Adrian Obleton

AirOps is live in the Claude Directory today and I am pumped. Marketing teams can now access their complete AI search performance data, competitive intelligence, and content visibility metrics directly inside Claude. Ask a question, get the data, see the charts, and take action - all in one conversation. No switching between tabs. No export, upload, copy-paste. Just ask like you normally would and the data appears with charts, analysis, everything. What really excites me, though, is the partnerships angle: we didn't need months of build time to make this happen. No integration backlog. No debates. Just immediate interoperability that lets us innovate with partners at lightning speed. This is the golden age of partnerships. We can test joint value, tell stories together, and get into real co-selling conversations in weeks instead of quarters. Every major platform is moving toward MCP support (ChatGPT and other clients coming soon) which means we're just getting started. If you're thinking about how to activate your actual business context with AI instead of just pumping out generic content, let's talk. Huge shoutout to the Anthropic team for being incredible partners on this. Let's go! Available now in the Claude Connector Directory: https://lnkd.in/eH3tnwG4

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Hot take: if your SaaS is under 50K MRR, you do not need ChurnZero or Gainsight. You need 5 well-written emails and a simple checklist. Agree or am I missing the nuance?

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The canary in the coal mine signalling the death of SaaS systems of record platforms? Mid career sales people on the hunt. Late career have install bases that they mine. Farmer mentality. Early career aren’t optimizing they time yet to upskill to SaaS products with larger deal sizes. Likely selling “feature” SaaS. Marketing is still in denial. They think the next AI feature will save them. “What if we launch a chatbot and call it Bud?” Tracking the behavior of mid career sales people has always been telling to me.

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I’ve spent a lot of time talking with PE-backed RIA leadership teams who feel confident about their tech stack and I understand why. On paper, the numbers usually look fine. The problem is that the real cost of CRM decisions often doesn’t live in licensing or consulting fees. It shows up in advisor time lost to friction, ops teams maintaining workarounds, and M&A integrations that take longer than expected. Over time, that drag compounds. What surprised me most while writing this piece was how rarely those costs surface in board conversations, even though they can materially affect enterprise value at exit. Check out my article in @Advisorpedia that walks through where that value leakage tends to hide, and the questions investors can ask to surface it without dictating solutions. https://bit.ly/4bAjK8M #RIA #PrivateEquity #WealthTech #CRM

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GTM Engineer might be the hottest title in tech right now, but the work has always been there. Some wear the title. Others just do the work, connecting systems, stitching insights, enabling teams, and pushing go-to-market efforts forward without the fanfare. Whether your org calls them GTM Engineers, Revenue Ops, or just “the person who makes everything work,” they’re often the difference between activity and real impact. If that’s you, this asset will help. It’s a clear, no-fluff breakdown of what great GTM execution looks like and how to improve it. Built to support the people behind the motion. Enter Peel Playbook - GTM Engineer Edition (of course as a Talkable) Talk to it - steal the great ideas - link in comments

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CPO Playbook: Building Strategic Product Partnerships with Your CFO and Board In today’s market, capital efficiency is the new growth. Boards and CFOs are asking harder questions about ROI, and the Chief Product Officer can no longer afford to answer with features and timelines alone. To have true influence at the strategic level, great CPOs must evolve - from roadmap owners to financial storytellers. This playbook breaks down the 5 levers every CPO can use to connect product initiatives to business outcomes like ACV growth, CAC efficiency, and net retention - becoming indispensable to both the CEO, CFO, and the board. --- 1. Translating Product Strategy into Strategic Narrative Key Business Levers: ACV, NRR, NDR Your product strategy *is* your strategic narrative. Yet many boards hear about features and timelines, not outcomes. The best CPOs shift from talking about *what* they’re building to *why* it matters—anchoring product bets in financial upside. For example: - Launching premium features? That’s ACV expansion. - Driving adoption across new teams? That’s NRR lift. - Reducing churn through onboarding improvements? That’s NDR gain. The best test? Can your CRO, CFO, and CEO all articulate your product strategy - and how it drives financial performance? When product leaders speak the language of unit economics, they become strategic amplifiers—not cost centers. --- ✅ In the full version: - Turn your roadmap into a forecast of future cash flow - Build Product Ops as a unit economic cockpit - Drive retention, upsell, and valuation through PLG and PLS - Help your board understand how product powers margin and growth 👉 Subscribe to access the full playbook → Link in the comments

New podcast drop with Alex Oppenheimer is live 🎙️ We went deep on what really drives PLG - and why most teams get it wrong. Here’s what we covered: > What PLG actually means: your product should do the job of Sales/CS - guide, qualify, and tell the story inside the experience. > Why focus wins: one lever, done exceptionally well, compounds everything else - activation, retention, and CAC. > AI ≠ magic: Prompt bars and “wow” moments drive curiosity, but long-term adoption still comes from solving a real problem with guided context. > Why bring in specialists: Even great teams only tackle onboarding a few times. Repetition across dozens of SaaS products shortens the learning curve - and avoids the usual pitfalls. And a whole lot more in our 30-min chat… When I started Valubyl, the goal was simple: solve one painful, repeatable problem - self-serve onboarding and activation. At first, we were advisors - helping SaaS teams spot what wasn’t working and what to fix. Now, three years later, we’ve evolved into an execution-first agency - not just telling teams what to do, but actually doing it for them. Our core offer: In 2-4 weeks, we ship a redesigned onboarding experience your team can launch and see the results - no fluff, just outcomes. If you’re trying to turn more signups into revenue, see what great activation actually looks like, and get a better understanding of what it takes to win with PLG - this episode’s for you. Comment ALEX and I’ll DM you the link... ... just joking :) check the first comment.

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Unpopular opinion: most churn models are solving the wrong problem. They're trained on what an account looks like at the time of renewal (or churn): so they can only predict churn when it's too late to do anything about it. Hook’s ML prediction models look at your customer’s behaviour at 30, 60, 90, 120, 150 and 180 days out, giving you more accurate prediction, 6 months before renewal. That's the difference between reacting and actually changing the outcome: having trust-worthy signals. We've written up everything that makes our ML approach different. If renewals still feel like guesswork, this is worth a read. Get the one pager ⬇️

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I'm putting together the first State of GTM Engineering report. GTM Engineering is no longer a niche role. It’s quietly becoming one of the most leveraged functions inside modern growth teams - but almost no one has hard data on how the role actually works today. So we’re fixing that. Over the next few weeks, we’ll be publishing the State of GTM Engineering with Alex Lindahl and Maja Voje. The goal is simple: Define what GTM Engineers actually do day to day Show how the role differs by company stage, size, and region Break down tooling, scope, reporting lines, and compensation Surface the real bottlenecks, tensions, and impact behind the scenes Map where this role is heading over the next 3–5 years 👉 If you work in GTM Engineering, Growth Ops, RevOps, or a hybrid growth role, we’d love your input. 👉 The more responses we get, the more useful (and representative) the report will be for the entire GTM community. If you fill it out we'll send the final report to you a week early. We’ll share the findings publicly once it’s live - with real benchmarks, patterns, and more. Inspiration drawn from Peter Walker and Hamza Shad over the years reading their State of Seed reports via Carta! Survey link in comments. Appreciate all the help, and happy holidays! 🥳

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Two outbound teams. Same leads. Same script. Same product. Team A answers 15% of dials. Team B answers 58%. Same industry. Same geography. Different infrastructure. The gap isn't talent. It isn't timing. It's the behavioral signals their phone numbers send to carrier analytics engines before a single ring reaches the prospect. Legacy dialing concentrates 200–500 calls per day on a handful of numbers. Carrier algorithms flag that pattern as robocall behavior, regardless of whether the calls are human or AI. Adaptive dialing distributes volume across large, registered, whitelisted number pools. Each number stays within carrier velocity thresholds. The behavioral profile looks legitimate because it is. The answer rate difference between these two approaches is 10–20% vs. 40–65%. Not a script problem. Not a lead problem. Infrastructure. Full technical breakdown: https://lnkd.in/dS3DCB86

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Scott Davidson asked the most important question on the GTM Engineering live session I did last week. "How do you convince your internal team to use some of these new tools & interfaces?" Short answer: if you're convincing them, it probably won't get adopted. We work with companies across the spectrum of AI adoption - aerospace to financial services to healthcare. The commonality is the sellers do not care about what we do. They want to do their job, hit quota and go home. There were a few companies I expected to be late adopters. But then someone on the team organically started using Claude to read and summarize their emails (no CRM access). That was it. Just email summaries. Then it was 10K analysis, inbound automation, etc. Once they got a taste, they came back asking for more, but it was organic pull. Now they're automating large chunks of their workflow. Snowflake published a Notion doc on their AI SDR framework. (I'll link it in the comments!) Same methodology. Account research first. Then draft first messages with a human check. Three months later, reps were like "I trust it- you can just have send it." Let the demand come to you. Crawl, walk, run. Solve one problem extremely well. What's worked for others on solving change management?

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Zero-waste SaaS means every subscription is mapped, monitored, and evaluated for real value, not just cost. It’s a shift to TCO, utility, and business alignment as the basis for buying and renewals. Real-world results show the upside. A Fortune 500 manufacturer with $2.8M in annual SaaS spend consolidated its estate, eliminated 59 of 127 apps, improved compliance and integration, and cut costs by 35 percent. The blueprint is practical: • Discover and inventory everything. • Optimize usage and rightsize licenses. • Align purchases with business outcomes. • Replace redundant tools with flexible platforms. • Use data to drive renewals and negotiations. Measured outcomes include 30–35 percent annual SaaS cost reductions, faster onboarding by up to 65 percent, and strengthened security and compliance. Read more ➔ https://lnkd.in/g4uua49Q #ZeroWaste #SaaSProcurement #TechSpend #SaaSManagement #LeanProcurement #CostOptimization #AIinProcurement #VendorManagement #SaaSrooms

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In the last 8 years I've surveyed 4,000+ private SaaS companies to figure out what's going on in the industry. Never have I seen benchmarks go out of date so quickly. Back in 2022 the typical <$1M ARR startup had 13 people. "T2D3" was the name of the game. Hiring a big team was a flex & the highest quartile of <$1M ARR SaaS companies had 38 (!) people. As of last year that figure had dropped to 7 people on average. Expectations around ARR per employee are through the roof. AI agent workflows have become the 'thirst trap' of LinkedIn. The new flex is reaching the top of the Lean AI startup leaderboard. Let's separate hype from reality & find out what's *really* going on. Take the 9th annual SaaS benchmarks survey here: https://lnkd.in/ga9H2NZ2 This report is super special to me. I'm so excited to continue the journey with my friends at High Alpha (👋 Scott Dorsey, Kristian Andersen, Blake Koriath & Mollie Kuramoto). The full report will drop in the Fall & all participants will get early access to the data 👀 THANK YOU for your support 🙏 - Kyle #saas #benchmarks #finance

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Everyone is asking: “How does Target get back to growth?” Wrong question. The real question is: How do you invest $2B+ into growth while leaking $5B in execution? I analyzed ~200K+ customer signals across Target stores. (~1k google reviews / store) What showed up wasn’t surprising. But it was uncomfortable: The strategy is right The execution is broken Target’s CEO priorities: - Elevate guest experience - Scale Drive Up - Invest in AI + personalization - Increase store payroll What customers are actually experiencing: - “Understaffed, long lines, no help” - “Damaged / missing pickup orders” - “Out of stock, can’t find items” - “App says it’s there… it’s not” - “Every store feels different” This isn’t a CX problem. It’s an execution system problem. I modeled the impact based on publicly traded financial data: ~$5.1B in annual friction drag ~5% of revenue ~100% of operating income The CX industry keeps focusing on: - Surveys - Dashboards - Sentiment Meanwhile the real problem is: No experience system that connects customer signals to operational execution The companies that win won’t be the ones who “listen better.” They’ll be the ones who: Fix what the business does next Curious, how many companies are sitting on this exact gap right now?

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I audited a $50M GTM motion today. The core dysfunction wasn't product-market fit or sales talent; it was a compensation structure engineered for financial leakage. 💸 The executive team celebrated Q4 bookings, yet the underlying retained margin was eroding at an unacceptable velocity. Their commission model incentivized gross revenue acquisition, not profitable, long-term customer value. This created an arbitrage opportunity for low-value deals that consumed disproportionate resources post-close. The cost of acquisition was effectively subsidizing future churn, a hidden tax on their capital efficiency. This isn't a motivational problem; it's a systems engineering flaw in the revenue ledger. The market tolerates gross bookings vanity metrics only so long. True value creation is a function of net retention and a disciplined profit vector. 📊 Comment LEDGER and my bot will DM the advanced compensation architecture. #RevenueIntelligence #RevOps #SalesCompensation #QuantFinance #GTM

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It’s 2025 B2B SaaS Benchmarks Report release day! We curate everything we see/hear into what’s most relevant for $2-20M SaaS. We at also included an Interactive Quarterly Board Scorecard Tool in there, which you can use to get everyone on the same page at your next board meeting. We’ve been using it for 5 years at EVERY Jurassic Capital portco board meeting. Thought it was time to share. Here are some key highlights from this year’s report: 1. Retention Under Pressure - Gross Revenue Retention (GRR) dropped to 88% median for $5-20M ARR companies (down from 90%), with logo retention falling to 86% - Net Revenue Retention (NRR) declined to 101% showing expansion is becoming increasingly challenging - AI is making switching easier, forcing vertical SaaS to rely on unique datasets and specialized labor for stickiness ⠀2. Growth vs. Efficiency Trade-offs - ARR Growth continues declining for 4th consecutive year - $5-20M companies at 27% - Burn Multiples are stable at 1.5x while gross margins improved to 77% as companies focus on capital efficiency ⠀3. Sales Getting Tougher - Expansion ARR now 37% of total new ARR as new logo acquisition becomes harder - Sales cycles finally dropped 9% YoY in early 2025 after years of increases, showing some market softening and hopefully leading to growth later in 2025 ⠀4. Lean Operations - Teams are doing more with less: Series A companies down to 15 people (from 20 in 2023) - Hiring at historic lows with dramatic reduction in 2024 software hiring, likely due to AI improving efficiency in OpEx - OpEx % of Revenue is dropping for companies utilizing AI (with some re-deploying into Growth via Sales & Mktg), while COGS are increasing due to compute costs - Founder salaries plateau around $140k median after $5M revenue ⠀5. Market Recovery driven by AI, but cautious road ahead for equity-backed companies, driven by barbell effect - Growth/PE dry powder and re-opening of IPO window drove best first half for software M&A ever in quantity with median multiples slowly rising to 4.2x - Concentrated LP fundraising into fewer firms leading to fewer rounds at higher valuations: Seed/Series A up 30%/2% in valuations but down 48%/60% in volume - Seed and Series A companies are still ~2 years to graduate to the next round; Bridge rounds increased to record highs as Seed/Series A at 46%/36% of all rounds - AI companies command 41% valuation premium over those that aren’t defined as AI Huge thanks as always to the researchers (like Carta, Peter Walker, SaaS Capital, Benchmarkit, PitchBook, Pilot.com, Software Equity Group, ChartMogul, Maxio, and so many more) who actually find this data via surveys, their own reporting/software, etc., and then we get to curate down. You all rock. See full report below and access our Benchmarking Tool here: https://lnkd.in/exT4bkva

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PE operating partners keep telling me the same thing: '$12M in IT synergies modeled. 18 months in. Zero realized.' I keep hearing the same thing from PE operating partners: "We modeled $12 million in IT synergies. It's been 18 months and we've realized zero." The IT synergy model in a PE acquisition thesis almost always looks the same. Consolidate ERP licenses. Reduce headcount through shared services. Standardize the tech stack. Eliminate duplicate vendor contracts. The numbers add up on the spreadsheet. They rarely add up in reality. Here's why. The model assumes you can consolidate ERPs within the first 12 months. The actual timeline for ERP consolidation in a multi-entity environment is 18 months minimum, more commonly 2 to 4 years. The model assumes the acquired companies run similar systems. They almost never do. Company A is on SAP ECC. Company B runs JD Edwards. Company C built something custom in 2011 and the developer is long gone. Company D outsourced its ERP to a managed service provider with a 3-year contract you just inherited. The model assumes you can reduce IT headcount post-consolidation. But you can't consolidate until the systems are integrated, and you can't integrate without the people who understand the systems, and those people are the first ones to leave after an acquisition because they see the writing on the wall. So the operating partner sits in the quarterly review, looks at the IT line items, and sees costs going up instead of down. More vendors, not fewer. More consultants brought in to manage the integration. More interim licenses for systems that were supposed to be decommissioned 6 months ago. The frustration is legitimate. The thesis made sense. The math was right in theory. But the model didn't account for the actual complexity of making 4 different technology stacks work together while the business continues to operate. This is the gap where AI should live. Not replacing the systems. Not running a 3-year consolidation project. But sitting across all of them, building a unified context layer, and running agents that can do the cross-system work today while the long-term consolidation happens in the background. The PE firms I've talked to who get this are starting to think about AI infrastructure as a standard part of the 100-day plan. Not as a nice-to-have innovation project. As a practical tool to capture the synergies their model already assumes, without waiting 3 years for ERP consolidation to finish.

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RTO Mandates Spiking Post-Earnings? What Top Engineers Are Demanding to Stay. Post-Q2 earnings = RTO crackdowns at 5+ SaaS unicorns. But here’s what’s underreported: Stealth retention deals are surging. Top engineers aren’t quitting – they’re negotiating “flex clusters,” project-based remote quarters, and compressed weeks. For CTOs/VPs: Blanket mandates = leaky pipelines. Retention requires structured concessions. For Sr. Tech Talent: Your leverage is high. Demand flexibility in writing during comp discussions. Thoughts? What RTO concessions have you seen work? #RemoteWork #FutureOfWork #EmployeeRetention #TechLeaders #EngineeringManagement

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Most vertical SaaS founders building in construction think they're entering one market. They're not. They're entering three. GCs, specialty trades, and homebuilders have completely different buyers, budgets, sales cycles, and GTM motions. Treating them as one segment is the first mistake. It usually costs about 18 months to figure that out. I wrote a full breakdown of the construction SaaS landscape for the Vertical GTM Guild. What's in it: → Why Procore's real competitive moat has nothing to do with features (the free sub tier was the actual genius move) → The five mistakes that keep showing up across construction SaaS companies, documented so you don't have to discover them yourself → The field metrics that tell you whether your product is actually working on a job site vs. just passing demos → A Go/No-Go framework for whether your team is built for this vertical or just interested in the TAM Procore Technologies is at $10B. ServiceTitan IPO'd at $9B. The market is real. The GTM playbook to get there is nothing like what works in a normal B2B SaaS motion. 👇 Link in the comments. #VerticalSaaS #ConstructionTech #GTM #B2BSaaS

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