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How telecoms can use Big Data and Predictive analytics to grow profits

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They face a highly saturated mobile market and competition from both incumbents and tech entrants. As a result, they are locked in a fierce price war, and their average revenue per user (ARPU) has taken a nosedive. In the face of such a vicious battle, only those with a competitive advantage (other than reduced price) will succeed, while others will end up as completely commoditized utility service providers. To stop losing money, get a cutting edge over the competition, and develop a strong customer value proposition, most forward-thinking players choose to adopt telecom analytics, Big Data, and Data Science. And it is only logical as large quantities of data are a natural part of daily telecom operations, and they are going up.


Design thinking, AI and starting with what users need

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Any organization that wants to deliver targeted, personalized services and experiences needs to understand its customers inside and out: their wants, wishes, behaviors and attitudes. These days, the data to develop that understanding is abundantly available. The challenge is to extract meaningful customer insights from it and convert those insights into actions. By combining the principles of design thinking with the power of artificial intelligence (AI), CIOs have the tools to solve that problem and deliver powerful business results for their organizations. That's definitely true for the CIOs of the service providers we work with at Nokia -- telcos, mobile virtual network operators and the like.


Customer Churn Prediction Using Machine Learning: Main Approaches and Models

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Customer retention is one of the primary growth pillars for products with a subscription-based business model. Competition is tough in the SaaS market where customers are free to choose from plenty of providers even within one product category. Several bad experiences – or even one – and a customer may quit. And if droves of unsatisfied customers churn at a clip, both material losses and damage to reputation would be enormous. For this article, we reached out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn with predictive modeling. You will discover approaches and best practices for solving this problem. We'll discuss collecting data about client relationship with a brand, characteristics of customer behavior that correlate the most with churn and explore the logic behind selecting the best-performing machine learning models. Customer churn (or customer attrition) is a tendency of customers to abandon a brand and stop being a paying client of a particular business. The percentage of customers that discontinue using a company's products or services during a particular time period is called a customer churn (attrition) rate. One of the ways to calculate a churn rate is to divide the number of customers lost during a given time interval by the number of acquired customers, and then multiply that number by 100 percent.


AI in Telecom - Ripe for Innovation

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From 2021 to 2028, the worldwide telecom services industry will increase at a compound growth rate of 5.4%. By 2025, the market for Telecom Equipment is expected to develop at a rate of 11.23%. One of the main aspects fuelling this market is an increased investment in 5G infrastructure deployment due to a shift in customer preference for next-generation technologies and smartphone devices. Increased need for value-added managed services, a growing number of mobile users, and surging demand for high-speed data connectivity are all major market drivers. Over the last few decades, the global communication network has clearly been one of the most important areas for continuing technical advancement.


AI in Telecom -- Ripe for Innovation

#artificialintelligence

From 2021 to 2028, the worldwide telecom services industry will increase at a compound growth rate of 5.4%. By 2025, the market for Telecom Equipment is expected to develop at a rate of 11.23%. One of the main aspects fuelling this market is an increased investment in 5G infrastructure deployment due to a shift in customer preference for next-generation technologies and smartphone devices. Increased need for value-added managed services, a growing number of mobile users, and surging demand for high-speed data connectivity are all major market drivers. Over the last few decades, the global communication network has clearly been one of the most important areas for continuing technical advancement.