Overview / Methodology
Methodology
How these ICPs were defined
The framework here is standard B2B SaaS ICP practice, applied to PostMoney's repo docs, competitor research, and published benchmarks. Every number on this site rests on an assumption, and every assumption is on this page. There are no production users yet, so treat everything as a pre-launch hypothesis, instrumented to be corrected.
Definitions
ICP, persona, target market
Three levels of specificity. Confusing them is a common source of misaligned GTM: marketing targets people while sales targets companies. Sources: Factors.ai, HubSpot, Kalungi, per the research reference.
Ideal Customer Profile
A description of the firm that gets the most value from the product, fastest, with the best retention and expansion economics. Answers "which accounts do we target." A good ICP is falsifiable: for any real account you can say fit or no-fit in under a minute using observable data.
Buyer persona
The individual humans inside an ICP account: the champion, the economic buyer, the end user. Answers "how do we message and sell to the people inside those accounts." ICP first, personas second, per the consistent guidance across sources.
Target market
The whole addressable universe that could plausibly use the product, here "all VC firms and startup investors." Useful for TAM math, useless for prioritizing sales effort. Each deep-dive page on this site carries all three layers.
Completeness
What a complete ICP contains
Synthesized from Kalungi, Sybill, ZoomInfo, and Landbase. This is also the exact spine every deep-dive page on this site follows.
- Firmographics: fund size, AUM, portfolio count, team size and structure, geography, stage.
- Situational triggers: the observable events that turn a fit account into an in-market account (a new fund close, first LP report due, portfolio crossing the spreadsheet-breaking point, an ops hire joining).
- Pains mapped to features: the specific painful workflow, what it costs today, and which PostMoney feature completes the job.
- Buying process and cycle: who champions, who signs, budget source, self-serve vs demo, expected cycle length.
- Economics: ARPA under current pricing, assumed churn, gross-margin LTV, and the affordable CAC that follows.
- Reachability: the watering holes, communities, and channels where the segment can actually be found.
- Disqualifiers: the explicit signals that rule an account out. Sources argue disqualifiers protect the scarcest resource, founder time, better than qualifiers do.
Scoring
The scoring model
Weighted-attribute scoring, the most repeated method across the published frameworks (GTM Playbook, GTMonday, Ivris). Each of the ten candidate segments is scored 1 to 5 on eight dimensions, weighted, and ranked. Pain, willingness to pay, product fit, and reachability carry the most weight because they matter most pre-launch. The full matrix is on the Priorities page; the weights and cutoffs there match this table exactly.
| Dimension | Weight | What it measures |
|---|---|---|
| Pain intensity | 20% | Hair-on-fire or nice-to-have. The best single predictor of conversion and willingness to pay. |
| Willingness to pay | 15% | Budget exists, the buyer controls it, and the price clears approval without procurement. |
| Product fit today | 15% | How completely the shipped product does the segment's job without roadmap work. |
| Reachability / CAC realism | 15% | Findable cheaply in dense communities and warm networks, or only via expensive outbound and paid. |
| Market size | 10% | Enough accounts to matter. Deliberately underweighted: a winnable 2,000-account beachhead beats an unwinnable 50,000-account market. |
| Competitive intensity | 10% | Incumbents and switching costs. Displacing a spreadsheet is easier than displacing a tool. |
| Sales cycle length | 10% | Committee size, security review, procurement. Short cycles are mandatory at low ACV. |
| Expansion potential | 5% | Will the segment stick and grow: more companies means more overage revenue, structurally. |
Tier cutoffs
P0 weighted score 3.8 or higher. P1 3.3 to 3.79. P2 2.5 to 3.29. Below 2.5 is not an ICP. Cutoffs are a judgment call, stated once here and applied uniformly.
Beachhead overlay
Two pass-fail tests on top of the score, from the Crossing the Chasm lineage: can we name 100 specific accounts in the segment, and do its members talk to each other. Dense referral networks are what make a beachhead compound.
The Janz check
Christoph Janz's framework: at $99 to $449 a month these customers are "rabbits" that must be won nearly self-serve, by product, content, community, and referral. Any segment that requires multi-stakeholder enterprise selling fails, regardless of its score.
Formulas
The economics formulas
LTV = ARPA x 80% gross margin x expected lifetime in months, where lifetime = 1 / monthly churn, capped at 60 months. The cap keeps low-churn segments from implying absurd lifetimes, per the standard fix when the simple formula breaks.
Why 80% gross margin. Deliberately conservative for SaaS: PostMoney's COGS includes LLM inference, and AI-heavy COGS makes gross-margin adjustment more important than for typical 80 to 90 percent margin SaaS. Revenue LTV inflates the metric and causes overspending on acquisition.
Affordable CAC = LTV / 3, the standard 3:1 health benchmark (Fiscallion, Foundry CRO). Sanity-checked against payback: a $199/mo customer contributes about $160/mo at 80% margin, so a 6 to 12 month payback allows roughly $950 to $1,900 of CAC. Under 12 months is the general SaaS health line (Stackmatix).
Churn priors by segment type, from Optifai's 939-company benchmark and related sources: SMB-like segments 2 to 4 percent monthly, steadier institution-like segments 1.5 to 2 percent. Each ICP's assumed churn is stated in the canonical table on the Economics page, and every page cites that one table. Expansion via per-company overage is noted qualitatively as NRR upside and is not baked into LTV.
Stated plainly: the churn priors are borrowed B2B SaaS benchmarks, not observed PostMoney data. PostMoney has not launched and has zero production users. Every LTV and CAC figure on this site is an estimate built from these priors.
Channels
Channel CAC benchmarks
B2B SaaS averages from 2025 sources, per the research reference (Phoenix Strategy Group, Optifai, Understory, Upraw Media). Used to judge which channels pencil against each ICP's CAC ceiling.
| Channel | Typical CAC | Notes |
|---|---|---|
| Referral / partner | ~$150 | Cheapest recurring channel; needs an ecosystem to tap. |
| Inbound (blended content) | ~$200 | Slow ramp, compounding. |
| Organic SEO / content | ~$290 | Lowest long-term paid-equivalent cost; 6 to 12 month ramp. |
| Community | Low $, high time | No solid public dollar benchmark; behaves like referral, slow to build. Directional only. |
| LinkedIn paid | ~$980 | Precise targeting, expensive clicks. |
| Outbound, high-touch | ~$1,980 blended | Only pencils for large deals. |
These are medians, used as priors only. Published CAC benchmarks vary widely by methodology (blended vs paid, fully loaded vs media-only) and skew toward funded companies. They are order-of-magnitude planning numbers, not targets, and CAC rose an estimated 40 to 60 percent between 2023 and 2025 (Phoenix Strategy Group).
Disqualifiers
The anti-ICP
Who PostMoney does not sell to, written with the same rigor as the ICPs. The sources argue disqualifiers are often more valuable than qualifiers because they protect founder time from deals that will never close or will churn. If an account shows one of the signals below, do not pursue it, even inbound.
Institutional PE / growth equity
The Chronograph and Vestberry buyer: procurement, security review, and a sales team required. Fails the Janz check outright at PostMoney's price and motion. Signal: an RFP, a vendor-risk questionnaire, or "loop in our IT and compliance."
Large multi-stage platforms
Firms with internal data and platform teams build or buy institutional suites; PostMoney's honest review loop is redundant next to their headcount. Signal: a named data engineering function or an existing Standard Metrics / Chronograph contract.
Founders
Wrong side of the update. Founders send updates; PostMoney serves the investors who receive them. Signal: "I want to send my investors updates." Point them at founder tools and move on.
LP-portal-only buyers
Buyers whose defining need is an LP investor portal are asking for a feature PostMoney has not built; the positioning research lists the portal as a known gap. Signal: the first question is "can my LPs log in."
Fund admins wanting a suite
Buyers who need fund accounting, cap tables, valuations, or waterfalls belong to Carta, Allvue, Fundwave, and FundCount territory. PostMoney starts after the check is written and is deliberately not a back office. Signal: "does it do capital accounts."
Provenance
Sources and caveats
Source documents
tmp/postmoney-icps/research/product-brief.md: the single source of truth for product and pricing claims, compiled fromdocs/features.md,docs/subscriptions/overview.md, roadmap docs, and the competitor research.tmp/postmoney-icps/research/icp-best-practices.md: the single source of truth for framework and benchmark claims, with the original source named inline (HubSpot, Kalungi, ZoomInfo, Sybill, GTM Playbook, GTMonday, Christoph Janz / Point Nine, Phoenix Strategy Group, Optifai, Understory, Stackmatix, and others).docs/subscriptions/overview.md: the canonical V1 pricing table, Starter $99 / Portfolio $199 / Platform $299 with overages, 20% annual discount, 7-day trial.tmp/competitor-research/: the 29-vendor competitive workbook and microsite behind the anti-ICP reasoning and the under-served buyer thesis.- Per-ICP web research gathered during planning: example firms, community names, and persona details, cited in each deep-dive.
Caveats, all of them
- Pricing discrepancy: the competitor microsite (built May 2026) priced PostMoney at the older flat $99 + $10/company model. This site uses the current tiered V1 model everywhere; rival comparisons quoted from that site should be read against the tier table.
- Example firms named on deep-dive pages are illustrative fits drawn from public information. They are not a lead list and not an endorsement, in either direction.
- Personas are composites, fictional people assembled from public patterns, not real individuals.
- Public data on VC-tool churn, sales cycles, and win rates is thin. No reliable benchmarks surfaced, so segment churn uses general B2B SaaS priors, flagged wherever used.
- All scoring is pre-launch hypothesis. The plan is to log segment, channel, and trigger on every signup and recompute tiers on evidence, quarterly.