Mobile Data Monetization in Telecom: Top Strategies for Revenue Growth
In the telecommunications industry, a myriad of tools, technologies, and strategies has emerged to grapple with the colossal volumes of data it churns out. Efficiency and speed are non-negotiable in this data-driven sector. Innovations like the hybrid cloud, Geographic Information Systems (GIS), and various data management solutions have risen to meet the challenges posed by this data flood. However, it's crucial to remember that none of these solutions operates by itself. The engine driving their effectiveness is artificial intelligence (AI), with stress put on machine learning (ML).
AI-powered tools not only supercharge data processing and analysis but also bring valuable insights, better personalization, and enhanced data security to the table. In the fiercely competitive telecom landscape, AI and machine learning have risen to the forefront, acting as indispensable catalysts for efficiency, innovation, and growth.
At Flyaps, AI solutions have been our favorites. A decade of working closely in the telecom industry has granted us a unique perspective on main AI trends, challenges, and untapped opportunities. Armed with this wealth of knowledge, we're excited to discuss how telecom businesses can elevate their revenue by harnessing the formidable synergy of AI and data monetization in telecom.
Three strategic paths of big data monetization in telecoms with AI
There are three key avenues through which telcos can improve data monetization, fostering profitability and growth: by offering more services to their current customers based on customer data, creating new services for other companies, and improving their own business operations.
1. Providing additional services to existing customers
Communications service providers have access to a wealth of customer data, ranging from usage patterns to preferences. By harnessing customer data and applying advanced analytics, they can gain deeper insights into their customers' needs and behaviors. Thanks to this knowledge, they can introduce tailored services and packages, upselling and cross-selling to their existing customer base. For example, personalized data plans, content recommendations, and bundled services can enhance customer loyalty and increase average revenue per user (ARPU), which serves as a good example of data monetization.
2. Designing customized services for other organizations
Telcos can better data monetization through partnerships and collaborations. They can offer data analytics and insights to other industries, such as retail, healthcare, or transportation, to help these sectors make informed decisions, optimize operations, and target their customers more effectively. By doing so, telcos can diversify their income by providing data-as-a-service solutions, opening up a new line of business while enriching other industries with valuable data-driven insights.
3. Enhancing internal business processes
Data analytics can empower communication service providers to streamline their internal operations and reduce costs. By optimizing network infrastructure, managing customer service more efficiently, and predicting network maintenance needs, telecoms can enhance the quality of their services while reducing operational expenses. This operational efficiency not just translates into cost savings but also allows them to reallocate resources and investments into areas with higher growth potential.
Now, let's see how well-known telecom operators implement these strategies and increase their profits.
6 use cases of telco data monetization
Below, we will discuss some examples that followed one of the previously mentioned data monetization strategies. Let's begin with the first one, which is offering more personalized services to current customers.
How Vodafone used AI and data-driven insights to optimize its personal recommendations and doubled its sales
Vodafone Italy has achieved a remarkable 50% share of the digital market by personalizing customer interactions across various digital touchpoints, including their app, push notifications, SMS, chatbot, paid media, and more. This AI and data monetization strategy in telecom was made possible thanks to two strategic collaborations.
Vodafone partnered with Persado, a renowned AI-generated language platform. This partnership allowed the telco to accurately predict which messaging components will yield the best customer response. Additionally, Vodafone joined forces with Adobe to deploy these personalized messages.
Here’s how it works. Vodafone's creative team makes messages, then Persado breaks them down into code pieces, figures out the best way to use those pieces, and finally, Vodafone uses Adobe Target to send personalized messages to the right customers.
As a result, Vodafone has observed a significant increase in response rates, with a response rate elasticity of 150% across more than 100 messaging experiments. By leveraging data insights and Persado's tools, Vodafone customized message elements like emotion and descriptions, leading to doubled sales compared to the prior month.
How T-Mobile's customer acquisition soared 400% with AI video emails
Personalized videos within email communications are an important part of T-Mobile’s strategy for AI and data monetization in the telecom industry. Based on customer data and big data analytics, these videos are tailored to individual customers, making the content more relevant and engaging. This level of personalization fosters stronger connections between the brand and its customers, while maintaining broad reach. By using AI, T-Mobile achieved a remarkable 400% increase in customer acquisition.
According to a study by the American Marketing Association New York, many U.S. consumers have negative attitudes towards personalized ads, especially when users didn’t know the data was collected from them. T-Mobile's approach, which relies on AI and customer data willingly provided, aligns with customer preferences for more user-friendly technologies like virtual reality, AI, and omnichannel solutions. This approach ensures personalization and enhances the customer experience without crossing into the realm of being invasive or "creepy."
How AT&T uses an AI-based solution to help retailers, QSRs, shopping malls and others gain a deeper understanding of their operations and customer activity
This case serves as an example of the second strategic path in AI and data monetization within the telecommunications industry. AT&T leverages AI and ML to develop innovative services for other companies. They accomplished this through their video intelligence solution, AT&T Operational Analytics.
AT&T Operational Analytics applies AI and ML algorithms to analyze existing video footage. This process involves monitoring and analyzing various elements, such as objects, assets, and property captured by video cameras. By extracting insights from video data, AT&T's solution empowers businesses to gain a deeper understanding of their operations and customer activity.
The AI-driven system provides real-time actionable insights and reporting based on the analyzed video data. These insights can offer a comprehensive view of business operations, helping companies monitor and evaluate their performance. In doing so, it enables organizations to make faster and more informed decisions.
AT&T's solution is equipped to generate alerts when specific events or conditions are met. These alerts are customizable and can be delivered through various communication channels, such as text messages or email. By proactively identifying events, the system ensures that personnel are promptly informed, allowing them to take appropriate actions in response.
Reports and dashboards generated by the AI platform provide a unified and comprehensive view of activity across different locations. These reports are designed to be customizable, ensuring that businesses can focus on the metrics and data that matter most to them. They can be accessed on a range of devices, including computers, tablets, and mobile devices, offering flexibility in how the information is consumed.
AT&T's approach maximizes the value of a company's existing video camera infrastructure. By repurposing these cameras for data collection and analysis, businesses can reduce costs, simplify their systems, and accelerate the deployment of video intelligence solutions.
How Deutsche Telekom is enabling the creation of new services for healthcare companies through the application of AI
Here's another illustration of the second strategic direction in AI and data monetization in the telecom sector. Deutsche Telekom is supporting the development of innovative healthcare services using AI, such as FUSE-AI, a startup based in Hamburg.
FUSE-AI's AI system helps radiologists diagnose tumors in MRI scans quickly and accurately. It automatically identifies and categorizes tumors, distinguishing between benign and malignant cases. To power this intelligent algorithm, FUSE-AI relies on the Open Telekom Cloud, Deutsche Telekom's cloud infrastructure that provides the flexibility, scalability and security needed for efficient medical image analysis.
The Open Telekom Cloud provides AI systems with access to robust computing resources tailored to their models and the data needed for analysis. This cloud-based approach eliminates the need for organizations to invest in high-performance computing and ensures secure data storage. The on-demand nature of cloud resources means that users only pay for what they use.
FUSE-AI aims to provide round-the-clock, on-demand diagnostic support for radiologists from the cloud. The scalability and flexibility of the Open Telekom Cloud facilitates the deployment of this solution, providing radiologists with a valuable tool for tumor detection.
How Deutsche Telekom uses AI for advanced network monitoring
Once again, we turn to Deutsche Telekom as a prime example, showcasing how service providers can harness the power of AI and data monetization within the telecom industry to streamline their internal operations.
Deutsche Telekom Global Carrier, the global wholesale arm of Deutsche Telekom, has harnessed the power of AI for advanced network monitoring. This innovative solution is the result of a collaboration between Deutsche Telekom, BENOCS and Anodot, a company specializing in autonomous enterprise monitoring. The AI-driven network monitoring service not only monitors network data but also has the ability to detect changes in traffic behavior and flow.
This implementation has delivered significant benefits, particularly in terms of cost reduction and operational efficiency, achieved by filtering and delivering only relevant anomaly alerts, effectively eliminating unnecessary noise from the alert system.
According to Carsten Bruns, Vice President Internet & Content Services at Deutsche Telekom Global Carrier, The integration of Anodot's anomaly detection technology with BENOCS' Flow Analytics has accelerated the identification of irregularities and problem resolution.
How Verizon utilizes AI and machine learning to optimize its supply chain inventory
Verizon introduced the OnePlanning program to bring together and standardize its various supply chain processes and data sources. The solution uses advanced statistical models, predictive analytics, and automation to calculate inventory targets, optimize inventory levels, and enable faster, more accurate decision-making, ultimately leading to improved data monetization.
By breaking down silos and enabling real-time data sharing and communication among team members, OnePlanning improves supply chain visibility and collaboration with partners, including suppliers. AI and ML contribute to more accurate forecasting by enabling the use of best-fit models, accounting for seasonal changes, and improving safety stock calculations.
Verizon's transformation program has improved working capital by assessing inventory availability, supplier affordability, and equipment allocation to specific stores. It also reduces transportation costs and positions Verizon for future advances in AI and machine learning.
We've discussed the theory and presented real-life success stories. Now, let's explore how Flyaps can assist you in achieving comparable results.
How Flyaps deals with AI and data monetization for telecom
When it comes to data management and data monetization in the telecom industry, generic, off-the-shelf solutions are not enough. What's communication service providers need is customized AI solutions, backed by industry experts. These customized solutions are the linchpin to effectively address the telco’s unique requirements, overcome its specific challenges, and open new revenue streams. At Flyaps, we're ready to help you navigate this complex landscape and transform your network data into a valuable asset that drives business growth. Having a solid track record of developing products that are actively used by more than 100 telecom companies, including industry leaders such as Orange Group or Yaana Technologies, our extensive experience has given us a deep understanding of the unique challenges and requirements within the telecom sector.
Ready to transform your telecom business with AI-based systems for data monetization? Just drop us a quick message and we will lead the way!
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