Part 2 of 2: Scaling the Program – Ramping Up Optimization
John Stewart
VP Product @ Rate | Innovative Product Executive | Fintech & Mortgage Solutions | Driving Growth Through Data and Digital Transformation | Personalization SME
Last week, I shared the fundamentals of building an optimization program—from tracking and testing to iterative improvements and personalization. But understanding the principles is only half the battle—the real challenge is scaling the program into a sustainable, high-impact growth engine.
In this post, I’ll dive into how to operationalize and scale optimization within any organization. Just like flying a plane, launching an optimization program has three key phases:
? Takeoff – Setting up the right processes, tools, and mindset
? In-Flight – Running tests, ramping up traffic, and monitoring performance
? Landing – Analyzing results, iterating, and continuously improving
Let’s explore how to move from sporadic testing to a culture of experimentation that drives long-term business growth.
1. Build a Strong Testing Process
A structured process ensures that optimization isn’t just ad hoc—it’s embedded in how the company operates.
?? Test Preparation & Planning
?? Test Development & Execution
Ensure equal test splits (e.g., 50/50 vs. 90/10) and monitor for anomalies in the first 48 hours. To minimize negative impact ramp up traffic gradually from 10% to 100%% depending on the initial results
?? Post-Test Analysis & Sharing
?? Pro Tip: Keep a “Testing Playbook” to document past experiments—this helps new team members ramp up quickly and prevents repeated mistakes.
2. Increase Testing Velocity
To maximize impact, you need to run more tests, more often—without sacrificing quality.
?? Ways to increase test volume:
? Analyze site traffic and conversion rates - Allow you to understand sample sizing to reach statistical significance and build out test lanes to minimize test collisions
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? Parallel testing – Run multiple tests across different sections of the site that have limited interaction
? Segmentation testing – Tailor experiments for different user groups such as new vs. existing customers or category preferences
? AI-driven experimentation – Use machine learning to automate test targeting & analysis
?? Pro Tip: The best teams never run a test in isolation—every experiment should feed into the next round of hypotheses.
3. Build a Data-Driven Team
An optimization program is only as strong as the people behind it.
?? Hire data scientists, analysts, and UX researchers to strengthen insights.
?? Train marketers, product managers, and developers on testing best practices.
?? Create a cross-functional optimization team that meets regularly to align priorities.
?? Pro Tip: Democratize optimization—make it easy for every team to contribute test ideas based on their expertise.
Final Thoughts: Why Every Company Needs an Optimization Program
Optimization isn’t a one-time project—it’s a discipline and a mindset. Companies that successfully build and scale an optimization program treat experimentation as an ongoing process rather than a checklist item.
To recap, scaling optimization requires:
? A structured process – Clear workflows for testing, tracking, and iterating
? Testing velocity – Running more experiments, more often, without sacrificing quality
? A data-driven culture – Empowering teams across the organization to contribute ideas and insights
Just like flying a plane, the key to success isn’t just a smooth takeoff—it’s staying in-flight and ensuring every test leads to a better landing.
How has your company approached optimization at scale? Drop your thoughts in the comments—I’d love to hear your experiences! ??
#Optimization #Experimentation #ProductManagement #A/BTesting #DigitalStrategy #DataDriven