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Optimizely Overture
An AI-powered web platform that guides small-to-medium business marketers through Optimizely, simplifying A/B test setup and analysis to directly combat the platform's highest source of customer churn.
The solution elevates user confidence and engagement by integrating a proactive AI assistant, clear visual guides, and a "Launch Readiness" monitor, ensuring customers can consistently run successful experiments.
My Role
Product Designer
Skills
End-to-end Design
Interaction Design
Information Architecture
Visual & UI Design
User Flows
Team
3 Product Designers
1 UX Researcher
1 Optimizely Product Manager
Optimizely Head Of Product
Timeline
6 months
Key FeaturesMeet Opal: The Proactive AI Assistant
Thoughtfully embedding Optimizely’s existing AI, Opal, across all stages of test set-up: from creation to launch, analysis, and iteration. This accelerates test setup and design while guiding customers through best practices: empowering all to run smarter, more effective experiments.
Show Don’t Tell: Unveiling the Connections During Experiment Setup
Overture organizes key set up elements in one clear, interactive node layout paired with the visual editor. This makes it easy for users to follow each step, build their experiment smoothly, and see their progress at a glance.
Launch Confidently: Checkpoints for Every Step
A redesigned preview, Launch Readiness monitor, and a new publish center to ensure customers can launch without worry.
ImpactOverture's design solution is projected to boost user confidence, accelerate experimentation, and improve retention
45%
Fewer experiments running longer than two months.
With clearer statistical guidance, users can reach significance more quickly and confidently conclude tests.
50%+
Churned ARR (Annual Recurring Revenue) pain points directly addressed for SMBs.
Targeted solutions now make experimentation faster, easier, and more accessible for this segment.
ContextCustomers face the frustration of campaign concepts getting stuck in complicated setup, test failures, and slow feedback loops
Optimizely’s Web Experimentation (WebEx) is a platform that helps businesses compare different versions of their digital experiences in order to figure out what resonates most with their customers and improve key performance metrics.
The ProblemA/B testing platforms like Optimizely’s Web Experimentation (WebEx) can be a powerful but confusing tool to use for newcomers
Small-medium businesses who use Web Experimentation struggle to launch successful tests and leave the platform for other solutions that fit their experimentation needs or abandon experimentation efforts altogether.
Meanwhile, Web Experimentation customers can pay more to get dedicated support during onboarding, but for those who don’t purchase this service, Optimizely’s Customer Success team can intervene as a last resort to re-engage struggling users, creating additional workload and delays.
Current Interface
Experiment Landing Page
No guided onboarding or in-product support to help set up first experiment
Visual Editor Interface
Inconsistent & unclear terminology with little to no built-in explanations or tooltips
Support Help Center
Heavy reliance on external documentation
Variant Preview
Lack of consistent, visibility of system status
ResearchThe main insight I discovered:
Participants lack confidence in setting up and launching A/B experiments on Optimizely because they have insufficient guidance and feedback to validate their choices. This uncertainty undermines trust in both their results and the platform itself.
To understand What the early experiences of self-serve users on the Web Experimentation platform are and what was going wrong we:
➜ Conducted desk research, consisting of competitive analysis and a heuristic evaluation of the platform.
➜ Spoke with 5 Subject Matter Experts (Optimizely employees)
➜ Ran 6 contextual usability studies with professionals who have A/B testing experience but have not used Optimizely WebEx
Key Findings:
Many customers quit because of issues using the platform, due to lack of help, hidden steps, and roadblocks within the test-setup process. Our findings revealed the need to fully transform the setup process to improve the customer experience.
➜ Click for the final Research Report
Design ChallengeHow might we create an experience that guides users to launch their first test, use that initial success to build confidence, and reveal the compounding value of experimentation — all while teaching best test practices?
Mapping the Experiment Set-Up JourneyAll of those methods and sessions helped us put together a Journey Map to break down the complexity of setting up an experiment task flow
It helped us capture user emotions along the way and highlight where people got stuck, allowing us to prioritize pain points and uncover key opportunities for improvement.
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We split the Web Experimentation journey into 4 phases including account creation, create a new project, set up experiment being the bulk of the journey, and ending with see results.
Design IdeationExploring 2 different layouts to reduce complexity and embed help and guidance exactly where needed
We started ideation, rapidly brainstorming around 8+ high-level concepts that addressed our how might we statements. Not all of our ideas at this stage involved AI, but we definitely kept AI and Opal at the forefront of our minds during this process knowing that Opal is a huge priority for the future of Optimizely.
Once we were done brainstorming ideas individually, we came together and created themes by affinitizing similar ideas, and we down selected to two ideas that we found to be the most promising:
concept 1Word Clouding
Users describe tests in their own words ➜ AI generates setup + highlights input mapping.
concept 2Node Layout
Simplify setup by visualizing all components in one place, modeled as directed graphs of nodes.
Prototyping & Feedback LoopsEnsuring Concepts Fit with Real User Workflows
My co-designers and I mapped out a single user flow that combined both concepts into one experience and created low-fidelity Figma wireframes for a split-screen prototype:
Left: Opal AI word-cloud notepad for capturing goals and experiment parameters
Right: node-based layout visualizing the structure of the experiment
We tested this with seven marketing-adjacent professionals with A/B testing experience, grounding feedback in real workflows to ensure insights were contextual and reliable.
Prototype Screens
1. New experiment interface with a blank "notepad" or word clouding area for users to brainstorm their experiment ideas.
3. Deep dive into Node Layout: nesting Metrics under Event components + Opal AI suggestions.
2. Users enter in "notepad" content for Opal AI to generate an experiment on the right side in a node layout format.
4. Exploring how we might keep Optimizely's unique Visual Editor feature in this design.
Design DecisionMaking the node layout the backbone of the experience
While participants appreciated the Opal notepad, I advocated for prioritizing the visual node layout as the backbone of the experience because users anchored their understanding in visuals and preferred entering pre-formed ideas directly.
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We removed the AI notepad to reduce redundancy and align the workflow with how marketers naturally conceptualize experiments.
IterationRefining the Experiment Setup for Clarity and Confidence
Building on earlier insights, I refined the workflow in high-fidelity, focusing on moments that improved clarity and user confidence.
Early Opal Guidance
Opal guides initial hypothesis formation.
Users can generate test ideas with Opal, upload a test plan, or enter a pre-defined hypothesis directly into the input field. Once satisfied, they can refine it further in the hypothesis box before confirming, all before any nodes are generated.
Generating a hypothesis populates the node layout, revealing connections between events and metrics and reinforcing a visual-first workflow.
Opal offers contextual suggestions as users interact with nodes, supporting refinement and informed decisions without distracting from the workflow.
Driving Confidence during Preview
Side-by-side variant comparison improves preview confidence.
A slider overlay allows users to compare variants directly, making differences clear and helping them validate experiments before publishing.
Ensuring Statistical Significance at Launch
A centralized Publishing Center supports reliable launches.
Launch readiness checklists, runtime calculators, and scheduling tools help users finalize, share, and confidently launch experiments.
What our Sponsors are SayingShaping Optimizely's Web Experimentation roadmap and a Beta launch
“We’ve taken a ton of the feedback and already started implementing it…the research and recommendations are already being folded into Opal.
Today we are launching the first beta of the Optimizely Experiment QA Agent, which is the feature they talked about that gives warnings and updates and helps [the user] build a realy good test. It is being built into Opal and going into our beta today.”
— VP of Product, Digital Optimization
“I loved working with this team. Throughout the entire experience you could see they cared about making a better experience for the customers and making sure the customers felt confident in what they were doing…That’s great news that we are already starting to implement some things!”
— Optimizely Senior Product Manager
ReflectionContextual Unfolding
Designing an A/B testing workflow reinforced how important context and progressive guidance are when things get technical. I found journey mapping very helpful in shaping intuitive, step-by-step flows where we could make guidance feel naturally built-in. I also learned that good design offers clear direction with flexible options, a mindset I brought to designing for AI, making support timely and relevant to each user’s situation.
Balancing Design Systems
Working with Optimizely’s large design system taught me to start with a small, core set of components for fast, low-fidelity exploration, then selectively step outside the system when we needed to push the experience further. That balance between consistency and creative exploration helped the final solution feel both coherent and distinct.