Transforming video monetization
Role
Lead designer
Duration
Q1 2023 - Q4 2024
Company
JW Player
Primary program
Figma
OVERVIEW
Dynamic Strategy Rules
JWPlayer's Dynamic Strategy Rules transformed video monetization for digital publishers. I led the design of this solution that streamlined how publishers manage video experiences across their sites.
The product cut implementation time from weeks to hours while boosting ad revenue up to 59%. More importantly, it gave publishers unprecedented control over their video experiences without requiring technical expertise.
BACKGROUND
Where it started
In Q3 2022, our engineering team began building the foundation of what would become DSR. The initial vision centered around creating a visual decision tree interface.
I joined the project in Q1 2023, tasked with visualizing and refining this vision.
The challenge? Transform a complex backend system into an easy to use and understand interface that would allow publishers to implement sophisticated video strategies without heavy development resources.
RESEARCH
Understanding the user
First things first, I needed answers to some important questions. I needed to know more about the target users.
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By interviewing customers and internal stakeholders I gathered information about the potential product users and the typical workflow when creating and preparing advertising and video strategies. Here is where I learned about all the different personas that touch a strategy. There were 4 main characters that could be using DSR.
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By interviewing customers and internal stakeholders I gathered information about the potential product users and the typical workflow when creating and preparing advertising and video strategies. Here is where I learned about all the different personas that touch a strategy. There were 4 main characters that could be using DSR.
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Complex requirements: Publishers were struggling to balance competing needs: user experience, advertising requirements, and third-party integrations.
Resource coordination challenges: Every change, even minor, demanded extensive coordination among engineering, ad ops, product, and editorial teams, making the process tiresome and lengthy.
Limited flexibility: Existing tools were inadequate. Teams struggled to optimize ad placement and respond promptly to performance insights, hindering their ability to maximize revenue.
THE PROCESS
*Introduction to what we are building*
Now that we know the who, why, what for this project, it’s time to figure out the how. First I focused on identifying the main features users needed to create and implement their video strategies.
There were a few key elements for the user to define, starting with the essential components:
Placements: Areas of a publisher site where the strategy would be executed.
Strategy: The roadmap and setup for a publisher’s video monetization experience.
Conditions: Split branches by percentages or by user visit data.
Content Experience: The player settings and media that the user will see
Advertising Experience: The advertising settings and third party integrations
Triggers: Actions that occur when criteria is met…
Player visibility: hide the player when necessary
THE PROCESS - PHASE 2
Beta Phase
With key ideas in place and initial explorations underway, I could make some choices. Some features would resemble parts of the dashboard, so I focused on the tree's appearance, how experiences and conditions connect, and how to integrate them into the tree. I began to envision how the tree would look and work.
Design system update! While working on design, the company acquired a payments platform, so we needed to merge design systems. This meant redesigning parts for DSR. I updated the designs to fit the new system, ensuring consistency across the JW Player platform.
Updated designs used for user testing
Validation through User testing
I wanted to validate the design changes for the new product, so user testing was essential for validation.
This testing provided some very useful insights. Some glaring issues were highlighted and needed to be addressed such as:
This is a bit complicated to use. This tool will be used by people of all technical backgrounds and this needs to be kept in mind.
Users did not understand the core elements of the strategy.
Confusion around settings.
UI was hard to understand. The users had a hard time tracking where they were in the tree.
THE PROCESS - PHASE 3
Alpha phase
We started with an Alpha phase instead of a full launch, choosing a key customer partner to test the tool and provide feedback. This helped us collect useful data on its performance and find areas to improve. During this phase, we added major upgrades, such as automation triggers and better traffic management. We kept in close touch with our early users, setting up regular feedback sessions to help us focus on improvements and discover new optimization chances. This ongoing communication ensured we were building what our users truly wanted.
We made gradual UI improvements to the decision tree. User testing feedback showed that users wanted to see a summary of the node content. I also believed that changing the branch labels would help clarify their meanings.
⬆️ Before (Beta phase)
⬆️ Tree UI improved
⬆️ Content Experience page
⬆️ Advertising Experience page
THE PROCESS - PHASE 4
Improvements
More improvements to the tree.
Ran a workshop between design and engineering to come up with solutions to simplify creating a Strategy.
Workshop was a success
Solution from the workshop was a Reusable Component Library concept. To help simplify to concepts more, The Advertising and Content Experience configuration were merged to become the “JWP Video Experience”.
Advertising configuration page
Media Curation configuration page
JWP Video Experience
IMPACT & RESULTS
Results don’t lie
✅ 10% increase in video views
✅ 59% lift in revenue
Customers saw a 59% lift in revenue within the first three months of implementation
✅ 4 hour implementation
Implementation time dropped from an average of 2-3 weeks to 4 hours.
✅ Average ad impressions per embed saw a 129% increase
✅ reduced implementation time by 95%
✅ 10% increase in video views
✅ 59% lift in revenue
Customers saw a 59% lift in revenue within the first three months of implementation
✅ Average ad impressions per embed saw a 129% increase
LOOKING BACK
Key learnings
Start with the Right Problem
Early in the project, I noticed we were focusing too heavily on the interface challenges when the real problem was workflow bottlenecks. By stepping back and reframing our approach around team coordination, we created a more impactful solution. This taught me to always validate that we're solving the core problem, not just its symptoms.
Test at Different Scales
Through our phased rollout, we discovered that what worked for major publishers often overwhelmed smaller teams. This insight led us to develop scalable solutions that could grow with our users. Now I always ensure testing includes diverse user scales and scenarios.
Balance Power with Simplicity
Our biggest challenge was making complex functionality accessible without oversimplifying. Through multiple iterations, we found that progressive disclosure—showing basic options first but making advanced features readily available—was key to satisfying both novice and power users.
LOOKING FORWARD
Future enhancements
The success of Dynamic Strategy Rules opened up several exciting opportunities for future enhancement. A few features that I would have liked to explore and add to the tool are:
Workflow Intelligence
Future iterations could incorporate learning capabilities that adapt to each team's unique workflow patterns. This could include personalized shortcuts, automated routine tasks, and predictive suggestions based on past actions.
Enhanced Analytics
The next evolution of our tool could provide deeper insights into strategy performance, helping publishers understand not just what's working, but why. By connecting more data points, we could help publishers make more informed decisions about their video experiences.
Smarter Automation
We're exploring ways to leverage usage data to suggest optimal strategy configurations based on publisher goals and audience behavior. Imagine the system automatically recommending adjustments based on performance patterns across similar publishers.