MA_Group_Project

Welcome to SuRFM Documentation

Group 6 - Marketing Analytics Project for Streaming Services

Overview

In the competitive landscape of streaming services, maintaining customer retention and minimizing subscriber attrition are critical challenges. The SuRFM (Surfing the Subscribers RFM) Python package is designed to address these issues by providing a sophisticated analytical tool. Developed by Group 6 (Artur Avagyan, Elen Galoyan, Hrag Sousani, Ina Karapetyan, Khoren Movsisyan) for a Marketing Analytics Project, this tool leverages RFM (Recency, Frequency, Monetary) analysis to offer actionable insights into subscriber behavior patterns.

The Problem

Streaming services face significant challenges in retaining subscribers due to the dynamic nature of consumer preferences and the intense competition in the market. Identifying at-risk subscribers before they churn and understanding the factors influencing their decisions are crucial for maintaining a stable and growing subscriber base.

The Solution

SuRFM provides a robust solution by segmenting subscribers based on their engagement levels as indicated by their Recency, Frequency, and Monetary contributions. By analyzing these segments, SuRFM helps identify subscribers who are most likely to churn and offers tailored strategies to re-engage them.

Expected Outcomes

By implementing SuRFM, streaming services can expect the following outcomes:

• Improved Subscriber Retention: Targeted interventions based on RFM analysis help in retaining at-risk subscribers.
• Increased Customer Lifetime Value (CLV): By extending the retention span of subscribers, their lifetime value to the business increases.
• Enhanced Targeting Strategies: Data-driven insights facilitate more effective marketing and promotional campaigns.
• Operational Efficiency: Streamlined processes for identifying and addressing subscriber needs, reducing overheads related to customer management.

Features

• Data Input: Processes subscription data, including activity and transaction history.
• Analysis: Segments subscribers based on their RFM values.
• Insights and Actions: Provides actionable insights to enhance customer retention strategies.

Getting Started

To get started with SuRFM, ensure you have Python installed on your system and all dependencies are met. For detailed setup instructions and dependencies, refer to our Installation Guide.

Documentation Sections

• Home: Overview of the SuRFM package and its capabilities.
• API: Documentation of the API endpoints provided by SuRFM.
• Database: Details on the database schema, data generation, and how to interact with the database.
• Logger: Information about the logging setup used within SuRFM.
• Model: Explanation of the Customer Lifetime Value (CLV) model and its implementation.

Support

For any issues or inquiries, feel free to reach out to the repository maintainers for quick help.

Thank you for choosing SuRFM! We hope it helps you gain insights into your subscriber base and improves your retention strategies.

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