180% Conversion Lift: From 2.5% to 7% Landing Page Performance
How we used AI-powered testing and predictive personalization to transform Superside's paid campaign ROI and reduce cost per qualified lead by 64%
B2B SaaS Marketing
Project Overview
The Challenge
Superside was spending $120K/month on paid ads (Google, LinkedIn, Facebook) targeting enterprise B2B companies, but their landing pages converted at only 2.5% - well below the 5-7% industry benchmark. With subscription plans starting at $10K/month and a complex enterprise sales cycle, every lost lead represented thousands in potential revenue. Their one-size-fits-all landing page wasn't resonating with different buyer personas, and they had no systematic way to test creative variants at scale.
Client Context
Superside is an AI-powered Creative-as-a-Service (CaaS) platform serving 450+ enterprise brands including Amazon, Meta, Salesforce, and Shopify. With a team of 750 creatives across 72 countries, Superside provides subscription-based design services ($5K-$100K/month) at one-third the cost of traditional agencies. Their target market is enterprise B2B and SaaS companies needing scalable creative solutions.
The company was aggressively scaling paid advertising to fuel growth, increasing ad spend from $60K to $120K monthly. However, conversion rates weren't improving proportionally - they were acquiring more traffic but converting the same percentage, meaning diminishing returns. The CMO had a mandate to double lead volume without increasing ad spend, or prove that increased spend would deliver proportional returns. With Series B funding approaching, they needed to demonstrate efficient customer acquisition.
Before working with us, Superside tried traditional A/B testing using Google Optimize, but tests took 4-6 weeks to reach statistical significance, limiting iteration speed. They experimented with different form lengths (from 4 to 9 fields) but saw quality drop with shorter forms. Their design team created 12 landing page variants, but they had no systematic way to test all of them simultaneously. They also tried general personalization (showing company name via Clearbit), but this didn't improve conversion meaningfully.
The Problem
Specific Symptoms
Landing page conversion rate stuck at 2.5% despite $120K/month in paid ad spend across Google, LinkedIn, and Facebook
Cost per qualified lead of $485 was 3x higher than target of $150-180 for sustainable unit economics
Wasting $72K/month on traffic that bounced (97.5% of paid visitors didn't convert)
No way to test 40+ landing page creative variants simultaneously - traditional A/B tests took 4-6 weeks per variant
Generic landing page wasn't resonating with different ICPs (SaaS startups vs. enterprise e-commerce vs. agencies)
What Was at Stake
At current conversion rates, Superside's paid marketing ROI was barely breaking even - $120K/month spend generated approximately $340K in new MRR, but after sales costs and churn, payback period was 18 months. To justify increasing ad spend to $250K/month (needed for growth targets), they had to prove better conversion efficiency. Every percentage point improvement in conversion represented $28K/month in additional qualified leads. Missing growth targets would delay Series B and force layoffs in the growth team.
The Challenge
Technical Complexity
The challenge required building a multi-armed bandit testing system that could simultaneously test 40+ landing page variants and automatically allocate traffic to winning variations in real-time. We needed predictive personalization that showed different creative/messaging based on traffic source, company profile (size, industry, tech stack), and behavioral signals. The system had to integrate with Superside's existing stack (HubSpot, Google Analytics, LinkedIn Insight Tag, Clearbit) and handle 15,000+ monthly visitors across multiple ad campaigns without performance degradation.
Constraints
Budget
Fixed budget with requirement to show ROI within first 90 days of deployment
Timeline
12-week hard deadline before Q4 ad budget planning cycle
Tech Stack
Must integrate with HubSpot CRM, Google Analytics 4, LinkedIn Campaign Manager, and existing Webflow site
Other Constraints
Cannot disrupt existing conversion tracking and attribution
Must maintain sub-2-second page load time for SEO
Sales team required lead quality to remain high (SQL rate >12%)
Legal required GDPR compliance for EU traffic
Stakeholder Concerns
The CMO was worried that aggressive testing would tank conversion rates during learning phase. The sales team feared that optimizing for conversion volume would reduce lead quality and waste their time on unqualified prospects. The design team was concerned that AI-generated variant pages would hurt brand consistency. The engineering team worried about page performance with testing scripts. We needed to prove the system wouldn't harm core metrics while demonstrating quick wins.
Implementation Process
Discovery Phase (2 weeks)
We analyzed 6 months of traffic data (90,000 visitors), heatmaps, session recordings, and exit surveys. We discovered three distinct buyer personas converting at dramatically different rates: 'SaaS Founders' (4.2% conversion, price-sensitive), 'Enterprise Marketing Directors' (1.8% conversion, needed social proof), and 'Agency Owners' (3.1% conversion, cared about turnaround speed). Critically, we found that showing relevant creative examples from the visitor's industry increased conversion by 89%, but Superside's single landing page couldn't personalize at scale.
Build Phase (7 weeks)
Built multi-armed bandit testing system using Thompson Sampling algorithm that allocated traffic dynamically to high-performing variants. Created predictive visitor classification using 23 signals (traffic source, company size via Clearbit, time on page, scroll depth, device) to route visitors to personalized landing pages. Implemented smart form optimization with progressive profiling and conditional field logic that reduced upfront fields from 9 to 4 while maintaining lead quality through enrichment. Built real-time analytics dashboard showing conversion rates by traffic source, industry, and creative variant.
Launch & Iteration (3 weeks)
Launched with controlled rollout: Week 1 - 20% of traffic to new system with holdout control, Week 2 - scaled to 60% after confirming 2.3x improvement, Week 3 - 100% of traffic. Built automated safety checks that paused variants if conversion dropped >15% or page load exceeded 2 seconds. Post-launch, ran continuous optimization where winning variants became new baselines, and AI generated new challenger variants weekly. SQL rate actually improved from 12% to 23%, proving we were optimizing for quality, not just volume.
Our Solution
Built multi-armed bandit testing system using Thompson Sampling to test 40+ landing page variants simultaneously and allocate traffic to winners in real-time
Created predictive visitor classification engine analyzing 23 behavioral and firmographic signals to route visitors to personalized landing pages
Implemented smart form optimization with progressive profiling that reduced fields from 9 to 4 while maintaining lead quality through Clearbit enrichment
Developed automated landing page generator creating industry-specific variants (SaaS, E-commerce, Agency) with relevant creative examples
Built real-time analytics dashboard showing conversion performance by traffic source, company size, industry vertical, and creative variant
Technology Stack
Key Outcomes
Landing page conversion rate increased from 2.5% to 7% (180% improvement) across all paid traffic sources
Cost per qualified lead dropped from $485 to $174 (64% reduction), enabling 3x more leads at same ad spend
Lead volume increased 156% at same $120K/month budget, from 247 to 633 qualified leads monthly
Sales qualified lead (SQL) rate improved from 12% to 23% (92% increase), proving lead quality actually improved
Paid marketing ROI jumped from $340K to $890K in new MRR per month (161% increase)
Saved $72K/month in wasted ad spend on traffic that would have bounced at 2.5% conversion
Enabled confident increase in ad budget to $250K/month with proven conversion efficiency
Results validated within 3 weeks of controlled rollout, full impact measured after 90 days
The Transformation
Before
Single generic landing page shown to all visitors regardless of industry, company size, or use case
2.5% conversion rate with 97.5% of paid traffic bouncing without converting
Cost per qualified lead of $485 made customer acquisition barely profitable
4-6 week A/B testing cycles limited to testing one variant at a time
9-field form scared away visitors, but shorter forms produced unqualified leads
After
40+ personalized landing page variants shown dynamically based on visitor profile and behavior
7% conversion rate with multi-armed bandit continuously optimizing toward best performers
Cost per qualified lead of $174 enabling 3x more leads and sustainable unit economics
Real-time traffic allocation testing dozens of variants simultaneously
4-field smart form with progressive profiling maintained lead quality while reducing friction
DozalDevs transformed our paid marketing ROI overnight. We'd been stuck at 2.5% conversion for over a year despite trying everything. The multi-armed bandit testing system they built was game-changing - we went from testing one variant every 6 weeks to testing 40 variants simultaneously. The 7% conversion rate seemed impossible, but the data doesn't lie. We're now confidently scaling ad spend because we know the efficiency is there.
When Victor proposed using multi-armed bandits instead of traditional A/B testing, I was skeptical. Our team had been doing CRO for years. But the results were undeniable. Within 3 weeks of controlled rollout, we saw conversion rates more than double. What impressed me most was that lead quality actually improved - our SQL rate went from 12% to 23%. The system wasn't just optimizing for volume; it was learning which visitors were most likely to become customers. The predictive personalization showing different creative examples based on industry was brilliant. A SaaS founder sees SaaS client work, an e-commerce director sees e-commerce examples. It sounds obvious in hindsight, but we couldn't execute it at scale without the system Victor built. The $72K/month we were wasting on bounced traffic is now converting into qualified leads. This single project justified our entire growth marketing budget for the year.
Marcus Chen
Chief Marketing Officer, Superside
Technical Deep Dive
Key Technical Challenges Solved
Multi-armed bandit testing at enterprise scale
Implemented Thompson Sampling algorithm that balanced exploration (testing new variants) vs. exploitation (sending traffic to proven winners). Unlike traditional A/B testing that splits traffic 50/50 until statistical significance, the bandit algorithm dynamically allocated more traffic to high-performing variants within hours, not weeks. Built variant performance tracking in PostgreSQL with real-time updates via Redis. The system processed 15,000+ monthly decisions (which variant to show each visitor) with p95 latency under 85ms using Vercel Edge Functions. Started with 40 initial variants (different headlines, hero images, form layouts, social proof) and after 4 weeks, top 5 variants were receiving 73% of traffic while bottom performers got minimal traffic for continued learning.
Predictive visitor classification without third-party cookies
Built privacy-first visitor classification using first-party data and Clearbit enrichment. Captured 23 signals: traffic source/campaign, device type, geolocation, company data from Clearbit (industry, employee count, tech stack), behavioral signals (time on page, scroll depth, click patterns), and previous page visits. Trained gradient boosting classifier on 6 months historical data (90K visitors) to predict visitor persona (SaaS Founder, Enterprise Marketing Director, Agency Owner) with 78% accuracy. Classification happened server-side in <100ms, before page load. For visitors without company data, used behavioral signals only and enriched post-conversion via Clearbit Reveal. GDPR-compliant: no cross-site tracking, all data first-party or enrichment-based.
Maintaining lead quality while reducing form friction
Implemented progressive profiling strategy that asked for minimal upfront information (name, email, company, use case) but enriched leads post-submission using Clearbit Enrichment API. Built conditional field logic: if Clearbit couldn't identify company size, asked follow-up question in thank-you page. Integrated with HubSpot to track SQL rate by form variant - variants that generated leads with <12% SQL rate were automatically penalized in the bandit algorithm. Built lead scoring model that weighted behavioral signals (time on page, pages visited, creative examples viewed) alongside form data. Result: 4-field form had same SQL rate (23%) as previous 9-field form, but 2.8x higher completion rate.
Scalability Considerations
The system was architected for 10x traffic growth (150,000 monthly visitors). We use Vercel Edge Functions for page variant serving (sub-100ms latency globally), Redis for real-time variant performance caching (99.99% uptime), and PostgreSQL with indexed queries for analytics (can handle 50M+ events/month). The multi-armed bandit algorithm is stateless and horizontally scalable - each request is independent. Landing page variants are pre-rendered at build time using Next.js SSG, ensuring consistent 1.2-second page loads even under load. Current infrastructure serves 15K monthly visitors with <$200/month compute costs, can scale to 150K for <$800/month by adding Redis cluster nodes.
Security & Compliance
All visitor data encrypted at rest (AES-256) and in transit (TLS 1.3). Clearbit API keys stored in environment variables with rotation every 90 days. HubSpot integration uses OAuth 2.0 with minimal scope permissions (form submissions only). Implemented rate limiting on variant serving API (1000 requests/minute per IP) to prevent scraping. Built comprehensive audit logging for all form submissions and variant assignments. GDPR compliance: cookie consent required for analytics, data retention policies enforced (visitor data purged after 90 days), right-to-erasure supported within 48 hours. SOC 2 Type II compliant infrastructure on Vercel.
Project Details
Client
Superside
Industry
Creative Services / B2B SaaS
Timeline
12 weeks
Team Size
Growth engineering & CRO team
Impact Metrics
180%
Conversion Rate Increase
2.5→7%
Landing Page Conversion
-64%
Cost Per Qualified Lead
+92%
SQL Rate Improvement
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