🎬 OTT Subscription Forecasting and Market Insights

By Heena Shaikh | Data Science & Business Analytics

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💡 Project Overview

The OTT industry has transformed how audiences consume entertainment — but behind every streaming platform lies a complex web of subscription patterns, pricing strategies, and audience retention challenges.

To explore these dynamics, I built a Netflix-themed forecasting dashboard that predicts subscription growth for OTT platforms using ARIMA time-series modeling. The goal: uncover trends, seasonality, and growth patterns that can help businesses plan their revenue, pricing, and marketing strategies more effectively.

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🧩 Objectives

• Forecast subscriber growth for upcoming quarters using historical data (2014–2024).
• Identify trends, seasonality, and potential growth plateaus.
• Create an interactive, business-friendly dashboard using Streamlit.
• Provide strategic insights that can support pricing and retention strategies.

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🔬 Data Source & Preprocessing

I used a fictional Netflix subscription dataset, representing quarterly subscriber counts from 2014–2024.

Key steps:

• Cleaned and structured the dataset into time-series format.
• Handled missing values and ensured quarterly frequency consistency.
• Explored patterns using line plots and rolling averages to detect trends and seasonality.

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🧠 Forecasting Model: ARIMA

ARIMA (Auto-Regressive Integrated Moving Average) is one of the most trusted statistical models for time series forecasting.

Why I chose ARIMA:

1. Works well with consistent quarterly data.
2. Easy interpretability for business decision-makers.
3. Strong performance in short-term forecasting scenarios.
4. Using the ARIMA model, I forecasted the next 8 quarters (2025–2027) to estimate future subscription growth and visualize confidence intervals.

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🎨 Dashboard UI Development

I wanted this project to look as engaging as it is analytical — so I designed Streamlit dashboard in Netflix’s iconic red and black theme using Streamlit.

Dashboard Features:

Upload or use sample data (2014–2024).

Visualize historical trends and ARIMA forecasts.

Display growth predictions, lower/upper bounds, and quarterly patterns.

Export results and insights for presentation or business reports.

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📊 Key Insights

OTT subscriptions showed consistent growth with visible seasonal spikes every Q4 (year-end).

Forecasts suggest a 6–9% quarterly growth in upcoming years.

Identified potential saturation points post-2027 — useful for pricing and marketing planning.

Generated interactive visuals for executive-level reporting.

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🛠️ Tools & Technologies

Python (Pandas, Matplotlib, Statsmodels)

Streamlit for web app development

ARIMA for time series forecasting

Power BI for additional visual analysis

Excel for initial cleaning and trend validation

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💼 Business Relevance

This project is directly aligned with Subscription Strategy & Research roles like the one at Zee Entertainment, where understanding viewership patterns, pricing structures, and audience behavior is crucial.

By forecasting subscription data, companies can:

• Adjust pricing based on predicted growth.
• Anticipate churn and retention trends.
• Optimize marketing budgets during high-growth quarters.

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🚀 Key Learnings

Data forecasting can guide strategic business decisions beyond numbers.

Building dashboards that combine data science and design creates real impact.

Even fictional datasets can model real-world business logic effectively when structured correctly.

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🔗 Project Links

💻  GitHub Repository: OTT Subscription Forecasting and Market Insights

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❤️ Final Thoughts

Building this project was not just about code — it was about connecting data with decision-making.

As someone passionate about analytics and media strategy, this project strengthened my understanding of how data forecasting fuels business growth in the entertainment industry.