Forecasting the number of subscriptions Netflix will achieve in a time period is a vital business practice that enables them to plan, strategize, and make data-driven decisions. It enhances operational efficiency, financial planning, and content strategy, ultimately contributing to their success and growth in the highly competitive streaming industry. If you want to learn how to forecast the number of subscriptions for a streaming service like Netflix, this article is for you. In this article, I’ll take you through the task of Netflix Subscriptions Forecasting using **Python**.

## Netflix Subscriptions Forecasting: Process We Can Follow

Using techniques like **time series forecasting**, Netflix can estimate the expected number of new subscribers in a given time period and better understand the growth potential of their business. Below is the process we can follow to forecast subscription counts for Netflix:

- Gather historical Netflix subscriptions growth data
- Preprocess and clean the data
- Explore and analyze time series patterns
- Choose a time series forecasting model (e.g., ARIMA, LSTM)
- Train the model using the training data
- Forecast future Netflix subscription counts

So the process for forecasting subscriptions for Netflix starts with collecting a dataset based on the historical growth of Netflix Subscribers. I found an ideal dataset for this task. You can download the dataset from **here**.

In the section below, I’ll take you through the task of Netflix Subscriptions Forecasting using Time Series Forecasting and the Python programming language.

## Netflix Subscriptions Forecasting using Python

Let’s start this task by importing the necessary Python libraries and the **dataset**:

# Importing Necessay Python libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import plotly.graph_objs as go import plotly.express as px import plotly.io as pio pio.templates.default = "plotly_white" from statsmodels.tsa.arima.model import ARIMA from statsmodels.graphics.tsaplots import plot_acf, plot_pacf # reading the data data = pd.read_csv('Netflix Subscriptions.csv') print(data.head())

Time Period Subscribers 0 01/04/2013 34240000 1 01/07/2013 35640000 2 01/10/2013 38010000 3 01/01/2014 41430000 4 01/04/2014 46130000

The dataset contains subscription counts of Netflix at the start of each quarter from 2013 to 2023. Before moving forward, let’s convert the Time Period column into a datetime format:

data['Time Period'] = pd.to_datetime(data['Time Period'], format='%d/%m/%Y') print(data.head())

Time Period Subscribers 0 2013-04-01 34240000 1 2013-07-01 35640000 2 2013-10-01 38010000 3 2014-01-01 41430000 4 2014-04-01 46130000

Now let’s have a look at the quarterly subscription growth of Netflix:

fig = go.Figure() fig.add_trace(go.Scatter(x=data['Time Period'], y=data['Subscribers'], mode='lines', name='Subscribers')) fig.update_layout(title='Netflix Quarterly Subscriptions Growth', xaxis_title='Date', yaxis_title='Netflix Subscriptions') fig.show()

In the above graph, we can see that the growth of Netflix subscribers is not seasonal. So we can use a forecasting technique like ARIMA in this dataset.

Now let’s have a look at the quarterly growth rate of subscribers at Netflix:

# Calculate the quarterly growth rate data['Quarterly Growth Rate'] = data['Subscribers'].pct_change() * 100 # Create a new column for bar color (green for positive growth, red for negative growth) data['Bar Color'] = data['Quarterly Growth Rate'].apply(lambda x: 'green' if x > 0 else 'red') # Plot the quarterly growth rate using bar graphs fig = go.Figure() fig.add_trace(go.Bar( x=data['Time Period'], y=data['Quarterly Growth Rate'], marker_color=data['Bar Color'], name='Quarterly Growth Rate' )) fig.update_layout(title='Netflix Quarterly Subscriptions Growth Rate', xaxis_title='Time Period', yaxis_title='Quarterly Growth Rate (%)') fig.show()

Now let’s have a look at the yearly growth rate:

# Calculate the yearly growth rate data['Year'] = data['Time Period'].dt.year yearly_growth = data.groupby('Year')['Subscribers'].pct_change().fillna(0) * 100 # Create a new column for bar color (green for positive growth, red for negative growth) data['Bar Color'] = yearly_growth.apply(lambda x: 'green' if x > 0 else 'red') # Plot the yearly subscriber growth rate using bar graphs fig = go.Figure() fig.add_trace(go.Bar( x=data['Year'], y=yearly_growth, marker_color=data['Bar Color'], name='Yearly Growth Rate' )) fig.update_layout(title='Netflix Yearly Subscriber Growth Rate', xaxis_title='Year', yaxis_title='Yearly Growth Rate (%)') fig.show()

## Using ARIMA for Forecasting Netflix Quarterly Subscriptions

Now let’s get started with Time Series Forecasting using ARIMA to forecast the number of subscriptions of Netflix using Python. I will start by converting the data into a time series format:

time_series = data.set_index('Time Period')['Subscribers']

Here we are converting the original DataFrame into a time series format, where the Time Period column becomes the index, and the Subscribers column becomes the data.

Now let’s find the value of p and q by plotting the ACF and PACF of differenced time series:

differenced_series = time_series.diff().dropna() # Plot ACF and PACF of differenced time series fig, axes = plt.subplots(1, 2, figsize=(12, 4)) plot_acf(differenced_series, ax=axes[0]) plot_pacf(differenced_series, ax=axes[1]) plt.show()

Here we first calculated the differenced time series from the original time_series, removed any NaN values resulting from the differencing, and then plotted the ACF and PACF to provide insights into the potential order of the AR and MA components in the time series. These plots are useful for determining the appropriate parameters when using the ARIMA model for time series forecasting.

Based on the plots, we find that p=1 and q=1. The ACF plot cuts off at lag 1, indicating q=1, and the PACF plot also cuts off at lag 1, indicating p=1. As there is a linear trend in the subscription growth rate, we can set the value of d as 1 to remove the linear trend, making the time series stationary.

Now here’s how to use the ARIMA model on our data:

p, d, q = 1, 1, 1 model = ARIMA(time_series, order=(p, d, q)) results = model.fit() print(results.summary())

SARIMAX Results ============================================================================== Dep. Variable: Subscribers No. Observations: 42 Model: ARIMA(1, 1, 1) Log Likelihood -672.993 Date: Sat, 05 Aug 2023 AIC 1351.986 Time: 07:03:48 BIC 1357.127 Sample: 04-01-2013 HQIC 1353.858 - 07-01-2023 Covariance Type: opg ============================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------ ar.L1 0.9997 0.012 80.765 0.000 0.975 1.024 ma.L1 -0.9908 0.221 -4.476 0.000 -1.425 -0.557 sigma2 1.187e+13 1.57e-14 7.57e+26 0.000 1.19e+13 1.19e+13 =================================================================================== Ljung-Box (L1) (Q): 3.96 Jarque-Bera (JB): 4.62 Prob(Q): 0.05 Prob(JB): 0.10 Heteroskedasticity (H): 7.27 Skew: 0.54 Prob(H) (two-sided): 0.00 Kurtosis: 4.23 ===================================================================================

Now here’s how to make predictions using the trained model to forecast the number of subscribers for the next five quarters:

future_steps = 5 predictions = results.predict(len(time_series), len(time_series) + future_steps - 1) predictions = predictions.astype(int)

2023-10-01 243321458 2024-01-01 248251648 2024-04-01 253180570 2024-07-01 258108224 2024-10-01 263034611 Freq: QS-OCT, Name: predicted_mean, dtype: int64

Now let’s visualize the results of Netflix Subscriptions Forecasting for the next five quarters:

# Create a DataFrame with the original data and predictions forecast = pd.DataFrame({'Original': time_series, 'Predictions': predictions}) # Plot the original data and predictions fig = go.Figure() fig.add_trace(go.Scatter(x=forecast.index, y=forecast['Predictions'], mode='lines', name='Predictions')) fig.add_trace(go.Scatter(x=forecast.index, y=forecast['Original'], mode='lines', name='Original Data')) fig.update_layout(title='Netflix Quarterly Subscription Predictions', xaxis_title='Time Period', yaxis_title='Subscribers', legend=dict(x=0.1, y=0.9), showlegend=True) fig.show()

So this is how you can forecast subscription counts for a given time period using Time Series Forecasting and Python.

### Summary

Using techniques like time series forecasting, Netflix can estimate the expected number of new subscribers in a given time period and better understand the growth potential of their business. It enhances operational efficiency, financial planning, and content strategy, ultimately contributing to their success and growth in the highly competitive streaming industry. I hope you liked this article on Netflix Subscriptions Forecasting using Python. Feel free to ask valuable questions in the comments section below.