IDARE Enterprise AI predictive analytics platform background

Use Case

Predicting Monthly Revenue for Netflix User Base

Predicting the monthly revenue from the Netflix user base to maximize income generated from various user groups.

1Overview & Strategic Importance

Predicting Monthly Revenue for Netflix User Base

Regression Solution Media & Revenue

Problem Statement

Understanding user subscription behavior is essential for optimizing revenue strategies and reducing churn. Predicting monthly revenue is challenging due to factors such as subscription type, demographics, device preferences, and payment activity. Addressing this allows businesses to enhance pricing models and customer satisfaction.

Required Solutions

Developing ML models for revenue forecasting.
Identifying factors like tenure and device usage.
Optimizing pricing and marketing strategies.

Solution Objectives

Uncover trends in customer subscription behavior.
Develop accurate models for demographic-based revenue.
Identify factors contributing to Lifetime Value (LTV).
Support data-driven decision-making for growth.

Understanding the Problem

Revenue is influenced by plan types, age, and payment recency. Premium users contribute higher revenue but have distinct retention patterns.

AI models analyze historical trends to optimize acquisition strategies and personalized retention efforts.

2About the Data

Data Collection

Synthetic dataset representing a Netflix userbase, including subscription types, monthly revenue, country, and device preferences.

Major Parameters Description

Download Training Data

User ID

Unique identifier for each user.

Subscription Type

Plan type (Basic, Standard, or Premium).

Join Date

Date when the user subscribed.

Last Payment Date

The last recorded payment date.

Country

User location.

Age

User age in years.

Gender

Gender of the user (Male/Female).

Device Type

Device used (Smartphone, Tablet, Laptop, Smart TV).

Plan Duration

Duration of the subscription plan.

3Using iDareAI

Guided Mode Initialization

AUploading Dataset

Click on the **'Upload CSV or Excel Data'** button → Select a source for the dataset → Upload `netflix_revenue_train.csv`. The system automatically analyzes the file, extracts column descriptions, and identifies the top value-adding targets for prediction.

BChoosing Analysis Mode

Choose between autonomous machine learning or manual building. In autonomous mode, ask questions such as:

How to determine the monthly revenue trend for Netflix using this data?
Can revenue be maximized using this data?

Operation Using Autonomous Guided Mode

AQuery Response

Predictions generated were: $12.57 from Linear Regression, $13.33 from Random Forest, and $12.63 from LightGBM. Random Forest showed the lowest test error, indicating that selected demographics significantly impact revenue.

BAI Application

Model Fine-Tuning/Manual Model Building

ASelecting Prediction Target

'Monthly Revenue' column was selected as the target.

BSelecting Analysis Type

The analysis target is a numeric column. Hence, the 'Regression' analysis type is selected.

CSelecting Model Group/Item

DSelecting Features

Selected Subscription Type, Country, Age, and Device.

ESelecting Training Level

"Fast" training level with Linear Regression was used for stable performance.

AI Modeling Details

Linear Regression achieved a testing error of 11.70%.

Training Analysis Details

APredicted Target

BPredicted Trend

CError Trend

DFeature Importance

Improving Model Accuracy

ACustom Variable Creation

Derived features such as 'Subscription Length' and 'Device-Subscription Interaction' capture hidden user behavior patterns.

Variable Name	Formula
Subscription Length (Days)	DATEDIF(D2, E2, "D")
Tenure Category	IF(K2> 365, "Long-Term", "Short-Term")
Age Group	IF(G2<=25,"Young",IF(G2<=45,"Middle-aged","Senior"))
Device-Subscription Interaction	Device_Rank * Plan_Rank
Age-Tenure Interaction	Age_Group_Rank * Tenure_Rank

BAnalysis Details (Improved)

Accuracy increased through targeted feature engineering and selecting impactful variables.

4AI APPLICATION

Manual Model Building

The system leverages a trained Linear Regression model. Users can modify Subscription Type, Country, Age, and Device through radio buttons and sliders to observe real-time revenue impact.

AI Application Demo

Adjusting Age ranges and Device types dynamically updates the predicted revenue, helping refine business and acquisition strategies.

Saving the Project

Save your revenue analysis by clicking the icon at the bottom left corner of the interface.

Sharing the Project

Generate shareable links once the project is saved to distribute findings across strategy teams.

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