Streaming Analytics – Understanding Platform Popularity Across Age Groups
Xinru Wang
2025-11-10
Introduction
In this report, we explore whether age group influences people’s perferred streaming platform. Specifically, if certain platforms (Netflix, Hulu, Disney+, or Amazon) appeal more to specific age demographics. Using simulated survey data, we conducted analyses including descriptive statistics, data visualization, and a chi-square test of independence. We also calculate Cramer’s V to assess the strength of the relationship between age group and platform perference, and identify which specific age-platform combinations contribute the most to any significant difference.
Step 1 – Data Preparation
load and setup data:
platform_age_preference <- read_excel("Streaming Services and Age.xlsx")
summary(platform_age_preference)## AgeCat Platform
## Length:300 Length:300
## Class :character Class :character
## Mode :character Mode :characterstr(platform_age_preference)## tibble [300 × 2] (S3: tbl_df/tbl/data.frame)
## $ AgeCat : chr [1:300] "18–25" "18–25" "18–25" "18–25" ...
## $ Platform: chr [1:300] "Other" "Hulu" "Netflix" "Netflix" ...Contingency Table: Age Catorgry
table(platform_age_preference$AgeCat) ##
## 18–25 26–40 41+
## 100 100 100Contingency Table: Streaming Platform
table(platform_age_preference$Platform)##
## Amazon Disney+ Hulu Netflix Other
## 54 61 46 111 28Contingency Table: Age Catorgry + Streaming Platform
table(platform_age_preference$AgeCat, platform_age_preference$Platform)##
## Amazon Disney+ Hulu Netflix Other
## 18–25 4 22 23 47 4
## 26–40 11 25 16 41 7
## 41+ 39 14 7 23 17table_total <- table(platform_age_preference$AgeCat, platform_age_preference$Platform)Step 2 – Visualization
Stacked Bar Chart: Platform Preference Within Each Age Group
stacked_graph <- ggplot(platform_age_preference, aes(x = AgeCat, fill = Platform)) +
geom_bar(position = "fill") +
labs(
title = "Platform Preference Within Each Age Group",
x = "Age Group",
y = "Proportion",
fill = "Platform") +
theme_fivethirtyeight()
stacked_graph
Interpretation:
The stacked bar chart shows the proportion of streaming platform preferences within each age group. We can see that Netflix is the most popular platform among people aged 18–25 and 26–40. However, as age increases, preferences begin to shift. For adults aged 41 and older, Amazon becomes the most preferred platform instead of Netflix.
Clustered Bar Chart: Platform Preference Counts by Age Groups
clustered_graph<- ggplot(platform_age_preference, aes(x= AgeCat, fill = Platform)) +
geom_bar(position = "dodge") +
labs(
title="Platform Preference Counts by Age Groups",
x = "Age Group",
y = "Counts",
fill = "Platform") +
theme_fivethirtyeight()
clustered_graph 
Interpretation:
The clustered bar chart shows the number of people in each age group who prefer each streaming platform. We can see that Netflix has the highest number of users in the 18–25 and 26–40 age groups. Again, for adults aged 41 and older, Amazon becomes the most preferred platform, replacing Netflix as the leading choice.
Step 3 – Chi-Square Test of Independence
chisq.test(table_total)##
## Pearson's Chi-squared test
##
## data: table_total
## X-squared = 68.044, df = 8, p-value = 1.203e-11chi_total <- chisq.test(table_total)Interpretation:
The chi-square test showed a significant association between age group and streaming platform preference, χ²(8) = 68.04, p < .001. This means platform preference differs by age, so age and platform choice are not independent.
Step 4 – Observed, Expected, and Residual Values
Observed Values:
chi_total$observed##
## Amazon Disney+ Hulu Netflix Other
## 18–25 4 22 23 47 4
## 26–40 11 25 16 41 7
## 41+ 39 14 7 23 17Expected Values:
chi_total$expected##
## Amazon Disney+ Hulu Netflix Other
## 18–25 18 20.33333 15.33333 37 9.333333
## 26–40 18 20.33333 15.33333 37 9.333333
## 41+ 18 20.33333 15.33333 37 9.333333Residual Values:
chi_total$residuals##
## Amazon Disney+ Hulu Netflix Other
## 18–25 -3.2998316 0.3696106 1.9578900 1.6439899 -1.7457431
## 26–40 -1.6499158 1.0349098 0.1702513 0.6575959 -0.7637626
## 41+ 4.9497475 -1.4045204 -2.1281413 -2.3015858 2.5095057Interpretation:
After running a chi-square test, we looked more closely at the observed, expected, and residual values. Residuals show how different the observed values are from the expected values. The largest positive residual (more people than expected) was for adults aged 41+ choosing Amazon (39 observed vs. 18 expected). Older adults also chose “Other” platforms more than expected (17 observed vs. 9.33 expected). In contrast, the largest negative residual (fewer people than expected) was for the 18–25 age group choosing Amazon. Only 4 people chose Amazon in this group, even though about 18 were expected (residual = -3.30).
Step 5 – Contributions to the Chi-Square Statistic
Cell Contributions:
cell_contributions <- round((((chi_total$observed - chi_total$expected)^2) / chi_total$expected),2)
contributions_function <- function(x){
cell_contributions<- round((((x$observed - x$expected)^2) / x$expected),2)
cell_contributions
}
contributions_total <- contributions_function(chi_total)
contributions_total##
## Amazon Disney+ Hulu Netflix Other
## 18–25 10.89 0.14 3.83 2.70 3.05
## 26–40 2.72 1.07 0.03 0.43 0.58
## 41+ 24.50 1.97 4.53 5.30 6.30Percent Contributions:
percent_contributions <- round((cell_contributions / chi_total$statistic) * 100, 2)
percent_contributions_function<- function(x){
cell_contributions<-round((((x$observed - x$expected)^2) / x$expected),2)
percent_contributions<- round((cell_contributions / x$statistic) * 100, 2)
percent_contributions
}
total_percent <- percent_contributions_function(chi_total)
total_percent##
## Amazon Disney+ Hulu Netflix Other
## 18–25 16.00 0.21 5.63 3.97 4.48
## 26–40 4.00 1.57 0.04 0.63 0.85
## 41+ 36.01 2.90 6.66 7.79 9.26Heatmap of Percentage Contributions by Age Group and Streaming Platform:
pheatmap(percent_contributions,
display_numbers = TRUE,
cluster_rows = FALSE,
cluster_cols = FALSE,
main = "% Contribution to Chi-Square Statistic")
pheatmap_function <- function(x) {
contributions <- ((x$observed - x$expected)^2) / x$expected
percent_contributions <- round(((contributions / x$statistic) * 100),2)
return(percent_contributions)
}
pheat_total<- pheatmap_function(chi_total)
pheat_total##
## Amazon Disney+ Hulu Netflix Other
## 18–25 16.00 0.20 5.63 3.97 4.48
## 26–40 4.00 1.57 0.04 0.64 0.86
## 41+ 36.01 2.90 6.66 7.79 9.26Interpretation:
The cell that contributed the most to the overall Chi-Square statistic is the combination of older adults (41+) and Amazon, accounting for about 36% of the total χ² value. This means that viewers aged 41 and older chose Amazon far more often than expected compared to other age groups. In contrast, younger viewers (18–25) contributed a notable portion (16%) of the χ² value through lower-than-expected counts for Amazon, indicating that fewer young adults prefer Amazon than expected.
Step 6 – Effect Size (Cramer’s V)
cramerV(table_total)## Cramer V
## 0.3368Interpretation:
A Cramer’s V value of 0.34 indicates a moderate association between age category and streaming platform preference. This suggests that age has a noticeable influence on which platform people prefer. Specifically, older and younger viewers tend to show distinct viewing patterns.
Step 7 – Final Interpretation
The Chi-Square test revealed a significant relationship between age category and streaming platform preference, χ²(8, N = 300) = 68.04, p < .001. The largest contributions came from the 41+/Amazon and 18–25/Amazon combinations, with older adults choosing Amazon far more often than expected, while younger adults chose it far less. Cramer’s V = 0.34 indicates a moderate association, suggesting that age noticeably influences streaming choices. In real-world context, this means that streaming services could benefit from adjusting their marketing and content strategies to better target different age groups, as platform preferences shift with age.