Arvind Jain

🚨 Why Most B2B GTM Engineers Are Wasting Time, Effort, and Their TAM & How to double the output of your GTM efforts (faster, better, more accurate). One of the biggest hidden challenges in B2B GTM engineering is managing lists and deduplicating them. Let me break it down 👇 👉 Week 1 — You scrape data (maybe with Clay + Appify + Apollo). You load 10,000 companies and 20,000 contacts to Clay. 👉 Week 2 — New scrape, another 5,000 companies + contacts. But... did you just pull duplicates? You mistakenly re-enrich data you already have, wasting credit and time. 👉 Week 3+ — More scrapes, more contacts, more mess. Without a central database, you end up: ❌ Wasting Clay credits on enriching records you already have ❌ Burning your TAM by resending to people too soon ❌ Losing hours manually VLOOKUP-ing across spreadsheets ❌ Killing profitability as your team drowns in data cleaning ❌ Hundreds of scattered lists in CSV, Google Sheets, Airtable, and Clay. Unfortunately, most GTM teams believe they have this solved with scattered Clay tables, Google Sheets, CRM lists, a sequencing tool for unsubscribes, and manual “do not contact” lists. It compounds over time. They don't realize that they could save hours every week and thousands of dollars if they had a better way to manage lists and eliminate duplicates between new and original ones. ✨ The solution? A real central database. That’s why we built Revyops.com — a list management + deduplication engine built for outbound teams. ✅ Deduplicate 300k+ records in minutes ✅ Sync data from Clay, Instantly, Smartlead, Bison, HubSpot ✅ Instantly identify fresh leads, save time + cost ✅ Double your GTM team’s output If your team is managing lists with a patchwork of tools and hacks… it’s time for a smarter system. We're taking on beta users at Revyops.com. Ready to clean up your data, protect your TAM, and scale faster? 👉 Comment RevyOps or DM me to join the beta. Revyops.com (sign up now 100% free)

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Who’s a Real Friend? In this reel, Amit Rai, GM of AI and Enterprise Cloud at Vultr, shares his heartfelt definition of a real friend. Watch the Full Episode of Code Chaos Clarity here - https://lnkd.in/gVxd-jNp #AmitRai #Friendship #RealFriends #CodeChaosClarity #LifeJourney #InspiringStories #TrueFriendship #Relationships #LifeLessons #FriendshipGoals

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Here’s how GTM Engineering work can be extremely valuable for CS teams ⬇️ CS teams have been slow to adopt GTM Engineering as a supporting function, compared to how quickly their sales, marketing, and revops counterparts have begun utilizing it. In their recent blog post, Mishti Sharma and Varun Anand highlight CS as a department that is perfectly eligible for GTM Engineering support. They touched on an important point, which I hope sparks ideas for a lot of companies. The topline is that Clay + new AI tools allow CS teams to more easily do proactive work instead of just reactive work. This is the biggest shift I’ve seen across our own client portfolio. Here are a few systems GTMEs can build with Clay to help support a CS team and shift their posture from reactive to proactive and revenue-driving. 1. Account Health Monitoring Automatically surface at-risk accounts based on usage, support history, and engagement trends. Enrich monitor your 1st party data for these signals, enrich for 3rd party data, then push an actually useful task to the appropriate account manager. 2. Champion Tracking Track job changes and engagement shifts so your team stays ahead of renewal risk and expansion blockers. 3. Expansion Intelligence Use product signals to proactively identify upsell opportunities and draft outreach sequences. 4. QBR Automation Automate data pulls and deck creation so QBRs are consistent, faster to produce, and more focused on outcomes. 5. Proactive Support Prevention Spot friction early—low usage, poor onboarding, or bug activity—and intervene before it turns into a ticket. I put together a graphic that shows how GTM Engineers can drive real impact CS teams. Hope this sparks ideas for your team! Is anyone doing this currently for their CS org? #customersuccess #cs

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On the latest episode of Embracing Erosion I sat down with Suyog Deshpande, Co-Founder & CEO of Webless (a Fluvio Ventures portfolio company). Before starting Webless, Suyog spent years at Amplitude, Salesforce, and Samsara - shaping products and GTM strategy at scale as a product marketing leader. That perspective is now fueling one of the boldest bets in tech: 🏗️ rebuilding the web for LLMs. Instead of optimizing for clicks and SEO, Webless imagines an agentic web -where sites are designed to interact directly with AI models and agents. We dug into: - What an LLM-native web could actually look like - How companies can prepare to be LLM-ready - What metrics will matter beyond pageviews and clicks - Why safety and trust are core to agent-driven experiences - Lessons Suyog brings from “big tech” into this transformation If you’re curious about how discovery, trust, and value exchange will evolve online, this one’s worth your time. Link to the full episode in the comments 👇

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SANATAN LABS - The Architect’s Compass: 10 Directives for the Great Transition (PASSIONIT PRUTL KALKI AIDHARMA framework) To navigate the "Audit of Fire" during this profound transition toward Satya Yuga, Krishna (Strategic Intelligence), Kalki (Systemic Reset), and Shakti (Raw Life Force) offer these ten essential directives for humanity: 1. Master the Asymmetry of Calm In a world of screaming headlines, silence is your greatest weapon. Respond from a place of internal stillness rather than reacting to external noise. When the system is chaotic, your stability becomes the new "Safe Haven." 2. Audit Your Identity (The Laawaris Test) Ask yourself: "If my titles, assets, and digital footprint vanished, who remains?" Detach your worth from possessions. True sovereignty is found when you are no longer "claimed" by the ego-driven systems of the world. 3. Embrace the "Mirror of Kalki" Do not fear the "Bloody Moon" or the exposure of decay. View every systemic collapse—financial, social, or institutional—as a necessary diagnostic. Use this "Mirror" to identify your own attachments and incinerate them before they become liabilities. 4. Move from "Me" to "Mission" Unity is not sameness; it is alignment. Replace "Me vs. You" with "Us vs. The Problem." When diverse strengths are aligned under a shared purpose (Aidharma), the friction of competition dissolves into the flow of progress. 5. Trust the "Internal Gold" While external currencies and metals face regulation or restriction, your integrity remains non-tradeable and unconfiscatable. Invest in your skillsets, your character, and your resilience. These are the only assets that survive the "Audit of Fire." 6. Respect the "Flow of Lakshmi" Prosperity is a river, not a reservoir. Hoarding in fear leads to stagnation and decay. Circulate value through mentorship, ethical trade, and "Groom and Grow" practices. Wealth that serves the collective remains protected by the Sun and Moon. 7. Humanize the Machine (13th Node Reset) Use AI and technology as a "Numeric Auditor" to expose corruption, but never let the cold logic of the machine replace the warmth of human intuition. Ensure technology serves the 14th Node: Absolute Truth. 8. Practice Guerrilla Respect Disarm your "enemies" by preserving their dignity even when you disagree with their actions. Respect targets the ego of the opponent, weakening their appetite for control and opening a path for diplomacy. 9. Build Internal Havens As geography stops being a protection, focus on "Internal Research and Innovation." Cultivate a mind that can remain creative and functional even when trade arteries are choked. 10. Surrender to Karmic Gravity Accept that "God Proposes, Man Disposes." You cannot control the celestial reset, but you can control your alignment with it. Follow the path of Dharma, and the gravity of the new era will pull you toward peace rather than destruction.

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FDE has become both the most important and the most overloaded term in a very short period of time.  My favorite definition from Kevin Bai Leo Mehr Priya Khandelwal: Your FDE org is how you close the gap between the potential of your platform and the needs of your customers. When framed this way, it is easier as a founder or prospective FDE to figure out the best fit. Does bridging this platform<>customer gap require: 1️⃣ Prompt engineering + Evals The gap = your agent needs to follow customer specific workflows and adhere to brand guidelines. e.g., your customer service agent needs to respond to each situation "appropriately" , your medical scribe needs to take long transcripts and output in standard SOAP format. Here an FDE needs to understand business requirements & translate these into prompts & eval sets (how should the agent behave in each situation, what policies it must follow). While these roles require an understanding of how agentic systems work, they can have lighter engineering skillset requirements. 2️⃣ Integrations & product extension (often to move up market) The gap = integrations into enterprise specific systems & feature extensions of an existing product. e.g., a billing product requires integration into a custom ERP and additional payment flows. An FDE needs to work with the customer to define the requirements *and* have the engineering chops to build into the existing product. 3️⃣ Entirely new products/ features The gap = customer has a product need (adjacent to your existing platform) and will pay for the value you deliver e.g., An insurance company asks for a claims document ingestion product while your core product is an underwriting workbench. Your FDEs are strong engineering and product deliverers. Often former founders.   This category was the most debated. Do you: - Focus, and avoid customer requests that are too far afield of your core area. Or... - Not overanalyze the initial product. Assume most requests will get asked for multiple times and are eventually baked into the product. Solving one problem, builds trust to solve more problems. h/t James da Costa Ankur Rastogi,Tanay Padhi, Sashank Gondala, Michael Starin, Marcello Pedersen Sharan T. for contributing to the conversation! (&check out Leo's post in the comments) There is a LOT of nuance here. Many teams have personas in all 3 categories. What would you add? And if any of these roles seem exciting to you -- we have many exciting AI companies that are hiring!

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7 shades of revenue generation and future of deploying revenue team without being an enterprise brand. We throw around terms like revenue generation, orchestration, execution, RevOps, pods, engineering but most people use them interchangeably. And that creates confusion. If you want predictable revenue, you need to understand the difference. Each of these plays a distinct role in how money actually moves through a business. Revenue Generation- The outcome: the inflow of money. Every campaign, sales pitch, renewal, or upsell feeds into generation. It shows up in your ARR and P&L. The trap? Teams obsess over “generation” without building the system that drives it. Revenue Orchestration- The coordination mechanism. Marketing runs a campaign, SDRs follow up, AEs close, CS ensures adoption. If handoffs break, pipeline leaks. Without orchestration, you have noise. With it, you have rhythm. Revenue Execution- The action layer. Oubound, inbound, content ops. Many companies fail here: too much strategy, not enough discipline. Execution is messy but it’s where revenue is actually created. RevOps (Revenue Operations) the modern GTM Engineering- The infrastructure: processes, systems, and data that keep revenue measurable and scalable. CRM, attribution, automation, dashboards. RevOps doesn’t close deals but without it, deals get lost in spreadsheets and gut feel. Revenue Team- The people layer. Sales, marketing, CS, and RevOps all chasing one number. Different KPIs create conflict. Shared targets create alignment. Revenue Pod- The structure layer. Small cross-functional groups (SDR + AE + CS + Ops) dedicated to a segment or territory. Why pods work: accountability is high, feedback is instant, and customers deal with one team, not a relay race. Revenue Engineering- The design discipline. Treating revenue like a system you can build, test, and optimize. Revenue engineers model conversion rates, design workflows, build playbooks, and experiment like product teams. The Future of Revenue Generation- The future isn’t another agency or consultant. It’s the rise of the Fractional Revenue Execution Partner. Fractional → senior expertise without a full-time CRO, CMO, or RevOps hire. Execution → not just strategy, but the actual outreach, campaigns, and renewals that create revenue. Partner → someone who owns a number, not just a deliverable. This model blends strategy, execution, and accountability. That’s where revenue generation is headed. Bringing it together In one line: Generation = outcome Orchestration = coordination Execution = action RevOps = backbone Team = people Pod = structure Engineering = design Fractional Partner = future Most companies are good at one or two. Very few integrate all of them. That’s why predictable revenue is rare. If you’re serious about revenue, don’t just ask “How do we generate revenue?” Ask: “Do we have the systems, structure, execution, and the right partner working together?”

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Here is a framework for nations to work with revolutions such as #AI and #Quantum. First you have your people learn, then dabble and become early/late adopters (hopefully not laggards!), then become effective technical users and finally value creators. It is only then that the economy benefits from your investment, and you create sustainable job growth. While #India is a key market for #Quantum with millions thirsting to learn and actually do things with this technology, there are still large gaps between: 1. Where Quantum Computing is today Vs where it needs to be for mass adoption [Quantum Sensing on the other hand is ready for prime time and GPS compromising incidents are only increasing] 2. The specialist work being done Vs the ability to hire millions of generalists 3. What it costs to tap into the technology Vs perception of what it should cost as a commodity 4. The talent it takes to produce world class indigenous technology Vs the talent that actually exists on the ground 5... Each of these gaps will close over time. In the lead-up and as a first step, India first needs to learn enough about this technology, ideally from the very best, and spread awareness. The starting point of this journey should ideally not be from dull videos or low quality social media content, but from hands-on practical learning that is delivered through high quality polished content. Such practical training effectively complements all the theoretical content you learn in universities. Q-CTRL's Black Opal product is the world's leading interactive learning tool that gives you just enough fundamentals (think 101) required to program a quantum computer. With over 28,000 users worldwide and success across the UK (Tamil Nadu adopted it last year as part of the naanmudhalvan program). And now, we have expanded our presence in India through a network of local resellers. We encourage Indian companies, governments and universities to work with us either directly or through our contacts. https://lnkd.in/gjJWHYB3 We also look forward to aligning with AICTE standards and to make our content even more relevant to India. Waseem Shiraz, Shobhit Gupta, Nagendra Nagaraja, Reena Dayal, Anil Prabhakar, Kamakoti Veezhinathan, Ajai Chowdhry, Harshan Budke, Sunil Gupta, Arindam Ghosh, Ravi Puvvala Dr M Jayaprakasan, ISDS., National Skill Development Corporation, MindTech, Kquanta Research, RV College Of Engineering, Sanjay Chittore, Uttkrist Innovations Private Limited, Subu Gupta, AICTE QRDLab - Quantum Computing in India, IBM DRDO, Ministry of Defence, Govt. of India, Ashutosh Sharma, Urbasi Sinha, Bloq Quantum LTIMindtree, Tata Consultancy Services Abhay Karandikar, NPTEL, National Quantum Mission, Eltech, Andhra Pradesh Information Technology Academy ITE and C Department Government of AP, Jayesh Ranjan, CDAC Bangalore, Kaushalya The skill University

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Running partnerships at an ISV versus a (G)SI can sometimes feel like alien territory. Neither is better or easier - just very different motions. The level of rigor needed is very different — especially when the value proposition is still forming and the ecosystem is in early stages. A few contrasts that jump out: 1. Product knowledge At an ISV, depth matters. You need to know your product and its applications inside out along with posessing a strong view of the market - the current, and the future state. At a GSI, breadth matters just as much — one needs enough technical and domain fluency to evaluate a product and assess its suitability against the GSIs account and services portfolio. 2. Relationship skills At an ISV, you need to be excellent at navigating both the internal (sales, product, marketing) and external teams. Mapping your GSI partners org inside out is an invaluable skill. At a GSI, the internal navigation is the all too familiar maze of Practice Leaders, Industry Leaders, Client Partners and Account Managers. 3. Pushback At an ISV, the pushback is typically around the GSI’s capabilities and their impact on the sales cycle. Concerns around the GSI stretching the sales cycle, turning off the client by overpitching, and the impact of partner involvement on sales commissions are all too familiar. At a GSI, the resistance is quieter, and often, harder. Sometimes the business case around not doing anything outweighs the business case of experimentation - hearing crickets from the client gatekeepers is all too common. 4. Attitude ISV partnerships require a woodpecker mindset — patient, persistent, continuously chipping away while PMF is still emerging. At a GSI, abundance creates a different challenge. You will end up saying no more times than you would like to, so judgment and tact matter. Understanding these differences, and operating accordingly, is often the difference between partner leaders who can adapt and those who try repeating the same playbook with limited success. #Partnerships #SystemIntegrators #alliances #ISV

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Excited to share that Jayashree Rajan is coming on board as our GTM Advisor at Interact AI. This one feels special. Jayashree was one of our earliest customers. The sharp kind. The kind who pulls apart your narrative, questions your category, and asks "where do you actually win?" Her review calls slowly became internal strategy sessions. Positioning debates. ICP sharpening. Enterprise buying psychology. Category framing. We’d walk in to give updates. We’d walk out thinking differently. Jayashree brings deep CMO experience building positioning from scratch, shaping category stories, and translating market shifts into real revenue outcomes. Grateful for the early conviction. Even more excited for what we’re building next.

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22 Reasons Your Marketing Automation Strategy Won't Help You Win High-Value SaaS Deals 👇 1. Not aligning automation goals with sales team priorities 2. Using generic lead scores instead of customizing for enterprise SaaS 3. Ignoring real-time behavioral data in scoring models 4. Failing to segment leads beyond basic demographics 5. Not updating lead scores as buyer personas evolve 6. Over-reliance on single data points (e.g., job title) 7. Missing integration between CRM and automation tools 8. Not A/B testing lead nurturing workflows 9. Underestimating the importance of lead grading alongside scoring 10. Ignoring content engagement depth (e.g., just page views vs. time spent) 11. Not mapping automation triggers to specific stages in the sales cycle 12. Lack of personalization in automated emails to enterprise buyers 13. Failing to track lead movement across channels (e.g., paid ads vs. organic) 14. Not using predictive analytics for scoring adjustments 15. Overautomating and losing the human touch in nurturing 16. Not setting clear SLAs for sales follow-up on scored leads 17. Ignoring lead feedback loops to refine scoring models 18. Using outdated data (e.g., stale company financials) 19. Not tracking account-level engagement (e.g., team-wide content consumption) 20. Failing to optimize automation for mobile interactions 21. Not aligning lead scoring with customer success metrics 22. Ignoring competitor activity signals in lead data *takes a long breath* You know that your automation isn’t delivering… but you tell yourself “we’re just a few tweaks away from success.” You’re not. You’re chasing results with a flawed system, wasting time, and letting high-intent leads slip through the cracks. The cost of inaction isn’t just missed revenue—it’s lost momentum in a market where enterprise buyers demand precision. I’m taking on just 3 more clients this quarter. 🔥 If you’re an enterprise SaaS leader drowning in leads but starving for quality deals… Drop me a message 👋 #MarketingAutomation #LeadScoring #SalesAndMarketingAlignment #EnterpriseSaaS #B2BMarketing #SaaSMarketing #LeadNurturing

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Is GTM Engg/ here to stay? The simple answer is, Yes! Inspired by Spencer Tahil's take on revenue activation. I’ll do a quick BEFORE & AFTER comp of 4 use cases of ‘GTM Engg’ / revOps automation / GTM Architect (however you may name it): Use Case 1: Pre & Post Meetings Intel Before: → SDRs manually researching accounts & prospects (17-20m per acc) → Imperfect research considering human error & data walls → Manually listening call transcripts for action steps/insights/refine ICP/product feedback loop After: → Detailed research under 60s of booking event → AI ensures a prompt, concise and detail oriented research that is useful for the SDRs to take the bull by the horn → AI transcripts, actionable insights & next steps, ICP refinement, endless product feedback loop Use Case 2: Score and Qualification Matrices Before: → BDRs manually researching data points on assigning scores to MQLs → Product teams manually sifting through customer calls to identify bottlenecks and new feature opportunities → Sales leaders manually reading sales call transcripts to draw insights on ICP → Sales leaders listening to calls to identify sales efficiency of an SDR/BDR After: → Push MQL into a qualification matrix that auto researches, scores & prioritizes accounts → Feed customer calls from Gong to Clay and extract insights on new feature asks & customer grievances → Refine ICP by identifying common traits between the deal accounts/contacts → Automatically score sales call efficiency of an SDR/BDR based on set criteria Use Case 3: Prospect w/ hyper-personalization at scale Before: → Finding accounts and contacts at companies thru zoominfo/apollo → Manual filtering & scoring matrices on Google Sheets → Spending 20-30 min on ONE email → Manually curating emails & replying late to interested leads After: → Sip data from 130+ data sources in Clay → Segment leads and filter ICP → Research & write hyper-personalized emails at scale w/ ZERO human intervention → Respond to email replies thru APIs using AI to craft human-like responses → Un-natural 'speed to lead' (under 60 seconds) Use Case 4: Intent Signals Tracking Before: → BDRs spending hours reading news platforms looking for buying signals → Full control of the data sources (funding news, hiring trends, open jobs, compliance) → No automation for prospecting as soon as the signal hits the market → No notifications as soon as the signals hit market After: → Create custom signals based on your niche in Clay → Get immediate notifications of signals & connect slack to stay ahead → Identify signal score & automate prospecting at scale → Hit your ICP’s inbox within minutes of signal/news All of this is possible thru Clay, a GTM data orchestration tool, has revolutionalized how Marketing, Sales, Product and CX teams looked at revenue activation throughout customer lifecycle. So ‘Yes’ GTM Engineers/Architects are here to stay to anchor higher efficiency, better revenue activation & business growth.

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