In this day and age, it’s not a surprise that all telecommunication companies strive to circumvent their opponents by using the latest technologies and tools. And nowadays, when information is considered to be a fuel of the digital economy, big data analytics becomes the new driving force: a tool to attract more customers, and tailor offers to the existing ones; a method to predict network degradation events, and to know in advance the issue the client is calling about.‍

Still, before going further into use cases, we need to have a glance at the tools that make big data analytics accessible and clear to understand.

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The Tools

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There is a huge variety of BI tools on the market. Without diving into all of them, we’ve underlined 3 most commonly used ones:

1. Tableau - a business intelligence tool that allows to create highly interactive dashboards and explore complex datasets. Unlike Power BI, Tableau is used to handle large volumes of datasets when the amount of data is continuously increasing. Additionally, Tableau is recommended when a high level of customization and the ability to connect to a large number of data sources is a priority.
2. Microsoft Power BI - another visualization tool, this time developed by Microsoft that is used for creating reports and dashboards. Among its distinguishing traits are its integration with other Microsoft-developed products, fast data processing, and user-friendliness.
3. Apache Super set - is an open-source analytics platform that also provides visualization and reporting on the extracted data. High customizability and large integration pool, strong support for SQL queries, and ability to be modified according to company-specific needs are usually listed among its strengths.

These three tools (and others) all have their strengths and weaknesses, so the final choice between them rests on the goals and priorities of the company. If you are considering adopting a BI tool or planning on switching to another one, we can recommend the best one for your needs.

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Areas of Application

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BI and Analytics have four main areas of application: client personalization, network and connectivity optimization, operations analytics, and data commercialization.
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Client personalization

As in every business, the competition over the client base is high, so no wonder that client journey and experience become a focus of BI analytics. Client churn prevention, monitoring of client satisfaction, and further strengthening of relationships have the same priority as new client acquisition.
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Analytics of historical data, past and existing clients allows telecoms to build predictive models for the future. Personalization of offers and services based on past actions or call center inquiries, tailored recommendations, and smart assistants are the examples of utilizing the client data provided by analytics.
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Further use of collected and analyzed data can include customer operations optimization to improve call resolution and facilitate the work of a call center, for example, by using predictive analytics to predict client problems in advance rather than react to them later.

Network and connectivity optimization

Network and connection quality is also an area of constant improvement, as even high personalization won’t stop the churn if the network services are of no good quality.
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The main areas of BI and big data analytics utilization here are the prediction of network disruptions; network performance monitoring for latency, throughput, packet loss, and network congestion; capacity planning for stable operation; customer feedback analysis for rapid interventions and preventive measures in case of network failures, etc.
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The collected data can later be used in predictive modeling, where algorithms are trained to predict events like traffic surges to make more effective preemptive actions.

Operations analytics

It’s necessary not to forget that successful changes in client service are often accompanied by changes in the company operations. For the optimal service provision and utilization of employee strengths, inner bottlenecks should also become the target of analysis and elimination.
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Analytics of the company data can point to optimization problems, resource misallocation, and time-consuming processes that can be brought to the attention of the management. The under performance of departments may lie in outdated or unsuitable tools, and timely analysis will signal the need for a change in processes or tools to best fit telecommunication companies’ business purposes.

Data monetization

The collected client data doesn’t only end in personalized offers and services. Data sharing with other companies and businesses is an ordinary practice, although requiring data anonymization and corresponding to information quality standards.
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Here analytics serves as a way of clearly presenting the gathered user data: often-visited locations, demographics, most widely used mobile devices, etc. The data collected allows to gather insights into buying preferences, correlations between various age demographics and visited places, etc. to plan future marketing campaigns and banner placements, store openings, and in-store product layouts.

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Use Cases

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How do telecom companies apply BI analytics in practice? Get a peak in the following real-life use cases:

Fraud and revenue loss prevention

One of the real-life cases is a SIM box fraud when international calls are routed through a SIM box as local calls via a local SIM card. The fraudster makes a profit by using the price difference between the cost of international and domestic calls, while telecommunication companies face revenue losses and network congestion.
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Such fraud is common all over the world, where global companies lose 3-6%of total revenue or around 58B US$ yearly. To solve this issue, the CDR (Call Detail Records) analysis was proposed. The analysts of one African telecommunications company took into consideration the call volume, location data, and call destination, where suspicious SIM cards were predicted to make large volumes of calls, make calls to a large number of different destinations, or never move out of a cell. In the end, the predictive model was adapted to detect 95.98% of fraudulent calls.

Traffic Analysis

Another case includes real-time traffic analysis to allow telecommunication companies to ensure sufficient coverage and service quality at often populous places, for example in places of active traffic. Telecom, geospatial, and social data are used to provide insights into traffic congestion.
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The aggregated data from the above mentioned sources is then processed and analyzed to map the traffic with the road network. Besides ensuring optimal network connectivity for the future, telecommunication companies also use the analyzed information for navigation services. By using a navigation app with real-time data, travelers in the area will be warned about potential congestion, traffic speed, and suggested alternative routes.

Customer Churn prediction

For this case, the task is to analyze the historical data that includes customer data: demographic information, usage patterns, call records, billing history, customer service interactions, and feedback. The model is then developed using logistic regression, decision trees, random forests, or neural networks that are trained on past customer behavior, specifically those who switched to another telecom provider. After the model is validated, it is deployed to predict which customers are likely to churn in the future. The model assigns a churn probability score to each customer, indicating the likelihood of them leaving the service.
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Taking these insights into account, the company aggregates a pool of hesitant or dissatisfied customers it can work with. The measures to retain them then include personalized incentives, targeted marketing campaigns, improved customer service, or loyalty programs.

Increased transparency and operations optimization

The issues faced in these cases are often associated with unclear data management, reporting, and subsequent analytics. Our client was also confronted with a lack of clear indicators for business performance, time-consuming manual reporting, and disorganized information.
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During the project, the team implemented a Green plum database as a historical data warehouse for storing and managing the information and the BI tool Tableau for the managerial report creation. The tool made possible the visualization of more than 50 different indicators using only 4 dashboards, thus allowing for greater transparency and better understanding of the processes by the upper management.

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Conclusion

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In a modern world, BI and analytics provide limitless opportunities for telecommunication companies whether it’s client analysis or a company's own process of optimization. The amounts of data gathered may exceed thousands of terabytes, and with so much information available, no wonder that new analytic models and predictive algorithms appear every year.
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Still, even in advanced telecoms, there is always room for improvement. With BlitzBrain Data Analytics and Business Intelligence solutions, we offer:

1. A unified Data Intelligence platform for analytics and reporting needs;
2. Historical data analysis and process optimization;
3. Data sources integration and in-depth data relationship analysis.

Contact us to find out how we can help you implement BI and Analytics solutions that will suit your needs and goals.