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Website Analysis: User and Technical Insights of
Deep Dive into User Behavior, Technical Performance, and UX Enhancements Using Full Story Data
The median time to conversion stands at ** seconds, suggesting that users are quickly persuaded by the CTAs presented.
Total Users: The site attracted a total of ****** unique visitors or ****** unique sessions during the analysis period, indicating the engagement scale and the website's reach.

*Note: The total number of users captured by FullStory might not be the same as GA4.
Overview of Traffic and Conversion Funnel: Our website has experienced a substantial volume of traffic, with a total of ***** unique users visiting during the reporting period. A closer inspection of the conversion funnel shows a robust conversion rate of ******.

*Conversion Definition: For this report, we define a conversion as the user's engagement with a CTA on our site that leads to a Redirection page.
Analysis of device usage revealed that **% of users accessed the site via mobile, **% via desktop, and the remainder through tablets. This highlights the importance of optimising the site for mobile users.
Website Performance Analysis
Overall Site Usage
Funnel Conversion Rate
Device Breakdown
This report offers a comprehensive analysis of, focusing on user interactions, technical performance metrics, and user experience indicators from February 19 to March 19, 2024.

This report has two main goals. First, it will point out what parts of the website are doing a great job of meeting user needs. Second, it will find ways we can make the website better and improve user experience. We're using information from the Full Story tool to give a complete picture of how well the site worked during a certain time.
The data was collected over a specified period, from 19 Feb 2024 to 19 March 2024, capturing a broad spectrum of user interactions across different pages, devices, and sessions.
Data Collection
The analysis focused on aggregating and interpreting the collected data to uncover insights into user behaviour patterns, technical performance issues, and potential areas for user experience improvement. The process involved:
  • Quantitative Analysis: To measure and evaluate key performance indicators and user interaction metrics.
  • Qualitative Observation: Leveraging Full Story's session replay feature to observe user interactions and identify issues not immediately apparent through quantitative data alone.
By synthesising these insights, the aim was to develop a comprehensive understanding of the site’s performance from both a technical and user-centric perspective. The findings from this analysis serve as the basis for the recommendations provided in this report.

Note: The analysis excludes data from users in Malta and Spain, focusing on delivering insights that are most relevant to our primary audience segments.
Analysis Approach
This section delves into the performance of our website, evaluating both user engagement metrics and technical performance indicators to understand how the website meets user needs and where improvements can be made.
Introduction to Website Performance Analysis
This analysis was conducted exclusively using Full Story, a comprehensive digital experience analytics tool that offers insights into user interactions, behaviours, and experiences on our website. Full Story enables the capture of a wide array of user interaction data, including clicks, scrolls, mouse movements, and more, providing a granular view of how users navigate and experience the website.
Frustration Indicators:
Dead Clicks:
This indicates areas where users expected interactivity but found none.
In our analysis, we detected a significant number of dead clicks. Identifying and addressing these dead click hotspots is crucial for enhancing the overall user experience by ensuring that all interactive elements meet user expectations.
I identified significant occurrences of dead clicks on various non-interactive elements. This included non-clickable image elements, text elements, and offer elements on cards.

By addressing these issues and creating Jira tickets to track and resolve them, we substantially reduced the number of dead clicks. This improvement led to a notable decrease in user frustration and a significant enhancement in overall user experience.

Consequently, the reduction in dead clicks has also positively impacted our conversion rates, demonstrating the effectiveness of our UX optimizations. The proactive steps taken have resulted in a healthier, more intuitive website, ultimately driving better engagement and satisfaction among users.
Broken button:
Through detailed analysis using the Full Story tool, I identified two issues on our Guns N' Roses Slot page:
  1. The "Play for Money" button does not trigger any action upon clicking, creating a barrier in the user journey. This issue led to user frustration and loss of revenue.
  2. The navigation bar on this page is not responsive, possibly hindering user navigation and negatively affecting the user experience.
These issues were likely caused by frontend JavaScript problems, backend server issues, or broken links. We implemented the necessary code and server fixes to ensure that the "Play for Money" button and the navigation bar worked correctly.
After making these fixes, we conducted thorough testing under various conditions to ensure the issues were fully resolved.
Proposed Solution
I identified a problem on our slot pages regarding user expectations. When users click the "Play for Money" button, they expect to be taken to a casino where they can play for money. However, they are instead presented with three cards with different casinos to read reviews.
This misalignment between user expectations and actual outcomes could lead to user dissatisfaction, potential loss of trust, and increased drop-off rates.
Here we can see 60% of users had total exits:
  1. We considered redesigning the flow to align more closely with user expectations. This involved directing users with casino choices where they could choose where they wanted to play. We made it clear that they will be taken to a casino website when they click the CTA.
  2. A/B tests different approaches to see which one best meets users' needs and expectations.
  3. The implemented solution we continued to monitor.
Broken journey
Proposed Solution:
Scroll Map
Using Full Story's scroll map feature, I analysed user scrolling behaviour. The scroll map provides a visual representation of how far users scroll down a page, highlighting areas where user engagement is high and where it drops off. This helped me understand which parts of our pages are most effective and which areas might need improvement.
Performance Metrics
Load Times Analysis
The analysis identified pages with the longest load times. These pages are critical to the user journey and require optimisation to reduce bounce rates and improve engagement:
DOM Content Loaded: **** (**%) of users (or **** unique sessions) experienced a DOM Content Loaded time greater than 3 seconds, indicating potential delays in page usability.
HAR analysis:
First Contentful Paint (FCP): *** (**.*%) unique users or **** sessions had an FCP greater than 3 seconds, affecting the initial user engagement by delaying the appearance of the first-page content.

Implication: A notable portion of users waited more than 3 seconds to see the first content appear on the screen, which can negatively impact user engagement and satisfaction.

For most of the sessions, we detected that The BW was too low. This is usually due to a bad network connection:
The request time spent too much waiting for the browser to have available socket:
Largest Contentful Paint (LCP): *** (*.**%) unique users or *** unique sessions experiencing an LCP greater than 3 seconds, the data points to the need for optimising content loading to improve perceived speed.

  • Implication: The delay in loading the largest content element on the screen suggests performance optimisation issues that could degrade the user experience.
For most of the sessions, we detected that The BW was too low. This is usually due to a bad network connection:
Prioritisation of the optimisation of pages identified with long load times through image compression, code minification, and leveraging browser caching. Fast-loading pages are crucial for retaining users and improving engagement metrics.

Analysis of Behavioural Indicators, Content and Navigation:
Understanding how users interact with the elements on the homepage and navigate through our website provides essential insights into their preferences and potential areas for improvement.
Pattern analysis helped us to simplify and improve the website's architecture. This included optimising the menu structure and enhancing search functionality. We also reduced the number of subcategories in each dropdown menu, prioritising popular ones or combining related subcategories.
Homepage Metrics
I analysed what users search for on our website and it helped us ensure that key information is easily accessible:
I analysed each menu and each submenu option to see:
  • User Preferences: Which submenu options are the most frequently accessed, helping us understand user interests and priorities.
  • Navigation Efficiency: How effectively users can navigate through the site using the menu and submenu options, identifying any potential confusion or bottlenecks.
  • Content Relevance: The relevance of the content under each submenu, ensuring it aligns with user expectations and needs.
  • Engagement Levels: How long users stay on pages accessed through different submenu options, providing insights into which content is engaging and which may need improvement.
  • Conversion Pathways: Understanding which submenu options lead to the highest conversion rates, helping to optimize the site for better performance.
Technical Performance Deep Dive
Uncaught Exceptions: Uncaught JavaScript Errors
A closer examination of technical performance underscores the importance of optimising both loading times and interaction responsiveness to enhance overall user satisfaction.
Overview: During the analysis period, our tool recorded a total of *** uncaught exceptions (*** (*.*%) unique users). These uncaught exceptions are indicative of JavaScript errors that were not properly handled, which can disrupt the intended functionality and lead to a degraded user experience.

Implication: Uncaught JavaScript errors are errors that occur in JavaScript code that have not been captured and handled through error-handling mechanisms like try-catch blocks. These errors can interrupt the execution of the script and may lead to malfunctioning of web pages or applications, displaying an error message in the console. They indicate issues in the code that the developer did not anticipate or handle, such as calling a function that doesn't exist or trying to access a property of an undefined variable.

A significant majority of the uncaught JavaScript errors, specifically ****%, occurred with users operating on Safari Browser on various versions of iPhone operating systems.
*It allows developers to Identify Browser-Specific Issues: Certain JavaScript errors may only occur in specific browser versions or with certain rendering engines.
Dealing with such errors usually involves debugging the JavaScript code to find the source of the problem and adding appropriate error handling to manage or prevent the error condition.

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