California State University of San Marcos
Forecasting Netflix’s Subscription-Based Business Model
Ashton Heredia, Kyle Scott, Jeremy Dalen, Moshun Wong
OM 424: Advanced Business Analytics
Professor Majid Karimi
April 11, 2025
Problem Description
Our project will analyze Netflix’s historical performance data from 2017 to 2024 to forecast
subscriber growth and revenue trends across all the regions ( US & Canada, Europe, Latin
America, Asia-Pacific) for the next eight quarters from 2024 Q1 to 2025 Q4. In the streaming
industry market they are highly competitive, like Disney+ , and Hulu they are pussuring Netflix’s
market share, so that making an accurate forecast is essential for the strategic planning. Our
dataset will include quarterly subscriber metrics, regional for average revenue per user trend, and
competitive benchmarks, this will help us to identify the pattern like example seasonal
fluctuations, regional growth variations, also pricing sensitivity.
Using the data, we can:
●​ Develop an 8-quarter forecast using the time series analysis and regression modeling,
incorporating seasonal adjustments and the average revenue per user trend.
●​ Analyze the pattern by examining net subscriber changes and pricing impacts to identify
retention opportunities.
●​ Compare against the competitors like Disney+ and Hulu to contextualize Netflix’s
performance and industry positioning.
●​ Identify the growth drivers, like content release timing and regional pricing strategies, to
help optimize the future performance.
The final report and data set will include the regional growth projections, churn risk assessments,
average revenue per user optimization scenarios, and competitive insights. This extended
forecast helps to ensure Netflix can make data driven decisions while accounting the full
seasonal cycles and industry trends.
Any update to the “Second Draft Proposal”
(If you decide to make any changes to your Second Draft sections, you must include them in
your progress report.)
●​ Changes in Problem Description
Our current problem description significantly enhances the previous descriptive analytics
framework to provide netflix with deeper, and more actionable insights. While the previous
approach will focus on a 6 month forecast using the basic subscriber metrics, the current version
we expand the scope to 8 quarter projection with advanced modeling techniques and competitive
benchmarking.
●​ Extended Forecasting
○​ Previous: 6 month outlook for short term planning
○​ Current: 8 quarter forecast to capture the seasonal trends and long term
growth patterns.
●​ Regional Analysis
○​ Previous: High level global subscriber trends.
○​ Current: Breakdowns to UCAN, EMEA, LATAM, and APAC, including
the average revenue per user, and pricing sensitivity.
●​ Enhanced Competitive Benchmarking
○​ Pervious: General mentions of Disney+ and Hulu
○​ Current: Quantitative comparisons on subscriber growth, pricing, and
change in market share.
●​ Techniques
○​ Previous: Basic trend analysis
○​ Current: Time series decomposition, and regression modeling
●​ Descriptive Analytics
For the descriptive analytics of Netflix’s subscriber trends, we will be using the time series
visualization techniques to identify the historical patterns including the seasonal variations in
subscriber growth across different regions. We will be using the quarterly data from the excel
sheet name “Aux Data-Regression Analysis”, we will create a interactive line graphs in excel
and our final report to visualize the key metrics, this will be including:
●​ Quarterly subscriber growth trends for UCAN, EMEA, LATAM, and APAC
regions.
●​ Revenue per user fluctuations over time
●​ Seasonal Patterns
Tools & Execution:
●​ Excel Line Charts:
○​ Data range from “Aux Data-Regression Analysis” (including Date and
Subscribers)
○​ Features:
■​ Dynamic date axis formatting for clear quarterly segmentation
■​ Different color code trenlines for different region
○​ The purpose is to highlight growth trajectories and anomalies.
●​ Predictive Analytics
For the Predictive Analytics of Netflix’s subscriber trends, we will be using linear regression
analysis to forecast future subscriber numbers for Netflix for the next 8 quarters. Using quarterly
data from excel sheet, “Aux Data-Regression Analysis,” we are able to create a regression model
to help us create a forecast. We can also find historical subscriber trends by looking at quarterly
data in the excel spreadsheet such as “Paid Net Streaming Membership Additions” and the
“Average Paid Streaming Memberships During Period,” to create future forecasts through
moving averages.
Tools & Execution
●​ Excel Regression Analysis
○​ Data range from “Aux Data-Regression Analysis” (including Subscribers and
Dummy Variables)
○​ Features:
■​ Displays ANOVA and Regression Statistics to help find forecast
○​ The purpose is to create a forecast for the next 8 quarters using the data from the
analysis
●​ Prescriptive Analytics
For the prescriptive analytics of Netflix’s subscriber growth and revenue trends, we will be using
either What-If Analysis or Simple Maximization Method to model different outcomes based on
changes in pricing, content quality, and marketing due to Netflix evolving as a company and
continuously changing pricing. Using either What-If Analysis or Simple Maximization Method,
we can explore different scenarios to determine how KPIs change as Netflix changes pricing and
content quality. To display these scenarios with KPIs, we will use a worksheet named "Netflix
What-If Analysis".
Tools & Execution:
●​ What-If Analysis Table:
○​ The data will be sourced from NFLX worksheets with key metrics such as
price, subscribers, and revenue.
●​ Line Chart (Optional):
○​ Use axes to show how both subscriber and revenue respond to pricing.
○​ The purpose is to explore different scenarios to show how KPIs change
with pricing or content quality.
Plan Progress
(What has been completed?)
We are currently finishing Milestone 2 (Project Proposal Second Draft) and in the middle of
Milestone 3 (Project Progress Report) of our Forecasting Netflix's Subscription-Based Business
Model project. We haven't completed as much as we wanted based on the completion plan
deadlines since we had a slow start and unclear project objectives at the beginning, but we have
completed significant tasks and milestones for our project. For example Milestones 1 (Project
Proposal First Draft) and 2 (Project Proposal Second Draft) as a whole are completed, and a clear
and improved project objective has been developed, a dataset has been found and adjusted to
meet the project's needs, each business analytics has been improved in our project, and an
effective completion plan has been developed. In our Milestone 3 (Project Progress Report), we
have completed the Excel workbook setup for our dataset with raw data, performed some
regression analysis for quarterly forecasting, and created an automated and vba implementation
plan.
(What needs to be done?)
As part of our project and completion plan, we need to improve communication among each
other, work efficiently with time and group work, implement VBA and automate each business
analytics, develop a summary page with findings and results for key metrics and indicators, and
put it together as a final draft.
(Challenges?)
At the moment, we are facing challenges in our project related to prescriptive analytics, the
implementation of vba and automated in excel for our forecasting project, and the final phase of
the project with write-up and group communication. In the case of the project, we have a clear
idea of what needs to be automated in Excel using vba, but figuring out how to do so has been
challenging. There is uncertainty with the prescriptive analytics of the project in terms of
techniques, approaches, and automation processes. There has been little communication within
the group, mainly because we are working on it individually and on different time schedules.