Ted Blosser

When agents can talk directly to your apps through an MCP server, everything changes. Instead of a human jumping between Salesforce, Airtable, etc... an agent can do it autonomously: Summarize call transcripts for target accounts from Gong Pass those summaries to Inflection to create custom segments Draft a target account campaign based on these custom segments No brittle integrations. No copy-paste workflows. No “please log in and click this button.” The agent just acts — safely, deterministically, and across systems via multiple MCP servers.

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Anrok generated $300K in pipeline in 3 months by unifying marketing and sales outbound in Unify. Here's the playbook growth marketing lead Kathleen Kong, and founding SDR Rydian Searles used to make it happen:   Anrok was growing fast. But before Unify, their outbound was fragmented. Kathleen was running marketing campaigns in HubSpot, but every new campaign took forever to build, and segmentation was painfully slow. Rydian's SDR team was worse off, switching between 3 different tools to execute every sequence.   "We had the growth and the signals," Rydian said. "What we needed was a system that helped us act on them."   There was only one answer.... Unify. Kathleen started by building automated plays targeting new hires, website visitors, and accounts showing sales tax exposure via AI signals. Once marketing proved the model worked, they expanded Unify to the SDR team.   Suddenly, both teams were operating on the same page. Campaigns could be built 20% faster, SDRs could work 4X faster, and cold calls converted better with out-of-the-box AI insights.   This made it easy for Kathleen & Rydian to support Anrok's growth and expansion into new markets; easily spinning up new sequences without rebuilding a single workflow.   Kathleen summed up their experience best: "Unify gave us the flexibility to run everything in one ecosystem. We could move fast, measure impact, and stay aligned."   Ready to follow Anrok's lead and unify your teams? Use the link below to learn more.

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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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Struggling with sales and churn, especially if you're a legacy product? Your adoption issue is likely to blame, but it's not as surface level as it seems. Check this out - Your VP of Sales buys 100 licenses Onboarding is canned, the speeches have been said 1000x Within the first few min, reps tune out, think "I'll look at this later" They don't 10% of the reps do come back (🙋‍♀️), figure out it's valuable, become power users CSMs run QBRs, struggle to show ROI due to lack of adoption by the 90% "We'll do more training!" But, the moment has passed Tech consolidation becomes a thing and CXOs say, "What isn't getting used?" This product then hits the chopping block except for 20/25% of licenses But... where the real issue starts is when those CXOs and VPs change jobs Intent signals show an old buyer job hopped, so a rep reaches out "Hi hi hi, congrats on the change, want to buys us again?" 😃 "Eh, we really struggled with adoption, not interested." or "Eh, we really struggled with adoption but maybe a few of our reps want this." Take this and multiply it by every leader, AE who gets promoted into leadership, CXO who scoots to a new company/division and...you've got one hell of a leaky boat that snowballs YoY and starts to create more friction in selling, more churn, smaller deals, and a bad brand. Add to that that every software platform is trying to be everything-to-everyone, and displace those legacy tools (even if they don't really compete with them). The biggest question to ask is: does our onboarding actually teach its users how to succeed? Not how the tool works - no one cares how to push the buttons -, but how to succeed with it. If not, or if we assume they'll figure it out on their own, this is a house-on-fire situation. And if you're that above brand, you likely also need a modern brand refresh to show people what you're really made of. This alone can cure a lot of what's broken in your revenue model. #samsales

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Remember generic SaaS demos? Those days are DEAD. 👀 Your B2B buyer doesn't want a feature walkthrough. They want PROOF more than just features - they need to know that your solution fits their specific business reality. The harsh truth about today's B2B buying landscape: - Multiple stakeholders now involved in every decision - Generic demos fail to create conviction - Internal champions need self-serve environments they can share - Decision-makers want to see THEIR data, THEIR workflows, THEIR brand This creates a massive revenue bottleneck for most SaaS companies. What we've learned: The faster prospects can envision themselves using your product, the shorter your sales cycle. But maintaining custom demo environments is a silent profitability drain that most companies overlook. The Strategic Shift Winners Are Making: ✅ SaaS companies are moving from feature pitches to proof-based demonstrations ✅ They're building standardized-yet-customizable demo experiences letting buyers explore at their own pace ✅ Transforming demos from technical exhibitions to strategic weapons Here's the bottom line: Your demo strategy isn't just about showcasing product capabilities - it's about helping buyers see themselves in your solution's story. What's your approach to making demos more buyer-centric? Share below. 🚀

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RevOps is becoming the most leveraged role in SaaS. Boards expect efficiency, CROs need predictability, and CEOs want scale without bloating headcount. RevOps is where those outcomes get engineered. What top RevOps leaders are taking on right now: Building one operating system across Sales, CS, Marketing Wiring data and AI into plays, owners, and SLAs Creating lean handoffs, clean data, and a clean pipeline Improving forecast integrity with models, coverage, and assumptions the board can trust Metrics that show it’s working: Forecast accuracy within ±5% Time from signal → play under 24 hours Lift in NRR and win rate attributable to RevOps programs Pipeline per rep and CAC payback trending in the right direction Where this is heading: RevOps will co-own planning, forecast reviews, and board prep Three moves to make this quarter: Map the Revenue OS with data sources, systems, workflows, owners, and SLAs on one page Automate one cross-functional play, for example...product-usage spike → CSM alert → AE expansion task → exec email Publish a weekly Revenue Health Brief with forecast deltas, risk list, expansion list, top leaks, next actions The revenue engine is only as strong as the people who build it. ---------------------- Hey, I’m Nicholas 👋 I run Lucidly, the customer intelligence platform for revenue growth. I post every weekday morning (8:30am PST) about retention, expansion, and building a SaaS company from the ground up. Follow for ideas you can put to work the same day. DM anytime.

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A CMO’s weekly/monthly insights cadence part 2. Next up: Pipeline Progression Call: Weekly. This is a Sales X Marketing X Ops call. Each pipeline generating team: marketing, partner, outbound SDR, outbound BDR, fills out a memo style overview of pipeline week on week progress as well as highlighting any challenges. Initially it felt like a weekly call to track pipeline may not yield much because it would be a little too frequent to uncover impact from initiatives the week prior, but actually this intense discipline and focus on weekly pipeline generation by team has been a big unlock. Cross functional issues get surfaced much earlier and get unblocked much faster. Ownership and accountability has improved tenfold. It got everyone focused on discussing the same data vs relying on different views on separate dashboards that made threading the needle of the problem more difficult to diagnose. Importantly this is not a call for marketing to defend their pipeline position and lay blame on coverage gaps with other teams. I see this call as the time to work out how Sales, Marketing & Operations can pick the next best set of actions that will improve our chances of hitting that weeks budget pipeline target & close any gaps that may have built up. Pipeline Monthly Review: Monthly. The set-up for this call is similar to the weekly cadence, but the focus is on looking and uncovering monthly trends that require correction in order to remain on track for that quarters budget number. Are we generating in month pipeline at the same rate as we previously were? Is there a pipeline generation team that has built up a gap? If so what’s the gap, what are the 1-2 biggest causes of that gap and importantly what is our plan for correcting it? For issues identified in the previous monthly pipeline review are we seeing corrective trends in the data as a result of the actions we prioritised in month? Budget Committee: Monthly. This is a marketing leadership call where we build our monthly budget deployment plan. We'll reconcile the previous months spend and performance and use this alongside trending historical data and required deeper investigations to provide the detailed rationale for how we allocate spend for the month ahead. I like these calls to be lively debated. I want my leaders to bring forward strong business cases for how the $ are best allocated in order to hit our goals and how the data shows this is the best approach. It should take us a few iterations before we land on a plan that everyone is aligned on and happy to proceed with. If there is no discussion or debate then I question if my leaders are challenging the data and the approach enough. LinkedIn limits mean I can't fit a review of our final meeting cadence, which is: Quarterly full business review. This call is a deep dive into all of our create and capture demand activities by region and segment. More on that another time. #demandgeneration #b2bmarketing 

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EVERY ZoomInfo customer can connect Claude and ZoomInfo together right now- but how about in Slack ? With Claude? Here’s how to build a targeted prospect list and account plan without leaving Slack. (In <5 minutes) Step 1: Ask Claude directly inside Slack to build you a list I asked for… “25 CROs and VPs of Sales based in Boston, at software companies between 500–1k employees, with $100M+ in funding. Name, title, email, mobile, total funding.” It taps straight into your Zoominfo database and all comes back right there in the channel. It even flags who is on your do not call list. Step 2: Pick an account. Ask Claude to prep you for the call, directly in Slack. It uses Zoominfo to pull every conversation you've had with them - calls, emails, pain points they've raised, products they use. It comes back with the leadership you NEED to know, what's happening at the account right now and specific angles that could help you win. This works inside Claude too when you connect it up to our MCP server - you can even ask it to build you a custom interface to sort, edit, and work the data however you want. Zoominfo is going to show up EVERYWHERE. Slack, Claude, wherever you're doing GTM work, your data should follow you there. Every ZoomInfo customer already has access to this - connect ZoomInfo to Claude here: https://lnkd.in/eM-UbCJt

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outbound tip: identify specific, observable problems your prospect faces right now. not theoretical pain points from a buyer persona doc. actual problems you can see from the outside. property managers in october need gutter work. SaaS companies hiring 10+ salespeople need pipeline infrastructure. companies with broken DMARC settings can't reach inboxes. these aren't assumptions. they're facts you can verify before sending a single email. when you can point to something real and urgent, your outreach stops feeling like spam and starts feeling like help.

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Is your CPQ capable of supporting separate sales motions? Mary Wike, VP of Sales Ops at Quilt Software, scaled quoting operations while preserving the unique GTM motion of two parallel brands. With DealHub.io CPQ, Quilt were able to create a shared, scalable quoting foundation that adapts to each brand's needs. Read the full story here 👉 https://lnkd.in/dUY7xGE2 #SalesOps #GTMmotions #CPQflexibility

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This CS Agent just replaced a $200,000 SaaS tool 🤯 And it’s a sign of what’s coming. GTM was built in the image of SaaS. →One tool for every function. → Dashboards everywhere. → Disconnected workflows patched together by process. But the SaaS-Native GTM model is breaking: → Too many tools. → Too many gaps. → Too much headcount. → Too many teams stuck reacting - instead of moving with intelligence. And now AI is exposing the real issue. Because it can understand, reason, act and learn. But SaaS-Native GTM misses the context, intelligence and memory. Recently, we worked with a $250M ARR SaaS business - where Customer Success was struggling to scale: → NRR was falling → Retention weak → CSAT in decline → Just 20% of customers actively engaged → No visibility into product usage → Sales handovers were non-existent → No insights flowing to Product or Marketing → Playbooks untouched since 2023 → Support inbox chaos → CSMs firefighting and burning out They were preparing to hire 8 more CSMs. And they were already spending $200,000 on a well-known CS platform. But that platform couldn’t help. → It was disconnected. → Outdated. → Siloed from CRM, product, support, and the rest of GTM. So we replaced it. Not with another tool. With GTM Intelligence Infrastructure and an AI-native CS Agent. Here’s what changed: ✅ Context was unified - CRM, usage, support, and calls all connected ✅ Signals were identified - risk and expansion surfaced in real-time ✅ Agents orchestrated action - plans, workflows, follow-ups ✅ Memory was built - learning from every customer touchpoint ✅ Slack became the interface - insights and recommendations delivered where work happens The CS Agent didn’t just alert. It acted: → Detected risk or opportunity → Explained context and surfaced reasoning → Set objectives based on account maturity and goals → Referenced ICP, persona, and past plays → Generated a dynamic plan → Guided the CSM with meeting prep, follow-up, and next best action → Reported and escalated when needed → Monitored resolution and learned continuously → Shared insights with Product, Marketing, and Sales - automatically This is what a system that thinks looks like. And the results? → 45% increase in CSM capacity (more active customer conversations, zero added headcount) → 250% increase in expansion pipeline → $200K platform fully replaced → CS team re-energised, focused, and aligned with GTM And this is just the beginning. GTM teams don’t want another disconnected tool. They need infrastructure that adapts. Agents that guide. Systems that learn. We’re now replacing legacy platforms across sequencing, forecasting, ABM, and CS - with one GTM system powered by context, intelligence and memory. If you build the infrastructure now - You gain compounding insight, action, and advantage. 📩 Tired of legacy SaaS tools in GTM? DM me. ✉️ Building GTM with AI? Get in touch. 🔔 Follow for more insights as we we build AI-native GTM.

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Would you pay your top sales rep $300+/hour to update Salesforce fields? You already are… 6 hrs/week on manual data entry. Another 4 hrs on forecast calls. 3 hrs more chasing contract approvals through legal. That's 13 hrs/week not selling. GTM engineering exists to get those hours back. I've tracked the evolution of GTM roles for 20+ years from sales operations to revenue operations to what we're seeing now. The shift to GTM engineering recognizes that speed requires code. The early signal came from LinkedIn, where Brian Frank ran global sales ops with a dedicated software engineering team. They couldn't wait for vendor roadmaps or IT queues. They needed to move faster, so they built their own solutions. That’s now the pattern. GTM engineers are rebuilding how GTM teams operate. They're writing code that eliminates the friction between a rep's time and a rep's quota. They're automating the admin work that buries sales teams. They're building systems that make selling time the default. The focus is ruthlessly practical: How do we reduce non-value-added time? How do we make forecasts accurate without endless meetings? How do we eliminate back-and-forth between sales, deal desk, and engineering? When founders ask me about this role, I point to time allocation. If your reps are spending more time in systems than with customers, you need someone who can code solutions, not document processes. GTM engineering is about giving sellers their time back. Everything else follows from that.

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Most revenue teams still rely on outdated GTM playbooks. 🐰 Jen Igartua is rewriting the whole thing. No hype. Just the actual tools and systems Go Nimbly is using to hit targets in a tough market. Here’s where you can catch Jen live at SaaStock USA this month: 1. Center Stage Interviewing Keith Rabkin (President at PandaDoc) Topic: The Art of Building for the Long Run: Lessons from a Unicorn Journey 2. GTM Forum Keynote With Everett Berry (Head of GTM Engineering at Clay) Topic: GTM Strategy Rebuild: Must Haves for Success in 2025 and Beyond 3. Founder Roundtable Topic: AI Tools That Actually Move the Needle If you're serious about scaling revenue in 2025, take notes. Jen will walk you through: • How her team personalises outreach at scale • The tools they use to automate the pre- and post-meeting grind • And how to increase seller capacity without growing headcount This is AI First for revenue. Built by operators, not armchair experts. Founders: bring your GTM lead, your head of sales, and a notepad. You’ll leave with a clearer plan than most boardrooms. See you in Austin. 

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Agents need the right context to get to the right answers. Context engineering makes that possible. But let’s be real: you will never move all your data into one store. In RevOps alone, CRM sits in Salesforce, enrichment in Apollo, calls in Gong, notes in Slack and Notion. By the time ETL creates a “single source of truth,” the deal is already slipping away. The fix is Agentic Flows. Pull just what you need in the moment — the latest customer email thread, a support ticket from Zendesk, or usage data from your internal store — and let AI Agents guide reps instantly. That is what Dhisana AI Agentic Flows deliver: cutting through silos and powering real-time selling. 👉 RevOps leaders: do not wait for silos to disappear. Orchestrate them.

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Your GTM stack is an expensive mess. AI-native companies figured out why. Quick one. How many tools does the average B2B sales rep use to close a deal? Eight. Eight platforms stitched together with Zapier and prayer. 28% of reps hit quota last year. Down from 44% the year before. 72% of their time goes on non-selling activities. Cold email response rates have fallen 41% since 2019. We spent a decade buying tools to make selling easier. We made it harder. The average B2B org runs 305 applications. 53% of licences sit unused. It's $2 of sales and marketing spend for every $1 of new ARR. That's not a tooling problem. That's architectural debt. And the typical response - swap vendors, retrain the team, hire another SDR - is treating symptoms whilst the disease spreads. AI-native companies figured this out. Their response isn't to buy better suites. It's to build composable systems from scratch. Cursor hits $1B ARR with 300 people - $3.3M per employee. Traditional SaaS median? About $112K. That's not a rounding error. That's a different operating model entirely. I tracked 210 GTM roles across OpenAI, Clay, ElevenLabs, Notion, and Anthropic this month. 70% explicitly require AI building capabilities. Not "familiarity with AI tools." Actual building skills. Verkada automated 80% of their SDR workflows - 120 meetings a month versus the typical 20. Rippling doubled YoY cold email performance. Not by working harder. By building better systems. Most companies are assigning juniors to "experiment with AI tools." Nice UX, lightweight workflow tweaks. But when you're trying to 5x total turnover, getting a junior to explore ChatGPT plugins isn't going to move the needle (harsh but true). The companies making this work are hiring senior folk with commercial acumen and giving them product mandates. Sprint cycles. Release notes. Measured outcomes. I wrote up the full breakdown - the economics, the company implementations, and why the gap between intent and execution is a seniority problem, not a technology problem. Read the full post here →

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Every sales org has a pipeline: Stage 1. Stage 2. Forecast. Commit. You can recite it in your sleep. But when it comes to renewals? Crickets.... There’s no real-time view of customer risk. No weekly trendline. No forward-looking model that projects retention. Isn't that kinda bonkers? You FORECAST growth, but you DISCOVER churn...usually when it’s too late to fix. The big mistake is presuming retention is a lagging indicator, when it would be more accurate to define it as a neglected one. Think about it...most CS dashboards read like a rearview mirror: - Usage metrics. - Ticket volume. - NPS. Useful? Sure. Predictive? Nah. Because churn doesn’t start when a customer says they’re leaving. It starts months earlier when their exec sponsor stops showing up. If you can spot the risk early, you can unlock more levers: - Re-engage power. - Reset the value narrative. - Escalate to commercial. - Offer roadmap visibility. - Re-map success metrics post-reorg. But none of that works if your team can’t see it coming, which is why you need a churn forecast...just like you do for revenue. Build a churn pipeline. Treat it like sales. Some things to build into this: 1. Define churn stages. Just like deals, risk evolves: - Stage 1 (Early): No exec alignment, lagging adoption, stakeholder turnover. - Stage 2 (Mid): Value not realized, weak champion, limited engagement. - Stage 3 (Late): Renewal live, buyer pushing back, pricing/legal blockers. Assign clear criteria. Surface it in Gainsight, CZ, or Salesforce. Manage it like opportunity stages. 2. Assign prevention plays. Each stage should trigger a play: - Stage 1 = exec outreach + roadmap session. - Stage 2 = commercial review + value plan. - Stage 3 = C-level call + tailored retention offer. Codify this. Don’t let reps improvise. 3. Forecast it weekly. Revenue leaders inspect pipeline weekly. CS should do the same with churn. Ask: - How many accounts moved stages? - What’s projected retention by segment? - What % of at-risk logos have exec visibility? Also, share your churn forecast with the CFO. If the CFO sees new logo pipeline every Monday, but doesn’t see churn risk until Q4? That's no bueno. Start sending a weekly churn report with: - Total ARR at risk. - ARR by churn stage. - Stage progression. - Active recovery plays. - Forecasted retention (with confidence levels). This reduces surprises while also forcing accountability. Why? Because churn is rarely a CS failure, but it's often a product gap, pricing mismatch, or sales promise that aged poorly. When churn is forecasted, it becomes EVERYONE'S problem. Which it is. tl;dr = you don’t “discover” churn. You forecast it. You stage it. You act on it. Just like sales.

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Boosting sales efficiency starts with unit economics. How much would a 1% lift in win rate be worth to each salesperson, and to your business, in bookings and quota attainment? What if your average deal size grew by $1,000 or $10,000? What if your average sales cycle shrank by 1 day or 10 days? Small shifts. Big outcomes. At Mastiff Software, we help Sales, Marketing, RevOps and other GTM leaders quantify these impacts, then go a level deeper to uncover the detailed patterns behind their most productive and efficient sales motions, segments, and campaigns. We also flag the inefficient ones, the deals and motions that drain time for minimal return, so you can focus where productivity exceeds the goal. Want to see what this looks like for your team? Try the simple calculator linked in the comments as a starting point to estimate your potential impact. Then let’s explore how to pinpoint the sales scenarios and account attributes that drive your best unit economics.

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