Operationalizing Discovery Research for Scalable Experimentation

Discovery research often gets trapped in decks or Google docs.

It rarely makes the jump to actual tests with measurable outcomes.

To close that gap, you need a system that turns qualitative insight into testable, prioritized hypotheses at scale.

In this email, we’ll cover how to:

• Build a structured pipeline from research to experimentation
  

• Use mental models to translate behavior into hypotheses
  

• Balance research depth with testing velocity
  

• Ensure discovery work creates impact, not just documentation

We've spent the last 13 years building and refining this process. I’m going to lay out exactly how we do it at Surefoot.

Building a Systematic Research-to-Hypothesis Pipeline

Translating research into testable actions starts with consistent data collection, clear prioritization, and centralized documentation. This process ensures discovery work doesn’t stall before reaching implementation.

1. Begin by collecting structured qualitative data using a tool like the Revenue Friction Roadmap.

2. Validate findings using quantitative tools like GA4, funnel reports, heatmaps, and session recordings.

3. Reframe friction points into clear opportunity statements.

• For example: “Users can’t find sizing info” becomes “Make sizing info more visible on mobile PDPs.”

4. Prioritize test ideas using the PIPE framework:

• Probability of success, based on user signals.

• Impact on key metrics like conversion rate or AOV.

• Problem severity and how frequently the issue occurs.

• Effort required to design, build, and QA the test.

5. Document each step in a centralized repository:

• Finding → Opportunity → Hypothesis → Test Result → Learning.

6. Define KPIs at the hypothesis stage to align the team on measurement.

• Primary metrics might include conversion rate or add-to-cart rate.

• Supporting metrics could include scroll depth, click-through rates, or time on page.

7. Review each hypothesis with a cross-functional team to avoid implementation bottlenecks.

8. Maintain a steady pipeline rhythm with 30-day research cycles, 90–180 day test roadmaps, and monthly roadmap reviews.

From Behavior Patterns to Testable Hypotheses

Behavior patterns only become useful when they’re connected to actionable hypotheses. This requires moving from “what users are doing” to “why they’re doing it” to “how we can shift that behavior.”

1. Start by identifying consistent behavior patterns from research or analytics.

• For example: users exiting PDPs without clicking into any tabs.

2. Determine the root cause based on qualitative insight or UI analysis.

• The issue could be that key information is hidden in low-visibility components.

3. Create a hypothesis that connects an interface change to a measurable behavior shift.

• Example: “If we show the size guide inline on mobile PDPs, users will be more likely to add to cart because they can evaluate fit without friction.”

4. Ensure every hypothesis includes:

• The specific change being tested.

• The predicted behavior shift.

• The metric being used to track performance.

5. Use the PIPE framework again to prioritize hypotheses that are low-effort and high-leverage.

6. Move approved hypotheses into a centralized backlog so that testing can begin as soon as resources are available.

Balancing Research Rigor with Testing Speed

You don’t need to choose between research depth and execution speed. A well-scoped process can deliver signal without slowing the team down.

1. Use structured methods to maintain research rigor.

• Run usability tests with at least 5–10 participants per device type.

• Cross-validate user feedback with behavioral analytics before moving to test.

2. Avoid launching tests based on anecdotes or personal opinions.

• Every hypothesis should be grounded in observed friction or user pain points.

3. Increase testing velocity by front-loading the strategy.

• Maintain a backlog of scoped and prioritized hypotheses.

• Use modular, single-variable test designs to isolate learnings.

• Set up standardized workflows for design, development, QA handoff, results reporting, and getting next steps from the learnings into work queues.

4. Adopt a clear operational cadence.

Weeks 1–3: Run interviews, behavior audits, and review funnel metrics.

Week 4: Launch the first test.

Month 2–4: Scale findings into a structured 90-day roadmap.

Key Takeaways

Strong discovery means better conversion of insights into action. Structuring your process ensures every user's pain point gets translated into a meaningful opportunity.

• Discovery research is only valuable when it feeds into scoped, measurable hypotheses.
  

• PIPE scoring helps teams avoid the trap of chasing high-effort ideas with unclear impact.
  

• Centralized repositories prevent insights from being lost and allow learnings to compound.
  

• Modular tests and cross-functional alignment reduce delays between insight and launch.
  

• The best programs close the loop. Research drives testing, and test results guide the next round of discovery.

How to take action moving forward:

• Review your last few research projects. How many insights led to measurable experiments?
  

• Refactor your documentation so every insight ends with an opportunity statement or test hypothesis.
  

• Re-score your testing backlog using PIPE to re-prioritize based on friction, not assumptions.
  

• Run a 30-day sprint focused on identifying and launching low-effort, high-impact hypotheses tied to real user behavior.

Looking forward,

Brian

P.S. Ready to grow revenue without having to grow traffic? Let’s talk.