Everyone is talking about the importance of data – and yet many questions concerning their practical use remain unanswered. Nicolas Fröhlich describes some possible applications for carriers.
Whenever digitalization is the subject of discussion, the issue of the use of data always follows. Important areas for the analysis of mass data (data analytics) are the creation of (customer) profiles, the derivation of measures, or the detection of errors through the examination of correlations. Moreover, data also serve as a basis for the algorithms of artificial intelligence (AI). Machine learning, a subset of AI, uses data to identify regularities and develop new business models.
The possibilities for utilization are enormous. But what these opportunities mean for industries of all kinds is barely comprehensible for many companies. Studies such as the one conducted by the eco Association of the Internet Industry project that companies in Germany will save €330 billion in costs by 2025 and generate an additional €150 billion in revenues over 2019 thanks to AI. But what does this mean for carriers?
Data thinking as an approach for the development and realization of use cases
The conduct of data projects poses several challenges. Many data projects of the present day fail because of two factors: one is a lack of data quality or the availability of usable data and the other is the lack of knowledge in the field of data processing. Furthermore, the establishment of a separate data science team is financially very costly and the investment will not be recouped for a number of years. Data protection and access rights represent another challenge, of course. In this respect, many framework conditions must be taken into account because the rules are becoming increasingly strict.
We also see in practice that, in many cases, no one has any concrete ideas for the use of data. What data are available, how can they be used meaningfully, and what additional data would still be needed? In terms of innovation, data are a topic that, from the perspective of carriers, can be exploited to create value for customers and users. One valuable approach might be found in data thinking, but that is not the primary concern of this article.
The overall range of applications is truly extensive. In the following, three types of applications that could illustrate the use of data in the telecommunications industry are described in more detail:
- Data analytics as a means of identifying gaps in mobile network coverage
- Customer retention through sentiment analysis
- Projections of future network use
Data analytics as a means of identifying gaps in mobile network coverage
We see a fundamentally relevant application in the use of data analytics to identify gaps in mobile network coverage. The focus in this case is above all on customer perception. Phone calls while driving on the highway or traveling on a train are often dropped when the connection is lost. Users tend to remember such interruptions clearly and to perceive them as a sign of poor service. Gaps in coverage may be caused by a broad range of different factors. The lack of a mobile network tower in the region is an obvious cause. Yet connections are also lost in regions where there is actually adequate coverage. One possible cause here is the sector swap between the individual cells of a mobile network tower. Gaps of this type are not obvious to observers.
Historical data from individual cases and other parameters such as dropped phone calls can be evaluated using data analytics and the associated algorithms. The coverage gaps determined in the past are “subjected” to an analysis that leads to the determination of certain patterns that are then applied to the current network. Previously undetected gaps in coverage can be identified early and eliminated faster. Customer perception becomes more positive since users notice an improvement in network quality (e.g., along the routes they take when commuting) or do not even experience any gaps in coverage and are subconsciously happy to have a seamless connection (Internal Project Documentation, Detecon).
Customer loyalty through sentiment analysis
Another area of application in the private customer sector is the analysis of customer behavior with the goals of avoiding possible contract terminations or the loss of customers to other providers and the generation of attractive service offers for customers. This is a broad field for any telecommunications company – and at the same time, one that is highly relevant if they are to understand their customers better. Customer mood analysis comprises a number of methods that all serve the processing of information. The final objective of the analysis is the assessment of positive or negative responses on the part of customers to services or products. The analysis of the collected data also makes it possible to detect current trends and to react to customers’ problems in real time. The analysis can be based on such data as feedback from social media sources, number of products purchased, products with a defined term, or even calls to support lines. Customer service can proactively contact customers to offer new mobile communications contracts to them, an effective means of warding off the termination of contracts. By using artificial intelligence, carriers can continuously broaden their understanding of their customers (Datameer, Top 8 Telecommunications Big Data Use Cases, 2019).
Projections of future network use
A final example for the use of data is the projection of future use of network capacity. The analysis of large quantities of data can be used to generate a deeper insight into capacity utilization, which is a time-consuming process for telecommunication providers unless the analysis of the algorithms is automated. The differences in the use of capacities result from the many different applications in industry or in the private customer sector. They include a wide range of video streaming services at the end user’s domicile as well as the interconnection of machines in a large industrial factory for more efficient production. The use of autonomous vehicles in warehouses that are all connected to the internet will make even greater demands on carriers’ network capacities in the future. Depending on the time and devices, network demand fluctuates and the use of the application is affected; a common example is poor video quality during streaming. Previously generated data can produce reliable prognoses and the network can be controlled more efficiently (Datameer, Top 8 Telecommunications Big Data Use Cases, 2019).
Using data to generate value
The examples above relate to a part of a carrier’s core portfolio. There are, however, more possible opportunities such as the smart home, television, or the use the services provided by OTT players. Today as well as in the future, telecommunications providers will continue to expand their portfolios of cloud services through partnering or services they have developed themselves. Data accrue virtually everywhere and in massive quantities, and they can be used to create value for users and for companies themselves (automation). It is important to never lose sight of the benefits for companies and customers. The examples described above should serve as inspiration to identify even more use cases for carriers. A structured approach to the identification of use cases is essential; blind analyses without a specific direction are a waste of time and energy. The method of data thinking is one possible solution.