Optimizely Announces for Amazon Personalize; Launches New Features at Opticon20

Optimizely introduces new features for Amazon recommendation engine

Optimizely today announced the launch of Optimize for Amazon Personalize, a machine learning (ML) solution from Amazon Web Services (AWS) that makes it simpler for companies to create personalized recommendations for their customers at every digital touchpoint.  The new integration will enable customers to use experimentation to determine the most effective machine learning algorithms to drive greater customer engagement and revenue.

Optimizely for Amazon Personalize enables software teams to A/B test and iterate on different variations of Amazon Personalize models using Optimizely’s progressive delivery and experimentation platform. Once a winning model has been determined, users can roll out that model using Optimizely’s feature flags without a code deployment. With real-time results and statistical confidence, customers are able to offer more touchpoints powered by Amazon Personalize, and continually monitor and optimize them to further improve those experiences.

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“Successful personalization powered by machine learning is now possible,” says Byron Jones, senior director of product at Optimizely. “Customers often have multiple Amazon Personalize models they want to use at the same time, and Optimizely can provide the interface to make their API and algorithms come to life. Models need continual tuning and testing. Now, with Optimizely, you can test one Amazon Personalize model against another to iterate and provide optimal real-time personalization and recommendation for users.”

What Does This Mean for Brands?

Until now, developers needed to go through a slow and manual process to analyze each machine learning model. Now, with Optimizely for Amazon Personalize, development teams can easily segment and test different models with their customer base and get automated results and statistical reporting on the best performing models. Using the business’ KPIs with the new statistical reports, developers can now easily roll out the best performing model. With a faster process, users can test and learn more quickly to improve key business metrics and deliver more personalized experiences to their customers.

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Optimizely Announces New Capabilities at Opticon20

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 StatsEngine 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 the 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 events 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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Speaking with Toolbox Marketing Claire Vo, chief product officer, Optimizely added “Businesses cannot survive in today’s environment by guessing. 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.”