When CAC Fell and Lead Volume Rose — But Retention Quietly Collapsed

“On paper, everything was working. Amoeba helped us see that some growth was quietly setting us back.”— VP Growth

‍Company Snapshot

  • Industry: B2B SaaS (Marketing Analytics & Automation)
  • ‍ARR: ~$120M (growing 45% YoY)
  • ‍GTM Motion: Product-led growth with paid acquisition engine
  • ‍Primary Channels: Paid search (Google, Bing), paid social (LinkedIn, Meta), inbound content, product virality
  • ‍Customer Model: Freemium land-and-expand with multi-seat enterprise upsell
  • ‍Average Deal Size: $18K annual contract, expanding to $67K by year two
  • ‍Primary Stakeholders: CMO, VP Growth, VP Customer Success, CFO

The Challenge

The company had just closed its strongest growth quarter on record:

  • CAC decreased 15% — from $1,850 to $1,572 per customer
  • Lead volume surged 30% — driven by aggressive paid social expansion and conversion rate optimization
  • MQL-to-SQL conversion held steady at 28%, demo-to-close at 31%
  • Monthly new logo acquisition up 22% quarter-over-quarter

Marketing dashboards were glowing. The executive team celebrated the efficiency gains. Budget conversations centered on doubling down: scaling the highest-performing channels, increasing spend on campaigns with the lowest cost-per-acquisition.

Six months later, a different story emerged.

Customer Success began surfacing troubling patterns in their weekly business reviews:

  • Net revenue retention dropped from 118% to 104% for cohorts acquired in Q3
  • Time-to-first-expansion increased by 40% — customers were taking longer to add seats or upgrade plans
  • 90-day churn spiked from 6% to 11% among newer customers, particularly those from paid social channels
  • Product engagement scores declined — daily active usage dropped 18% for accounts acquired during the high-volume quarter
  • Support ticket volume per new customer increased 35%, with many expressing confusion about product fit

The disconnect was jarring. The acquisition funnel looked healthy. But the customers flowing through it were fundamentally different — and leadership didn't see it coming.

Why it mattered

The company was making weekly six-figure spend decisions based almost exclusively on short-term efficiency signals: CPL, CAC, MQL volume, pipeline velocity. But these metrics told an incomplete story.

What they didn't capture:

  • Whether cheaper leads were actually qualified leads
  • Whether higher volume was attracting customers with genuine product need
  • Whether faster conversions meant better-fit buyers or just more aggressive sales tactics
  • Whether paid social scale was pulling in customers who would stay and grow

By the time Customer Success flagged retention issues, the damage was done. Months of acquisition spend had been allocated to channels generating high volumes of low-quality customers. The result:

  • $2.3M in recoverable ARR at risk from increased early-stage churn
  • 18-month payback periods stretching to 26+ months for recent cohorts
  • Expansion pipeline eroding as newer customers showed weaker product engagement
  • CAC payback assumptions breaking down — customers weren't staying long enough or expanding fast enough to justify acquisition costs

The real risk wasn't just financial. It was strategic. If leadership continued optimizing for volume and efficiency without visibility into customer durability, they would compound the problem — scaling channels that delivered fast growth but slow revenue decay.

The core question became unavoidable: Which growth was truly durable, and which was quietly eroding the business?

Where Amoeba fit

Amoeba was introduced as a Decision Intelligence layer to bridge the gap between acquisition performance and long-term customer outcomes. Instead of treating marketing, sales, and customer success as separate reporting silos, Amoeba connected them into a unified view of growth quality:

What Amoeba Did

1. Linked acquisition signals to downstream customer behavior
Amoeba ingested data from the company's marketing automation platform, CRM, product analytics, and billing system. It mapped every lead source, campaign, and channel to cohort-level retention, expansion velocity, product usage, and lifetime value.

For the first time, the team could see:

  • Which campaigns generated customers who stayed beyond 12 months
  • Which channels drove leads that expanded within 6 months
  • Which targeting strategies attracted high-engagement users versus trial-and-churn behavior

2. Identified early indicators of quality degradation
Amoeba flagged leading signals of retention risk before they appeared in revenue metrics:

  • Declining product engagement in the first 30 days
  • Slower time-to-value realization
  • Lower feature adoption rates
  • Weaker buying intent signals at acquisition

These indicators became visible within weeks of a customer signing up — not six months later when they churned.

3. Surfaced trade-offs between efficiency and durability
Amoeba didn't just report metrics. It highlighted decision trade-offs:

  • "This paid social campaign has 20% lower CAC but drives customers with 35% higher 12-month churn"
  • "This content channel converts slower but produces customers with 2.1x higher LTV"
  • "This landing page variation increases conversions by 18% but attracts users who engage 40% less with core features"

This shifted the conversation from "Which channel is cheapest?" to "Which growth is actually healthy for the business?"

What changed

Marketing Could See Beyond the Funnel

The VP of Growth stopped optimizing campaigns purely for cost-per-lead. Instead, she began testing for quality-weighted acquisition — prioritizing campaigns that generated leads with strong engagement signals and expansion potential, even if initial CAC was slightly higher.

Growth Experiments Became Smarter

A/B tests were no longer judged solely on conversion rate lifts. The team now evaluated tests based on:

  • 60-day product engagement
  • Time to first expansion
  • 12-month retention probability

If a landing page variation increased sign-ups but attracted lower-intent users, it was killed — even if it "won" on traditional metrics.

Cross-Functional Alignment Improved

Customer Success and Marketing began working from shared success criteria. Instead of arguing over "lead quality" anecdotally, they used Amoeba's cohort analysis to agree on what "good growth" looked like — and where acquisition strategies needed adjustment.

Retention Risk Became Predictive, Not Reactive

Amoeba's early warning signals allowed the team to intervene before customers churned:

  • Onboarding workflows were redesigned for at-risk cohorts
  • Customer Success proactively engaged accounts showing low engagement patterns
  • Marketing stopped investing in channels generating consistently weak retention signals

Impact

Financial Outcomes:

  • $1.8M in at-risk ARR recovered through earlier intervention on retention signals
  • CAC payback period stabilized at 16 months (down from 26 months for at-risk cohorts)
  • Net revenue retention rebounded to 115% within two quarters
  • Marketing efficiency improved 23% when measured by CAC-to-LTV ratio, not CAC alone

Operational Improvements:

  • Decision velocity increased — leadership could reallocate budget with confidence, knowing which channels supported long-term health
  • Cross-functional friction reduced — shared data replaced siloed opinions
  • Growth strategy matured — from volume-obsessed to value-focused

Strategic Confidence: The CMO could now walk into board meetings and explain not just how much they grew, but how durable that growth was — and why marketing investments were positioning the company for sustainable scale, not just vanity metrics.

Before vs after

Before Amoeba

  • Acquisition success defined by CAC, lead volume, and conversion rates
  • Retention treated as a downstream problem for Customer Success to solve
  • Growth decisions optimized for short-term performance and quarterly targets
  • 6–9 month lag before quality issues surfaced in revenue metrics
  • Channel investment decisions made in isolation from customer outcomes

After Amoeba

  • Acquisition evaluated by cohort durability, expansion velocity, and engagement signals
  • Retention risk surfaced within 30–60 days, not quarters later
  • Growth decisions aligned to long-term customer value, not just funnel efficiency
  • Real-time visibility into which growth was healthy and which was hollow
  • Marketing spend allocated based on predictive customer outcomes, not reactive reporting

See This Applied to Your Growth Strategy

If your marketing dashboards look healthy but Customer Success is raising red flags — or if you're scaling acquisition without confidence in long-term retention — you may be optimizing for the wrong signals.

Request a Decision Intelligence Preview to see how Amoeba maps your acquisition performance to customer durability — and where your growth strategy may be creating hidden risks.

Request a Decision Intelligence Preview

‍

‍