4.2× conversion lift on enterprise traffic through AI-assisted qualification architecture
Enterprise site converting <0.8% with no qualification layer and zero pipeline visibility.
- 4.2×
- Conversion Rate Lift
- $1.2M
- Q1 Pipeline Added
- 98
- Lighthouse Score
- 6 wks
- Full Payback
vs. pre-engagement baseline
net-new qualified deals
from 31 at engagement start
on total project investment
01 — Business Challenge
The problem
Meridian's marketing site was generating significant enterprise traffic but converting at less than 0.8% — well below the industry benchmark of 2.4% for asset management firms. There was no qualification mechanism, no CRM integration, and sales teams were manually reviewing every inbound contact with no intent data.
No lead qualification layer
All inbound submissions were treated equally regardless of intent, AUM, or company size. Sales time was wasted on unqualified contacts.
Zero pipeline visibility
No CRM connection meant pipeline data lived in spreadsheets. Attribution was impossible and forecasting was guesswork.
Legacy site architecture
PHP monolith on shared hosting with 4.2s average LCP. Enterprise prospects were abandoning before engaging with content.
No conversion infrastructure
A single generic contact form served all traffic. No segmentation, no progressive disclosure, no behavioral triggers.
02 — Technical Constraints
Constraints we engineered around
Multi-entity compliance
Three regulated entities under one parent required separate compliance footers and content gating by visitor jurisdiction.
No existing CRM
HubSpot had to be provisioned and configured as part of the engagement — no existing pipeline data to migrate from.
Legacy hosting dependency
Domain was tied to existing PHP site. Zero-downtime cutover was a hard requirement with no staging window.
03 — Solution Architecture
What we built
Full-stack replacement built on Next.js 16 App Router with Vercel Edge delivery. Lead qualification embedded at the acquisition layer — not bolted on after the fact.
AI Lead Scoring Engine
OpenAI-powered lead scoring evaluating company size, role, behavioral signals, and stated AUM range. Scores 0–100 with automatic routing thresholds.
Behavioral Conversion Layer
Segment tracks scroll depth, dwell time, and content consumption patterns. Progressive CTA disclosure triggers at intent thresholds.
CRM-Integrated Pipeline
HubSpot deal creation automated on qualification score ≥72. Stage transitions trigger internal Slack notifications and sales assignment.
Edge-Rendered Pages
All public pages rendered at Vercel Edge with ISR. No cold starts, sub-50ms TTFB globally, full Core Web Vitals compliance.
04 — Conversion Engineering
How we moved the numbers
Three primary conversion mechanisms were re-engineered: the primary CTA flow, the contact qualification form, and the post-submission nurture sequence.
| Metric | Before | After | Change |
|---|---|---|---|
| Lead form completion | 11% | 41% | 3.7× lift |
| Qualified lead rate | 8% | 34% | 4.25× lift |
| Sales response time | 18h avg | 4 min | Auto-routing |
| Page load (LCP) | 4.2s | 0.9s | 4.7× faster |
05 — Performance Improvements
Technical performance delta
| Metric | Before | After |
|---|---|---|
| Lighthouse Score | 31 | 98 |
| LCP | 4.2s | 0.9s |
| CLS | 0.34 | 0.02 |
| TTFB | 1,400ms | 48ms |
06 — Results
Commercial outcomes
Results
Full payback in 6 weeks. Pipeline operating at 4.2× pre-engagement baseline.
- 4.2×
- Conversion Rate Lift
- $1.2M
- Q1 Pipeline Added
- 98
- Lighthouse Score
- 6 wks
- Full Payback
vs. pre-engagement baseline
net-new qualified deals
from 31 at engagement start
on total project investment
Services used in this engagement
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