u/Goran-CRO

Most early-stage B2B SaaS teams are working on the wrong problem.

Not because they're bad at their jobs — but because symptoms look like the problem.

"We need more pipeline" → they hire an SDR.

"Conversion is low" → they A/B test landing pages.

"CAC is too high" → they pause paid and blame the channel.

These are symptoms. The actual constraint is usually (at least) one layer below.

Here's the sequence I use to diagnose what's actually broken:

Foundation → Strategy → Execution

0. Problem + ICP (before everything) Is the problem urgent AND important? Both, not one. If customers are managing fine with a spreadsheet and some willpower, you don't have a distribution problem - you have a switching cost problem.

Clayton Christensen's JTBD and SPICED framework are useful here: what's the trigger that makes someone switch from their current workaround to you? That trigger is your real positioning hook.

Narrower ICP also means lower CAC and faster iteration cycles. It feels like a constraint. It's actually a multiplier.

1. Positioning April Dunford's framework is the clearest I've seen: who are you best for, compared to what alternative, and why does it matter to them? The "alternative" is usually a spreadsheet or an internal process - not a SaaS competitor. That reframe changes everything about how you message.

FletchPMM's message architecture adds the layer most people miss: a hierarchy of messages by use case and buyer role, not a single positioning statement.

2. Product & PMF Stop looking at sign-ups. Look at retention curves, organic referral rate, and feature adoption depth. Those are the signals.

Itamar Gilad's Total Impact Matrix is worth a look - balances customer value against business value so you're not optimizing one at the expense of the other.

Pre-define your success, guardrail, deterioration and quality metrics before you run experiments. (Spotify's model for this is the best real-life example.)

3. Pricing & Packaging Flows from positioning - not market comps. Price the unit of value your ICP actually experiences (usage, seat, outcome). If you're pricing based on what a comparable Series A company charges, you've skipped the thinking.

4. GTM Motion Deal size + buyer complexity + business model → motion. Not trends.

$5K ACV + single buyer = PLG or low-touch.

$50K ACV + buying committee = sales-assisted.

Copying a competitor's GTM without their stage, resources, and timing is how you burn runway while feeling busy.

5. Channel Strategy Last. Not first.

Three questions before spending: Does search demand exist for your category? Can you reach your ICP at viable unit economics? Do you have the conversion infrastructure to make a test meaningful?

The diagnostic I actually use:

🔴 Can't get meetings → Problem/ICP layer

🔴 Meetings don't convert → Positioning/Pricing layer

🔴 90-day churn → PMF layer

🔴 CAC unsustainable → GTM/Channel layer

🔴 "Channels don't work" → Unit Economics layer

Most of the time, the team is working one or two layers above the real constraint.

What layer is your company currently stuck on?

B2B Saas Growth Diagnostic: Dependency Map

Situation I've seen play out more than once in B2B SaaS:

The company does everything right — proper attribution, experienced paid-search support, and weekly performance reviews. Numbers look healthy. They scale.

Six months later, someone finally lines up the right data:

• Average CAC: $1,100 → within benchmark, no red flags
• New customer CAC: $2,900 → had been climbing quietly for 3 quarters

The average looked fine the whole time. Older, efficient cohorts were masking what it actually costs to acquire a new customer right now.

The distinction that matters:

Average CAC = total spend ÷ all customers ever acquired

Marginal CAC = incremental spend ÷ incremental customers (this cohort only)

In scaling mode, average CAC is a rearview mirror. You're not buying your history — you're buying the next 50 customers. And those get more expensive as channels saturate.

How it typically unfolds:

$5k/month → works well Scale to $20k → CAC "still looks fine" Push to $50k → efficiency silently collapses One quarter later: payback is 24 months, LTV:CAC is broken, CFO freezes the budget

The inflection point was in the data the whole time. Nobody was watching the marginal number.

The fix is straightforward: track CAC cohort by cohort. Compare what this month's new customers cost versus last month's. If that's climbing faster than ARPA, adding budget makes the problem worse, not better.

Has anyone here actually built marginal CAC tracking into their reporting, or is blended/average still the default at most companies?

Situation I've seen play out more than once in B2B SaaS:

Company does everything right — proper attribution, experienced paid search help, weekly performance reviews. Numbers look healthy. They scale.

Six months later, someone finally lines up the right data:

• Average CAC: $1,100 → within benchmark, no red flags
• New customer CAC: $2,900 → had been climbing quietly for 3 quarters

The average looked fine the whole time. Older, efficient cohorts were masking what it actually costs to acquire a new customer right now.

The distinction that matters:

Average CAC = total spend ÷ all customers ever acquired

Marginal CAC = incremental spend ÷ incremental customers (this cohort only)

In scaling mode, average CAC is a rearview mirror. You're not buying your history — you're buying the next 50 customers. And those get more expensive as channels saturate.

How it typically unfolds:

$5k/month → works well Scale to $20k → CAC "still looks fine" Push to $50k → efficiency silently collapses One quarter later: payback is 24 months, LTV:CAC is broken, CFO freezes the budget

The inflection point was in the data the whole time. Nobody was watching the marginal number.

The fix is straightforward: track CAC cohort by cohort. Compare what this month's new customers cost versus last month's. If that's climbing faster than ARPA, adding budget makes the problem worse, not better.

Has anyone here actually built marginal CAC tracking into their reporting, or is blended/average still the default at most companies?

Average CAC vs Marginal CAC

Marginal CAC inflation x Payback Period

Situation I've seen play out more than once in B2B SaaS:

Company does everything right — proper attribution, experienced paid search help, weekly performance reviews. Numbers look healthy. They scale.

Six months later, someone finally lines up the right data:

• Average CAC: $1,100 → within benchmark, no red flags
• New customer CAC: $2,900 → had been climbing quietly for 3 quarters

The average looked fine the whole time. Older, efficient cohorts were masking what it actually costs to acquire a new customer right now.

The distinction that matters:

Average CAC = total spend ÷ all customers ever acquired

Marginal CAC = incremental spend ÷ incremental customers (this cohort only)

In scaling mode, average CAC is a rearview mirror. You're not buying your history — you're buying the next 50 customers. And those get more expensive as channels saturate.

How it typically unfolds:

$5k/month → works well Scale to $20k → CAC "still looks fine" Push to $50k → efficiency silently collapses One quarter later: payback is 24 months, LTV:CAC is broken, CFO freezes the budget

The inflection point was in the data the whole time. Nobody was watching the marginal number.

The fix is straightforward: track CAC cohort by cohort. Compare what this month's new customers cost versus last month's. If that's climbing faster than ARPA, adding budget makes the problem worse, not better.

Has anyone here actually built marginal CAC tracking into their reporting, or is blended/average still the default at most companies?

Average CAC vs Marginal CAC