Rheem Smart Home Ecosystem: Redesigning Professional Data Sharing for Energy Efficiency
Leading comprehensive research and design strategy to unify IoT experience across water heaters, AC systems, and mobile applications—navigating a complex multi-actor ecosystem while challenging fundamental assumptions about smart home adoption.
Company: Harman International (Samsung) × Rheem Manufacturing
Role: Senior UX Researcher & Research Strategy Lead
Duration: 12 weeks (May–August 2018) | Part of 18-month consultancy tenure
Team: Led cross-functional team (offshore/onshore) within Harman's Huemen Design consultancy
Impact: 30% UX improvement, product launched to market, unified ecosystem architecture serving 4 distinct user personas
The Context
When a Traditional Appliance Manufacturer Faces the IoT Revolution
Rheem Manufacturing, a 100-year-old leader in water heating and HVAC systems, faced an existential challenge: transition from standalone appliances to connected home integration or risk losing market share to tech-native competitors like Nest and ecobee.
Their EcoNet system had the technical capability—water heaters and AC units could connect to smartphones—but adoption was stagnant, user confusion was high, and different teams across the organization held varying perspectives on what users actually needed from IoT connectivity.
The Strategic Stakes
This wasn't a UI problem. It was a strategic alignment crisis masked as a usability issue:
Energy efficiency regulations were tightening, making connectivity a competitive necessity
Competitors with intuitive smart home experiences were gaining market share
As a large manufacturing organization, different departments brought valuable but sometimes divergent perspectives based on their specialized expertise
The company needed to demonstrate IoT competency to retain Fortune 500 HVAC partnerships
The Fundamental Challenge
Like many large organizations optimized for manufacturing excellence and operational efficiency, Rheem's structure had created specialized silos where different departments developed deep expertise in their domains. Engineering teams championed technical capabilities, product teams considered market positioning, and sales understood contractor relationships. Some believed users wanted detailed data analytics. Others insisted people "just wanted the water heater to work" and wouldn't care about saving a few dollars. Each perspective held merit, but needed empirical validation to identify the optimal path forward.
The Ecosystem Challenge
Designing for Three Actor Groups Across Multiple Devices
One of the project's core complexities was that this wasn't simply "user research"—it required mapping an entire business ecosystem where three distinct actor groups interacted with four different device types.
The Three-Actor Value Chain
1. Homeowners (Direct Users)
Navigate purchase decisions (often contractor-influenced), installation, daily usage, troubleshooting, and maintenance. Pain points: intimidated by technology, uncertain about value proposition, overwhelmed by data they didn't understand.
2. Contractors (Purchase Influencers - 70% of Sales)
Recommend products and bear responsibility for connectivity issues. Critical insight: While Rheem's engineering teams had strong relationships with contractors, systematic behavioral research on their needs represented an important opportunity given contractors' influence on 70% of purchase decisions. Contractors felt uncertain about their role when problems arose and needed better support to confidently recommend smart technology.
3. Technicians (Installers & Repair)
Required different technical terminology than consumer interfaces. Needed diagnostic data, error codes, and system specifications—completely different mental model from homeowners.
The Multi-Device Complexity
Each actor group interacted with multiple devices requiring consistent terminology and interaction patterns:
Smartphone app (iOS & Android - EcoNet)
Water heater displays (3 different form factors)
AC unit controls
Physical thermostats
The design challenge: Create a unified system serving all three groups without confusion through inconsistent terminology, information hierarchies, or interaction patterns.
The Strategic Approach
Systematic Hypothesis Testing Through Provotyping
Rather than simply "asking users what they want," I designed a research strategy that would systematically test internal stakeholder assumptions while uncovering latent user needs.
Research Architecture: Balancing Velocity with Rigor
Working within Harman's Huemen Design consultancy—a 24-hour design operation with teams across time zones—I led the complete discovery-to-delivery cycle with aggressive timeline constraints:
Stakeholder Interviews - Surfaced diverse internal perspectives across departments and translated them into testable hypotheses
End User Interviews (15 participants) - Two groups: contractors/facility managers and smart neighborhood residents
Hypothesis Testing via Provotyping - Low-fidelity prototypes testing stakeholder assumptions about data preferences
Heuristic Evaluation - Nielsen principles assessment of existing products
Competitive Analysis - Benchmark against Nest, ecobee, and emerging smart home platforms
The Provotyping Methodology
Drawing from my doctoral research, I applied a provocation-based research approach that divided user interviews into two phases:
Phase 1: Understanding current behavior and pain points without bias
Phase 2: Introducing low-fidelity provocations (paper prototypes without labels) to test stakeholder hypotheses
This approach forced participants to focus on meaning rather than visual details, revealing psychological preferences that high-fidelity prototypes would have masked.
The Information Spectrum Exploration
I created 10 different visualization sets exploring information across a spectrum:
Device-centered (neutral: temperature, status)
Personal preferences (individual goals, custom alerts)
Family dynamics (shared savings, household comparisons)
Community insights (neighborhood energy use, environmental impact)
The Discovery
Four Behavioral Personas: Psychological Diversity in Smart Home Adoption
The research revealed something critical that internal stakeholders hadn't anticipated: users had fundamentally different psychological relationships with smart home technology. We couldn't design one interface for one "user"—we needed flexible architecture serving four distinct motivational profiles.
Key Insight: "Want to receive education to help them make better lifestyle decisions"
Behavior: Motivated by environmental impact beyond cost savings. For these users, "being better in a sustainable way" was an intrinsic motivation that drove self-learning and social interactions to share knowledge. Interested in sharing data if it creates collective benefit, wanted community-level insights and neighborhood comparisons.
Design Implication: Visualizations showing environmental impact (not just cost), educational content about conservation, transparent data sharing options, community features for knowledge exchange.
2. The Frugal Minded
Psychological Driver: Financial optimization through data analysis
Key Insight: "Can make decisions on wattage and usage and unplugs things based on reports"
Behavior: Comfortable with detailed graphs and numerical data. Wanted dollar amounts, not percentages. Willing to experiment: "Felt he would test the experience with the water heater to see how low he could go and then would check the app to see what the changes caused as far as money."
Design Implication: Detailed financial reports with customizable views, prominent dollar values, comparison features, predictive savings calculations.
Behavior: "Feels overwhelmed by data" (EU 6). "Doesn't have much time to view too much information" (EU 6). Satisfied with basic functionality. Leaves budgeting to spouse. "Not interested in reports. Would look at it if there is something outrageous" (PAR 1).
Design Implication: Simple, minimal default interface. Alerts only for critical issues. Hide complexity unless specifically requested. Smart defaults that "just work."
The Paradigm-Shifting Discovery
The Nonchalant persona challenged the fundamental assumption driving the entire project—that EVERYONE wants data analytics and optimization capabilities.
This discovery represented a significant portion of the user base who would abandon the app if it demanded too much attention. Designing for mainstream adoption meant designing for people who DON'T want to optimize everything—a critical insight for market expansion.
4. The Enthusiastic
Psychological Driver: Curiosity and technological exploration
Key Insight: "Curious about ecological justice but unsure of the next steps"
Behavior: "Liked that there were research opportunities" (EU 15). Wanted tutorials and educational content in-app. "Feels like they are not utilizing all the app features" (EU 11). Would try to repair the water heater themselves (EU 8).
Design Implication: Advanced features and customization options, in-app tutorials, system transparency (show how it works), reference guides explaining modes.
Strategic Insights
Challenging Assumptions: What We Learned
Insight 1: "Wasting" vs "Saving" — The Psychology of Loss Aversion
One of the most strategically important discoveries emerged from how participants talked about money:
"$5 saved doesn't feel like much. But $5 wasted? That feels like A LOT."
This insight fundamentally reframed the value proposition. Internal stakeholders who believed "users won't care about saving a few dollars" were partially right—but completely wrong about the psychological framing.
The product wasn't about "save money" (weak motivation). It was about "avoid wasting money" (strong anxiety/regret prevention). This distinction drove messaging, feature prioritization, and how data was visualized.
Strategic Impact
This single insight unified conflicting stakeholder perspectives. The technology WAS relevant—not because it saved a few dollars, but because it prevented the psychological pain of waste. This reframe changed product positioning and marketing messaging.
Insight 2: The Experimentation Behavior Pattern & Interface Trust Differential
One of the most valuable discoveries wasn't about what users wanted to see—it was about how they wanted to learn:
"Felt he would test the experience with the water heater to see how low he could go and then would check the app to see what the changes caused as far as money." — Participant PAR 6
This revealed a learning-through-experimentation pattern that stakeholders hadn't anticipated. Users didn't want to be told optimal settings—they wanted to discover them through safe exploration.
Critical Usability Finding: Users felt confident they could recover from mistakes using the app, but this feeling was completely absent when using hardware screens. They were afraid of using physical controls because they couldn't return to initial settings. This app-vs-hardware trust differential had major implications for feature placement and onboarding design.
Design Implication: Rather than prescriptive "you should do this" messaging, we designed for curiosity and experimentation in the app, showing results of changes over time without judgment. Hardware interfaces prioritized safety and reversibility cues.
Insight 3: The Contractor Ecosystem Gap
Contractors were critical gatekeepers but felt abandoned:
"Lack of education and knowledge about product offerings by Rheem"
"Contractors concerned about their role and responsibility with connectivity issues"
Not comfortable pushing smart technology they didn't understand
The Hidden Problem: Rheem was trying to sell a consumer product without adequately supporting the professionals who influenced 70%+ of purchase decisions. This was a business model problem, not just a UX problem.
Insight 4: Privacy Wasn't the Concern—Value Was
Contrary to assumptions, participants were surprisingly open to sharing data:
"Sharing data with others will help spread out responsibility" (EU 11)
"Fine with sharing information with others" (EU 7)
"Has no privacy concerns" (EU 6)
The real barrier wasn't privacy—it was uncertainty about value. People needed to understand the benefit before they'd engage with features.
Strategic Opportunity Identified: Customers recognized that contractors and customer service would benefit from having access to event history and consumption data to provide tailored help. This validated a data-sharing architecture that could support the entire service ecosystem—transforming privacy from a barrier into a value proposition when properly framed.
Insight 5: Contextual Learning — When Users Want Education vs. Reference
Users revealed a sophisticated understanding of when they wanted different types of support:
Open to Education During:
Installation (high attention, motivated to understand)
Troubleshooting (problem-solving mode, willing to learn)
Want Reference Information During:
Regular dashboard interaction (quick decisions, not teaching moments)
Daily engagement (guidance for informed choices, not lessons)
The Engagement Challenge: Customers were satisfied with EcoNet overall, but many needed specific triggers at the right moment to explore more features. The opportunity: machine learning could provide money-saving information precisely when users were making decisions—"If you lower the temperature now, you'll save $12 this week."
Design Implication: Create context-aware interfaces that shift between educational and reference modes based on user activity. Use predictive analytics to surface specific, actionable savings opportunities ("$12 this week") rather than generic encouragements.
Insight 6: The Autonomy-to-Coaching Progression (Self-Determination Theory in Practice)
Perhaps the most theoretically significant finding emerged from understanding user motivation evolution:
Foundation: Technology as Tool for Autonomy
The first condition for engagement was user autonomy—customers needed to feel in control, making their own decisions. Technology that removed control (prescriptive automation) was rejected. This validated Self-Determination Theory's emphasis on autonomy as a fundamental psychological need.
Evolution: Technology as Coach or Conscience
Once autonomy was established, some users (particularly Eco-Conscious and Enthusiastic personas) welcomed technology that helped them "be better." However, this coaching role was only acceptable AFTER autonomy was proven—not as a replacement for it.
The Intrinsic Motivation Discovery: While all customers agreed saving money was important, for some users the ideal of "being better in a sustainable way" was an intrinsic motivation that drove self-learning and social interactions to share knowledge. These users saw EcoNet not as a utility tool, but as a platform for personal growth and community contribution.
Design Implication: Create progressive engagement pathways: Start with autonomy (full user control), then offer coaching features as opt-in enhancements for users who demonstrate readiness. Never force optimization—invite it.
Based on research insights, I led the design strategy for a unified information architecture that could serve four distinct psychological profiles across multiple devices and three actor groups.
Core Design Principles
1. Progressive Disclosure Based on Persona
Default to simplicity (serving The Nonchalant), with clear pathways to complexity (serving The Enthusiastic and Frugal Minded). Users opt-in to data density rather than being overwhelmed by default.
2. Frame Information as "Waste Prevention" Not "Savings"
Alerts and notifications emphasized avoiding waste ("You're using 40% more energy than last month") rather than celebrating savings. Loss aversion drives action more than gain anticipation.
3. Support Experimentation with Safe Feedback Loops
Rather than prescriptive recommendations, the interface showed consequences of user choices, encouraging learning through exploration without judgment.
4. Unified Terminology Across Devices
Created a three-tier terminology system:
Consumer Language (Default): Simple, benefit-focused terms
Technical Language (Toggle Available): HVAC standard terminology for contractors
Cross-Brand Recognition: Acknowledged when terms matched competitors (Nest, ecobee)
Information Architecture: Three-Tier Hierarchy
High Tier (New Features): Simplified dashboard emphasizing at-a-glance status and "waste prevention" alerts
Medium Tier (Enhanced Existing): Detailed settings, scheduling, and historical data for engaged users
Low Tier (Maintained Current): Basic device control for contractors and technicians during service calls
Component Library & Visual Standardization
Created a comprehensive design system ensuring consistency across:
EcoNet mobile app (iOS & Android)
3 water heater display form factors
AC unit controls
Web dashboard for contractors
This wasn't just visual consistency—it was cognitive consistency reducing the learning curve when users interacted with multiple Rheem devices.
The Impact
Measurable Business Outcomes
30% UX Improvement based on user feedback metrics following redesign implementation
Product Successfully Launched to Market — The EcoNet system went to market with the unified architecture serving the entire Rheem smart home portfolio
Internal Stakeholder Alignment — Research evidence bridged diverse departmental perspectives, providing empirical foundation that accelerated product decision cycles by 40%
Scalable Framework — The flexible architecture approach became the template for future Rheem IoT products beyond water heaters and AC systems
Strategic Impact
Challenge to Tech-Optimist Assumptions: The "Nonchalant" persona discovery fundamentally changed Rheem's product philosophy. The organization's willingness to challenge their initial assumptions demonstrated strategic openness to external research insights. The company recognized that serving the mainstream market meant designing for people who DON'T want to optimize everything—a critical insight for market expansion beyond early adopters.
Multi-Stakeholder System Thinking: Demonstrated that consumer IoT success requires designing not just for end users, but for the entire ecosystem including influencers (contractors) and service providers (technicians). This led to contractor-specific features in subsequent releases.
Research-to-Design Velocity: By working within Harman's 24-hour design operation (India/US teams), we compressed a traditionally 6-month research cycle into 12 weeks without sacrificing rigor—proving that strategic research can operate at startup velocity within enterprise constraints.
Reflections
What I Learned About Ecosystem-Level Research
This was my first experience leading comprehensive research strategy for a Fortune 500 client within a high-velocity consultancy environment, and it fundamentally shaped how I approach complex product ecosystems.
The Power of Hypothesis-Driven Research
Rather than open-ended exploratory research, translating diverse stakeholder perspectives into testable hypotheses via provotyping created immediate alignment. When stakeholders saw their perspectives validated or reframed with evidence, decision-making accelerated dramatically.
Multi-Actor Research Requires Different Methods
Researching contractors required different recruitment, different interview protocols, and different insight synthesis than homeowner research. The research architecture itself must reflect ecosystem complexity.
Traditional market segmentation (age, income, tech-savviness) failed to predict behavior. Psychological motivations (Eco-Conscious vs Nonchalant) were far more predictive of feature adoption and long-term engagement.
Constraints Drive Innovation
The 12-week timeline and 24-hour team operation forced ruthless prioritization. Every research activity had to either:
Create immediate business value
Build reusable research infrastructure
Challenge a critical assumption
This constraint made the research more strategic, not less rigorous.
If I Were to Do This Again
Start with contractor research earlier - We uncovered the "70% purchase influence" insight late in the project. This should have been a discovery phase priority.
Prototype the "wasting vs saving" framing - This insight emerged organically, but we could have validated it more systematically with A/B message testing.
Document the persona prioritization framework - We needed a clearer method for deciding when to prioritize Nonchalant simplicity vs Enthusiastic depth in specific features.
What I'm Most Proud Of
Not the features that shipped, but the thinking frameworks I helped establish:
The "wasting vs saving" psychological reframe that changed product messaging
The four persona psychological profiles that became Rheem's user segmentation model
The three-tier terminology system that balanced technical accuracy with consumer clarity
The provotyping methodology that accelerated assumption-testing
These frameworks don't disappear when the researcher leaves. They become part of how the organization thinks.
