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The Future of Optimizely for Technical Teams

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Optimizely has been reimagining the future of progressive delivery and experimentation, improving every part of the platform to empower technical teams to build, ship, and iterate faster. Learn about the latest enhancements to Optimizely Full Stack and the Optimizely Data Platform, and get a sneak peek at the upcoming roadmap.

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The Future of Optimizely for Technical Teams

  1. 1. The Future of Optimizely for Technical Teams Anna Barr Product Manager OPTIMIZELY Zack Liscio Sr. Product Manager OPTIMIZELY
  2. 2. Introductions Shipped this year PDEX recap Roadmap strategy & plans Unifying around Flags New ways to work with data Q&A Agenda
  3. 3. 50New Features & Products
  4. 4. Customize feature delivery Targeted Rollouts SHIPPED 2020
  5. 5. How can we align progressive delivery and experimentation?
  6. 6. Key Concepts Flag: a juncture in code where a decision is made Rule: conditions for what to deliver and why Ruleset: the full set of Rules for to a Flag Variation: the payload a Flag delivers
  7. 7. Flags always deliver a variation ● Experiments & Targeted Delivery have the same “shape” ○ Simplest variations: On and Off ○ Even the simplest On / Off toggle is an opportunity to experiment ● Allows for seamless transitions between Targeted Delivery and Experiment Experiment Rollout
  8. 8. Interacting with Flags ● No breaking changes ○ Migrate to Flags without codebase updates or SDK upgrades ○ Standalone Experiments => Flags with Rules ● Coming Soon: Decide ○ Provides a single, unified implementation decide(flag_key) Run an A/B test or MAB Turn feature on for specific users Customize a feature by audience
  9. 9. Understand the impact of changes to your deployed Flags, and use that data in other places where it will be useful. Governance Automation Monitoring Build workflows and processes around Flag deploy, management, and cleanup. Flags as a Foundation Control access and ownership of Flags, and define oversight and change management policies.
  10. 10. What is our data vision?
  11. 11. Dynamic Flexible Extensible When new information becomes available, the event pipeline should handle asynchronous, in-place updates to existing datasets. Actionable insights come in all shapes and sizes. Events should not conform to rigid structures, and neither should the consumption patterns of those events. The data used to run statistical models should be the same data used to create business-level KPIs. Data as a Product
  12. 12. Enriched Events Event-level dataset containing all records you send to Optimizely Enriched with experiment and user attributes from the server With a partitioning scheme that serves more of your use cases
  13. 13. Enriched Events Analyze experiment results with any SQL or data analysis tool Join Optimizely data with other data sources in your data warehouse Monitor experiment impact using your own dashboards
  14. 14. Flag Monitoring Is my flag configured correctly? Where is my flag evaluated? How often is my flag evaluated?
  15. 15. Stream Service Create filtered data streams using a generic subscription API Consume and use Optimizely data in real time Make product decisions faster and more efficiently
  16. 16. Event Debugger Inspect and validate your event instrumentation Uncover missing events Ensure the right attributes are being sent with your events
  17. 17. Join us for Q&A on slack
  18. 18. Experiment everywhere Run thousands of experiments Embrace hypothesis thinking Source ideas from everyone LOREM IPSUM
  19. 19. First Item Second Item Third Item LOREM IPSUM
  20. 20. Experiments we run through Optimizely are very easy to make changes to, and deploy on the spot.” Greg Sherwin Senior Principal Engineer Farfetch