meet so many needs. Tailor every feature to your ICP. Your first version is your Minimum Sellable Product (MSP). Make the MSP easy to use and beneficial. With customers using your product, continually collect feedback to improve: 1 - Offering 2 - Marketing messages 3 - Sales calls 4 - Pricing structure 5 - Demo flow If you reach $1M ARR, congratulations! Your product has real demand. Next, it’s time to think about scaling to $10M ARR. More on that soon.

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When we first met Jon + Pål a year ago, they were deep in figuring out how to unlock the next phase of growth: building their outbound sales motions. With 500K+ devs using the platform, Sanity had built a strong PLG motion and they were looking to see if they could spot enterprise/upsell opps from all this activity. They took a bet on Koala — that they now call a gamechanger. I'm particularly proud of how we've iterated together – working with Jon to deliver quick early value during the POC and then working with both Jon and Pal to layer in lots of new data & use cases together. Two weeks in:  - Sellers now have self-serve access to website, docs, and product data.  - We’re already getting pinged with meeting highlights sourced with Koala! Fast-forward one year:  - Koala has become the go-to daily tool for sellers. - They integrated G2 intent and PLS data to further identify top accounts in-market.  - Outbound has become their main lead source! What’s next? (We're not done yet!) - Migrating from their legacy ABM platform — with Koala’s intent data, they’re unlocking use cases like ad retargeting, segmentation, and more. I think what made it all really click for me was when Michael, a top SDR @ Sanity shared how with Koala: 😍“Outbounding is fun again!!” 😍 For us, that’s what it’s all about: building a platform that sellers actually want to use, and we can’t wait to watch more magic happen at Sanity! Check out the case study here 👇 https://lnkd.in/gg-d_uW5

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I've invested in over 300 startups over the last 5 years, and there is one common trait that nearly all failed early stage startups share - they don't run a proper customer discovery process. You can read about the importance of customer discovery all over the internet, but that practical application of it among first time founders is still extremely low. We look at over 10,000 deals each year Forum Ventures and I see firsthand just how few founders actually excel in this process. One subset of founders simply doesn't do it at all. It's a classic archetype - build product in a vacuum without running any validation cycles. Typically the founder profile here skews more technical/product where founder-led sales feels out of character and not an area they enjoy spending time. Often founders think they can "product their way" to initial customers, but more often than not that new feature set they are excited about fails to generate user demand, simply because they never asked anybody if they care about what they were building. There is a classic startup'ism - first time founders focus on product, second time founders focus on distribution - and it rings very true within this subset. The second group of founders actually understands the importance of running discovery and leans into it early in their build process. While they have the best intentions, ultimately they come up short because in practice they don't actually know how to run an unbiased process. Discovery that focuses around a feature/solution set will skew toward lukewarm positive responses from potential customers, "sounds interesting, let me know when the beta is ready". The reality is no one is going to tell you that your baby, in this case your product, is ugly when it's far easier to just smile and nod. Founders often take this false signal as validation and race to ship a product that ultimately falls flat on its face. There is an art to soliciting feedback from a potential customer. The process emphasizes questions on their existing workflows, bottlenecks they experience in their day to day, understanding what motivates them and how they measure success in their role. This is a critical area we emphasize when evaluating investment opportunities, and a process we are very hands on in assisting our portfolio companies. Having a strong network is a massive advantage when launching a startup but close network relationships have the potential to give false signals across a customer discovery process. There are ways to combat this by being very deliberate when once solicits feedback from their network, but there are definitely bonus points for net-new out of network customer validation.

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Many AI-Native SaaS companies are taking too much of an opinionated approach to workflows for enterprises It's making it more challenging to get companies to buy and successfully implement. Instead of trying to build a "one size fits all" solution, here’s three approaches I’ve seen work incredibly well when going after the enterprise: 1. Start with a low-code/no-code custom workflow builder and layer on AI Features A company like Fuse Finance is making it super easy for lenders, credit unions and community banks to adopt as they have a customizable UI with agentic tools being built on top that deliver clear ROI in many ways. Unlike a customers current software, which require tons of engineering time to make any changes (which most banks don't have the resources for), Fuse now makes it 10X easier to make any changes to the process while still harnessing the power of AI. Companies with custom workflow builders find it not only easier to sell, but have quick implementation timelines, easy user onboarding, strong customer satisfaction scores + NPS and lower numbers of CX tickets vs. opinionated user interfaces that require a ton of change management 2. Build on top of the industry standard, outdated 3rd party app Datasnipper, a $1B startup backed by Index did exactly this. How? They built AI to empower Auditors with an intelligent automation platform, sitting on top of excel. By building on top of excel, Datasnipper's users feel right at home and it reduces the friction of change management in a big way for all parties involved. 3. Go into the company with forward deployed engineers, build custom workflows and integrations and then find ways to productize it for other customers Palantir is the obvious case study here and people are catching onto this playbook. My thesis is that we'll see dozens of companies launch with this consultative approach, going after Accentures ~$65B of revenue. What many VCs used to mistake as a services company model is now widely accepted as a new GTM strategy based on a two key things: 1. Palantir's success 2. Unlocked acceleration of software development driven by AI Firms, including ours, General Catalyst, Elad Gils and Jared Kushners are all partnering up with large enterprises to launch new companies that have an easy time selling in what feels like are custom builds, but in the longterm are clearly going to be products. Working on an AI software solution with either of these three approaches? We'd love to chat.

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SOOOO many PE acquisitions happening right now (Good companies that got bad VC terms) Problem??👇 Once you acquire them, you realize their ops suck. ↳ (unsophisticated) very few internal systems or processes 🤦‍♂️ ↳ everything is “growth hacked” 🥴 ↳ they’re not positioned for maximum valuation 🙄 ↳ teams are under utilized (but often EXTRAORDINARILY talented) ↳ no concept of capital origination… seeing a lot of this on Captal.co So how does Captal help?? Companies can quickly restructure to maximize VALUATION Hint: on average each company that joins, results in the formation of 4.3 new orgs (🤫 family offices love us for this) Cheers 🥂 to #raisingvaluations

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Great read on the state of SaaS in 2024. Biggest take-aways (especially GTM): - 𝗚𝗿𝗼𝘄𝘁𝗵 𝗗𝗲𝗰𝗹𝗶𝗻𝗲: ARR growth is at its lowest in years, with companies relying more on expansion than new logos. - 𝗦𝗮𝗹𝗲𝘀 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Sales efficiency has dropped, with the net magic number falling below 1.0x for the first time. - 𝗣𝗿𝗼𝗳𝗶𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗣𝗿𝗲-𝗜𝗣𝗢: More SaaS companies are achieving profitability before going public. - 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗙𝗼𝗰𝘂𝘀: Free Cash Flow (FCF) margins are improving, but the "Rule of 40" remains stagnant. - 𝗔𝗜'𝘀 𝗙𝘂𝘁𝘂𝗿𝗲 𝗜𝗺𝗽𝗮𝗰𝘁: GenAI could shift the "Rule of 40" to the "Rule of 60," but this is expected in 12+ months. - 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆: ARR per FTE is up, but operational costs per employee are rising due (inflation and AI investments). Report by Claire Davis and Emre Garih at ICONIQ Capital #tech #startups #saas

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The journey from $1 to $10M in ARR is typically an exercise in finding your first channel-market fit as a GTM/Marketing leader and/or founder. After prioritizing the top 1-3 channels you believe are most likely to be a fit for your product, finding channel-market fit is typically an exercise in experiments and iterations. If you’re flying blind bc you don’t have the systems, processes, and data hygiene setup to measure and test quickly inside a framework, it’s going to be a difficult journey. Check out the podcast I did with the team at Evron for more mistakes to avoid on your journey from $1M to $10M ARR.

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AI could kill ARR as we know it. And, in doing so, it could kill SaaS metrics as we know them ⤵ Annual recurring revenue (ARR) is the building block of SaaS metrics. And it's the basis of SaaS valuation multiples. It's (usually) high margin, predictable and growing. Which means SaaS companies are (usually) on track to become highly profitable at scale. AI throws this off. What's not classic ARR: - Charging per successful AI resolution (Intercom, Zendesk) - Charging per credit used (Clay) - Charging per task completed (11x) - Charging per photo edited (Imagen) - Charging per demand package generated by AI (EvenUp) - Charging per conversation (Salesforce) We're moving away from charging for *access* to software and to a model of charging for the *work delivered* by AI & software. This might mean greater volatility. Variable margin profiles. Seasonal revenue. Project-based, non-recurring use cases 🤯 Welcome to the new "ARR": annual *re-occurring* revenue. Some implications of this shift: 1️⃣ Spending much more time unpacking the components of revenue. 2️⃣ Moving away from ARR multiple valuations to looking at last 12 month revenue (or, even better, last 12 month margin $). 3️⃣ Looking much more closely at revenue concentration -- I suspect there will be a far wider variance between the smallest & largest accounts. 4️⃣ Measuring newer things like "time to ramp" and "share of wallet" as predictors of future success. --- I unpacked this shift with CJ Gustafson in his Run the Numbers podcast (check the comments). And shoutout to Dave Kellogg for a great keynote on this topic 🙏 🎁 For more insights on SaaS growth & pricing, check out Growth Unhinged — my free weekly newsletter: https://lnkd.in/exTbjKaM #ai #saas #finance

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