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Sales Principles

What guides us?

Sales converts qualified interest into committed revenue. Marketing optimizes for attention. Sales optimizes for trust. The distinction matters because trust requires skin in the game — accountability needs a who, not a what.

Value Creation

Sales creates value by reducing the buyer's risk of a bad decision.

Buying B2B software is hard. The decision-maker has usually never bought that type of software before. There are countless options that look similar. Forty percent of B2B purchase processes end in no decision — not because the product failed, but because the buyer couldn't navigate the choice.

The salesperson who guides the buyer through that fog — who admits when they're not the best fit, who communicates tradeoffs honestly — earns the right to close.

PrincipleWhat It Means
Trust over transactionThe higher the deal value, the more human presence matters. The lower the deal value, the more AI can handle end-to-end.
Guide over pitchBuyers want perspectives on the market, help comparing alternatives, and education on outcomes — not feature lists.
Pain before productEvery sales conversation starts from validated pain, not your roadmap.
One channel, one productUntil $1M ARR, focus beats breadth. Prove one channel works before expanding.
Skin in the gameAccountability closes deals. AI can draft the email; a human must own the relationship.

Essential Data

The data that drives sales decisions. Without these, you're guessing.

Data DomainWhat It ContainsDecisions It Drives
PipelineDeals by stage, value, age, probabilityResource allocation, forecast, hiring
ICP profilesTarget archetype with psycho-logic (what they say vs do)Pursue/nurture/disqualify
Activity historyOutreach sent, responses, meetings, proposalsChannel effectiveness, rep performance
Conversion ratesStage-by-stage close rates, cycle timesBottleneck identification, funnel design
Revenue actualsARR, MRR, deal size, CAC, CLVUnit economics, growth rate, payback period
Engagement signalsContent downloads, email opens, website visits, LinkedIn activityTiming of outreach, deal priority
Win/loss reasonsWhy deals closed or diedICP refinement, pitch improvement, product feedback

Impact of bad data: Wrong ICP = wasted discovery calls. Stale pipeline = false revenue forecast. Missing win/loss reasons = repeating the same mistakes.

Glossary

TermDefinition
ICPIdeal Customer Profile — the archetype of who buys, defined by psycho-logic not just demographics
CACCustomer Acquisition Cost — total sales + marketing spend to acquire one customer
CLVCustomer Lifetime Value — total revenue from a customer over the relationship
ARRAnnual Recurring Revenue — normalized annual value of recurring contracts
MRRMonthly Recurring Revenue — monthly value of recurring contracts
Pipeline coverageRatio of pipeline value to quota; healthy = 3-4x
Sales velocity(Deals x Avg Value x Win Rate) / Avg Cycle Length = revenue per time period
POV pitchPoint of View pitch — starts with market insight, not product features
CLOSERClarify, Label, Overview, Sell the vacation, Explain concerns, Reinforce the Decision
HiTLHuman-in-the-Loop — the new sales role where humans orchestrate AI execution
ACVAnnual Contract Value — yearly value of a single contract
SDRSales Development Representative — owns prospecting and qualification
AEAccount Executive — owns discovery through close
RevOpsRevenue Operations — systems, data, and process that support the revenue team
NRRNet Revenue Retention — measures expansion minus churn in existing accounts
BANTBudget, Authority, Need, Timeline — classic qualification framework
Discovery callFirst real conversation where the salesperson listens for pain, not pitches

Context

Questions

What is the minimum set of data a founder needs to track before hiring their first salesperson? At what deal size does the trust advantage of human sales stop compounding?