A/B Testing Consultant

Expert A/B testing consultant with proven track record. I help SaaS platforms, e-commerce sites, and B2B services run statistically rigorous experiments that drive measurable conversion lifts through disciplined methodology and practical implementation.

Proven Experimentation Results

18%
Trial-to-Paid Lift
Sustained over 3 quarters
35%
Conversion Increase
SaaS onboarding optimization
27%
Faster Experiment Cadence
11 days → 8 days turnaround
A/B testing dashboard showing experiment results and statistical analysis
Expert A/B testing consulting combines statistical rigor with practical implementation

A/B Testing Methodology & Approach

I've built and executed experimentation programs for B2B SaaS platforms, e-commerce sites, and high-traffic applications. My approach combines statistical rigor with practical implementation, ensuring experiments deliver actionable insights, not just numbers.

Core Methodology

  • •Signal-first discovery: Map friction points using session replays, product analytics, and support transcripts before designing experiments
  • •Statistical guardrails: Proper sample size calculations, significance testing (95% confidence), confidence intervals, and automated analysis that flags invalid results
  • •Implementation sprints: Lightweight ES5-compatible variations that don't derail roadmap work, with feature flags for safe rollouts
  • •Shared analytics dashboards: Real-time experiment status, uplift ranges, and confidence intervals visible to marketing, product, and leadership

Tools & Platforms

  • Optimizely Edge & Web: Client-side and server-side experimentation
  • Custom experimentation frameworks: Full-stack platforms with feature flags, A/B tests, and multivariate tests
  • Analytics integrations: Google Analytics 4, Mixpanel, Looker dashboards
  • Statistical analysis: Proper significance testing, confidence intervals, sample size calculators

A/B Testing Services

Experimentation Framework Setup

Build end-to-end A/B testing infrastructure: platform selection, implementation, analytics integration, and team training.

  • • Platform evaluation and selection
  • • Technical implementation
  • • Analytics and tracking setup
  • • Team training and documentation

Experiment Design & Analysis

Design statistically rigorous experiments, calculate sample sizes, and analyze results with proper significance testing.

  • • Hypothesis formation
  • • Sample size calculation
  • • Statistical significance analysis
  • • Results interpretation and recommendations

Ongoing Optimization

Continuous experimentation cadence: identify opportunities, design tests, analyze results, and implement winners.

  • • Experiment roadmap planning
  • • Regular test design and review
  • • Performance monitoring
  • • Compounding wins strategy

Training & Enablement

Train your team on A/B testing best practices, statistical significance, and building an experimentation culture.

  • • Statistical significance workshops
  • • Experiment design training
  • • Results interpretation guidance
  • • Best practices documentation

A/B Testing Consultant Pricing

Hourly Rates

£150–£250/hr

UK rates

$175–$275/hr

US rates

Project-Based

£5k–£15k

Framework setup & initial tests

£15k–£40k

Comprehensive programs

Retainers

£3k–£8k/month

Ongoing experiment design & analysis

Frequently Asked Questions

An A/B testing consultant designs, implements, and analyzes experiments to improve conversion rates. I help you set up experimentation frameworks, calculate proper sample sizes, ensure statistical significance, interpret results, and build a culture of data-driven decision making. This includes everything from strategy and hypothesis formation to technical implementation and results analysis.
A/B testing consulting rates vary by engagement type. Hourly rates: £150–£250/hour (UK) or $175–$275/hour (US). Project-based: £5k–£15k for experimentation framework setup and initial tests, £15k–£40k for comprehensive programs. Retainers: £3k–£8k/month for ongoing experiment design and analysis. Pricing depends on whether you need strategy-only, full implementation, or ongoing optimization.
Typical results include 10–35% conversion rate increases for well-executed experiments. In my case studies: 18% trial-to-paid lift sustained over three quarters, 35% conversion increase for SaaS onboarding, and 27% faster experiment turnaround. Results depend on your starting point, traffic volume, and commitment to the experimentation process.
It depends on the engagement. For strategy and design-only work, you'll need engineering resources to implement experiments. For full-stack implementations, I can build experimentation platforms, feature flag systems, and analytics integrations using tools like Optimizely Edge, custom frameworks, or your existing stack. Many clients prefer a hybrid: I design experiments and provide implementation guidance, while your team handles deployment.
Most A/B tests run for 1–4 weeks to reach statistical significance, depending on traffic volume and the size of the expected effect. Initial setup and framework development: 2–3 weeks. First experiments: 4–6 weeks (including design, implementation, and reaching significance). Comprehensive programs: 3–6 months for multiple experiment cycles and compounding wins.
A/B testing is a methodology for comparing two versions of a page or feature. Conversion rate optimization (CRO) is the broader discipline that includes A/B testing, but also encompasses UX research, funnel analysis, user interviews, heatmaps, session replays, and strategic prioritization. I use A/B testing as the validation tool within a comprehensive CRO framework.

Ready to Start A/B Testing?

If you're ready to build an experimentation program that drives measurable conversion lifts, let's discuss your specific needs and how A/B testing can help.

Book a Free Strategy Call

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