Mobile VAS Services – AI-driven mobile data monitoring and offerings are now escalating the usage of many mobile VAS services. In the telecom industry. Competitive forces in the telecommunications industry come not solely from traditional mobile providers but also from unorthodox competitors like those operating over the top (OTT). One can access different streaming services such as Netflix, Hulu, Skype, and What’s App by downloading their applications from various companies’ app stores.

Moreover, there exist digital content “oases” that furnish their exclusive streaming facilities. Thanks to these services, subscribers can enjoy streaming video and audio almost free of charge.

AI and Data Science-Based Mobile VAS

A point to make with clear understanding is that “the change or technology shift that 5G and subsequent technologies will bring will not be the same as from 3G to 4G.” The shift from 4G to 5G will change the whole game and bring major consequences and complexity to almost every industry.

Mobile VAS Services

The mobile value-added services market is predicted to cross $1 billion in the next 5–7 years. In order to achieve these numbers, a lot of diversity in its business model, applications, and style of content delivery is needed.

In order to proliferate the telecommunications market, where almost everyone has a smartphone, it won’t be easy without artificial intelligence and data science capabilities. The way it’s designed, where it just captures subscriptions and delivers static and monotonous content, it’s going to die soon.

The vas service providers have to pick the correct needs and technology to make sure they go hand in hand with  4G, 5G, machine learning and big data-based services.

Telcos & Declining Revenues

Telcos face flat growth due to market maturity, demand saturation, the commoditization of connectivity services, and advanced mobile devices. To counter these business challenges, telecom companies in all markets must go beyond VAS (value-added services). This will not only bring them differentiation but will also attract new customers and boost margins and ARPU (average revenue per user).

AI-based software is helping to solve distribution channel challenges. With such software, after feeding data to it, they can help improve customer visibility, behavior, patterns, and controls. For financial transactions over the POS, it even helps to stop the fraudulent transaction in an automatic manner. Machine learning capabilities are turning mobile operators’ biggest issues, sales and service distribution, around.

From a pure business perspective, a mobile value-added service is an extra service for which subscribers pay extra. Content-based services are on the rise, with examples of educational content for school and university books at very low cost and various tips for health, information technology, day-to-day life, astrology, managing relationships, business, financial advice, etc., and many more.

Contents Related Challenges

One of the significant challenges encountered by content providers is to customize their content according to the geography, market, and subscribers’ personal preferences. If this is not paid attention to, the content may end up annoying the subscriber.

Mobile VAS Services

With the rising popularity of video entertainment comes a corresponding need for a top-notch quality experience (QoE), especially in instances where viewers are shelling out a premium for access or when advertising is being used to make a positive impact.

Research conducted through a Google search reveals that Orange France’s collaboration with the music streaming platform Deezer was cited as an illustration during the webinar. As a result of the collaboration, Deezer witnessed a remarkable surge in its French clientele, with numbers reaching up to 1.2 million from a mere 25,000.

Orange brings an innovative and unique solution to its audience with comprehensive billing and data analysis capabilities, resulting in a more captivating sales proposition and heightened data engagement among its client base. Sophisticated machine learning algorithms were utilized to enhance sales and provide personalized content based on individual preferences.

Points to Note:

All credits, if any, remain with the original contributor only. In this post, we have covered some high-level mobile VAS services with a reliance on AI. In the coming weeks, blog posts will talk about each of these emerging bundles in detail.

Books and Reference Material

  • Research through the open internet, news portals, white papers, and imparted knowledge via live conferences and lectures.
  • Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.

Feedback & Further Question

Do you have any questions about Telecoms, AI, Deep Learning or Machine Learning? Leave a comment or ask your question via email. Will try my best to answer it.

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Posted by V Sharma

A Technology Specialist boasting 22+ years of exposure to Fintech, Insuretech, and Investtech with proficiency in Data Science, Advanced Analytics, AI (Machine Learning, Neural Networks, Deep Learning), and Blockchain (Trust Assessment, Tokenization, Digital Assets). Demonstrated effectiveness in Mobile Financial Services (Cross Border Remittances, Mobile Money, Mobile Banking, Payments), IT Service Management, Software Engineering, and Mobile Telecom (Mobile Data, Billing, Prepaid Charging Services). Proven success in launching start-ups and new business units - domestically and internationally - with hands-on exposure to engineering and business strategy. "A fervent Physics enthusiast with a self-proclaimed avocation for photography" in my spare time.

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