Telecom Offer Recommender System

Welcome to my Telecom Offer Recommender System project! In this repository, I aim to build an intelligent offer recommendation system for telecom products and services. With this system, my goal is to enhance customer satisfaction, boost revenue, and foster customer loyalty within the telecom industry.

Business Overview

Offer recommendation in the telecom industry involves suggesting personalized offers to customers based on their usage patterns, demographics, and other relevant factors. This approach is instrumental in:

• Increasing customer satisfaction by providing relevant and valuable offers.
• Raising revenue through higher customer spending and reduced churn.
• Improving customer loyalty by creating a better customer experience.

My project leverages advanced algorithms and big data techniques to achieve these goals, ultimately leading to long-term growth for telecom companies.

Why Offer Recommendation is Important

• Increase Customer Satisfaction: By understanding customers better and offering relevant deals, I enhance their satisfaction.

• Boost Revenue: Providing valuable offers leads to increased customer spending and reduced churn, ultimately growing revenue.

• Improve Customer Loyalty: Personalized offers create better customer experiences and stronger relationships, resulting in higher loyalty.

Why Data Science?

Telecom companies possess vast customer data, but they often struggle to use it effectively. My goal is to harness this data to suggest relevant and valuable offers, ultimately increasing customer satisfaction and revenue.

Aim

My project's aims include:

• Perform problem-specific Exploratory Data Analysis (EDA).
• Understand the importance of offer recommendation.
• Build a robust offer recommendation system for telecom companies.

Data Description

My dataset consists of 98,230 customers with 73 unique features, including customer demographics, personal information, and usage data.

Tech Stack

• Language: Python
• Libraries:
  • Pandas: For Data Analysis and Manipulation
  • Numpy: For performing mathematical operations on data
  • Scikit-learn: For model building

Approach

In this project, I will construct a collaborative-filtering system based on the user. Here's a simplified overview of my approach:

• Identify the n-most similar customers for a given customer.
• Utilize a churn-rate approach to determine the most successful offer among similar customers.
• Choose the most successful offer for my target customer.

My algorithm will be trained on a subset of the dataset containing customers who have received specific offers (A, B, C, D, E, F, G, H, I, or J) in their 'offer' field. I aim to apply this algorithm to the 'No Offer' group as well. This problem is treated as an unsupervised learning problem, and validation is a challenge due to the absence of ground truth data.

Project Takeaways

Throughout this project, I gained knowledge in the following areas:

• Conducting Exploratory Data Analysis (EDA).
• Encoding categorical features using Labelencoder.
• Feature engineering techniques.
• Understanding and implementing distance metrics like Cosine Similarity, Manhattan distance, and Euclidean distance.
• Choosing an appropriate distance metric for a specific problem.
• Determining the minimum threshold parameter.
• Building a collaborative offer recommendation system.
• Bootstrapping the offer recommendation system.
• Testing the algorithm in a production environment.

Feel free to explore this repository! It's been a pleasure working on trying to advance the telecom industry's offer recommendation capabilities.