Gap Analysis in Optimizely Web Experimentation

What is Gap Analysis in Optimizely Web Experimentation?

Gap Analysis in Optimizely Web Experimentation is a systematic approach to identifying disparities between the current and desired states of a webpage or digital experience.

It entails assessing current outcomes, defining desired objectives, and identifying performance gaps. These gaps serve as the foundation for hypotheses development and experimentation.

Through various experiment types, changes are implemented and metrics like Conversion Rate are assessed. Data analysis validates results, leading to permanent implementation of successful changes and refinement of unsuccessful ones.

Gap Analysis ensures data-driven, goal-oriented optimisation, fostering continuous improvements in line with your organisational goals.

How to perform Gap Analysis in Optimizely Web Experimentation

How to Conduct Gap Analysis

In the context of Optimizely Web Experimentation, “Gap Analysis” refers to the process of identifying discrepancies or gaps between the current state or performance of a webpage or digital experience and the desired state or performance. Gap Analysis is a crucial exercise in the optimisation process and involves the following key steps:

Assess the Current Outcomes

This involves a comprehensive evaluation of the existing webpage or digital experience. Metrics and data are collected to understand how the current version is performing, including user engagement, conversion rates, bounce rates, and other relevant key performance indicators (KPIs). For example you may find that your e-commerce site has an average conversion rate of 5%.

Define the Desired Outcomes

In this phase, you establish clear goals and objectives for what you want to achieve through optimisation. This could include improving conversion rates, reducing bounce rates, increasing user engagement, or achieving specific business outcomes. E.g. after researching you discover that the industry standard conversion rate is 7%, this becomes your target rate.

Identify the Gap

Gap Analysis involves comparing the current state data with the desired state goals. It identifies where discrepancies or gaps exist between the two. These gaps highlight areas that need improvement or optimisation to align with the desired objectives. From above you know that the you want to increase the conversion rate by an absolute rate of 2%, or a 40% relative increase (5% to 7%).

Develop Your Hypotheses

Once the gaps are identified, you formulate hypotheses about what changes or variations could bridge these gaps and improve the performance of the webpage or digital experience. These hypotheses serve as the basis for experimentation.

Experiment and Test

With hypotheses in place, you design and conduct experiments using Optimizely Web Experimentation, through the various types of experiments that can be performed, from standard A/B tests to Multi-Arm Bandit for time critical campaigns. These experiments involve implementing changes or variations to the webpage and measuring the impact on key metrics, in this case Conversion Rate.

Analyse the Data

After running experiments, data is collected and analysed to determine whether the proposed changes have closed the gaps and brought the performance closer to the desired state. Within the experiments results screen, review Statistical Significance alongside Confidence Intervals to validate the results. Use this to determine which changes have impacted the move towards the desired Conversion Rate.

Optimise and Iterate Through Gap Analysis

Based on the analysis, successful changes may be implemented permanently, while unsuccessful ones are refined or abandoned. Gap Analysis is an iterative process, and the cycle continues until the desired state is achieved. Based on your results, review and update your experimentation roadmap to take into account the lessons learned.

Gap Analysis in Optimizely Web Experimentation is a systematic approach to identify, prioritise, and address performance gaps on webpages or digital experiences. It ensures that optimisation efforts are data-driven, goal-oriented, and result in continuous improvements aligned with organisational objectives.

Leave a Comment