VIEW
IN ROOM

TEAM
Sunny Lee - UX Design
Angela Argentati - Product Management
Justin Morgan - Solutions Architect (iOS)
Andrew Bradnan - iOS Development
Harrison Shapley - Android Development
Alexander Biemann - Android Development
Mike Gourdin - Quality Assurance
Jonathan Bergeron - UX Research
Amy Roark - UX Copywriter
Dave Kover - UX Standards
Vidhya Subramanian - Accessibility

DURATION
15 months

CLIENT
Best Buy - Seattle Technology Development Center

TOOLS
Sketch, Zeplin, UserTesting, UserZoom, InVision, MIRO, Keynote, 3DStudioMax, VRay, Blender, Reality Composer, Photoshop, Illustrator, After Effects, Lottie, Optimizely, Adobe Analytics, OpinionLab, and JIRA.

PLATFORMS
iOS: ARKit, SceneKit, RealityKit, QuickLook
Android: ARCore, Sceneform, ModelViewer

OVERVIEW

MVP

CUSTOMER PROBLEM

Weighing what can feel like a myriad of factors can make selecting an appropriate TV complex and frustrating. Balancing space planning, often involving manual measurement, adds another layer of difficulty.

Our in-home advisers were finding that not only did just one-third of customers arrive at stores with precise measurements, many customers misunderstood measurement classifications, frequently confusing width with diagonal screen size. This confusion often led to purchasing TVs that were smaller than anticipated, resulting in dissatisfaction and potential returns.

SHOPPING JOURNEYS

In our initial research phase, we conducted a moderated ethnographic study involving 18 participants to document their TV shopping experiences. By employing contextual inquiry, we gathered qualitative data within participants’ natural environments, yielding deeper insights into the challenges they encountered.

TARGET DEMOGRAPHIC

We analyzed and identified our target audience by evaluating demographic trends. While baby boomers appeared to be a strong candidate due to their disposable income and traditional TV consumption habits, our independent study revealed that familiarity with augmented reality (AR) technology declined significantly among participants over 35. Considering the high volume of media content marketing directed at Generation Y, we aligned our early adoption strategy to focus on users aged 30–40.

USER CONTEXT

DESIGN CONSIDERATIONS

Next, I conducted a competitive analysis to provide the team with a clearer understanding of the market landscape. This analysis helped identify customer expectations and informed our strategic positioning.

FEATURE PRIORITIZATION

3D MODELS

Our usability study, UXR surveys, competitive analysis, and user reviews all pointed to a common insight—users sought confidence in the product. They expected models to be accurate in scale, form, and representation. To build trust, we recognized the need to move beyond programmatic TV solutions.

Developing our first set of TV models required over eight months from initiation to completion. I established an evaluation framework to assess six potential partners based on modeling and texturing capabilities, quality, cost, speed, and real-time geometry optimization. This process, including interviews and analysis, took approximately one month. While contract negotiations and production progressed, I collaborated with our partner to develop 3D modeling guidelines, define quality assurance criteria, and coordinate with Android and iOS developers to establish architectural requirements for ARKit and ARCore platforms.

What we designed was a 3D model prototype that dynamically adapted based on placement, ensuring accurate mounting representation by showing or hiding the feet depending on whether the TV is placed on a floor or wall.

CONCEPT DEVELOPMENT

Given the uncertainties in development, we adopted an agile approach. I began by creating low-fidelity paper prototype screen flows to address key user needs. The user flow integrated key functionalities, including an entry point, virtual object placement, object dimensions, price and size comparisons, and photo capture for shared purchases.

PLACEMENT HELP

Initial testing explored strategies to improve virtual TV placement rates, which were at first low. A/B testing indicated that users who viewed a tutorial before starting the experience achieved higher placement rates, but the added step increased drop-off rates. To balance usability and guidance, I introduced an on-demand tutorial button, enabling users to access help when needed. This approach significantly improved placement rates without compromising user engagement.

CUSTOMER FEEDBACK

At this point, engineering resources were tight and my team couldn’t afford to provide any additional development support. Best Buy was also transitioning from UserTesting.com to UserZoom so usability testing would be temporarily unavailable for four months due to app versioning and integration challenges. Rather than waiting for engineering resources to open up, or for integration, I reached out to our data analytics partner, Dana Joachim. I discussed with her the importance of implementing a simple feedback form so that we could continue to capture user insights. She agreed and offered resources from her engineering team. To minimize development effort, we deployed OpinionLab’s out-of-the-box web solution, ensuring a rapid go-to-market strategy.

Our goal was to collect actionable feedback efficiently. To maximize engagement, we kept the feedback question concise. After two AR sessions, a snack bar prompt invited users to provide input. Within the first two weeks, we received over 800 responses. Notably, nearly 20% of comments contained actionable feature requests, which I systematically organized into an affinity map.

SYNTHESIS

By categorizing user feedback, we identified evolving priorities, including improved wall detection, perceived scale accuracy, error reduction, and overall value enhancement. These insights informed iterative improvements, ensuring a more user-centric approach to product development.

DYNAMIC MESSAGES

Based on usability test observations and customer feedback, I developed the first set of dynamic messages for “pre-placement” events. These messages guided users through the virtual object placement process by analyzing light, space, and user progress. 

By popular demand, we also introduced “post-placement” messages—concise hints that helped users refine their TV placement. These appeared on the first launch and dismissed upon action completion.

SHARED ANALYSIS

To further assist decision-making, we added a photo feature, allowing users to capture and share snapshots of their scene.

LESSONS LEARNED

• Customer success in AR depends not only on ease of use but also on motivation. Mobile web users engage more successfully than those accessing AR directly from the app.

• Industry-standard 3D model formats (e.g., USDZ) display inconsistencies across iOS environments, requiring careful optimization.

• Features like “save for later,” “take/share photo,” and “dimensions” did not directly increase revenue or conversion rates but strengthened user engagement and purchase confidence.

NEXT STEPS

• Assist users in selecting the appropriate resolution and proprietary brand technologies based on their environmental lighting conditions.

• Expand access points to include the search bar, home screen, product details and specifications, mobile web, and explore social marketing strategies.

• Facilitate connections between AR users and in-store experts through video chat for personalized product advice.

CASE STUDIES