Unlocking Content Performance Insights with ANOVA

Understanding content performance is crucial for enhancing user engagement and optimising digital services. Whether managing a government website or a private enterprise, accurately assessing content success can significantly impact outcomes. In this blog series, we’ve examined how data-driven techniques can go beyond standard analytics. While previous posts focused on the pillars of content measurement, today we delve into a more sophisticated statistical analysis using ANOVA (Analysis of Variance). This analysis aims to test key hypotheses regarding content engagement, session duration, and landing page performance by leveraging data from two websites: one in the public sector and the other in the private sector. The results offer valuable insights into how various factors influence user behaviour across these platforms.

To discover how Storm can help you improve content measurement through Google Analytics 4, speak to our team.

Leveraging Advanced Data Science Techniques

Data analysis in today’s digital landscape necessitates more than merely reviewing surface-level metrics from tools like Google Analytics. To gain deeper insights into user interactions with your content, advanced statistical methods are essential. Conducting data-focused analyses helps identify which metrics are genuinely relevant and impactful, ensuring decisions are grounded in actionable insights rather than assumptions.

These techniques require expertise in data science and proficiency in specialised tools like R or Python, extending beyond basic reporting.

Here’s an overview of key techniques that data scientists employ to extract meaningful insights:

1. Descriptive Statistics
   Descriptive statistics summarise data using averages, medians, variances, and distributions, aiding in understanding general trends and identifying outliers in user behaviour.
2. Hypothesis Testing
   Hypothesis testing allows for making inferences to determine if differences between groups (e.g., content categories or traffic sources) are statistically significant. This is a vital tool for informed decision-making.
3. ANOVA (Analysis of Variance)
   ANOVA compares the means of three or more groups to assess if there are significant differences among them. In this study, ANOVA was employed to evaluate whether engagement metrics varied across content categories and traffic sources.
4. Chi-Square Test
   The Chi-Square test examines relationships between categorical variables, such as whether different types of landing pages are associated with varying levels of user engagement.
5. Time Series Analysis
   Time series analysis is useful for identifying patterns, trends, and seasonal variations in data collected over time, essential for understanding how engagement or traffic evolves weekly.
6. Granger Causality Test
   This advanced technique tests whether one time series can predict another, such as whether changes in traffic from a particular source influence engagement metrics over time.
7. Linear Regression
   Linear regression models the relationship between a dependent variable (e.g., engagement) and one or more independent variables (e.g., traffic source or session duration), aiding in understanding these relationships and facilitating future predictions.
8. Natural Language Processing (NLP)
   NLP techniques analyse and interpret human language in text data, helping to gauge user sentiment, categorise content, and extract insights from customer feedback or reviews.
9. Clustering Models
   Clustering groups similar data points, allowing for the segmentation of users or content based on behaviour, which helps organisations identify distinct user segments and tailor their content strategies accordingly.
10. Classification Models
    Classification models, including logistic regression and decision trees, predict user engagement or categorise content based on input features, helping optimise content strategies.

Each of these techniques provides a unique perspective on analysis, requiring specialised knowledge. This is why organisations need data scientists to select appropriate methods and accurately interpret results.

Case Study: Content Performance Analysis Using ANOVA

In this case study, we utilised ANOVA to test three key hypotheses and evaluate content performance on two websites—one from the public sector and one from the private sector. ANOVA is a statistical method used to compare the means of multiple groups and determine whether observed differences are statistically significant or merely due to random variation. The goal is to assess whether various factors, such as content categories or traffic sources, influence user engagement.

ANOVA Framework

For each hypothesis, ANOVA tests the Null Hypothesis (H₀) against the Alternative Hypothesis (H₁):

• H₀ (Null Hypothesis): There is no significant difference between group means (e.g., engagement metrics are identical across content categories or traffic sources).
• H₁ (Alternative Hypothesis): There is a significant difference between group means (e.g., engagement metrics vary significantly across different content categories or traffic sources).

The process involves calculating an F-statistic, which measures the ratio of variance between groups to variance within groups. If the F-statistic is sufficiently large and the corresponding p-value is less than the chosen significance level (commonly 0.05), we reject the null hypothesis (H₀), concluding that significant differences exist between the groups.

Hypotheses Tested

Here are the three hypotheses we evaluated using ANOVA:

1. Engagement metrics are the same across different content categories (H₀: no difference, H₁: there is a difference).
2. The average session duration is the same across all traffic sources (H₀: no difference, H₁: there is a difference).
3. Engagement metrics are the same across different types of landing pages (H₀: no difference, H₁: there is a difference).

Engagement Metrics Used

Key engagement metrics such as session duration, engagement rate, and pages per session were analysed. We employed traditional GA4 metrics like engagement rate and average session duration while also integrating custom events implemented by Storm ID. These events include content conversion, which tracks when a user clicks on an internal or external link or stays on a page for 30 seconds while scrolling 75% down, and page read, which tracks when a user stays on a page for 30 seconds and scrolls 75% down.

Implementing these custom events is crucial because relying solely on traditional GA4 metrics—especially GA4’s engagement rate—can provide an incomplete or misleading picture of user behaviour. GA4 defines "engaged sessions" broadly, categorising any session that lasts over 10 seconds, results in a conversion, or includes at least two pageviews as "engaged." However, this broad classification does not always reflect meaningful user interactions. For example, a session that lasts just over 10 seconds may not genuinely represent engagement, as users could be classified as engaged even if they quickly bounce away.

Conversely, custom events allow for precise tracking of meaningful user actions, providing deeper insights into content interactions. By monitoring specific behaviours—like scrolling down a page or clicking internal links—you can measure true engagement, enabling more informed content strategy decisions. Customising these events to align with your business goals or user interactions offers richer insights than out-of-the-box GA4 metrics, which often oversimplify engagement.

Statistical Results

Here’s what the ANOVA tests revealed for each hypothesis:

For Hypothesis 1 (Engagement metrics across content categories):

• Public sector site: The F-statistic indicated a significant difference in engagement metrics across content categories (p-value < 0.05), leading us to reject the null hypothesis (H₀) and accept the alternative (H₁). This suggests that some content categories are more engaging than others, indicating the need for tailored strategies.
• Private sector site: The F-statistic similarly indicated a significant difference in engagement metrics across content categories (p-value < 0.05). Thus, we reject the null hypothesis (H₀) and accept the alternative (H₁), implying that some content categories are more engaging in the private sector as well.

Engagement metric	p-value (Public Sector)	p-value (Private Sector)
Content Conversion Event	0.00	4.85e-9
Engagement Rate	3.29e-8	8.93e-5
Page Read Event	0.00	1.99e-6

For Hypothesis 2 (Session duration across traffic sources)

• Public sector site: A significant difference in session duration across traffic sources was detected (p-value < 0.05), leading us to reject H₀. This indicates that certain traffic sources attract more engaged users.
• Private sector site: In contrast, session duration did not vary significantly by traffic source (p-value > 0.05), so we fail to reject H₀, indicating that different traffic channels do not significantly impact session duration. However, it’s important to note that other engagement metrics, such as content conversion events, page read events, and engagement rate, did show significant differences across traffic sources.

Engagement metric	p-value (Public Sector)	p-value (Private Sector)
Avg. Session Duration	2.05e-02	0.12530
Content Conversion Event	6.69e-91	0.00012
Page Read Event	8.82e-92	0.00017
Engagement Rate	2.99e-18	0.00791

For Hypothesis 3 (Engagement metrics across landing pages)

• Public sector site: Engagement metrics reveal a statistically significant difference across landing page content groups (p-value < 0.05), except for average session duration, which did not demonstrate significant variation.
• Private sector site: Traditional engagement metrics from GA4 (average session duration and engagement rate) do not show a statistically significant difference across landing pages. However, custom events indicate variations in engagement performance among the landing pages.

Engagement metric	p-value (Public Sector)	p-value (Private Sector)
Avg. Session Duration	0.1256	0.3015
Content Conversion Event	0.0000	0.0012
Page Read Event	0.0000	0.0003
Engagement Rate	0.0005	0.5225

Conclusion

The ANOVA analysis uncovers significant variations in engagement metrics across content categories, traffic sources, and landing page groups for both public and private sector websites. For Hypothesis 1, we identified statistically significant differences in engagement metrics across content categories in both sectors, highlighting the importance of customised content strategies to enhance user engagement.

In Hypothesis 2, the public sector site exhibited significant differences in session duration across traffic sources, suggesting that certain channels attract more engaged users. Conversely, for the private sector site, session duration did not vary significantly by traffic source. This distinction emphasises the necessity for tailored marketing strategies that consider the specific performance of different traffic sources to optimise user engagement.

For Hypothesis 3, while the public sector revealed significant variations in engagement metrics across landing page content groups, traditional metrics like average session duration and engagement rate did not show such differences in the private sector. Instead, custom events highlighted variations in user engagement, indicating that standard metrics may overlook critical insights.

It is important to note that while we examined one public sector and one private sector website, these findings should not be interpreted as universally applicable across all public and private sector websites. The results presented in this analysis should be viewed as case-specific rather than indicative of broader trends.

Integrating custom events with traditional GA4 metrics is essential for capturing a comprehensive view of user behaviour. GA4’s broad classification of "engaged sessions" can obscure genuine interactions, making the implementation of specific tracking mechanisms aligned with business goals crucial. Custom events provide deeper insights into user engagement by accurately reflecting meaningful actions.

Furthermore, thorough analysis is vital for identifying relevant metrics that truly represent user interactions. Since no single technique offers a universal solution, organisations should collaborate with data scientists to apply appropriate methodologies. By leveraging advanced statistical techniques and custom events, organisations can gain valuable insights that enhance their content strategies across various sectors.

If you’d like to explore deeper insights into your website’s performance or develop a custom content measurement plan, get in touch with our team. We specialise in data-driven solutions that go beyond basic metrics, helping you unlock the full potential of your digital service.

To discover how Storm can help you improve content measurement through Google Analytics 4, speak to our team