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Eppo

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"Data-driven growth with streamlined experimentation solutions."

Case Study
Frequently Asked Questions
This company's service helps businesses conduct trustworthy experiments efficiently, from simple A/B tests to complex AI personalizations. With a focus on being user-friendly, the platform aims to reduce analysis times, safeguard data, and ensure reliable results. By leveraging a warehouse-native design, it empowers teams to independently manage testing and decision-making processes, leading to smarter business strategies and improved performance.
Eppo Stated Claims
These are public claims Blurbs AI believes to have been made by Eppo.
Secure and data-native design
Eppo claims that their platform's zero-copy, warehouse-native architecture ensures trustworthy experimentation by naturally safeguarding data privacy and accuracy.
Faster experiment cycles
Eppo claims that their CUPED++ acceleration significantly reduces the duration of experiments, giving quicker insights without sacrificing rigor.
Self-serve data capabilities
Eppo claims that their out-of-the-box reporting and slice-and-dice capabilities remove analysis bottlenecks, empowering teams to drive insights autonomously.
EXPERIMENTATION PLATFORM
FEATURE FLAGGING
AI PERSONALIZATION
WAREHOUSE NATIVE ARCHITECTURE
A/B TESTING
STATISTICAL ANALYSIS
DATA PRIVACY
SELF-SERVE EXPERIMENTATION
BlurbSTAR Case Study
Coinbase & Eppo
Eppo transformed Coinbase's experimentation, saving time, cost, trust.
40%
Faster experiment analysis
Millions
In fixed cost savings
1.
Situation
Coinbase's CIFER system struggles
→
Internal system CIFER was inadequate for scale.
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Lack of sophisticated tools delayed product launches.
→
Operational inefficiencies increased costs and frustration.
Coinbase faced significant challenges with its in-house experimentation system, CIFER. This system was insufficient for a rapidly growing enterprise, leading to issues in trust and inefficiency. CIFER lacked sophisticated statistical tools, causing delays and inconsistent experimentation practices. Business teams couldn't self-serve experiment results, and product launches were delayed due to inconsistent data interpretations. The internal system also required costly attempts to build advanced features, which increased operational expenses and frustrations. These challenges slowed decision-making and innovation, compromising the effectiveness and trust in Coinbase's experimentation processes.
2.
Task
Find a scalable solution
→
Identify an external, robust experimentation solution.
→
Ensure it complies with security standards.
→
Achieve cost savings and operational efficiency.
The task at hand was to find a robust solution to replace the internal system, CIFER, and to restore trust in the experimentation processes at Coinbase. Tuhin Ghosh and Jeff Bliss spearheaded the search for an external platform that would meet technical and business needs while complying with Coinbase's security protocols. The new solution needed to support effective experimentation with advanced features, such as statistical tools and intuitive UI, to ensure business teams could trust the results and make better decisions. Additionally, the goal was to achieve cost savings and operational efficiencies without disrupting existing infrastructure.
3.
Action
Selecting Eppo for experimentation
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Eppo seamlessly integrated, no data migration.
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Comprehensive statistical tools offered.
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Quick support enabled fast transition.
After a four-month evaluation of Eppo and Statsig, Coinbase chose Eppo as their experimentation platform. Eppo's warehouse-native solution seamlessly aligned with Coinbase's existing infrastructure, eliminating costly data migration. Eppo offered comprehensive statistical features, including CUPED++ and sequential testing, which ensured data reliability. Moreover, Eppo demonstrated expertise in handling large-scale operations and provided exceptional support for a quick transition. This platform standardizes experimentation processes and enables data scientists to focus on strategic tasks, enhancing overall efficiency across Coinbase.
4.
Result
Significant cost savings, restored trust
→
Saved millions by avoiding an in-house rebuild.
→
Experiment analysis time reduced by 40%.
→
Restored organizational trust in data experiments.
The adoption of Eppo resulted in immediate improvements at Coinbase, including millions in fixed cost savings, by avoiding the expenditure on an in-house rebuild. The analysis time for experiments reduced by 40%, allowing faster decision-making. Trust in experimentation was rebuilt across the organization, fostering a proactive approach to leveraging data for strategic projects. The new platform equipped Coinbase with the necessary tools to enhance innovation and growth, positioning experimentation as a key driver in improvement efforts.
Keywords
COINBASE EXPERIMENTATION
EPPO PLATFORM
COST SAVINGS
DATA-DRIVEN DECISION-MAKING
TRUST IN EXPERIMENTATION
EFFICIENCY
INNOVATION
WAREHOUSE-NATIVE
SEQUENTIAL TESTING
Eppo
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