Applying breakthrough solutions from other verticals to telecommunications (Reader Forum)

Today, businesses are transforming to meet growing consumer and user demand for instant, accurate results on everything from ATM transactions to video analytics for online gaming. Companies that meet the demands of our current economy by transforming their infrastructure will succeed now and in the future.

Telecom companies, which are taking a financial hit in the transition to 5G, can take advantage of new revenue streams that other verticals are experiencing by meeting the needs of today’s economy. For example, let’s look at potential value-added applications, such as fraud detection and prevention, image and video analysis, online gaming, and product recommendations in mobile commerce.

Specifically, when mobile operators upgrade from 4G to 5G, the resulting improved network capacity and quality will come at a huge cost due to money spent on spectrum and densification and upgrading. network infrastructure level. At the same time, there is no significant (if any) increase in average revenue per user (ARPU) for the enhanced mobile broadband (eMBB) use case.

That’s why it makes sense for telcos to focus on providing some of the value-added applications themselves instead of acting like a “dumb pipe” – a bandwidth provider that just transfers bits and bytes between the customer’s device and the Internet in general.

Several non-telecom applications can be very profitable. Among them:

Fraud detection and prevention

It is estimated that in areas such as retail, travel, hospitality and entertainment, organizations lose an average of $4.5 million each year due to fraudulent online transactions. Yet only 51% say they prioritize fraud prevention. We’ve been part of PayPal’s 30x reduction in fraudulent transactions that would otherwise be missed. The benefits are tangible.

Multinational universal banks can and do benefit from ultra-low latency, as well as the ability to scale seamlessly to share fraud rules across platforms and facilitate machine learning consistently with uptime. millisecond response for 99.999% of transactions. So how is this relevant for telecom operators? Typical areas of fraud in the telecommunications context include identity theft and impersonation, registration of stolen devices under a bring-your-own-device (BYOD) program, and apps mobile payment. In each area, telcos can take advantage of massively scalable, highly consistent, and lightning-fast data solutions in the back-end, paired with respective front-end applications.

Image/video analysis

According to Mordor Intelligence, the facial recognition market was valued at $3.72 billion in 2020, but is expected to grow to $11.62 billion in 2026. online identity, healthcare, automotive, smart home and access control.

For example, companies are using data platforms to power facial recognition solutions for banking. Anyone entering a branch using the solution undergoes facial recognition to verify their identity. A data platform plays a key role not only by storing the metadata of human face contour, but more importantly, by providing this data to the AI ​​module in real time to facilitate a quick and transparent decision.

Image and video analysis is a key growth area for telecom operators. Edge infrastructure can aggregate field data captured by CCTV cameras or other input devices and then transmit it in the desired format to a central location that hosts the System of Record (SoR) database. Ideally, a data platform that can be deployed to both Edge and SoR systems works well in this context. Additionally, Cross Data Center Replication (XDR) technology can do the magic in the background by automatically replicating data between the edge database and the SoR database. This allows the SoR database to access real-time data aggregated by the edge and make it easily accessible to the AI ​​module for insight generation without compromising speed and relevance.

Again, what is at stake for telecom operators? Physical locations and telco buildings (e.g., signal access points and cable/telecom hubs) in metropolitan areas can facilitate edge deployments and aggregate field data for the SoR database main in more central locations. This program enables companies to offer personalized services based on image and video analysis to businesses and local and federal governments.

Online games

In 2020, the global online PC gaming market was worth $42.2 billion and is expected to reach $46.7 billion by 2025. However, the online mobile gaming market is expected to witness a much higher increase with a double-digit CAGR.

Technology must address key priorities with the ability to store, manage and retrieve massive volumes of player and game information; provide instant response time and rapid, iterative development; provide high availability; and provide a distributed database system that runs on low-cost commodity servers.

For example, India-based Dream11 had operational issues in 2020. It solved this problem by using a real-time data platform, which enabled it to manage more than 100 million sports fan users. , to grow by 30% and reach one million transactions per second in less than 15 milliseconds of latency.

Telcos hold a key advantage here as they own the relationship with the end user, including direct visibility into device usage patterns. The main opportunity here is to keep monitoring their in-game engagements, extract insights, analyze the data in the back-end, and then offer upsells or cross-sells while users are still on their mobile devices. From a database perspective, the requirements are to provide real-time subscriber profile information to front-end applications and the back-end analytics engine.

Product recommendation in mobile commerce

The pandemic caused an explosion in online purchases of goods and services with some $105 billion added to e-commerce in the United States in 2020. Consumers were also paying attention to what others were buying. A study found that product recommendations can increase product sales by 11%. It’s no news that in the big bucket of e-commerce, a critical subset is mobile commerce (m-commerce).

Real-time data platforms can process data at the edge and combine it with SoR to power recommendation engines. The best can also efficiently power petabyte-scale AI/ML inference models to support e-commerce and retail engagement systems applications for real-time decisions with a fraction of the servers. required by other technologies.

While many companies have rushed to improve their online e-commerce products and services to meet consumer demand over the past two years, Wayfair, one of the leading online furniture retailers, was already at the cutting edge of e-commerce. Years ago, Wayfair was looking to make its environment highly scalable, dramatically increase the flexibility of its data architecture, and drastically reduce server footprint. Wayfair now operates the platform for customer rating and segmentation, online event tracking, on-site advertising and recommendation engines. It has shrunk its ad-tech server footprint to an eighth of its previous size and uses both cloud and on-premises storage to scale up or down as needed to control costs.

Similar to the online gaming use case, telcos are also in a unique position to track their subscribers’ app usage. Telcos can bring insights from cross-app analytics and offer it to individual apps (e-commerce/m-commerce apps in this case) to shorten the sales cycle and expand the basket. In some developing markets, it is not uncommon to see telecom operators offering virtual shopping malls and acting as an integrated e-commerce/m-commerce application platform – a “super application of applications” .

It is in the interest of enterprises to use a vertically independent real-time data platform to take advantage of the huge potential for cross-referencing use cases across different industries. We look forward to seeing how telcos take market-leading non-telecom use cases and adopt them to serve new audiences during the height of today’s economy.

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