Conducted a detailed analysis of Netflix content viewership data for 2023 to determine optimal content types. Examined total hours viewed, growth rates, and patterns including seasonal, monthly, and weekly viewership. Aims to enhance content strategies for Netflix to boost viewership.
- Total Records: Initiated with 24,812 records in dataset.
- Records After Cleaning: Post removal of duplicates, null values, and invalid entries (e.g., zero or negative hours viewed), retained 19,158 records for analysis.
Utilized Python with following libraries:
- Pandas: For data cleaning and analysis.
- Plotly: For generating bar charts and line plots to display results.
Assessed viewership hours for Shows and Movies:
- Shows: 74.63 billion hours
- Movies: 44.89 billion hours
Insight: Shows outperform Movies in viewership. Recommend prioritizing development of engaging Shows.
Evaluated viewership based on content language:
- English: 92.38 billion hours
- Korean: 13.19 billion hours
- Non-English: 8.02 billion hours
- Japanese: 5.00 billion hours
- Hindi: 0.85 billion hours
- Russian: 0.08 billion hours
Insight: English content dominates viewership, followed by Korean. Suggest increased investment in English and Korean content to maximize audience reach.
Investigated viewership growth from 2015 to 2023:
- English and Korean content exhibited highest growth rates (as seen in yearly trends).
Insight: Rising popularity of these languages indicates sustained production in English and Korean to leverage growth trends.
Analyzed viewership by release season (based on release date):
- Winter: 19.13 billion hours
- Fall: 17.71 billion hours
- Summer: 16.53 billion hours
- Spring: 16.18 billion hours
Insight: Winter yields highest viewership (based on release dates), potentially due to holidays. Propose scheduling major Show or Movie releases in Winter to optimize viewership.
Examined viewership per month (based on release date):
- December: 7.93 billion hours (Top 1)
- June: 6.22 billion hours (Top 2)
- May: 5.22 billion hours (Bottom 2)
- July: 4.98 billion hours (Bottom 1)
Insight: December records peak viewership (based on release dates), likely holiday-driven. Advise planning significant releases in December and June for enhanced viewership.
Investigated viewership by release day:
- Friday: 28.45 billion hours
- Thursday: 16.49 billion hours
- Wednesday: 12.34 billion hours
- Saturday: 4.20 billion hours
- Tuesday: 3.98 billion hours
- Monday: 2.80 billion hours
- Sunday: 1.32 billion hours
Insight: Friday releases achieve highest viewership, possibly due to weekend viewing habits. Recommend scheduling new content releases on Fridays to capture larger audiences.
Formulated assumptions during analysis:
Hours_Vieweddata reflects total views per title, lacking specific viewing timestamps. Thus, seasonal, monthly, and weekly analyses rely solely on release dates, not actual viewing dates.- Analysis provides foundational overview of content data study, yet lacks precision for seasonal or weekly patterns due to absence of view timing data.
With enhanced dataset including:
- Viewer region or country
- Genre (e.g., Action, Drama, Anime)
- Periodical views (e.g., daily/weekly per title)
- Additional variables (e.g., age group, device type) Future analysis could yield deeper insights. Plan to conduct Content Strategy Analysis Version-2 with such data for improved utility.
- Prioritize production of Shows due to higher viewership compared to Movies.
- Allocate resources to English and Korean content given dominant viewership and growth trends.
- Target Winter (particularly December) for major releases to capitalize on holiday viewership spikes (based on release dates).
- Schedule new content drops on Fridays to align with peak weekend viewing.
Intend to source enhanced dataset with details on region, genre, and daily views. Aim to perform more precise analysis in Content Strategy Analysis Version-2 subsequently.
Author: [Pavan Yellathakota]
Date: MAR 2025
You can reach out to me through the following channels:
- Email: pavanyellathakota@gmail.com
- LinkedIn: Pavan Yellathakota
For more projects and resources, check out:
- GitHub: Pavan Yellathakota
- Portfolio: pye.pages.dev