November 29, 2017

Prediction In Telecom: What Is It?

Companies that have hundreds of thousands of customers know how to anticipate the future. And for this, they do not have to hire clairvoyants. They are assisted by IT professionals who have recently used machine learning for more accurate predictions. The behavior of each client can now be more accurately profiled and this does not require a crystal ball to do so, it comes from the mass of data collected to date.

They determine which client is close to leaving the competitor; calculate how much money each client can bring for the entire time of interaction with the company and how much it would be reasonable to spend on its attraction, maintenance, and retention.

What It Is

People have long been trying to predict what will happen in the future. Businesses have been using a mathematical tool for this for at least a couple of decades. Earlier forecasts of financial indicators were calculated on paper and in simple programs like MS Excel. Then came the systems of Business Intelligence: employees of companies manually entered data sets and the systems built forecast models, used average industry indicators and proposed general recommendations.

Today, thanks to the development of machine learning technologies and especially Deep Learning (in-depth training), predictive analytics can provide much more accurate predictions based on the analysis of a huge amount of data collected from a variety of sources. These sources can be internal: accounting reporting, logistics information, billing systems, CRM, and external: data of partners, distributors, companies from other industries, public information from social networks and so on. And all of this data can be collected and analyzed in near real-time: forecast models are built in advance so that you can influence a developing scenario before it becomes critical to operations.

Such tools are successfully used to predict possible malfunctions in complex equipment, engines, and various other mechanisms. This can save significant funds for many companies around the world. One example, which can be read on their site, is from Trenitalia, which is part of the Italian State Railways holding company.

These same methods can be used to analyze people’s behavior – to better understand what their own employees require and, of course, to establish closer interaction and better relationships with customers. Predictive analytics helps to identify when something can “break” in the relationship between the company and a particular person and take timely action; it can predict when a customer is likely to need a certain product or service.

Such tools work in companies with a large number of clients, from several thousand people or more. On smaller volumes of data, it is more difficult to identify correlations in the behavior of clients. But companies that work in the mass market: banks, online and offline retailers, telecoms, transport companies and others – can benefit greatly from the use of such tools.

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