The evidence

Why the method works. With sources.

Every function in the program has a published conversion baseline. These are real industry numbers, not promises. Here is the full source list, the case studies, and the honest caveats.

A letter from Jake

Why this is set up to work, not just sound like it.

Every number on this page has a source. Not a promise, not a pitch, a source. I put them here for one reason: you should be able to check the math before you trust us with your pipeline.

Here is why the program is built the way it is.

The machine is measured against real baselines, from week one. 3.43% average reply rate on B2B cold email. About 25% of positive replies book a meeting. About 20% of qualified opportunities close. Those are industry medians with named sources, and they are the numbers we plan from. Not the numbers we hope for, the numbers that actually hold up in the market.

The growth levers are in the model, with honest timing. Email returns $36 for every $1 spent, versus $2 to $3 for paid. Content sources 15 to 30% of pipeline by month 18. AI-referred visitors convert 2.4 to 4.4x better than organic. But those legs ramp over months 4 to 18. Closed revenue lags spend by 3 to 6 months. That is why the pilot is 90 days, and why months 1 to 3 are outbound-led by design: you see the first leading indicators move while the keeps building channels build underneath.

We track the indicators that predict the outcome, not vanity metrics. From day one we report reply rate, reply-to-meeting, SQL-to-close, and content inbound. Those four tell you, within weeks, whether the machine is on track. You do not wait a quarter to find out.

The caveats are on the same page as the numbers. These are industry medians, not guarantees. Attribution studies are practitioner methodology, not peer-reviewed proof. If the evidence had holes, hiding them would make the whole page untrustworthy, so they are right here in the open. That honesty is part of the product: you are buying a system that has been run, measured, and published, not a story.

And because the baselines are published, the guarantee can be written. We agree your qualified-pipeline target in dollars before launch. Miss it in the first 90 days, and we keep working free until it lands, up to 3 months. Month one: if you do not feel the value, full refund and you keep everything we built. A guarantee is only worth offering when you know the machine can hit the number.

Check the numbers. Read the sources. Then book the call and we will model your market with the same math.

J. McMahon

Jake McMahon

Founder, Pipeline by ProductQuant

The cost of doing nothing is measurable, even when the pipeline is not.

01Buying signals that age out before anyone tests the message against them.

02Months of spend with no shared baseline for reply, meeting, and close performance.

03Compounding evidence and authority built by competitors while your market stays unmeasured.

The market does not wait for certainty. It rewards the team that starts measuring and acting.

1 · The full-stack case

Why a machine beats one channel.

This program is six functions run as one system, not a single channel retainer. The evidence for integrated growth is what makes that defensible.

Distribution is the binding constraint, not product. 92% of SaaS dies within 3 years, and the root causes are no market need (42%) and premature scaling (70%), which operators diagnose as messaging, ICP, and distribution failures.

CB Insights (SaaS failure causes) · interpreted for GTM

Integrated full-funnel programs show roughly 2x goal attainment versus single-channel approaches.

Anteriad, 2026 attribution study

A single machine, signals to content to outbound to social to operator, is the honest, cheap way to buy 12+ months of keeps building pipeline work while the founder sells and builds.

This program’s operating thesis

The honest caveat: there is no peer-reviewed RCT proving integrated beats single-channel for B2B SaaS specifically. The 2x figure comes from attribution studies with practitioner methodology. We say so plainly because the rest of the evidence is real.

2 · Per-function benchmarks

The numbers behind each deliverable.

These are the industry medians we build the machine on and track against from week one.

Function	Benchmark	Source
Outbound	3.43% average reply rate on B2B cold email	Instantly, 2026
Outbound	~25% of positive replies book a meeting	Martal
Outbound	0.8–1.4% overall email-to-meeting	Martal, SalesHive
Outbound	~20% of qualified opportunities close	SalesHive, OutboundSalesPro
Outbound	Signal-led outreach closes 15–25% vs 5–10% generic	Dupple
Email / newsletter	$36:1 email ROI vs $2–3:1 paid	Industry ROI studies
Newsletter	$0.40–3.20 revenue per subscriber / month at scale	Attrifast (B2B SaaS RPS)
Newsletter	B2B open rate 38–43%, CTR 3–4%	Mailchimp, GetResponse
Content + AI-search	Content drives 15–30% of sourced pipeline by month 18	DerivateX, MV3
Content + AI-search	Content CAC is 40–70% cheaper than paid	DerivateX, First Page Sage
Content + AI-search	~51% of B2B buyers start research in an AI assistant	G2
Content + AI-search	AI-referred visitors convert 2.4–4.4x better than organic	Semrush, Exposure Ninja
Content	70% of monthly traffic comes from prior-month posts (keeps building)	HubSpot, Ahrefs
Social	LinkedIn typically sources 10–17% of B2B pipeline	Unify, OS-CEO
Funnels	Lead magnet capture 25–40% on engaged traffic	MailerLite, Interact
Cost anchor	Fully-loaded US team for these roles: $28K–46K/mo	Salary surveys (Payscale, Sortlist, EverestX, SalesHive)

3 · The model

The math we put in front of you.

Bottom-up, using the medians above. Shown as a range because closed revenue lags spend.

Step	Value (median case)	Basis
Outbound sends	2,500–10,000 /mo	10K promised delivered; model uses quality-send median
Replies	86–343 /mo	3.43% reply rate
Booked meetings	~20–30 /mo	~25% of positive replies book
Qualified opportunities	~8–11 /mo	~47–50% of held meetings
Closed deals	~1.5–2.3 /mo	~20% of qualified opps
New ARR booked	$32K–52K /mo	$20–26K ACV per deal
Return on the $4,750/mo program: roughly 6–11x monthly ARR multiple (before the 3–6 month lag). Conservative case: ~2x ARR in year one.

The honest caveat: this is a modeled output from industry medians, not a guarantee. Revenue lags spend by 3–6 months. The growth levers (content, newsletter, social) ramp over months 4–18, so months 1–3 are outbound-led by design. That is why the pilot is 90 days and we track reply rate, reply-to-meeting, SQL-to-close, and content inbound from day one.

4 · Proof the concept works

Founders who did it, and the programs with receipts.

Public, real-number evidence. We source it because the concept should be proven before you trust us.

The only way that you could learn about Retool in the early days was for me to email you and tell you about it.

David Hsu, Retool · read.first1000.co/p/brute-forcing-your-way-to-default

We had a list of companies we knew were our ideal customer profiles, and we did some outbound sales.

Amit Bendov, Gong ($1M ARR in 12 months) · firstmillion.club/p/gong

Almost all of our initial traction came from me sending out cold emails. It got us to our first million dollars in revenue.

Steven Goh, Proxycurl · indiehackers.com/post/how-cold-emailing-grew-my-b2b-startup-to-100k-mrr

Program	Result	Source
Grove Digital	9x on tool investment (new MRR vs $1.5K/mo spend)	Grove Digital case study
Alchemail staffing client	~675x: $2.1M closed vs ~$3.2K/mo fees over 6 months	Alchemail
SalesHive B2B SaaS startup	104 meetings booked via outbound	saleshive.com/case-studies
Martal worked example	5,000 sends → 51 meetings → ~1 closed deal (0.02% email-to-close)	martal.ca/conversion-rate-statistics

The honest counterpoint: one operator (Fraction) saw cold outbound degrade after Google’s Feb 2024 spam policy and AI filters. This is exactly why the program is signal-led outbound plus owned channels (content, newsletter, list), not spray-and-pray blasts. The machine is designed for the post-2024 environment.

5 · Cost of doing it yourself

What the same team costs in-house.

Fully-loaded US market rates (salary surveys, 1.3–1.5x load). This backs the comparison table on the offer pages.

Role	Loaded monthly (US)	Source
Content writer (0.75–1.0 FTE)	$5,500–9,800	Payscale (loaded $88–117K/yr)
Social / growth specialist (0.75–1.0 FTE)	$6,700–10,800	Sortlist, EverestX
SDR (1.0 FTE)	$9,200–13,300	Assay, SalesHive, Prospeo
Email marketer (0.5 FTE)	$3,700–4,200	Coursera / Glassdoor
Marketing lead / PM (0.25–0.5 FTE)	$2,700–8,300	Growigami
Total: $28K–46K/mo for a working 3–5 person team, mixed part/full time, plus 3+ months to ramp. The program runs the whole machine for $4,750/mo.

See the numbers for your company.

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