Rocket Lawyer Rocket Lawyer ยท Mobile Conversion
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Case Study ยท Rocket Lawyer

Mobile Conversion Optimization

Failed fast on desktop. Won big on mobile.

5%

Mobile Conversion Lift

2.34%

Peak Checkout Rate

90%

of U.S. Revenue

Role: Senior Product Manager, Legal Docs Team

๐Ÿš€ Introduction

Rocket Lawyer's Legal Documents product line was the company's core revenue engine, contributing 90% of U.S. revenue. Even small improvements to conversion rates here had outsized business impact. My mission was to find and unlock those gains through systematic A/B testing and data-driven optimization.

Overview

The document creation flow (where users answer questions to generate legal documents like NDAs, leases, and wills) was the primary conversion funnel. Improving gross conversion rates (GCR) here meant more users completing documents and entering the checkout flow, directly driving revenue growth.

Mission Alignment

This work advanced Rocket Lawyer's mission to make legal services affordable and accessible. By reducing friction in the document creation experience, we helped more people successfully complete the legal tasks they came to accomplish.

๐ŸŽฏ The Problem

Conversion rates had room to grow, but the question was where to focus. Desktop vs. mobile? UX changes vs. performance improvements? We needed a systematic approach to identify the highest-leverage opportunities.

Users & Stakeholders

  • Consumers creating legal documents (NDAs, leases, wills, etc.)
  • Mobile users with different needs than desktop
  • Design, engineering, marketing, and leadership teams
  • Enterprise partners relying on the document platform

Key Problems to Solve

  • Identify which changes would actually move conversion rates
  • Understand differences between desktop and mobile behavior
  • Quantify the impact of site performance on conversions
  • Build a repeatable testing methodology for continuous improvement

๐Ÿง  Strategy & Solution

We developed a structured A/B testing methodology and ran experiments across the document creation funnel. When our initial desktop hypothesis failed, we pivoted to mobile and found the win we were looking for.

A/B Testing Methodology

Established a repeatable process: ideation, pitch, brief, experiment, launch. Great ideas could come from anyone. Briefs captured impact, theme alignment, user journeys, and designs before experiments ran.

Desktop Experiment

We hypothesized that hiding the document preview on shorter documents would increase conversions. The control won by 10%, teaching us that the preview builds trust and demonstrates value before checkout.

Frictionless Mobile

The site was responsive but not optimized for mobile. We designed a "frictionless" mobile interview, removing distractions and streamlining the experience. This variant outperformed control by 5%.

Performance as a Lever

Built custom analytics tools to link site performance with conversion performance. This revealed that speed improvements directly impacted checkout rates, leading to our highest recorded rate of 2.34%.

โš™๏ธ Platform Capabilities

Beyond the UX experiments, we invested in tooling and infrastructure that enabled data-driven optimization:

Performance Analytics

Built custom analytics to map user performance against key metrics: GCR, registration, and checkout rates. This made the relationship between site speed and conversions visible and actionable.

A/B Testing Framework

Established a repeatable experimentation pipeline with clear stages: ideation, pitch, brief, experiment, launch. This enabled continuous testing and learning across the funnel.

Mobile-Optimized Interview

Productionized the "frictionless" mobile document interview after validating the 5% conversion lift. Streamlined experience became the new default for mobile users.

Experiment Briefs System

Created structured experiment briefs capturing impact, theme alignment, user journeys, and designs. This documentation enabled better decision-making and knowledge sharing.

๐Ÿ› ๏ธ Product Walkthrough

The document interview flow guides users through questions to generate their legal document. We tested UX changes on desktop first, learned from the failure, then adapted for mobile where we found the conversion lift.

Rocket Lawyer Homepage
1. Rocket Lawyer Homepage
Initiatives Backlog & Planning
2. Initiatives Backlog & Planning
Desktop Control: Show Preview (Won)
3. Desktop Control: Show Preview (Won)
Desktop Variant: Hide Preview (Lost)
4. Desktop Variant: Hide Preview (Lost)
Mobile: Frictionless Experience (Won)
5. Mobile: Frictionless Experience (Won)

๐Ÿค Execution & Key Decisions

The path to results wasn't linear. We had to be willing to let data guide our decisions, even when it meant abandoning our initial hypothesis and pivoting to a different platform.

๐ŸŽฏ Learning from "Failure"

We hypothesized that users were copying content from the preview on shorter documents. But hiding the preview backfired: control won by 10%. The data taught us that the preview builds trust and shows value before checkout.

๐Ÿ—๏ธ Platform-Specific Thinking

The site was responsive but not truly optimized for mobile. Instead of treating mobile as smaller desktop, we designed a "frictionless" experience by removing distractions and streamlining the flow.

๐Ÿค Performance Investment

Building custom analytics to link site speed with conversion data wasn't glamorous work, but it revealed a clear opportunity. Performance improvements directly lifted checkout rates to an all-time high.

๐Ÿ“ˆ Outcomes

5% Mobile Conversion Lift

The mobile-optimized document interview drove a 5% increase in gross conversions, a significant win for a product line contributing 90% of U.S. revenue.

Record Checkout Rate

Performance optimization work pushed checkout rates to 2.34%, the highest recorded conversion rate, by linking site speed improvements directly to conversion outcomes.

Repeatable Testing Framework

The A/B testing methodology (ideation, pitch, brief, experiment, launch) became a lasting asset for the team to continuously identify and validate conversion improvements.

Custom Analytics Tooling

The performance-to-conversion analytics tools enabled ongoing optimization by making the relationship between site speed and business outcomes visible and actionable.

๐Ÿšง Challenges

Finding conversion wins required navigating failed experiments, shifting priorities, and building alignment across teams.

Initial Hypothesis

Desktop Experiment Backfired

We suspected users were copying content from the preview on shorter documents and not converting. We tested hiding the preview, but control won by 10%. Users valued the preview more than we expected.

The Insight

Preview Builds Trust

The "failed" experiment taught us something valuable: the preview builds trust in the product, shows real-time document updates, and demonstrates value before checkout. This informed future product decisions.

New Approach

Mobile Was Underserved

The site was responsive but not truly optimized for mobile. We saw an opportunity to remove distractions and streamline the experience. The "frictionless" mobile interview became our next experiment.

Parallel Track

Performance Visibility

We suspected site performance was impacting conversions but had no way to prove it. Building custom analytics to link performance metrics with conversion data required cross-functional investment but unlocked a new optimization lever.

๐Ÿ’ก Lessons Learned

Negative Results Are Still Results

The desktop experiment "failed" but taught us that the preview builds trust and shows value. That insight was worth more than if we'd simply not run the test.

Responsive โ‰  Optimized

A responsive site isn't the same as a mobile-optimized experience. Designing "frictionless" meant removing distractions and streamlining for mobile-specific constraints, not just shrinking desktop.

Performance Is a Conversion Lever

Site speed isn't just a technical metric. It directly impacts business outcomes. Making that relationship visible enabled targeted optimizations with measurable results.

Process Enables Continuous Improvement

Ideation, pitch, brief, experiment, launch. A repeatable methodology isn't overhead. It's how you find wins consistently and learn from losses.