How Data Science/Analytics can Change the Telecom Industry

Today, every company should care about its data, and these days it is a must to have a data science team who can focus on every tiny piece of a data. Telecom companies receive huge data sets from multiple different sources, and this data can be turned into valuable information that can be used to analyze and improve many issues. Below we explore a few such opportunities for improvement.

With data science, and by applying machine learning algorithms on these data sets we receive, we can  create a variety of models and touch points. By doing so we can predict customer problems before they actually occur. In some cases, issues can be addressed and remedied remotely, reducing the number of dispatches and reducing the number of customer calls resulting in a much better customer experience.

We can also recommend products to customers by applying the machine learning models to user navigation on the company product catalogue or quoting system.

Increase revenue:

By applying machine learning (ML) models, we can Identify the best source for leads and see which leads are being converted to opportunities and then assign resources based on the analysis so that we can receive even more leads. It’s not easy to go through thousands of emails that the marketing department receives, and we might miss some important leads because of that. So, we can apply ML algorithms to marketing emails and find the leads or any other important information that we identify as meaningful.

Further, we can analyze what products are used most by current customers and improve the efficiency of those products. Also, ML allows us to collect data points from current customers so that we can improve the efficiency of our products and packages.

Fraud Detection:

By analyzing CDRs, we can detect important data points like call drops and detect fraud. We can analyze behavior in the CDRs and then dig deeper into the raw data and see if we can find any fraud and may be prevent from happening it in the future.

Increase Security:

Cyber-attacks are very common these days and they keep evolving. Using historical and current information, ML algorithms can detect future attacks and increase the organization’s security. Data science techniques can also point out the vulnerabilities in a company’s information security systems.

By using Endpoint detection system data, ML can detect if any suspicious programs or executables are running in any environment. Intrusion Detection System (IDS) and Associate Rule Learning (ARL) are the most used in ML to detect cyber security.

Most commonly used machine learning tools are:

• Power BI
• IBM Watson 
• Azure Machine Learning studio
• Apache Mahout
• Google Cloud AutoML
• RapidMiner

These are just a few ways that data science can positively impact your business. Taking the time and energy to set build out a data science team for your organization will be time well spent!