Driving Adoption & Growth at Scale
Muck Rack
Overview
Muck Rack is a leading PR software platform used by thousands of communications professionals. I joined to tackle two critical challenges: low feature adoption despite heavy investment in new capabilities, and a need to build a culture of experimentation to drive sustainable growth.
This case study covers two interconnected initiatives that fundamentally changed how the product team operated.
The Challenge
Adoption Challenge: The team had invested significantly in building a powerful analytics dashboard, but adoption remained stuck at 20%. Users weren't discovering the value, and the feature risked becoming shelfware.
Growth Challenge: Product decisions were based on intuition rather than data. There was no systematic way to test hypotheses, measure impact, or learn from experiments. This made it difficult to optimize the user journey and justify investments.
The Solution
I led a dual-track approach:
Track 1: Dashboard Adoption - Deep user research to understand barriers to adoption - Redesigned onboarding flow with contextual guidance - Implemented progressive disclosure to reduce cognitive load - Created "aha moment" experiences showing immediate value
Track 2: Experimentation Infrastructure - Built company's first experimentation framework - Established hypothesis-driven development process - Created dashboards for real-time experiment monitoring - Trained teams on statistical significance and test design
Iterations
User Research & Discovery
Objective: Understand why users weren't adopting the dashboard
Conducted 20+ user interviews and analyzed usage data. Found that users didn't understand the value proposition and felt overwhelmed by the initial experience.
Onboarding Redesign
Objective: Create a guided path to value
Designed new onboarding flow with interactive tutorials, sample data, and clear next steps. Reduced time-to-value from days to minutes.
Contextual Recommendations
Objective: Surface relevant features at the right moment
Built recommendation engine that suggested dashboard views based on user context and behavior patterns.
Experimentation Platform
Objective: Enable data-driven product decisions
Implemented feature flagging, A/B testing infrastructure, and experiment tracking. Created playbook for running effective experiments.
Activation Optimization
Objective: Improve new user activation rates
Ran series of experiments on signup flow, first-run experience, and early engagement triggers. Iterated based on data.
Impact
Key Results
- Dashboard adoption increased from 20% to 60%
- 52% improvement in user activation rates
- 15+ experiments launched in first quarter
- 3x increase in feature discovery
- Reduced time-to-value by 70%
- Established experimentation as core practice
Key Learnings
- Adoption is an onboarding problem, not a feature problem—users need guidance to discover value
- Experimentation culture requires infrastructure AND education—tools alone don't change behavior
- Quick wins build momentum—start with high-impact, low-effort experiments to demonstrate value
- Measure leading indicators—activation signals predict long-term retention better than vanity metrics
- Cross-functional collaboration amplifies impact—growth is a team sport involving product, engineering, design, and marketing