Story of Building a Telecom Data Analytics Solution
Telecom data is quite complex - consisting of hundreds of continuous and categorical variables that capture the details of millions of users consisting of plans, services, roaming, phone/SMS usage, revenue, and, cost, etc. Through interactions with customer leadership, we arrived on the business objective of our solution as optimizing the existing plans and services and maximizing the profit. We used statistics and ML-based feature selection techniques and combined with domain knowledge to recommend the features to the Subject Matter Experts (SMEs). Using our solution the SMEs were able to identify the end-user plans that were not profitable and able to come up with strategies to maximize the profits, tailor existing services and offer new services. In this crisp talk, I will share the details on how data science played a role in assisting the telecom customer to derive valuable insights.
In this talk, I will share my knowledge and experience in building a data science solution for telecom domain.
Here is an outline of my talk:
1. Define business objectives and translating the customer requirement into a data science problem for telecom domain.
2. Explain the data - Telecom end-user data contains thousands of attributes. What are the attributes required for the business goals? How do we use domain knowledge and data science to do feature selection?
3. What’s the data processing pipeline? How to keep telecom SMEs involved in feature selection and identifying the opportunities to maximize the profits?
4. Details of categorical feature selection techniques and the ML model that assisted the telecom SMEs.
5. The takeaway message from Telecom data analytics.
PhD in Applied Mathematics with multiple years of work experience in data science and ML. Currently working as a Data Scientist at SAP Labs. Prior to SAP, worked at Shell India ML group as a data scientist.