** Netflix vs. Hulu: Analyzing Customer Retention Strategies - Netflix
Project Objective
What are the customer rentention strategies presented at Netflix? Step 1 & 2: Install and Load libraries
#install.packages("gclus")
#install.packages("ggpubr")
#install.packages("tidyverse")
#install.packages("readxl")
library(ggpubr)## Loading required package: ggplot2library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## âś” dplyr 1.1.4 âś” readr 2.1.5
## âś” forcats 1.0.0 âś” stringr 1.5.1
## âś” lubridate 1.9.3 âś” tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## âś– dplyr::filter() masks stats::filter()
## âś– dplyr::lag() masks stats::lag()
## â„ą Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(gclus)## Loading required package: clusterlibrary(readxl)###Step 2: Import Netflix data and clean data
rev_df <- read_excel("Netflix Excel (2).xlsx")
head(rev_df)## # A tibble: 6 Ă— 4
## Subs Pricing Revenue Rating
## <dbl> <dbl> <dbl> <dbl>
## 1 149. 13.0 4.52 0.75
## 2 152. 13.0 4.92 0.68
## 3 158. 13.0 5.25 0.93
## 4 167. 13.0 5.47 0.93
## 5 183. 14.0 5.77 0.86
## 6 193. 14.0 6.15 0.96###Step 3: Summarize the data
summary(rev_df)## Subs Pricing Revenue Rating
## Min. :148.9 Min. :12.99 Min. :4.520 Min. :0.5700
## 1st Qu.:190.4 1st Qu.:13.99 1st Qu.:6.055 1st Qu.:0.7425
## Median :211.4 Median :13.99 Median :7.410 Median :0.9000
## Mean :206.2 Mean :14.39 Mean :7.010 Mean :0.8325
## 3rd Qu.:225.0 3rd Qu.:15.49 3rd Qu.:7.940 3rd Qu.:0.9425
## Max. :260.3 Max. :15.49 Max. :8.830 Max. :0.9600Interpretation: On average, there are approximately 206.2 subscriptions, with a price ranging from $12.99 - $15.49, and revenue averaging around $7.01. The customer ratings tend to be relatively low for Netflix , with a mean rating of 0.83. This suggests that ratings do not vary much and are generally below 1. The data also mentions that while pricing and revenue have moderate ranges, the ratings are consistently closer to the lower end of the scale, with a maximum rating of 0.96.Step 4: Set up to create pairwise scatterplot matrix
pairs(~ Subs + Revenue + Pricing + Rating, data = rev_df)
Interpretation: Subscriptions and revenue are highly positively correlated (0.99), which means that as subscriptions increase, revenue also increases significantly. Pricing is moderately correlated with both subscriptions (0.90) and revenue (0.90), however, ratings show very weak or negative correlations with all other variables, indicating that customer ratings do not have a strong relationship with subscriptions, revenue, or pricing. Step 5: Analyze the correlation matrix for the dataset
corr <- cor(rev_df)
corr## Subs Pricing Revenue Rating
## Subs 1.000000000 0.8971548 0.98587540 -0.004053675
## Pricing 0.897154846 1.0000000 0.89904480 -0.353124961
## Revenue 0.985875397 0.8990448 1.00000000 -0.058104709
## Rating -0.004053675 -0.3531250 -0.05810471 1.000000000Interpretation: This data suggests a distribution with most values clustered between -2.24 and 2.33, indicating that the majority of observations are slightly negative or neutral, with a few extreme positive outliers. Step 6: Create the summary model for the data
model <- lm(Subs ~ Revenue + Pricing + Rating, data = rev_df)
summary(model)##
## Call:
## lm(formula = Subs ~ Revenue + Pricing + Rating, data = rev_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.550 -2.240 -1.397 2.326 8.173
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -68.188 38.772 -1.759 0.0977 .
## Revenue 18.426 2.421 7.610 1.05e-06 ***
## Pricing 8.387 3.328 2.520 0.0227 *
## Rating 29.444 10.201 2.886 0.0107 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.583 on 16 degrees of freedom
## Multiple R-squared: 0.982, Adjusted R-squared: 0.9786
## F-statistic: 290.2 on 3 and 16 DF, p-value: 3.722e-14Interpretation: The regression results suggest that Revenue, Pricing, and Rating are significant predictors of Netflix's customer-related outcomes, with Revenue showing the strongest positive relationship (p-value < 0.001). This implies that Netflix’s customer retention or engagement strategies should focus on maximizing revenue, optimizing pricing strategies, and improving customer satisfaction (ratings) to drive better customer outcomes.