Funnel Intelligence Agent
A 4-agent pipeline: a Collector pulls per-channel funnel data, an Analyzer flags statistical bottlenecks, a Hypothesis Generator turns each into A/B tests via Claude, and a Report Builder assembles the roadmap.
About this case study
Problem: a single blended conversion funnel hides where campaigns actually break. A channel with weak signup conversion could have a mobile UX bug, a targeting problem, or nothing wrong at all — and telling those apart by eyeballing one dashboard stops working past two or three traffic sources. Approach: a 4-agent pipeline positioned as "a junior growth analyst who never sleeps." A Collector pulls funnel data per traffic channel, an Analyzer flags statistical bottlenecks (z-score vs. the cross-channel baseline for that step, not a fixed percentage threshold — channels genuinely behave differently), a Hypothesis Generator turns each bottleneck into 2-3 concrete A/B tests via Claude, and a Report Builder assembles the prioritized roadmap. Result: this demo runs against a synthetic SaaS funnel with two deliberately planted issues, so there's something real for the Analyzer to find. It correctly surfaces the compounding paid_social mobile-friction problem among the top bottlenecks and proposes matching, testable hypotheses — live, on the page.
Methodology Note
Funnel Intelligence Agent is the first built module of a broader growth-analytics architecture: today it diagnoses funnels, next in line are customer lifecycle and channel-level revenue attribution, on the same 4-agent pattern (Collector → Analyzer → Hypothesis Generator → Report Builder). Built and validated on a controlled synthetic scenario — I'll run the same thing on your real data on a call.
← See where this diagnostic approach came from: the SolidVisa case study
Example run
SyntheticSaaS Demo — 2026-06-18 to 2026-07-18
Funnel analysis for SyntheticSaaS Demo (2026-06-18 to 2026-07-18) across 7 traffic channels: 6 bottleneck(s) identified. Top issues: paid_social/landing_view (3.0% vs cross-channel baseline); paid_social/signup (15.2% vs cross-channel baseline). Generated 9 A/B hypotheses across 3 channel/step combination(s). Recommend starting with quick wins before strategic bets.
Funnel (aggregate across all channels)
Channel comparison
| Channel | Volume | Overall conversion | vs. avg |
|---|---|---|---|
| direct | 12,000 | 4.27% | ▲ 3.35% |
| referral | 8,000 | 1.23% | ▲ 0.31% |
| 15,000 | 0.36% | ▼ -0.56% | |
| organic_search | 40,000 | 0.29% | ▼ -0.62% |
| paid_search | 60,000 | 0.16% | ▼ -0.76% |
| organic_social | 25,000 | 0.08% | ▼ -0.84% |
| paid_social | 90,000 | 0.03% | ▼ -0.89% |
Top bottlenecks & A/B roadmap (6 total found, top 2 prioritized)
[paid_social] Drop-off at 'landing_view' is 7.0% (1.5 standard deviations above the cross-channel mean of 4.0% for this step).
Control: Generic landing page headline and hero image not tied to specific ad campaigns
Variant: Dynamic landing page hero that mirrors the exact value prop, imagery, and copy used in the triggering paid social ad (message match)
Primary: landing_view to next-step conversion rate · Guardrail: bounce rate / avg time on page · n≈4,000/variant
Control: Current landing page with multiple competing CTAs, dense copy, and heavier page weight
Variant: Streamlined above-the-fold section with single clear CTA, reduced asset weight for faster load, and minimal competing links
Primary: landing_view drop-off rate · Guardrail: page load time / mobile conversion rate · n≈4,000/variant
[paid_social] Drop-off at 'signup' is 87.8% (1.7 standard deviations above the cross-channel mean of 72.6% for this step).
Control: Current signup form with full field set (name, email, password, phone, company) shown to all traffic sources
Variant: Paid social visitors see a reduced form with only email + password; additional profile fields deferred to post-signup onboarding
Primary: signup completion rate (paid_social segment) · Guardrail: post-signup activation rate (7-day) · n≈2,400/variant
Quick wins
- Match landing page hero to paid social ad creative/messaging (paid_social / landing_view)
- Simplify above-the-fold layout to reduce load/cognitive friction (paid_social / landing_view)
- Simplify Signup Form Fields for Paid Social Traffic (paid_social / signup)
Strategic bets
- Channel-specific landing page variant for paid social traffic (paid_social / landing_view)
Live access is available on request — I personally review each one.