22 resources from Growth Unhinged we point people to, and the questions each answers.
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Shows the actual arithmetic of list building, cutting 66,000 companies down to 5,700 real targets, then tiering them by how much human effort each deserves. Also honest that signal-based plays alone are not enough, since most of your market shows no signal at any given moment.
Shows how average contract value really grows as a company scales from seed to IPO, and makes the case that most of the gain comes from usage and packaging rather than straight price rises. Changes what you go and fix first.
Answers the two packaging questions that actually come up: when to bundle a new feature into a tier and when to sell it as an add-on, and how to give each tier a job. Short, and every principle is testable against your own pricing page.
Takes the three models most companies use (flat fee, feature tiers, per seat), names how each one fails, and gives specific repairs like price escalators, fair use policies and lite user seats. Diagnostic rather than inspirational.
Poyar replaces the sales funnel with a five stage new user journey (discover, start, activate, convert, scale) and attaches benchmark ranges to each. It is how you find out which stage is actually broken.
A free, topic sorted index into two hundred plus issues of original GTM benchmark research. The metrics and benchmarks section is the fastest way to find out what good looks like this year rather than in 2019.
Survey data from over eight hundred private companies, with an efficient growth matrix that plots CAC payback against NRR so you can locate yourself rather than just read averages.
Gives you the four-part structure for positioning against an alternative (what they use now, what is wrong with it, your different approach, why that is better) and the rule to pick an enemy your buyer already resents.
Shows the specific places AI earns its keep in outbound (closed-lost re-engagement, champion job-change tracking, micro-campaigns off a signal cluster) rather than arguing about whether AI is good. The distinction between deterministic and agentic steps is the useful bit.
From
Growth Unhingedby Kyle Poyar and Brendan Short15 min read
Closed-lost re-engagement: an agent watches for signal clusters, pulls the last call transcript, finds the objection that killed the deal, then drafts the reopen.
Micro-campaigns: when signals stack on one account, build a 50 to 100 contact list with campaign-specific copy (example: 13 companies hiring Data Engineers who named a competitor).
Warm-path ranking: a relationship graph scores every possible intro into a target account by likelihood of success.
Tier 1 accounts route to a human for review; lower tiers send automatically.
Twenty five tactics from someone who did it over five years and shows his own numbers, including the deflating and useful finding that viral posts drove 14 percent of his growth while ordinary good posts drove 55 percent.
The implementation half. Poyar's framing that moving from subscriptions to usage is as big a shift as moving from on-prem to SaaS is the warning to take seriously before you tell your board you are switching next quarter.
The margin defence most companies are actually reaching for. Poyar's number is the one to remember: roughly 70 to 80 percent of token consumption comes from about 10 percent of users, which is why a flat seat price on an AI product bleeds quietly.
Survey data from 230 B2B software and AI companies, so you can see what your peers are actually charging rather than what conference talks claim. Notable findings: hybrid pricing is now dominant and AI products carry thinner margins than classic SaaS.
Seven hybrid pricing patterns with named companies (GitHub, Shopify, Intercom, Zapier), plus Poyar's point that the enemy is not subscriptions, it is inflexible upfront commitments.
Poyar counted the actual job postings rather than the LinkedIn noise, which is the most useful sanity check available on whether the GTM engineer role is real yet. The free preview alone is worth reading before you open a req.
Names precisely which classic metrics stop working when revenue is consumption based and inference costs eat gross margin, then proposes replacements. The free preview alone reframes the problem.
Poyar's argument: ARR is becoming untrustworthy for AI-native companies, and DAU/MAU break when the product runs as digital labour or inside another product via MCP.
He does not trust LTV for any AI product right now, given experimentation budgets and the shipping pace of Anthropic and OpenAI.
Mixed monetisation splits revenue into high-margin platform and low-margin tokens, so a single gross margin line hides the business.
Actual job posting data instead of opinion: GTM posts down 15 percent, SDR posts down 21 percent, support down 37 percent, while GTM engineering headcount doubled and AI native companies more than doubled their SDR teams. It is the antidote to both the everything is automated and nothing has changed camps.
The build order, stage by stage: TAM and stakeholder mapping, account research, CRM cleanup and enrichment, then signal tracking. Note that the unglamorous data hygiene comes before any clever play, which is exactly where most teams skip ahead.
Worked examples rather than theory, across hundreds of tracked price changes: who repackaged well (Ahrefs, Loom, Pipedrive), who took the backlash (Docker at 67 to 80 percent), and what separated them. Read it to calibrate how big a move you can make.
The case for the cadence itself. Poyar's point that pricing can now be stale within six months, and his recommendation to run win/loss at least quarterly, is what turns pricing from an annual argument into a standing function.
The 8 percent median and the full distribution across 200 B2B products, which tells you fast whether your filter is too loose or you have a different problem entirely.