Optimization glossary

Experimentation framework

What is an experimentation framework?

An experimentation framework is a systematic approach to testing different versions of a product or feature to determine which one performs best. It is a data-driven process that helps businesses make informed decisions about their product development and marketing strategies. As an active part of any experimentation program, they are highly utilized by product teams and development teams.  

Product experimentation consists of a set of guidelines and practices that organizations use to conduct experiments and optimize their products. It involves comparing different versions of a product against each other to isolate and start conducting feature tests for each feature. In short, identify the performance indicators to measure, develop a hypothesis, design the experiment, and analyze the results.       

The results are then measured using key metrics, such as conversion rate, customer retention rate, engagement rate, and more. By understanding how customers interact with products, companies can optimize their offerings and better meet customer needs. It helps businesses minimize risks, create better user journeys and generate more revenue in less time.  

Sample experimentation framework

How does an experimentation framework work?

Having an experimentation framework enables organizations to prioritize and quickly launch new products or services without expending too much time or resources on the development. This approach helps ensure that only the most successful features are implemented while providing valuable feedback about what customers value most when it comes to their user experience.   

Let's take the example of an A/B testing framework you can use:  

  • Gather data: Utilize an analytics tool, such as Google Analytics, to identify sections of your website or app that receive significant traffic. Pay attention to pages with high bounce rates or drop-off rates, and target these for improvement. Additionally, consider consulting heatmaps, social media feedback, and surveys to discover new opportunities for enhancement.  
  • Establish objectives: Define the metrics to determine the success of a variation compared to the original version. Objectives can vary and may involve actions like button clicks or product purchases in a given time.  
  • Develop hypothesis: Once you establish the objectives, generate ideas for A/B testing and explain why you believe the proposed changes will outperform the current version. Prioritize these ideas based on their expected impact and the level of implementation complexity.  
  • Create variations: Make tweaks to specific elements of your website or mobile app. These changes can include altering button colors, rearranging the order of page elements, hiding navigation elements, or implementing custom modifications. It is important to thoroughly test your experiment to ensure the different versions function as intended.  
  • Execute: Launch the experiment and wait for visitors to participate. After you drive visitors to your site, they'll be randomly assigned to either the control or variation of your experience. Monitor their interaction with each version, record the data, and compare it against the baseline to evaluate performance.  
  • Result validation: Depending on the size of your target audience, it may take some time to obtain statistically significant results. Reliable experiment results will indicate when the outcomes have reached statistical significance, as it would otherwise be challenging to determine whether your changes have truly made an impact.  

If you notice a measurable difference in performance between the test versions, apply the insights to other website pages with a focus on conversion rate optimization (CRO) 

However, if your experiment yields no clear result, view the experiment design as a learning experience and generate new hypotheses to test in future experiments.  

Why experimentation framework is important?

Product experimentation helps you identify and iterate which versions of a product or feature are most effective at achieving their desired outcomes, such as increasing user engagement, driving conversions, or improving customer satisfaction.   

Based on experiment results, this approach can help businesses reduce the likelihood of costly mistakes and improve customer experience. You can:  

  • Make data-driven decisions: Enable informed decisions based on solid evidence and data.  
  • Improve user experience: Enhance user satisfaction, engagement, and conversion rates.  
  • Mitigate risks: Identify potential negative impacts or issues before implementation.  
  • Optimize resources: Allocate resources to initiatives with higher potential.  
  • See evidence-based insights: Provide an objective evaluation of user behavior and preferences.  
  • Have faster time to market: Accelerate the deployment of successful changes or features.  
  • Build scalable systems: Applicable across various platforms and touchpoints.  
  • Align with business goals: Ensure experimentation efforts connect with strategic objectives.  
  • Foster innovation and creativity: Encourage exploration of new ideas and solutions.  

Types of feature or product experimentation frameworks

If you’re looking to build a culture of experimentation in real-time, several types of experimentation frameworks can work as a starting point, including:

1. A/B testing 

An A/B testing framework involves testing two versions of a product or feature with a single variable changed between them. Run experiments using baseline metrics to collect data. For example, you might want to test a landing page or homepage on your website with two different headlines or a call to action (CTA) to see which one performs better and gets more clicks.   

2. Multivariate testing 

A multivariate testing framework involves testing multiple variables across multiple versions of a product or feature. For example, a business might test several different versions of a product page with different combinations of colors, layouts, and messaging to determine which combination performs best.   

3. Lean hypothesis testing 

The lean experimentation framework is designed to test new ideas quickly and inexpensively. It involves creating a minimum viable product (MVP) that contains only the essential features necessary to test the hypothesis. The MVP is then tested with a small group of users, and the feedback is used to improve the product.   

4. User testing 

The user testing framework involves gathering feedback from stakeholders about the variants of a product or features through surveys, focus groups, or user testing sessions. It helps you identify potential usability issues or areas for improvement.   

Optimizely and experimentation framework

Having an experimentation framework is a critical component of product development and marketing. It enables businesses to test new ideas that use data-driven decision-making for maximum user engagement and satisfaction. By using an experimentation framework, businesses can reduce risks, and create better user journeys and checkout options to increase revenue and customer loyalty.  

With Optimizely’s experimentation platform, enjoy all the functionalities of a framework you want alongside trustworthy and fast data from the stats engine. Validate your results and know that you have the right products that are delivering the best value to your customers. Get started here to see how feature experimentation really works