Netflix Press Releases

Public Relations Team Analyzes Netflix Films and TV

Author

Sojung Chu

Published

October 5, 2025

Netflix logo

1. Executive Summary

This report analyzes Netflix’s data collection of its film and TV, with the goal of translating viewing patterns into clear, compelling stories that celebrate success, spotlight momentum, and frame opportunities for growth. The Public Relations (PR) team proposes 3 press releases:

  • STRANGER THINGS Season 5 Sets Sights on No. 1 as Netflix’s Most Viewed English Language TV Season, While Season 4 Holds No. 2
  • Hindi Titles Enter Netflix Global Top 10, Reflecting the Power of India’s Population and Viewership
  • Momentum in Season-Over-Season Growth Fuels Netflix’s Next Global Hits

2. Background

As one of the leading global streaming platforms, Netflix has invested heavily in the production of original films and television series that appeal to diverse international audiences (Netflix, 2024). This strategy aligns with the company’s emphasis on creating what its executives have described as “gourmet cheeseburgers,” which refers to high-quality productions that balance creativity and accessibility for mass-market appeal (Hastings, 2021). To support strategic communication efforts, Netflix’s Public Relations team leverages data-driven insights to highlight titles that exemplify commercial success and critical recognition. This project draws upon Netflix’s publicly available Top 10 data to identify trends in popular programming, offering a foundation for press materials that celebrate the platform’s most successful and widely viewed content (Netflix, 2024).

3. Methodology

The following methods were used to obtain and prepare the data for analysis.

3.1 Data Aquisition

The PR team acquired data from Netflix’s TuDum Top 10, the official companion site that publishes data on Netflix’s most popular shows and movies. The selected data sets include the Global Top 10 and Country-wide Top 10.

Code 
if (!dir.exists(file.path("data", "mp01"))) {
  dir.create(file.path("data", "mp01"), showWarnings = FALSE, recursive = TRUE)
}

GLOBAL_TOP_10_FILENAME <- file.path("data", "mp01", "global_top10_alltime.csv")

if (!file.exists(GLOBAL_TOP_10_FILENAME)) {
  download.file("https://www.netflix.com/tudum/top10/data/all-weeks-global.tsv",
    destfile = GLOBAL_TOP_10_FILENAME
  )
}

COUNTRY_TOP_10_FILENAME <- file.path("data", "mp01", "country_top10_alltime.csv")

if (!file.exists(COUNTRY_TOP_10_FILENAME)) {
  download.file("https://www.netflix.com/tudum/top10/data/all-weeks-countries.tsv",
    destfile = COUNTRY_TOP_10_FILENAME
  )
}

3.2 Data Cleaning

The structure was examined for the global top 10 data and revealed that when an entry for the season_title is missing, it is being read as a string instead of a proper NA value.

Code 
if (!require("tidyverse")) install.packages("tidyverse")
library(readr)
library(dplyr)
library(scales)

GLOBAL_TOP_10 <- read_tsv("data/mp01/all-weeks-global.tsv")

str(GLOBAL_TOP_10)

To fix this, the data was transformed into proper NA values.

Code 
GLOBAL_TOP_10 <- GLOBAL_TOP_10 |>
  mutate(season_title = if_else(season_title == "N/A", NA_character_, season_title))

3.3 Data Import

For the second, similarly structured database, the transformation was applied during import. As a result, two data objects GLOBAL_TOP_10 and COUNTRY_TOP_10 were obtained with season_title of both data sets having proper NA values.

Code 
COUNTRY_TOP_10 <- read_tsv("data/mp01/all-weeks-countries.tsv", na = c("", "N/A"))

4. Campaign Overview

Key findings from the exploratory data analysis (EDA) conducted in R using the dplyr package across the two datasets:

Code 
num_countries <- COUNTRY_TOP_10 |>
  summarize(num_countries = n_distinct(country_name))
  • Netflix appears in 94 countries in the dataset.
Code 
top_non_english_global <- GLOBAL_TOP_10 |>
  filter(category != "Films (English)") |>
  group_by(show_title) |>
  summarize(total_weeks = max(cumulative_weeks_in_top_10, na.rm = TRUE)) |>
  slice_max(total_weeks, n = 1)
  • Squid Game has spent the most cumulative weeks in the global top 10, with a total of 32 weeks.
Code 
longest_film_global <- GLOBAL_TOP_10 |>
  filter(!is.na(runtime)) |>
  distinct(show_title, runtime) |>
  slice_max(runtime, n = 1) |>
  mutate(runtime_minutes = round(60 * runtime))
  • Until You Burn is the longest film (English or non-English) to have ever appeared in the Netflix global top 10 and is 2855 minutes long.
Code 
# build one winner per category, by total global hours
top_global_by_category <- GLOBAL_TOP_10 |>
  group_by(category, show_title) |>
  summarize(total_hours = sum(weekly_hours_viewed, na.rm = TRUE)) |>
  slice_max(total_hours, n = 1) |>
  ungroup()

# present as an interactive table (pretty titles + rounded hours)
winners_dt <- top_global_by_category |>
  format_titles() |>
  arrange(Category) |>
  datatable(options = list(searching = FALSE, info = FALSE)) |>
  formatRound("Total Hours", 0)

Table 1: The Highest Total Hours of Global Viewership in Each Netflix Category

Code 
longest_tv_run_country <- COUNTRY_TOP_10 |>
  filter(category == "TV") |>
  group_by(country_name, show_title) |>
  summarize(longest_run = max(cumulative_weeks_in_top_10, na.rm = TRUE)) |>
  ungroup() |>
  slice_max(longest_run, n = 1)
  • Money Heist had the longest run in a country’s top 10, lasting 127 weeks in Pakistan.
Code 
country_under_200 <- COUNTRY_TOP_10 |>
  group_by(country_name) |>
  summarize(weeks_available = n_distinct(week), last_week = max(week, na.rm = TRUE)) |>
  ungroup() |>
  filter(weeks_available < 200)
  • Netflix provides over 200 weeks of service history for all countries within the data set except Russia. Netflix ceased operations there as of 02-27-2022, which is the last week recorded.
Code 
total_squid_game_global <- GLOBAL_TOP_10 |>
  filter(show_title == "Squid Game") |>
  group_by(show_title) |>
  summarize(total_hours = sum(weekly_hours_viewed, na.rm = TRUE)) |>
  ungroup()
  • The TV show Squid Game has 5,048,300,000 total number of hours watched across all three seasons.
Code 
library(lubridate)

# Total 2021 hours for Red Notice, then convert hours -> views using runtime (1h 58m = 118/60 h)
red_notice_2021 <- GLOBAL_TOP_10 |>
  filter(show_title == "Red Notice", year(week) == 2021) |>
  summarize(total_hours_2021 = sum(weekly_hours_viewed, na.rm = TRUE)) |>
  ungroup() |>
  mutate(
    runtime_hours = 118 / 60, # 1 hour 58 minutes
    approx_views_2021 = total_hours_2021 / runtime_hours
  ) # hours ÷ hours-per-view
  • Red Notice received approximately 201,732,203 views in 2021.
Code 
# US films that debuted below #1 but later reached #1 — table in your preferred style
us_films_debut_then_num1 <- COUNTRY_TOP_10 |>
  filter(country_name == "United States", category == "Films") |>
  arrange(show_title, week) |>
  group_by(show_title) |>
  summarize(
    debut_week      = min(week, na.rm = TRUE),
    debut_rank      = weekly_rank[which.min(week)],
    first_week_at_1 = if (any(weekly_rank == 1)) min(week[weekly_rank == 1]) else as.Date(NA_character_)
  ) |>
  ungroup() |>
  filter(debut_rank > 1, !is.na(first_week_at_1)) |>
  select(show_title, debut_week, first_week_at_1)

# most recent film to hit #1
most_recent_us_late_number1 <- us_films_debut_then_num1 |>
  slice_max(first_week_at_1, n = 1)

# present as table
num1_dt <- us_films_debut_then_num1 |>
  format_titles() |>
  arrange(desc(`First Week At 1`)) |>
  datatable(options = list(searching = FALSE, info = FALSE))
  • In the US, 44 films debuted below #1 but later reached #1. The most recent was KPop Demon Hunters, which hit #1 on 08-10-2025.

Table 2: Films Reached Number 1 on Netflix in the US Without Debuting There

Code 
top_tv_debut <- COUNTRY_TOP_10 |>
  filter(category == "TV") |>
  group_by(show_title, season_title) |>
  summarize(
    debut_week = min(week, na.rm = TRUE)
  ) |>
  ungroup() |>
  # Join back
  inner_join(COUNTRY_TOP_10, by = c("show_title", "season_title", "debut_week" = "week")) |>
  group_by(show_title, season_title) |>
  summarize(
    debut_countries = n_distinct(country_name)
  ) |>
  ungroup() |>
  slice_max(debut_countries, n = 1, with_ties = FALSE)
  • The TV show/season Emily in Paris (Emily in Paris: Season 2) hit the Top 10 in the most countries during its debut week, appearing in 94 countries.

5. Proposed Press Releases

The following are suggested press releases to highlight Netflix’s recent successes.

5.1 STRANGER THINGS Season 5 Sets Sights on No. 1 as Netflix’s Most Viewed English Language TV Season, While Season 4 Holds No. 2

Code 
# Top 5 most-viewed English-language TV seasons
top5_eng_tv_seasons <- GLOBAL_TOP_10 |>
  filter(category == "TV (English)") |>
  group_by(show_title, season_title) |>
  summarize(total_hours = sum(weekly_hours_viewed, na.rm = TRUE)) |>
  ungroup() |>
  slice_max(total_hours, n = 5) |>
  arrange(desc(total_hours))

eng_top5_dt <- top5_eng_tv_seasons |>
  format_titles() |>
  arrange(desc(`Total Hours`)) |>
  datatable(options = list(searching = FALSE, info = FALSE)) |>
  formatRound("Total Hours", 0)

# Total viewership and length of popularity
st_global <- GLOBAL_TOP_10 |>
  filter(category == "TV (English)", show_title == "Stranger Things") |>
  summarize(total_hours = sum(weekly_hours_viewed, na.rm = TRUE), weeks_global = n_distinct(week)) |>
  ungroup()

# How many countries had the show in its top 10
st_countries <- COUNTRY_TOP_10 |>
  filter(category == "TV", show_title == "Stranger Things") |>
  summarize(num_countries = n_distinct(country_name)) |>
  ungroup()

Table 3: Most Viewed Netflix English Language TV Season

Netflix’s global phenomenon Stranger Things returns in late 2025 for its fifth and final season, aiming to become the most viewed English language TV season on Netflix and going out strong. Stranger Things Season 4 currently sits at #2, while Wednesday: Season 1 holds the top spot. With a legacy few can match, Stranger Things across all four seasons has 2,967,980,000 total hours viewed globally. It also sustained popularity across 20 weeks in the global Top 10, and resonated with audiences in 93 countries. With the finale approaching, the series aims to close its story in a captivating way that tops the charts.

5.2 Hindi Titles Enter Netflix Global Top 10, Reflecting the Power of India’s Population and Viewership

Code 
library(ggplot2)
library(scales)
# --- Assumption: average Hindi* views per Indian subscriber per week ---
views_per_sub_week <- 5

# --- Ensure Date columns ---
GLOBAL_TOP_10 <- GLOBAL_TOP_10 |> mutate(week = as.Date(week))
COUNTRY_TOP_10 <- COUNTRY_TOP_10 |> mutate(week = as.Date(week))

# --- India weight per show = share of its country-weeks that are India ---
india_weights <- COUNTRY_TOP_10 |>
  group_by(show_title) |>
  summarize(
    india_rows = sum(country_name == "India"),
    total_rows = n()
  ) |>
  ungroup() |>
  mutate(w_india = if_else(total_rows > 0, india_rows / total_rows, 0))

# --- Monthly India-proxy (weighted views), start at 2024 ---
india_proxy_monthly <- GLOBAL_TOP_10 |>
  inner_join(india_weights, by = "show_title") |>
  mutate(month = floor_date(week, "month")) |>
  filter(month >= as.Date("2024-01-01")) |>
  group_by(month) |>
  summarize(
    total_views_weighted = sum(weekly_views * w_india, na.rm = TRUE),
    weeks_in_month       = n_distinct(week)
  ) |>
  ungroup() |>
  arrange(month) |>
  mutate(est_avg_subs = total_views_weighted / (views_per_sub_week * weeks_in_month))

# --- Keep ONLY Aug 2024 & Aug 2025 and compute YoY growth ---
india_aug <- india_proxy_monthly |>
  filter(month %in% as.Date(c("2024-08-01", "2025-08-01"))) |>
  mutate(Month = format(month, "%b %Y")) |>
  arrange(month) |>
  mutate(Month = factor(Month, levels = c("Aug 2024", "Aug 2025")))

aug24 <- india_aug$est_avg_subs[india_aug$Month == "Aug 2024"]
aug25 <- india_aug$est_avg_subs[india_aug$Month == "Aug 2025"]

rate_change_pct <- if (length(aug24) == 1 && length(aug25) == 1 && !is.na(aug24) && !is.na(aug25) && aug24 > 0) {
  (aug25 / aug24 - 1) * 100
} else {
  NA_real_
}

# --- Plot ONLY Aug 2024 vs Aug 2025, with YoY % on the 2025 bar ---
ggplot(india_aug, aes(x = Month, y = est_avg_subs)) +
  geom_col(width = 0.6) +
  geom_text(aes(label = comma(round(est_avg_subs))), vjust = -0.25, size = 3.2) +
  geom_text(
    data = subset(india_aug, Month == "Aug 2025" & !is.na(rate_change_pct)),
    aes(label = paste0(sprintf("%+.1f", rate_change_pct), "% YoY")),
    vjust = -2.0, size = 3.3, fontface = "bold"
  ) +
  scale_y_continuous(labels = comma, expand = expansion(mult = c(0, 0.15))) +
  labs(
    title = "Estimated India Subscriber Base: Year-over-Year",
    x = NULL,
    y = "Estimated average subscribers (per month)"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(size = 10),
    panel.grid.minor.x = element_blank()
  )

Code 
format_titles <- function(df) {
  colnames(df) <- colnames(df) |>
    str_replace_all("_", " ") |>
    str_to_title()
  df
}

# Ensure Date columns
GLOBAL_TOP_10 <- GLOBAL_TOP_10 |> mutate(week = as.Date(week))
COUNTRY_TOP_10 <- COUNTRY_TOP_10 |> mutate(week = as.Date(week))

# Titles disproportionately present in India (soft proxy for Hindi)
# Tweak thresholds if needed
min_india_weeks <- 2
min_share_india <- 0.25

india_dominant_titles <- COUNTRY_TOP_10 |>
  group_by(show_title) |>
  summarize(
    india_rows  = sum(country_name == "India"),
    total_rows  = n(),
    india_weeks = n_distinct(week[country_name == "India"])
  ) |>
  ungroup() |>
  mutate(share_india = if_else(total_rows > 0, india_rows / total_rows, NA_real_)) |>
  filter(
    !is.na(share_india),
    india_weeks >= min_india_weeks,
    share_india >= min_share_india
  ) |>
  distinct(show_title)

# Global debut (first week seen in GLOBAL_TOP_10) for those India-dominant titles
hindi_global_debuts <- GLOBAL_TOP_10 |>
  inner_join(india_dominant_titles, by = "show_title") |>
  group_by(show_title, season_title) |>
  summarize(global_debut = min(week, na.rm = TRUE)) |>
  ungroup() |>
  filter(global_debut >= as.Date("2024-01-01")) |>
  arrange(desc(global_debut)) |>
  slice_head(n = 10)

# Optional: show as an interactive table (same styling you use elsewhere)
hindi_debuts_dt <- hindi_global_debuts |>
  format_titles() |>
  arrange(desc(`Global Debut`)) |>
  datatable(options = list(searching = FALSE, info = FALSE))

As Hindi originals continue to break through globally, Netflix is seeing clear momentum from India’s massive, fast-growing audience. Using a Hindi-viewing proxy, our August snapshot shows an estimated 429,620 Indian subscribers, 18.1% year-over-year growth from 363,627 in August 2024. Recent Hindi titles have entered the Global Top 10, underlining both the scale of India’s streaming demand and its growing influence on worldwide viewing. Note: Our approach treats India-dominant titles (by country concentration) as a practical proxy for “Hindi.” It’s an approximation, but it consistently surfaces the titles most likely to be Hindi-led.

5.3 Momentum in Season-Over-Season Growth Fuels Netflix’s Next Global Hits

Code 
# Biggest season-over-season (SoS) jumps in global TV viewership — tidyverse style

sos_season_totals <- GLOBAL_TOP_10 |>
  filter(str_starts(category, "TV"), !is.na(season_title)) |>
  mutate(
    season_number  = parse_number(season_title),
    language_group = if_else(str_detect(category, "\\(English\\)"), "English", "Non-English")
  ) |>
  group_by(show_title, season_title, season_number, language_group) |>
  summarize(total_hours = sum(weekly_hours_viewed, na.rm = TRUE)) |>
  ungroup() |>
  group_by(show_title) |>
  arrange(season_number, .by_group = TRUE) |>
  mutate(
    prev_total_hours = lag(total_hours),
    growth_pct       = 100 * (total_hours / prev_total_hours - 1)
  ) |>
  ungroup() |>
  filter(!is.na(prev_total_hours), prev_total_hours > 0, is.finite(growth_pct)) |>
  arrange(desc(growth_pct))

# Top 5 SoS growth seasons overall
sos_top5_overall <- sos_season_totals |>
  slice_max(growth_pct, n = 5, with_ties = FALSE)

# Convenience objects for inline prose
top1 <- sos_top5_overall |> slice_head(n = 1)

noneng_count <- sos_top5_overall |>
  summarize(n = sum(language_group == "Non-English")) |>
  pull(n)

avg_growth_top5 <- mean(sos_top5_overall$growth_pct)

top5_list <- sos_top5_overall |>
  transmute(label = paste0(show_title, " S", season_number)) |>
  pull(label) |>
  paste(collapse = ", ")

Fueled by record season-over-season gains across serialized TV, Netflix today celebrates a slate of franchises scaling to new heights and announces potential expansion commissioning in fast-growing international markets. The biggest leap came from Black Mirror (Season 6), which surged 2676.3% versus its prior season (from 5,900,000 to 163,800,000 total hours). Notably, 1 of the Top 5 growth breakouts are Non-English and evidence of accelerating demand beyond English-language markets, while the Top 5 averaged 1629.3% SoS growth overall (standouts include Black Mirror S6, Money Heist S5, Ozark S4, Good Girls S4, Love Is Blind S2). Building on this momentum, Netflix will deepen investment in high-growth regions (content development, local marketing, and talent partnerships) to extend multi-season franchises and capture rising engagement where audiences are expanding fastest.

6. Conclusion

In conclusion, the PR team report emphasizes Netflix’s success with data-based metrics. There are three suggested press releases from the findings.