Telecom

Data science plays essential role in the development of the telecom industry.

Telecommunications companies can use data science to improve customer satisfaction and the quality of their services and processes.

Telecommunications companies have been successfully using data as well as statistical and mathematical models to optimize their products and services for many years. Dealing with “Big Data” is also not a novelty in the industry, but is usually part of daily business.

The methods from the areas of data science, machine learning and AI are new and exciting for all telecommunications companies, which often result in a significant increase in model quality compared to conservative, statistical approaches. In combination with the extensive databases in the Telco sector, so many new use cases and scenarios arise that can have a positive impact on business.

GMR Consulting’ services for Telecommunication companies

Our data science team supports you in making the best possible use of your data from operations, CRM and the network to improve your customer relationships, processes and products. Some of our services include:

• Predictive maintenance: Use predictive maintenance methods to identify problems in your devices

• Operational Analytics: Model the properties of your technical systems and prevent failures.

• Customer Analytics: Model the behavior of your customers and identify churn and upselling signals.

• Demand forecasting: Model the expected network load using machine learning models and plan capacities.

• Quality Analytics: Analyze your quality data to uncover connections and derive measures.

• IoT / connected devices: Collect data from beacons and other IoT devices in real time for analysis and modeling.

Why business should consider data science and data analytics

• Generation of data reports so that companies can anticipate the future, facing decision-making at different levels and in various organizational areas, based on reliable data.

• Avoid incomplete, imprecise or fragmented, duplicate or inconsistent data, achieving the acquisition of quality data.

• Analysis of variables and records of detected anomalies.

• Generation of alerts against possible failures in the processes.

• Expansion of the vision of business processes, with the possibility of improvements in the quality of products and services.

• Optimization and efficiency of processes in real time.

• Improvements in the customer experience.

• Monetize data for quantifiable revenue and opportunities.

Data types for Big Data analysis in telecommunications

The data collected, processed and analyzed by Big Data Analytics platform is grouped into four fundamental blocks:

• Web and Social Media

For a telecommunications company, it is essential to know information about its clients or potential clients: interests, tastes, jobs, affective relationships, their behavior patterns on the network, etc. they can be collected from social media environments as fundamental information to design truly profitable advertising and promotional campaigns.

• M2M (Machine To Machine)

Within the scope of the Internet of Things, the term M2M refers to the technologies that allow connection to other devices thanks to the use of sensors or event meters (speed, temperature, pressure, meteorological variables, chemical variables such as salinity, etc.), data that is transferred through networks (wired, wireless or hybrid) to other applications to be processed and thus have analyzed information.

• Data transactions

This analysis includes data such as billing or call records, transactional data that can also be analyzed to establish patterns of customer behavior with which the company can predict possible cancellations, control fraud or carry out loyalty actions.

• Information generated by clients

Users, in their relationship with the operator, generate a large amount of unstructured data (emails, phone calls, and documents) that, thanks to Big Data analytics, we can categorize based on their relevance for further analysis or discard.