Telecommunications and Data Analytics

Alexander Graham Bell patented the world’s first telephone back in 1876 — and since then, a lot has changed in the telecommunications industry.

From landlines and pagers to now having the world at our fingertips, the telecommunications industry makes interaction and connection possible on a global scale via smartphones, the internet, wireless technology, and more. Companies stay focused on building technologies that enable various?datasets?to be transferred everywhere, whether they’re in the form of text (e.g., messages), speech (e.g., calls), audio (e.g., music), video (e.g., video clips), or visual (e.g., pictures).

Telephone providers (both landline and mobile), satellite businesses, cable companies, and internet service providers comprise the main businesses in the telecom industry. In order to provide better products and services, as well as attract more customers, they employ data analytics tools — such as, predictive analytics, machine learning, data mining, and more.

This is why leading Business Intelligence (BI) companies — such as, Cubeware — are actively developing solutions for the telecom industry to ensure that users can enjoy quality networks, services, and connections.

Here are some of the key ways in which data analytics has improved the telecommunications industry:

1. Optimize network capacity

Network optimization refers to the resources and strategies?used to analyze and improve network capacity and efficiency — in other words, to provide the best network service possible, regardless of the level of congestion (commonly seen in overpopulated areas). The initial step in an optimization procedure is to establish and evaluate a set of network performance indicators — such as, latency, congestion, delay, connectivity, and more.

Using data analytics, telecom companies can accurately analyze and regulate network capacity, construct predictive capacity forecasts, and even develop plans for possible network expansion in locations that have existing network coverages.

For example, using descriptive analytics, telecom companies can store and analyze terabytes of real-time data — such as, network usage, network speed, network users, and more — to determine what the current network traffic is like, as well as to identify severely populated places where the network capacity?may be reaching its?thresholds.

From there, companies can even construct forecasting algorithms with predictive analytics — based on historical and current datasets surrounding a location’s population growth — to determine its anticipated network capacity.

In order meet that anticipated output in terms of network bandwidth, telecom companies can also employ prescriptive analytics to develop necessary expansion plans. This approach shows how various action variables can be tweaked in order to reach the targeted output, thereby expediting the network optimization process.

2. Offer optimal telecom plans for users

With the increased competition in the telecom industry, companies are constantly vying for higher market shares by offering the best plans and rates across their products and services — this includes data plans, mobile plans, network speeds, network coverages, and more.

Using data mining, telecom companies can study various current and historical datasets — such as, customers' responses to alternative pricing schemes, product sales, service offerings, competitor prices, and more — in order to identify positive correlations and patterns between a number of variables. This then helps them determine what their ideal offerings, packages, and price points should be, thereby boosting their sales and customer retention.

For example, according to Appinventive, Vodafone has been using big?data and Artificial Intelligence (AI)?to better understand customer demands and provide faster services. By monitoring customers' voice call durations and data usage behaviors, Vodafone has been able to provide them with the most suitable services and packages..

3. Provide better connectivity everywhere

In the recent decade, having an internet connection at all times has become an absolute necessity for many. However, the downside to this is that not every place experiences this privilege due to their unfortunate location — such as, rural areas or areas prone to natural disasters.

However, telecom companies can employ data analytics tools — such as, heat mapping and diagnostic analytics — to analyze the geographical characteristics of different locations, identify places that don’t have internet coverage, and understand why internet connectivity is scarce to none there. By understanding these reasonings, companies can then develop other alternatives to provide a stable connection to users everywhere.

For instance, the launch of Starlink — a telecommunications satellite that aims to provide satellite internet coverage worldwide — is a huge gamechanger for the telecommunications industry. Instead of relying on onsite cell towers to make calls or browse the web, people around the globe can seamlessly access the internet via Starlink satellites, rendering it an ideal option for rural areas.

4. Manage customer churn rate ??

A customer churn rate refers to the percentage of customers that have stopped using or purchasing your company’s products or services during a certain time period — or in telecom terms, started porting out to other networks.

Long-term customer engagement requires a significant amount of commitment and effort. Offering the cheapest network plan provides a good foundation, but there are other factors that affect a customer's satisfaction and loyalty — such as, offering excellent customer care services, providing loyalty benefits, giving rebates, and more.

It is quite common to observe that a considerable number of customers tend to?discontinue their telecom company's subscriptions every year owing to various issues — such as, inconsistent coverage, incompatible plans, and poor customer care. With data analytics, telecom companies can manage their customer churn rates by analyzing customer behaviors, identifying existing and potential issues, taking appropriate measures, and forecasting future demands.

With data mining tools, telecom companies can analyze a myriad of data points and millions of network consumption trends to anticipate their customers’ interests, identify churn threats, and develop strategies to rectify them. Additionally, telecom providers can also use machine learning tools to crawl the web for any mentions of their brand — especially those of concern or complaint — in order to resolve any issues and encourage customer retention.

In summary, the benefits of utilizing data analytics?in the telecommunications industry are plenty — from optimizing network capacity to managing customer churn rates, telecom companies can improve their overall competency and performance through data-driven insights and decisions. ?

To learn more about data analytics, visit us at www.cubeware.com. In addition to building end-to-end data analytics and BI solutions, Cubeware regularly curates educational articles on the most relevant components of the data analytics industry.