A team of data scientists at O2 Czech Republic are building advanced neural networks to gain accurate context of its mobile network. Better context into mobile networks and subscribers means better prediction of customer behavior, more personalized services, and scaling superior customer experiences from thousands to millions. 

Translating this to a mathematical equation and words that grab the attention of a data scientist, an algorithm can determine the vector, or relationship distance between an input and its context.

For example, if your colleague asked, “How much money you have in your pocket,” you may think they are asking to understand if you can afford the lunch you are eating together. But rather they were asking to understand how much economic impact you have.

This is an example of “Word2Vec,” a deep learning algorithm used to understand a group of words. Why are you saying what you are saying in an effort to understand context. While this is a linguistic mathematical equation, the same principal can be applied to a telecommunications company to better understand the context of its network to improve customer experience and reduce customer churn.

Answers and Outcomes

O2 Czech has created an advanced neural network, applying deep learning techniques to personalize the customer experience, scaling to 8M mobile subscribers across the Czech Republic and Slovakia.

Every phone emits two types of distinct data patterns which can be used to better understand the context of its users – Cell_IDs and SIM Card_IDs.

Cell_IDs are cell tower IDs and call routes that emit geolocation data which can determine the optimal sequence of calls in an effort to reduce dropped calls, busy signals, and other call quality concerns. These Cell_IDs are dynamic, changing based on a given location.

A SIM Card_ID is unique to the individual SIM card within the device. Think of your SIM Card_ID as a uniquely identifiable feature to you. While Cell_IDs are dynamic, a SIM Card_ID is permanent. You only get one. This ID, when paired with your customer account profile data, depicts your relevant data such as cellphone type, mobile usage, call patterns, gender, demographics, and other account-related data points.

Understanding how to optimize its network and relate cell tower and SIM card data gives O2 Czech the ability to better predict customer experiences, and paths to churn. This scales personalization of its services to millions of subscribers.

For example, an inner city has a high concentration of tech-savvy individuals who primarily text and stream video may require different network requirements than a low-density rural area where users are older and primarily place voice calls. Matching network optimization with relevant, highly contextual personalized offers drives customer loyalty and increases in O2 Czech Republic’s mobile, fixed, and internet-based offerings.