Way back in 2008, Dan Siroker, the Director of Analytics for the Obama campaign, pioneered one of the earliest data-driven election campaigns in history.
The innovative website methodology involved creating two variations of web pages, randomly directing users to each, and then analyzing which variant attracted the most clicks.
Nowadays, we know this simply as A/B testing. Back then, they called it "Wait a minute, you're allowed to do that?"
This approach, supplemented by numerous call-to-action strategies, A/B testing of subject lines, and form optimization, essentially laid the groundwork for the discipline of marketing experimentation as we know it today.
What does this have to do with Optimizely?
Good question: After the 2008 campaign, Dan Siroker founded Optimizely, monetizing the tools he created and used to conduct marketing experimentation throughout the campaign.
He even brought with him one of the best endorsements in the world: His marketing experimentation arguably won the 2008 election.
At its core, Optimizely relies on the power of A/B testing to compare different versions of web pages or app screens. By presenting users with variations of a page, Optimizely enables businesses to determine which version performs better based on predefined metrics.
Optimizely integrates with websites and mobile apps through a simple SDK which allows Optimizely to track user interactions, such as clicks, scrolls, and form submissions for subsequent analysis.
Once an experiment is live, Optimizely's machine learning algorithms work behind the scenes to analyze the collected data in real time. These algorithms identify statistically significant differences between variations, helping businesses determine the winning version quickly and confidently.
Optimizely's platform goes beyond basic A/B testing, offering advanced features like multivariate testing and personalization, which allows for the simultaneous testing of multiple elements on a page, while personalization enables targeted experiences based on user segments or behavior.
As you embark on your experimentation journey, it's crucial to evaluate your specific requirements and consider factors like technical capabilities, scalability, and pricing. While Optimizely is a well-established player, exploring alternatives like Statsig can offer a more tailored and efficient approach to optimizing your digital experiences.
Optimizely's visual editor allows marketers to create variations without coding. You can drag and drop elements, modify text, and adjust layouts easily.
Personalization is a key capability in Optimizely. It enables delivering tailored experiences to specific user segments based on attributes like location, device, or past behavior. This helps drive higher engagement and conversions.
Optimizely supports multi-page funnel testing. Instead of just optimizing individual pages, you can test entire user journeys spanning multiple steps. Identifying the best paths can significantly boost overall conversion rates.
Feature flagging is another core Optimizely capability. It allows controlled rollouts of new features to a subset of users. You can gradually expand availability while monitoring performance and gathering user feedback.
While Optimizely offers a solid set of experimentation tools, Statsig provides a more technically sophisticated platform. It's proven by large customers like OpenAI, Notion, Atlassian, Flipkart and Brex.
Statsig is also typically less expensive, with extensive volume discounts for enterprise customers. The generous free tier makes it accessible for companies of all sizes to get started with experimentation.
Optimizely's visual editor lets marketers create variations without coding. You can drag and drop elements, modify text, and adjust layouts easily.
With Optimizely’s personalization, you can deliver tailored experiences to specific user segments based on location, device, or past behavior, helping drive engagement and conversions.
Optimizely supports testing entire user journeys, not just single pages, allowing you to optimize multiple steps in the funnel to improve overall conversion rates.
Optimizely’s feature flagging enables controlled rollouts, gradually releasing new features to a subset of users while monitoring performance and gathering feedback.
Using a tool like Optimizely can significantly improve your product development process. By enabling data-driven decision-making, you can reduce guesswork and make informed choices based on real user behavior.
However, while Optimizely is a powerful tool, it may not be the best fit for every company.
Statsig offers a more technically sophisticated platform, proven by large customers like OpenAI, Notion, Atlassian, Flipkart, and Brex. It's also less expensive, with extensive volume discounts for enterprise customers and an extremely generous free tier.
Statsig's advanced features include feature flags, dynamic config, and experimentation, which let users safely test and roll out new functionality, customize their app's behavior without redeploying, and run A/B tests to optimize user experience.
Statsig also offers powerful analytics to help understand user behavior and make data-driven decisions.
Some key technical advantages of Statsig include:
Ability to run experiments on back-end systems and algorithms, not just front-end UI
Rigorous statistical methodologies for automated analysis of experiment results
Scalability to handle hundreds of concurrent experiments with billions of events
While both Statsig and Optimizely are powerful experimentation platforms, they have some key differences. Statsig takes a more developer-centric approach, with experiments defined directly in code. This allows for greater flexibility and control over the experimentation process.
In contrast, Optimizely provides a visual editor that enables non-technical users to create and manage experiments. This can be advantageous for teams where not everyone has coding expertise. However, this ease of use comes at the cost of some advanced functionality.
Take an inside look at how we built Statsig, and why we handle assignment the way we do. Read More ⇾
Learn the takeaways from Ron Kohavi's presentation at Significance Summit wherein he discussed the challenges of experimentation and how to overcome them. Read More ⇾
Learn how the iconic t-test adapts to real-world A/B testing challenges and discover when alternatives might deliver better results for your experiments. Read More ⇾
See how we’re making support faster, smarter, and more personal for every user by automating what we can, and leveraging real, human help from our engineers. Read More ⇾
Marketing platforms offer basic A/B testing, but their analysis tools fall short. Here's how Statsig helps you bridge the gap and unlock deeper insights. Read More ⇾
When Instagram Stories rolled out, many of us were left behind, giving us a glimpse into the secrets behind Meta’s rollout strategy and tech’s feature experiments. Read More ⇾