🚀 How to build a repeatable sales engine?

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3 tactics to build repeatable sales engines

⬆️ Ruthlessly focus and say ‘No’ to wrong customers

• Prioritize design partners and early adopters looking for strategic advantage against the competition 

• Define ideal customer profiles and maximize for repeatability: 

  • Do they experience similar pain points?

  • Do they share similar workflows?

  • Are they buying for similar outcomes that are high ROI for them?

  • Would the same messaging convert all of them?

• Pick 2-3 buyer personas among all high-intent leads—ignore the rest even if they are willing to pay

  • Ask: “If the product can serve anyone, who is it deliberately not for right now?”

  • Develop deep expertise about the personas, pain points and outcomes, and show it in every touchpoint from cold email to final demo

  • Shape messaging of content, digital marketing, case studies or events around the buyer personas and avoid generic messaging

📶 Build a sales recipe

• Map the buyer's journey through four phases

  • Awareness: How do perfect prospects first discover you — and how do you make it happen more?

  • Interest: What turns a casual look into genuine curiosity about your product?

  • Evaluation: How do tire-kickers become serious buyers?

  • Conversion: What's the final push that turns maybes into yes?

• Analyze every win to reverse-engineer what moved the deal forward through each stage, which elements mattered, and which were just noise

• Turn each early deal into a learning opportunity, by asking these questions:

  • Which messages hit home?

  • Who are the essential players in the room?

  • What's the perfect meeting sequence?

  • How do we maintain momentum between touchpoints?

  • Which deal terms matter most?

  • Where do we draw hard lines?

🌎 Establish and track metrics

• Define sales funnel into 5 stages:

  • Meetings Set: Number of first meetings booked

  • Meetings Executed: First meetings that actually happened

  • Opportunities Qualified: Opportunities that meet qualification criteria (e.g. align with ICP)

  • Trial or Pilot Kickoff(if applicable): Number of trials or pilots

  • Closed-Won: Deals won

• Calculate conversion rates between each stage to identify the biggest bottlenecks and address them quickly

2 traps to avoid

🚨Confusing early wins with repeatability

• Early wins from founder hustle ≠ scalable sales motion

• Without a proven process where X leads reliably convert to Y customers, new sales hires just accelerate burn

• Ensure deals come from a repeatable process (not friends, favors, or luck) before scaling the team

🚨Hiring sales reps before filling the pipeline

• Beware of investors pushing to hire more salespeople in order to meet quota and hit growth targets

• Simply adding more salespeople will not drive growth—it might just do the opposite because salespeople are expensive

• Invest first into demand gen, marketing, and growth, and hire when the team is overwhelmed and do not have time to talk to leads

• Prioritize capital efficiency by focusing on reps with higher conversion rates

1 tool to leverage

📖 Best practice on sales funnel metrics

• Funnel conversion rates reveal >50% of what matters—track them even if the process is messy and still work in progress

• Use this waterfall planning template by Emery Rosansky, VP GTM at First Round Capital (previously shared considerations when adding sales pilot), to build a sales forecast and calculate how many first meetings are needed to hit revenue targets

Bonus: 1 trend to spark startup ideas

📈 AI Infrastructure is entering Act Two: Era of Experience

• Act One focused on breakthroughs in algorithms (e.g. backpropagation, convolutional networks,transformers), fueling foundational models, compute, and data labeling

• Act Two shifts from “Can we solve this?” to “What should AI solve, and how to measure true progress?”

  • State-of-the-art research is moving beyond algorithm improvements towards enabling AI to interface effectively and purposefully with reality 

  • Enterprises are scaling AI from proof-of-concepts to customer-facing deployments (e.g. Walmart’s announcement) 

• New frontiers to build for:

  • Environments: Interactive techniques to support real-world learning (e.g. high-fidelity task curation and scalable generation of reinforcement learning environments/gyms)

  • Evaluations: Shift to focus on real-world parameters instead of benchmarks and leaderboards (e.g. LLM-as-a-judge, continuous methods and proprietary frameworks)

  • Systems: Shift from model-first to system-first design—infrastructure to support production-quality AI systems and agentic workflows across components (e.g. memory, knowledge retrieval, reasoning, and inference optimization)