Business Intelligence and Big Data in Telecommunications

The telecommunications sector is one of the most competitive markets that is constantly experiencing changes related to the introduction of breakthrough technologies and the emergence of new competitors. Comprehensive knowledge about the customer, attractive price, and the right moment to launch a new product are factors that are essential for gaining competitive advantage in this sector. Analytical tools create a working environment as well as the basis and patterns for making effective decisions in the area of telecommunications. On the example of the SAS Telecommunication Intelligence Solution package, it can be seen that they can support primarily the following:

■ Effective add-on and supplementary sales of various packages and services. Acquiring new customers is not the only way to increase the revenues of telecommunications operators. An appropriate offer for new products or their richer versions addressed to current customers usually results in an increase in both the value of these customers and their loyalty. The implemented cross- sell and upsell solutions enable accurate matching of new product offers as well as newer and richer product versions to the customers of the telecommunications operator.

■ Management of marketing campaigns: Telecommunication companies have experienced that the strategy of customer relations should be developed in detail, and communication with them carried out in several stages, using a variety of channels (radio, television, direct mail, telemarketing, e-mail, and the Internet). By integrating analytical tools with techniques used to manage marketing campaigns, it becomes possible to effectively plan and coordinate marketing campaigns, monitor their implementation, and analyze the results to maximize their effectiveness.

■ Customer profitability analysis: Customer profitability, analyzed from the beginning of the contract, is in-depth knowledge that gives telecommunication operators answers to questions about currently obtained rate of return, expected rate of return, customer value, or customer life cycle. BI&BD tools enable the analysis of customer profitability, thanks to the built-in mechanisms that enable assigning revenues and costs to individual customers/ contracts. These tools also allow the analysis of customer profitability in additional perspectives, such as tariff plan, month of activation, marketing promotion, distribution channel, terminal, acquisition, or other defined unitary characteristics of the contract.

■ Retaining valued customers: Competition in the telecommunication market has increased the cost of customer acquisition, making it a standard to identify customers willing to leave. Anticipating this phenomenon and creating strategies to reduce it is a difficult undertaking that requires the analysis of a huge volume of data and the use of advanced data mining techniques. Predefined analytical models created with the use of BI&BD enable operators to quickly learn the factors affecting customers’ departure to the competition. Not only do they identify customers willing to leave but also the reasons for this phenomenon (SAS, 2020).

Business Intelligence and Big Data in Energy Sector

Energy demand management and forecasting customer behaviors are becoming the main elements of analyses carried out by energy enterprises. Tire nature of changes in the energy market as well as the increasing volume and diversity of collected data cause an increase in demand for the use of Big Data technology and various analytical systems. According to SAS (2013), Big Data analysis models in energy enterprises relate to three main areas: business and economic analyses of the enterprise, analyses of the power network operation, and analyses of relations with customers, including those related to behaviors in the area of choosing energy-related services and energy-saving behaviors.

Energy enterprises supply energy through a dedicated infrastructure—the electrical distribution system. Supervision over its proper functioning is of special importance to them. Analytical systems play a key role in managing such an energy network (Pamuta, 2013). They enable, inter alia, the optimization of energy sources work and optimization of the process of connecting new distributed energy sources (Leeds, 2012). They provide extensive and detailed analysis of the smart metering infrastructure, asset management, network management in crisis situations, as well as visualization of network operation using geospatial intelligence. Furthermore, they allow the prediction and analysis of failures related not only to the technological process of the network and device operation but also to weather phenomena and cybercrime. They contribute to the reduction of technical and nontechnical losses (preventing theft of electricity) and increase the quality of services provided through the electrical distribution system.

Changing the model of relations with customers becomes the focal point of the reorganization of energy enterprises. In smart electricity networks, consumption, i.e., the demand side, is gaining in importance, which requires the introduction of many new services emerging on the market. Demand-side management is one of these basic services. The active participation of the recipient in the energy market forces the supplier to collect and compile a large amount of data and information. Based on these data, BI&BD tools enable building a recipient’s energy consumption profile and determine motivations for actions related to effective energy management (Pamufa, 2013). According to the GTM Research report, Greentech Media division (SAS, 2013), the most used analytical tools in 70 energy enterprises in North America are solutions from SAS, IBM, and Opower.