Data Monetization in the telecom industry

Summary

In a session on data monetization in the telecom industry, a seasoned data scientist discussed the challenges telecom operators face due to the rise of OTT services and changing consumer preferences. The talk emphasized the need for digital transformation, exploring both internal and external data monetization strategies. A case study featuring a partnership between ORANGE and Condor Electronics highlighted how telecom data can optimize store locations and enhance customer targeting. The presentation underscored the importance of leveraging insights for competitive advantage in a rapidly evolving market.

Takeaways

• 📉 Traditional telecom services are declining due to the rise of Over-The-Top (OTT) services, projected to reach $86 billion by 2026.
• 📱 Customers are shifting from voice communications to messaging, affecting telecom revenue streams.
• 💰 Telecom operators face financial strain from costly investments in new technologies like 5G and ongoing price wars to retain market share.
• 🏢 The entry of Mobile Virtual Network Operators (MVNOs) disrupts customer experiences, leading to higher customer expectations.
• 🔄 Telecom companies are undergoing digital transformation to transition from traditional service providers to digital solution providers, offering services like cloud computing and fintech.
• 📊 Internal data monetization involves leveraging data science and machine learning to enhance customer service and optimize operations within telecom companies.
• 📈 External data monetization focuses on selling insights derived from data assets to third parties instead of raw data.
• 🌍 A case study highlights a partnership in Algeria where telecom data is used to optimize store locations for an electronics retailer.
• 📍 The store optimization project involves analyzing competitor locations, customer movement, and demographic data to identify high-potential areas.
• 📊 The final product includes a dashboard providing a comprehensive view of store performance and area potential, aiding decision-making for retail expansion.

Q & A

What is the primary focus of the session?

The session focuses on data monetization in the telecom industry, particularly external data monetization.

Who is the speaker, and what is their background?

The speaker is Fatigue, a data scientist and marketing analytics expert with over 13 years of experience in the telecom industry.

What are the recent challenges faced by telecom operators?

Telecom operators face challenges such as declining demand for traditional services, the rise of over-the-top (OTT) services, high infrastructure costs, disruptive technologies, and increased customer expectations.

How has the digital revenue landscape changed from 2018 to 2023?

There has been a significant drop in traditional telecom services, while non-traditional services like digital games and OTT videos have seen growth, driven by mobile data usage.

What strategies are telecom companies adopting to cope with market changes?

Telecom companies are engaging in digital transformation to shift from traditional communication services to offering digital solutions in various fields, including IT, fintech, and IoT.

What is internal data monetization in telecom?

Internal data monetization involves leveraging data assets to enhance customer service and optimize operations through analytics, machine learning, and AI technologies.

Can you explain what external data monetization is?

External data monetization involves selling insights derived from data assets to third parties like retail, banking, and government, rather than selling raw data itself.

What is a specific use case presented for external data monetization?

A use case involves a partnership between the leading telecom operator in Algeria and a household electronics company, where insights from telecom data are used to optimize store location and performance.

How do telecom companies identify optimal store locations for their partners?

They analyze competitor store locations, customer demographics, foot traffic patterns, and internal telecom data to calculate potential areas for new store openings.

What is the significance of the final dashboard tool mentioned?

The dashboard tool provides a 360-degree view of store performance and location potential, enabling businesses to make informed decisions about store openings and closures.