Shubham Bhargav

If you plan to run a pricing experiment, but it takes your team 6 months to implement, you are already behind. Most SaaS companies have the same problem. They blame their billing tool. Or Salesforce. Or engineering bandwidth. But when I ask where their monetization bottleneck actually is, the answer is always the same: everywhere. 6 different systems. Each one claiming to be the source of truth. Hard-coded entitlements scattered across the codebase. Product teams can't ship without touching hard-coded billing logic. GTM can't test without filing engineering tickets. It's not a billing problem. If it were, you'd be out of business - you wouldn't be collecting cash. It's a pricing architecture problem. At Vercel, they average 5-6 pricing changes per month. They built pricing like they build product. Here are 4 pillars of pricing architecture that Fynn Glover, Benjamin Papillon, Shar Dara defined at the Schematic Monetizing AI Summit: 1/ 𝐔𝐧𝐢𝐟𝐢𝐞𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐂𝐚𝐭𝐚𝐥𝐨𝐠 One schema. One source of truth. Every plan, feature, entitlement, and price defined once and inherited everywhere. When product and billing share the same catalog, new SKUs appear across all systems instantly. 2/ 𝐃𝐞𝐜𝐨𝐮𝐩𝐥𝐞𝐝 𝐄𝐧𝐭𝐢𝐭𝐥𝐞𝐦𝐞𝐧𝐭𝐬 Stop hard-coding plan logic into your product. Every time you write "if planId == Enterprise, enable feature X," you're cementing your feet. Let business teams change packaging without touching code. 3/ 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐌𝐞𝐭𝐞𝐫𝐢𝐧𝐠 AI features are usually priced per usage. Customers hate buying if they can't predict how much they will actually pay. They fear the huge, unpredictable bill. Enable tools to estimate and monitor usage. 4/ 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐏𝐥𝐚𝐧𝐞 𝐟𝐨𝐫 𝐆𝐓𝐌 Your growth team should be able to spin up a Black Friday promo or a custom Enterprise plan without bothering a single engineer. Your growth and product teams should create new plans, adjust limits, trigger experiments, and launch promotions without waiting on engineering. Here's what happens when you get this right: → Pricing changes go from quarters to hours. → Developers ship product code, not billing code. → GTM iterates on monetization continuously instead of waiting in a queue. Pricing is what makes money. And the winners are the ones who treat pricing like product. Your pricing architecture: Can your team launch a new tier today without engineering? Can you unbundle a feature into an add-on? #SchematicPartner

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Since I started building B2B SaaS products 12 years ago, I’ve been through it all some products got acquired, some are still growing, some are still in progress, and yes, some failed spectacularly. Every success and failure has left a lesson. Here’s what I’ve observed on why B2B SaaS startups struggle or thrive: 🚀 Problem First, Features Later Building a “cool product” isn’t enough. Customers care about outcomes, not features. The moment you solve a real problem, adoption becomes easier. 💡 Product-Market Fit Can’t Be Skipped Even the best ideas fail if they don’t fit the market. If users churn or adoption stalls, it’s a clear signal: rethink, pivot, or refine. ⚡ GTM is as Important as the Product Building is only half the battle. Without visibility, marketing, partnerships, and outbound strategy, your product remains invisible. 💰 Cash Discipline is a Superpower Revenue matters more than vanity metrics. Many startups burn through cash chasing growth without a repeatable revenue engine. 💬 Customers Are Your Compass Ignoring feedback is deadly. Your early users are partners—listen, adapt, iterate. 📈 Scaling Too Soon Kills Focus Adding features, hiring aggressively, or entering multiple markets before nailing core value dilutes energy and slows growth. 👥 Team Alignment Makes or Breaks You Ideas are nothing without execution. A focused, mission-driven team with clear ownership is everything. ⏳ Patience, Persistence, and Iteration Win The path to product-market fit and sustainable growth is long. Don’t rush; keep learning and iterating. Building B2B SaaS is a marathon, not a sprint. After 12 years, the one constant is this: keep building, keep learning, keep improving. 💬 Founders, what’s the hardest lesson your SaaS journey has taught you? Share below—I’d love to learn from your experiences. #B2BSaaS #SaaSFounders #StartupLessons #ProductMarketFit #CustomerSuccess #Entrepreneurship #FounderLife #SaaSGrowth #BusinessStrategy #TechStartups

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Too many teams treat “cost control” like a last-minute clean-up. This dbt Labs on dbt post shows how we turned it into an everyday habit using the dbt Fusion engine to free up budget and build faster: ✅ 25% spend reclaimed ✅ Workflows that run lean ✅ Cost awareness baked into daily dev, no heroics needed Read the blog for more: https://lnkd.in/eErCcvGp

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🚀 “The fastest-growing products don’t chase more features — they chase the right metrics.” Most teams drown in DAUs, MAUs, signups, churn. But as Shreyas Doshi says: 👉 “A metric only matters if it meaningfully changes what you do next.” The problem isn’t measurement. It’s measuring everything except what actually drives behavior. 📌 What the best product teams measure Great metrics are: Understandable → explainable in 10 seconds Actionable → change what you prioritize Behavioral → reflect real user intent Comparative → help choose between options strong NSM →Reflect true user value & Predict long-term growth AARRR Funnel: Acquisition → Activation → Retention → Revenue → Referral ⭐ If retention is weak, nothing else matters. And the most important behavioral signal? 👉 The Aha Moment — the instant a user feels value. Great teams measure and optimize this moment relentlessly. 📊 The KPI stack every PM should use Input metrics → what teams control Output metrics → what users do Outcome metrics → business change Counter metrics → guardrails against tunnel vision Lifecycle metrics → early/mid/late-stage focus differences Good KPIs are (Company → Product → Team → Feature): Specific→Time-bound→Aligned to feature intent→Embedded into PRDs→ Laddered through the KPI Pyramid ⚡ What the fastest-growing companies optimize Activation moments (first value) Time-to-value Self-serve onboarding Expansion revenue (land → expand) Sales velocity Emotional triggers that drive habit formation 🧠 The Modern Metrics Toolkit (Decision Engines) - North Star Metric (NSM) - AARRR Funnel - KPI Pyramid - Input / Output / Outcome metrics - Counter Metrics - Leading vs Lagging - Behavioral & Aha Metrics 1️⃣ Slack — The “2,000 Messages” Breakthrough Slack discovered a simple truth: 👉 Teams that sent ~2,000 messages almost never churned. This wasn’t a vanity metric — it was Slack’s Aha Moment: Why it mattered: Network effects kicked in→Knowledge began compounding→Switching cost skyrocketed→Slack became “how we work” So Slack optimized everything to get teams there faster: Team onboarding > individual onboarding Zero-friction early features (channels, file sharing, search) Integrations with tools teams already use Slack’s growth formula: 👉 Accelerate collaboration → accelerate habit formation. 2️⃣ CRED — The Reward Habit Loop CRED realized: 👉 Paying bills doesn’t create habit. Feeling rewarded does. Their true success metric became Reward Redemption Rate, not bill payments. CRED’s habit loop: Trigger → bill/reminder Action → pay Reward → scratch cards, exclusive brands, surprise wins Investment → coins → redemption → dopamine → repeat Everything in the product pushes users toward feeling value, not just earning it. CRED’s growth formula: 👉 Turn a routine bill payment into a reward ritual. 🔖 If you had to bet on ONE metric to change your product’s trajectory this quarter — which one would it be, and why?

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This GTM pivot helped Confluent stop wasting time on leads that would never convert. How they navigated through this challenge & built a $11Bn empire is something every DevTool should deeply study! 🙌 In 2014, Kafka’s creators had left LinkedIn to build Confluent and deliver Kafka as a fully managed, enterprise-grade streaming platform. Earlier Confluent was selling to non Kafka users as well. This heavily fogged their pipeline! 👀 They realized quickly that if they continue pitching to these users, it would be a waste of time. They were also looking at two other challenges: 1. How to find these companies? Open source usage is trickier to track (& this was 2014) 2. Even if they’d narrowed down the companies that are built on Kafka, not everyone would be ready to buy Confluent... So, they started pulling intent signals from everywhere. - Think job ads for Kafka roles, - LinkedIn profiles that mention Kafka skills, - Who’s reading their blogs and joining their webinars, - How many Kafka devs a company has, - And who’s trying out Confluent Cloud or the self-hosted version. Then they fed all that into their sales and marketing engine, so every outreach hit exactly the right people at the right time. Too often I see DevTool teams freeze on one isolated signal - only to chase dead ends. The real pay-off comes when you monitor developer activity deeply and layer the signals together to map which companies are in market, in pipeline, & ready for a sales conversation! It’s not 2014 anymore. It’s far more easier to do this for companies today, than it was for Confluent, a decade ago. We’re making this happen for 100+ Developer focused companies😃 Exciting times if you're running Dev GTM! 🚀 P.S - Linking the full story of Confluent in the comments below.

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After scaling multiple SaaS solutions, here are three lessons that work almost in every case: 1. Optimize for retention before scaling acquisition. Churn can kill growth. 2. Tech debt isn’t optional—but manage it wisely. Pay it down strategically to maintain high velocity. 3. Solve real problems. Features that address user pains aren't optional—they're essential. We’ve compiled our findings into a special handbook on SaaS Product Management. Check it out here: https://lnkd.in/e-rVwMPj

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RevOps isn’t drowning in tools. It’s buried in the decisions no one ever unmade. Every automation someone built “just for now.” Every field added “in case we need it later.” Every enrichment tool, integration, and sync job that mostly works...we think? 🤷‍♂️ Tech debt in Salesforce is kinda like cholesterol. You don’t feel it until the heart attack. And by the time leadership notices, you’ve got 4 flows writing to the same Opportunity field, 200+ fields with null values, and an API rate limit warning that sounds like a smoke alarm. It’s not about aesthetic. It’s not about pride. It’s about velocity. - Every net-new process takes twice as long to build. - Reps don’t trust the data, so they don’t follow the playbook. - Your reporting is half duct tape, half faith. - You’re stuck reacting to problems instead of enabling scale. And here’s the really shitty part: No one owns the cleanup! Everyone agrees it’s important and no one touches it. It's bonkers. That said, the fix isn’t more tooling. It’s organizational courage. 1. Start with GTM motion, not system design. If it doesn’t support core revenue workflows then just kill it! Stop debating the naming convention of a field that no one uses. 2. Triage like an ER, not a museum curator. Don’t optimize everything. Prioritize what drives conversion, forecast accuracy, and rep productivity. 3. Give someone the mop AND the authority. You can’t fix tech debt on nights and weekends. Assign ownership. Put it on the roadmap. Attach it to revenue outcomes. 4. Run tests in the sandbox, not your anxiety spiral. You don’t have to delete everything today. But stop defaulting to inaction just because someone might complain. tl;dr = if your RevOps roadmap doesn’t include debt reduction, you’re building a trap. You don’t scale by adding more. You scale by removing friction.

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SoberingTruth™ #1: Shipping faster with AI is not a competitive advantage. Most SaaS teams just haven't found out yet. Our visionary Sr. VP Jean-Philippe Leblanc wrote a post on managing AI token costs in engineering budgets (https://lnkd.in/e2iZp7_K) and touched on Value Per Token, a conversation we all engineering leaders are (or will be) thinking about, so let's talk about it. Sprint velocity is up. The board slides look great. But most companies track token costs in one dashboard and product metrics in another. Those two have never met. So you know what you spend on AI. You know which features customers love. What you can't say is whether AI-built features deliver more or less customer value per token than the human-built ones. The people closest to the work sense this. SoberingTruth™ #2: Nobody actually knows if customers care about what's being shipped faster. And the faster the team ships, the harder it becomes to pause and ask, because asking might reveal that speed was never the real bottleneck. The how: connect what you spent to what customers got. 1) Tag what the LLM built. Every feature, fix, or improvement where LLM-generated code made it to production gets a flag. Doesn't need to be exact, "roughly 80% LLM, 20% human" is plenty. You just need traceability. 2) Measure customer value by watching behavior, not by asking. For each shipped feature, track what your product team probably already tracks: adoption rate, usage frequency, retention impact, expansion signal, or keep it simple and tag it with human-defined expected customer value in a scale of 0-5 (yes, zero inclusive) - ideally by your super Product Manager. The only new step is linking those numbers to how the feature was built. 3) Measure token cost per feature. Total tokens consumed to produce the code that shipped, including the dead ends, the iterations, the "no, not like that" cycles. It's in your LLM provider logs already. 4) Divide one by the other. That's your Value per Token. Simplicity Rulez! The number will be rough. That's fine. SoberingTruth™ #3: Rough and directional beats precise and nonexistent. One team that tried this found their highest-VpT work was small quality-of-life fixes: faster search, better error messages. The lowest? Ambitious new capabilities with single-digit adoption eating 10x the tokens. Track VpT over quarters. It tells you where to point the LLM, when the model is actually improving, and whether you're converting tokens into customer value, or just burning them faster. Stop measuring how fast you ship. Start measuring whether any of it lands. What's the most token-expensive feature your team shipped this year that customers barely touched? Come on gang, shred this to pieces, let's improve together! #AI #SaaS #ProductStrategy #LLM #EngineeringLeadership

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WunderGraph Cosmo is helping a major company RELEASE 50 TIMES A DAY...... WTF??? 🚀 A fast-scaling engineering org supporting over 50,000 customer websites was hitting serious friction: • Backend-for-frontend layers had diverged across teams • Every feature required multiple pull requests and redundant logic • AI automation efforts stalled due to inconsistent conventions • Product velocity slowed just as customer demands were increasing And a major launch was on the line. Rather than rebuild everything from scratch, they took a progressive approach. They adopted a new architecture centered around a unified GraphQL layer using WunderGraph Cosmo. The shift allowed them to: • Share logic across teams • Consolidate review and deployment workflows • Eliminate redundant transformations • Improve onboarding and operational clarity The team moved to a monorepo, replaced REST endpoints module by module, and used shared TypeScript types to connect services. Cosmo’s schema visualization helped engineers understand how systems fit together without needing deep GraphQL expertise. They’re now releasing to production up to 50 times a day...with fewer engineers and faster cycle times. The new architecture didn’t just reduce complexity. It gave product teams a clear path from idea to release, without bouncing between frontend, BFF, and domain layers. Keep in mind they did a bunch of changes as well to the architecture. Cosmo played a key part, but is not the only reason why they are able to release 50 times a day now. We’ll share the full story later this week. It’s a great example of how simplifying architecture can unlock both technical and organizational gains.

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The most expensive mistake you can make as a builder isn't a bad API call—it's working in isolation. Flowgrammer Café returns to Toronto on March 9th, and it's engineered for one thing: high-output shipping. This isn't networking. It's a momentum engine. A curated workspace where AI engineers, automation architects, and founders come to ship real work—fast. What makes this different: → Deep Work Environment: Zero panels, zero pitches, zero distractions. Just premium caffeine and conditions optimized for flow state. → The Hive Mind Advantage: Stuck on a recursive workflow bug or deployment mess? The person next to you might be your fastest path to the fix. → Accountability That Drives Output: Bring one target you're committed to shipping by end of day. This room makes procrastination feel expensive. Who this is for: Builders working with AI, n8n automation, no-code/low-code, or custom dev who are done with the conference circuit and ready to create momentum they can ride for weeks. The ROI is simple: A day of deep focus with people who move at your speed is worth more than a month of scattered Zoom calls. Spots are limited and these fill fast. If your backlog doesn't need motivation—it needs conditions—this is your room. ☕ Brew. Build. Belong. March 9th, 9 AM at Workhaus (901 King St W, 4th floor) Register now: https://luma.com/n9bz8sbk

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🌟 I had an absolutely fantastic conversation with Summer Poletti (rhymes with spaghetti) about the fascinating world of revenue! 🎉 As a technical founder, I shared some of my most challenging “fall on my face” moments while navigating the sales landscape. When two sharp, opinionated women in revenue hang out, the conversation doubles in fun — talk about a profit boost! We explored some truly enlightening topics, including: 🔍 How much of the AI-for-sales hype misses the mark 📉 The hidden dangers of poor data on growth potential ⚠️ The pitfalls of “set it and forget it” automations 🤔 The common challenges early-stage founders encounter regarding customer success 🤝 And why it's crucial for every startup to have a "flex person" in place before expanding to a full customer success team. Interested in learning more? Check out the link in the comments below! 👇 I’m excited to keep this conversation going!

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Hey Solutions Engineering team, how are you organizing all of the important documents during the POC/technical eval? During a POC or technical evaluation, key documents often get scattered across emails, Slack threads, or outdated links. Buyers can now securely upload files directly into the File Center of their shared Opine portal, making it easier than ever to collaborate on deal-critical documents in one centralized location. This update eliminates the chaos by giving buyers a dedicated space to contribute directly to the deal’s source of truth. Whether it's requirements docs, architecture diagrams, or product screenshots, everything stays organized and accessible in one place. Less back-and-forth and stronger alignment throughout the sales cycle. Opine keeps deal context clear and centralized, so your team can stay focused on what matters most: winning the deal. #sales #presales

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Last week, markets repriced a simple idea: If agents can operate workflows end-to-end, seat-based SaaS starts to look less like infrastructure and more like overhead. The uncomfortable question every CFO is now asking: "What happens to our software budget when one capable agent can cover work that used to require multiple tools—and multiple logins?" I spent last weekend analyzing this through three previous disruptions: 📍 IBM vs. the PC wave (1980s-90s) 📍 Netflix vs. Blockbuster (2000s-2010s)  📍 The cloud transition (2000s-2010s) The patterns don't repeat perfectly. But they rhyme relentlessly. Three insights that matter: → IBM's profitability trap: When your best business prints money, every pivot feels like stepping down a rung → Blockbuster's attachment problem: They couldn't cannibalize themselves before someone else did → Cloud's timeline lesson: Capability moves fast. Enterprise adoption moves at the speed of trust. Here's what makes agents fundamentally different: Cloud changed WHERE software runs. Agents change WHO does the work. That's not a product transition. It's an operating model shift. Full analysis: "The SaaS Wars Trilogy: When AI Agents Strike Back" Link in comments 👇 The real question isn't whether agents change enterprise software; it's whether they can. They will. It's the companies that dare to redesign their business around agents before customers force them to. Do or do not, there is no try. #EnterpriseAI #SaaS #DigitalTransformation #BusinessStrategy --- Views expressed are my own and do not represent my employer.

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You wouldn’t launch a product without testing it first. So why do we launch GTM motions without doing the same? When Sangeeta Chakraborty joined Miro as CRO, she didn’t default to a legacy sales playbook. Instead, she borrowed from product. She built pods. She built a backlog. She ran experiments. She learned fast before scaling. On this episode of The Revenue Leadership Podcast with Kyle Norton, Sangeeta shares how product thinking transformed her approach to go-to-market — and why it’s time for revenue leaders to stop operating in straight lines. You’ll also hear: ✔ Why “post-sales” is a broken term ✔ What CROs need to understand about financial models ✔ How to assess real sales talent beyond the resume ✔ What she looked for when choosing her next role 🎧 Listen to learn more: Spotify: https://lnkd.in/eX89pzau Apple: https://lnkd.in/ekTXVnba YouTube:  https://lnkd.in/eN6vUsWb

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I never thought .net core could be an API option for nextjs react. Accidentally my personal project is using it too. 😂

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