
Google Cloud for startup
What's special about this project?

0→1
Innovations
Back in 2023 Q3, Google Cloud website didn’t have a chatbot, so everything in this project was new

Evidence-based
despite limited access
With just 4 months and hard-to-recruit users, I bridged the gap through strategic research planning.

My Role as
a Student Leader
All execution was handled by the team, but I planned “when to do what” and ensured quality assurance
Cloud solution selection is Overwhelming
Google Cloud offers 100+ products, each with different pricing, integrations, and capabilities, making comparisons difficult. Users struggle to find the right solution without clear guidance.
I led the research from planning to execution, defining methods, sourcing participants, ensuring insights shaped design, and managing the project to keep execution on track.
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0→1 Hi-fi Prototypes
Back then (2023 Q3), Google Cloud website didn’t have a chatbot (though they do now in 2025), so everything we designed in this project was from 0 to 1.



Short project, lasting impact

Validated Hypothesis
Final Prototype SUS = 86.3% (high usability). Our also insights confirmed that helping users differentiate between similar cloud solutions is key to improving adoption rates

Comprehensive Hand-offs
Provided Google with raw data, interview protocols, insights, and participant connections to support ongoing improvements and future studies

Driving real implementation
Through 2 rounds of presentations, 6 Google executives expressed strong interest in further exploring the chatbot feature, which is now being implemented on the website
In problem-solving, I value knowing the "why" behind my decisions and taking ownership to test, refine, and prove their impact.
How can Google Cloud improve the product discovery experience for startup customers, ultimately increasing adoption rates of cloud solutions?
Ask myself:
Which stage is causing fiction?
Among them, which stage impacts adoption the most
When startup consumers search for solutions that align with their business objectives, how might Google Cloud assist them in differentiating between similar offerings on the platform?
Understand
What mental models do startup leaders use when evaluating cloud solutions to purchase?
Identify
What are the UX gaps between Google and its competitors in supporting cloud solution discovery?
Compare
What are cognitive biases and trust mechanisms that influence digital product purchase?
Balancing the feasibility & data significance
N=8 (hard to recruit)
Startup CTOs & Engineer Lead—key decision-makers in cloud adoption
Why this method?
In qualitative research, more interviews don’t alway lead to better insights—they lead to redundancy. Given the limited availability of our target users (Startup CTOs, CEOs, and Founders), I adopted a saturation approach to maximize insights while minimizing redundancy, given hard-to-reach participant pool.
Competitors
AWS, Azure
Why this method?
Competitive analysis was essential for identifying UX gaps in product discovery and differentiation. AWS and Azure are direct competitors recommended by the client based on previous Google research -- helpful since analysis helped uncover where Google Cloud could enhance AI-powered recommendations
Research Topics
Decision-Making, Online Purchasing, AI Trust
Why this method?
A fast, research-backed foundation to understand cognitive biases shaping decision-making, trust, and risk perception in cloud solution selection.
- Depth Over Volume: Startup leaders are hard to reach, making large-scale surveys impractical. Instead, in-depth interviews ensured rich insights from high-value participants.
- Behavior Over Self-Report: Mental models can’t be surveyed. We needed to see how users actually think, filter options, and make trade-offs—not just what they claim to do.
- Surveys Lack Reliability: Low engagement + varied expertise = statistically weak data. Instead of chasing incomplete numbers, we focused on actionable, behavior-driven insights.
Minimize the execution & expectation Gap

In-Person Recruitment@ Ithaca Social Events

In-Person Recruitment@ Ithaca Social Events
Recruiting the right participants is critical, but our target users were so rare —e.g. Startup CEOs, Technology Officers, and Startup Founders—who are also usually hard to reach due to their packed schedules. Therefore, I implemented a tiered recruitment strategy to balance efficiency and research validity.
- Direct Users – Current users of Google Cloud, providing firsthand insights
- Adjacent Users – Users of similar cloud platforms with overlapping needs
- Potential Users – Individuals exploring cloud solutions but not yet active users

Visualization I individually created to manage parallel research tasks
Research rarely goes exactly as planned. As the research lead, I strategically planned and assigned tasks to minimize gaps, ensuring efficiency through parallel research.
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I assigned research tasks
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I reviewed teammate's work to ensure quality
Results
Within 20 days, we successfully recruited 8 startup CEO/ CTOs, ensuring a diverse yet relevant sample.