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FEATURED CASE STUDY
Coinbase Saves Millions, Reduces Experiment Analysis Time by 40%, and Restores Trust in Experimentation with Eppo
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Eppo Vs. Optimizely
Maximize experiment velocity across teams, and master cause/effect relationships
Drive an experimentation culture
Empower all teams with experimentation
Eppo empowers all teams with experimentation, whether in product and engineering, or in go-to-market such as sales and marketing.
Maximize decision-making velocity
Eppo’s intuitive user interface (UI) and mature, proven features ensure maximum experimentation velocity.
Understand causal impact of drivers and outcomes
Empower smart decision-making with best-in-class, rigorous statistical methods built by experts.
Why customers choose Eppo vs. Optimizely
- Vision
Empower all teams with experimentation.
Including both technical and non-technical audiences.
Broad perspective: Eppo’s deep experience handles a broad range of industries and scenarios.
Build the best marketing team user experience
Not accommodating to technical staff such as data science and engineering.
Limited perspective: Sees experimentation primarily with a marketing lens and focus elsewhere (e.g., content management and customer data).
- Achieving the VisionMaximize decision-making velocity
Build a comprehensive solution from the ground-up
Warehouse Native: Integrates with all the leading cloud data warehouses. Provides transparency, trustworthiness, control, and security.
Plan right: Includes team-level organization and performance scorecards, a full experiment report builder, as well as a knowledge repository so that learnings from past experiments may be referenced, compared, and re-used.
Self-serve with intuitive UI: From simple on-off flags to gradual rollouts & full-blown experiments.
Build trust by understanding causation of outcomes: Measures are made with purpose. Underlying calculations are transparent, well-documented.
Collaborative customer success help: White glove service, to ensure maximum value.
Growth through
acquisitionAcquisition strategy
Not Warehouse Native: Requires you to send your data externally to a warehouse for analysis. That’s inefficient and dangerous.
No planning capabilities: No comprehensive experimentation planning to build and manage a comprehensive experimentation strategy holistically.
Inadequate experimentation features
No experiment health diagnostics
No analysis tools
Can’t use metrics that matter.
Inflexible: Proprietary stats engine (Harvard & Stanford, sequential testing), not flexible. 3rd-party integrations are cumbersome to set up.
Expensive to get help: Required to purchase add-on services of data scientists and professional services.
Why is Warehouse-Native Critical?
- Self-Serve Analysis
Decrease analysis time with Eppo using same-day analysis, translating to a faster decision-making process.
- Secure data
Shipping key business metrics to a 3rd party is a non-starter. As Eppo is warehouse-native, that’s not an issue.
- Minimize Costs
Eppo eliminates redundancy in storage, reduces pipeline development and management, and reduces experimentation run-times.
- Transparency, trustworthiness, & control
Optimizely’s data processing is opaque, creating trust issues. Eppo instills trust with powerful controls and a single source of truth for both business reporting and experimentation data.
- Discern the signal from the noise
Eppo’s enterprise-scale Performance Scorecards let you measure aggregate impact, align teams around inputs and outputs, and more.
Eppo ensures cost-effectiveness
- 1Storage
Eppo doesn’t store your data separately. It’s already in your cloud data platform.
- 2Reduces pipeline costs by 90%
Eppo is hyper-optimized to your warehouse.
- 3Reduce experiment operational costs
Eppo shortens experiment runtime with CUPED++
The brands that use Eppo
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