Hands On with Optimizely Analytics

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If you’ve been paying attention to Optimizely’s website, you may have noticed a new product offering: Optimizely Analytics. This is Optimizely’s warehouse-native analytics solution, which was launched after its 2024 acquisition of NetSpring. As a data-focused digital agency, we were excited to hear about the acquisition and have been digging deep into the capabilities and potential of this tool ever since we gained access. In this post, we’ll talk about what makes Optimizely Analytics unique, what use cases it serves, and what it looks like.

First, we’ll cover Optimizely Analytics’ “warehouse native” feature, the tool’s key differentiator.

What Does it Mean to be Warehouse Native?

The core innovation of Optimizely Analytics is its warehouse native approach, meaning its reports source data directly within data warehouses such as Snowflake, Databricks, Google BigQuery, and Amazon Redshift. This eliminates the need to extract data from these warehouses, providing several advantages:

Enhanced data security and privacy protection
Reduced costs by eliminating ETL and Reverse ETL jobs
Improved performance and speed
Simplified data governance

Additionally, Optimizely is the first DXP to offer warehouse-native analytics capabilities.

Being warehouse-native puts Optimizely Analytics in good company with other warehouse-native Customer Data Platform (CDP) vendors we partner with, like Hightouch and Rudderstack. It aligns with a broader industry push towards centralized data storage in an enterprise data warehouse and using this as the source of truth for multiple tools. For businesses who have opted into this warehouse-centric approach, there is added value for each warehouse-native tool added to their stack. For example, if you’re already capturing offline conversions in your warehouse to send to Google Ads with Hightouch, you can use those offline conversions as part of your experimentation reporting using Optimizely Analytics. We’ve completed dozens of successful data warehouse projects incorporating warehouse-native tooling and have seen firsthand the transformative effect that this approach can have.

The Power of Event-Based Analytics

Optimizely Analytics specializes in user behavior reporting that depends on recorded “events” (i.e., user actions). Events are sequential, timestamped data points that describe an end-user's action. Typically, this data is sourced from owned digital properties such as a mobile app or website, but it can also be collected from third-party systems (e.g., ad impression data) or offline channels (e.g., conference attendance). Event data forms the basis of product analytics, marketing analytics, and experimentation analytics reporting.

This focus on event data differentiates Optimizely Analytics from general-purpose business intelligence (BI) tools like Tableau or PowerBI. These tools take a “jack of all trades, master of none” approach to reporting that treats all data equally and gives no special attention to event data. In practice, this makes building behavior-focused event reports like funnel charts, user retention reports, or user journey reports challenging. In contrast, Optimizely Analytics has an internal model of your user and event data, which allows it to create these types of reports.

Use Cases Powered by Optimizely Analytics

Product Analytics

The product analytics field is crowded, but Optimizely Analytics stands out due to its warehouse-native approach. This approach makes it easy to place well-governed business metrics alongside noisier metrics collected from device telemetry. For example, a product analytics report showing net new customers could also display the impact on annual recurring revenue (ARR).

Optimizely Analytics’s semantic layer allows it to provide this seamless blend of product analytics data and business data. Optimizely Analytics provides administrators the ability to semantically define which datasets in their data warehouse contain "event" information and "actor" information (ex: end-users). With these definitions in place, report users can easily build funnel, retention, and user journey charts, which are difficult to build in traditional BI tools.

Experimentation

With Optimizely Analytics, customers using their Experimentation product can leverage data warehouse metrics when reviewing experimentation results. This unlocks the ability to take metrics like “percentage lift” and break them down by dimensions that aren’t available at exposure time. Similarly, you can view metrics not commonly available at exposure time. For example, viewing the lift in ad impression revenue when running an experiment on your recommendation engine. This capability allows Optimizely to compete head-on with other warehouse-native experimentation vendors such as Statsig and Eppo.

AI

Looking ahead, we feel confident that Optimizely Analytics will play an important role in the capabilities of Optimizely's AI solution, Opal. Marketers are expecting AI to help them build a content flywheel consisting of a few elements: content, personalized targeting, data, and insights. Each element serves as an input to the next.

An image showing a cycle with arrows in a circle following “Generate Content & Layout,” “Target with Personalization & Experimentation,” “Gather Performance Data,” and an arrow pointing to “Generate Insights” at the top of the cycle that says “Optimizely Analytics helps here.”

Optimizely Analytics can help close this loop by providing the insights necessary to determine the next content and personalization decision. Imagine a future where AI suggests your next blog article based on an in-depth analysis of how each customer segment has responded to each of your past blog articles. It then helps you build and personalize the article to the audiences that will appreciate it the most. This future isn't far off, but it will require good data as a first step.

Leverage Optimizely Analytics and its capabilities for your organization.

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Working in Optimizely Analytics

So, what does it look like to work in Optimizely Analytics? We'll demonstrate two critical components: bringing in your data and building a report.

Bringing in Your Data

Our first step is to connect Optimizely Analytics to our data warehouse. At Velir, we replicate our Google Analytics 4 data into Google BigQuery. Connecting to BigQuery was as easy as creating a new service account in Google Cloud and then filling out a few fields in Optimizely Analytics, as shown below.

A screenshot from Optimizely Analytics with fields for configuring a connection to BigQuery, including “Connection Type, Project ID, Service Account Email, and Service Account Key JSON,” with a “Health” option to test connection health by clicking a button.

Maintaining a semantic understanding of your behavioral and analytics events distinguishes Optimizely Analytics from other BI tools. While most BI tools treat all data tables the same, Optimizely Analytics understands that certain data tables contain data about users (called "Actors") and that other tables contain event stream data. Creating that understanding requires a few simple steps. You first identify the data you want Optimizely Analytics to consume. You then label your user data as an "Actor" or "Event," as shown below.

A screenshot from Optimizely Analytics in the “All Events” screen, where you can define an “Event Stream” with Event Stream fields filled in “All Events are events that occur at event_date_dt with event type event_name” and the Event Stream Properties field has “Session Identifier” set to “session_id”.

Once this is done, Optimizely Analytics can generate user funnels from event data and segment user cohorts using user data.

Building a Funnel that Blends Online and Offline Conversions

Now that Optimizely Analytics can access our user and event data, let's build a funnel report that blends website behavioral data with CRM data copied to our data warehouse. In this example, we want to understand our blog section’s impact on lead form submissions and sales as recorded in our CRM. We also want to see if there's a difference between all users visiting our blog and users arriving from our paid media (CPC) campaigns.

The results are shown below.

A screenshot from Optimizely Analytics of a report called “Blog to Sale Funnel,” showing Blog Views, Contact Us Page Views, and Lead Form Submissions broken down by CPC Traffic and Non-CPC Traffic.

As you can see, CPC is generating proportionally more Contact Us page views than non-CPC traffic and has contributed to three offline sales in this time range.

Leveraging Optimizely Analytics with Velir

Optimizely Analytics is a powerful tool that complements Optimizely’s existing product portfolio well. Its warehouse-native approach reduces data silos while enabling sophisticated event-based analytics that bridge online user behavior with offline business outcomes. The seamless integration provides immediate value for marketers and product teams already using Optimizely’s experimentation tools. As businesses continue to push for more unified data strategies, tools like Optimizely Analytics that connect directly to existing data warehouses will become increasingly valuable.

If you’re interested in deploying Optimizely Analytics or starting a path toward becoming more data warehouse-centric, we can help. We’ve helped brands large and small use Optimizely’s suite of products to build comprehensive data warehouses and drive valuable actions with data-driven insights. Reach out to us for help seamlessly integrating Optimizely Analytics into your marketing stack and turning your data into revenue.

Written by: Adam Ribaudo

Published: Apr 18, 2025