A/B Testing: Unveiling User Engagement

This case study delves into the intricate relationship between website themes and user behavior, using a comprehensive dataset from statso.io.

View the whole notebook below, also the details are followed the notebook.

Exploratory Data Analysis (EDA): A Glimpse into User Engagement

Our exploration began with an extensive dataset encompassing user engagement metrics and demographic information. Through Click Through Rates, Conversion Rates, Bounce Rates, Scroll Depths, and Ages, vivid visualizations unveiled patterns and differences associated with different themes. Box plots, scatter plots, histograms, and bar charts illuminated trends that fueled our curiosity to dive deeper.

Hypothesis Testing: The Influence of Themes Unveiled

Click Through Rate: We embarked on hypothesis testing with an intriguing question: Does the choice of website theme influence the Click Through Rate? Our null hypothesis suggested no difference, while the alternative hypothesis hinted at a distinct impact. With an independent t-test, we meticulously compared Click Through Rates between "Dark Theme" and "Light Theme". The verdict? A p-value of 0.048 led us to confidently reject the null hypothesis, underscoring the significant difference in Click Through Rates.

Conversion Rate: The journey continued with Conversion Rates, prompting us to examine whether themes played a role in user sign-ups. Null and alternative hypotheses were framed, and an independent t-test ensued. The outcome surprised us—while the Conversion Rate p-value stood at 0.635, we couldn't reject the null hypothesis. Themes seemed to have a negligible impact on conversions.

Added_to_Cart: Cart interactions emerged as the next focal point. Could themes guide users towards adding items to their carts? A chi-squared test scrutinized the data, and the results painted a consistent picture. With a p-value of 0.717, we upheld the null hypothesis—themes, it appeared, held little sway over users' cart interactions.

The Bonferroni Correction: Guarding Against Type I Errors

But, as any seasoned statistician knows, multiple hypothesis tests invite the specter of inflated Type I errors. Enter the Bonferroni correction—an armor against erroneous conclusions. We deployed this correction method to meticulously adjust significance levels, ensuring that our conclusions remained steadfast and reliable. Through simulated scenarios, we validated the correction's efficacy, observing an observed Type I error rate of 0.016—effectively aligned with the adjusted significance level of 0.0167.

Conclusion: Navigating User Engagement with Precision

In this captivating journey, our exploration and analyses have cast a revealing light on the intricate dance between website themes and user engagement. While Click Through Rates demonstrated sensitivity to theme changes, Conversion Rates and Cart Interactions remained steadfast in the face of aesthetic variations. Our meticulous deployment of the Bonferroni correction fortified our findings, offering a beacon for designers, developers, and marketers.

User engagement isn't just a buzzword—it's an art and a science. By fusing empirical insights with methodological rigor, we've not only deepened our understanding but also empowered decision-makers to navigate the virtual landscape with precision. Armed with data-driven insights, the digital realm now holds the promise of crafting user experiences that transcend trends and resonate with the heart of engagement.