Top User Segments by Average Monthly Social Media Spending

By abhishek.verma75000 · May 28, 2026

This analysis identifies the top-performing audience segments based on average monthly social media expenditure. User groups were segmented by age…

The analysis identified the top 10 user segments combining age group, country, and platform that generate the highest average monthly social media spend. A horizontal bar chart and supporting data tables have been produced showing these segments, filtered to only include groups with at least 3 users for statistical reliability.

From 2022 to 2024, new user account joins have been tracked across 36 months, totaling 2,000 new users. A line chart has been generated showing the monthly trend, making it easy to spot patterns and fluctuations over time. On average, about 55.6 new users joined per month.

The analysis reveals that different content types excel at different stages of the funnel. Entertainment drives the highest ad click rate (CTR of 0.1374), making it the best content type for capturing user attention. However, Stories leads in purchase conversion, with 27.75% of users actually completing a purchase. Two charts and several data tables were generated to visualize these differences — a grouped bar chart comparing click rates vs. conversion rates across content types, and a scatter plot showing how each content type is positioned on both metrics simultaneously.

The analysis compared influencers and regular users across three key metrics: followers, posts per week, and ad click rates. Two visualizations were generated — one showing average follower counts by user type, and another displaying posts per week alongside ad click rates side by side. Data tables with the full summary statistics are also available for deeper review.

The analysis reveals that LinkedIn drives the highest daily usage hours at 3.14 hrs/day, while Pinterest leads in engagement actions with 34.66 actions per user per day. Three charts were generated to visualize these findings: a bar chart of average daily usage hours by platform, a bar chart of average engagement actions per day, and a scatter plot comparing both metrics together across all platforms.

The analysis reveals how daily social media/screen usage hours relate to mental health scores and sleep disruption levels. A bar chart shows average mental health scores across different usage buckets (0–2h, 2–4h, 4–6h, 6–8h, 8–12h), and a second chart compares average daily usage hours and mental health scores across sleep disruption categories. The overall trend is clear: as daily usage increases, mental health scores tend to decline and sleep disruption worsens.

A correlation heatmap has been generated showing the relationships between all 11 key engagement and behavioral metrics. The heatmap uses a red-blue color scale where red indicates positive correlations and blue indicates negative ones, with annotations showing exact correlation values for each pair. The analysis reveals how daily usage patterns, social interactions, and content engagement behaviors relate to one another across the dataset.

A data table has been generated showing the distribution of sleep disruption categories across different peak usage time slots. The table breaks down the percentage of users reporting No impact, Mild impact, Moderate impact, and Severe impact for each time period, allowing you to compare how usage timing relates to sleep outcomes.

The analysis produced a grouped bar chart and data tables comparing the top 10 professions across three key metrics: average mental health score, daily usage hours, and monthly social media spending. The professions are sorted from highest to lowest mental health score, with monthly spend scaled by /10 in the chart for visual clarity. The grouped bar chart lets you directly compare all three dimensions side-by-side for each profession, making it easy to spot patterns between how much time people spend on social media, how much they spend, and how they rate their mental health.

The analysis successfully produced visualizations showing how ad click rates are distributed across different preferred content type categories. Two charts were generated: a box-and-whisker plot showing the median, interquartile range, and outliers for each content type, and a bar chart displaying the mean ad click rate with standard deviation error bars per category. Supporting data tables were also created with detailed statistics including IQR-based outlier detection results.