New capabilities enable customers to easily validate ideas, reduce risk, and ship better products faster

Optimizely, the world’s leader in progressive delivery and experimentation, at Opticon20 announced several new products and partnerships, including integrations with enterprise platforms that make it easier for customers to leverage progressive delivery and experimentation to drive more agile, competitive business decisions.

“Businesses cannot survive in today’s environment by guessing,” said Claire Vo, Chief Product Officer, Optimizely. “Teams are prioritizing progressive delivery and experimentation practices to navigate today’s unpredictable market and remain agile for future opportunities and challenges. Today’s announcements illustrate the high demand for these critical tools and Optimizely’s leadership in enabling smart, data-informed product development for the modern digital team.”

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Optimizely’s enterprise-grade platform supports billions of events daily and has conducted more than 2 million experiments to-date. In the past six months, over 1,000 COVID-19 and coronavirus related campaigns and experiments have launched as customers aim to minimize business risk and stay more closely aligned with changing customer behaviors and purchasing habits during the pandemic. Based on customer feedback and these usage trends, Optimizely unveiled several new features and capabilities in its portfolio today at Opticon20.

Optimizely Unveils New Version of Full Stack with Enhanced User Experience, New Capabilities

A new version of Optimizely’s flagship feature flag and in-code experimentation platform, Full Stack, includes new capabilities and flexibility to simplify deployment and speed time to value. Full Stack is available via 12 SDKs or as a microservice and powers brands including Alaska Airlines, BBC, Blue Apron, ClassPass, Compass, Lending Club, Sky Media, and StubHub, among many more customers.

• The new Full Stack includes an enhanced user interface that makes it the industry’s most robust enterprise-grade server-side platform to integrate progressive delivery and experimentation. The new design allows customers to create and manage feature flags, and set up experiments on those flags within one user interface for the first time, greatly streamlining how teams orchestrate flags, rollouts, and experiments. By uniting feature flags and experiments in a single progressive delivery and experimentation workflow, all flags become opportunities to experiment, and all experiments benefit from the safeguards flagging provides.

• The new version of Full Stack also features Optimizely’s new Decide API. Decide API replaces nine APIs with a simpler and more powerful API, helping reduce the complexity of individual Full Stack SDKs and make them easier for engineers to adopt. It unifies the implementation process for experiments and feature flags.

• In addition, the new Flag Monitoring capability in Full Stack brings the power of Optimizely’s data platform and analytics to the company’s progressive delivery feature set. Flag Monitoring provides developers a direct line of sight into how the code they deploy behaves as well as audience analytics detailing how users engage with a flag, which in turn allows teams to make crucial time-saving decisions. Like experiment data, flag data will be available for export and to integrate with other platforms such as Snowflake or Datadog.

The new user interface in Full Stack will be available starting in October. Decide API and Flag Monitoring will be generally available within Full Stack starting in Q4 2020. To learn more about these new tools, attend the Opticon20 session on September 16 at 9:35 a.m. PT: “The Future of Optimizely for Technical Teams.”

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Optimizely Unveils Stats Engine Service and Data Platform Enhancements

Optimizely has expanded its portfolio of data and analytics offerings to meet the increasingly sophisticated needs across product, engineering, data science, and growth marketing teams, and their ecosystem of data stacks. Optimizely is introducing enhancements to its experimentation data platform to drive higher standards of experimentation decision making.

• The new StatsEngine Service allows customers to use Optimizely’s proprietary model for A/B testing statistical analysis via an API. For the first time, customers can run Stats Engine on non-Optimizely datasets from external sources like a data warehouse or analytics tool. For example, users can join Optimizely decisions with private financial data and then run Stats Engine on anonymized metrics. They can also use Stats Engine to measure the impact of experiments on complex metrics like LTV, MAUs, and retention. In addition, customers can plot Stats Engine analysis charts in business intelligence (BI) tools like Tableau or Chartio. The Stats Engine Service will be available starting in Q4 2020.

• Optimizely’s Enriched Events Export, provides a new way to help teams integrate Optimizely Results data into their analysis workflows and data stack, and is generally available today. Customers with advanced analysis needs can now join Enriched Events with other data to develop machine-learning models and build custom reports and dashboards. Enriched Events Export supports event-level joins, with tags and metadata preserved so resolution is not lost. It features an intuitive schema, partitioned into decisions and conversions to make joins with internal data easier. It is enriched with useful information like session IDs and details regarding which experiments and variations were active when the event fired.

• Optimizely introduced new data tools today that also help avoid data silos within businesses. The new oevents is an easy command-line tool for data scientists and other technical users to load just the data they need in the Optimizely platform, without a production ETL job in place. A new Snowflake integration automatically loads Enriched Events Export data into a Snowflake instance with zero engineering work needed. In addition, a new Fivetran integration automatically loads Enriched Events and other Optimizely data into a destination of choice. All three of these new capabilities are generally available today.

• A new collection of integrations and tutorials for working with Optimizely data and APIs, called Labs, was also announced today. Labs is a collection of reference implementations and integrations for teams to extend Optimizely’s platform. It includes recipes for integrating Optimizely SDKs for specific programming language frameworks, reference code for sending data between Optimizely and different data providers, and Jupyter notebooks to enable more complex experiment analyses. All materials in Labs are open-source and hosted on Github.

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