Today businesses are transforming to meet the rising demand from consumers and users who want instant, accurate outcomes on everything from ATM transactions to video analytics for online gaming. Companies that are addressing the demands of our right-now economy by transforming their infrastructure will succeed now and in the future.
Telecommunications companies, who are experiencing a financial hit transitioning to 5G, can leverage new revenue streams that other verticals are experiencing by meeting the needs of the right-now economy. For example, let’s look at potential value-add applications, such as fraud detection and prevention, image and video analytics, online gaming and product recommendations in mobile commerce.
Specifically, when mobile operators transition from 4G to 5G, the resulting network capacity and quality improvement will exact a huge cost because of the money spent on the spectrum and densifying and upgrading the network infrastructure. At the same time, there is no meaningful (if any) increase in the average revenue per user (ARPU) for the enhanced mobile broadband (eMBB) use case.
That’s why it makes sense that telcos delve into providing some of the value-add applications themselves instead of acting as a “dumb pipe” — a bandwidth provider who just transfers bits and bytes between the customer’s device and the Internet at large.
Several non-telco applications can have a big payoff. Among them:
Fraud detection and prevention
It’s estimated that in areas such as retail, travel, hospitality and entertainment, organizations are losing an average of $4.5 million every year because of fraudulent online transactions. Yet only 51% report prioritizing fraud prevention. We have been a part of PayPal’s 30x reduction in fraudulent transactions that would be missed otherwise. The benefits are tangible.
Multinational universal banks can and do benefit from ultra-low latency, along with the seamless ability to scale to share fraud rules across platforms and facilitate machine learning consistently with millisecond response time for 99.999% of transactions. So, how is this relevant for telcos? Typical areas of fraud in the telecom context include identity theft and impersonification, stolen device registration in a “Bring Your Own Device” (BYOD) scheme and mobile payment applications. In each area, telcos can take advantage of massively scalable, strongly consistent, super-fast data solutions in the back-end, coupled with respective front-end applications.
Image/video analytics
According to Mordor Intelligence, the facial recognition market was valued at $3.72 billion in 2020, but that’s expected to jump to $11.62 billion in 2026. The technology is expected to be used more in security, online identity verification, healthcare, automotive, smart home and access control.
For example, companies are using data platforms to power facial recognition solutions for the banking industry. Anyone who walks into a branch office using the solution undergoes facial recognition to verify identity. A data platform plays a key role by not only storing the metadata of the contour of the human faces but more importantly, providing that data to the AI module in real-time to facilitate a fast and seamless decision.
Image and video analytics are key growth areas for telcos. Edge infrastructure can aggregate the field data captured by CCTV cameras or other input devices and then forward that in the desired format to a central location that houses the system of record (SoR) database. Ideally, a data platform that can be deployed at both the edge and SoR systems works well in this context. Additionally, cross-datacenter 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 the real-time data aggregated by the edge and make it readily available to the AI module for insight generation without compromising speed and relevance.
Again, what’s at play for telcos? Telecom companies’ physical locations and buildings (e.g., signal access points and cable/telco hubs) in metro areas can facilitate the edge deployments and aggregate field data for the core SoR database in more central locations. This scheme allows the companies to offer custom-developed services based on image and video analytics to enterprises and local and federal governments.
Online gaming
In 2020, the global online PC gaming market was worth $42.2 billion and is projected to hit $46.7 billion by 2025. However, the online mobile gaming market is expected to see a much sharper increase with a double-digit CAGR.
Technology should deliver on key priorities through an ability to store, manage and retrieve massive volumes of player and game information; provide instant response time and rapid, iterative development; deliver high availability; and supply a distributed database system that runs on low-cost commodity servers.
For example, India-based Dream11 was having operational issues in 2020. It solved this problem using a real-time data platform, which enabled them to handle more than 100 million sport fan users, grow 30% and achieve one million transactions per second in under 15 milliseconds of latency.
Telcos hold a key advantage here as they own the end-user relationship, including direct visibility of device usage patterns. The key opportunity here is to maintain oversight of their in-game engagements, extract insights, analyze data in the back-end and then make upsell or cross-sell offers while the users are still on their mobile devices. From the database perspective, the requirements are to provide subscriber profile information in real-time both to front-end applications and the back-end analytics engine.
Product recommendation in mobile commerce
The pandemic caused an explosion of online shopping for goods and services with some $105 billion added to e-commerce in the U.S. in 2020. Consumers were also paying attention to what other people were buying. One study found that product recommendations can boost product sales by 11%. It’s not news that within the larger e-commerce bucket, a critical subset is mobile commerce (m-commerce).
Real-time data platforms can process data at the edge and combine it with the SoR to power recommendation engines. The best ones can also efficiently power AI/ML inference models at petabyte scale to support e-commerce and retail Systems of Engagement applications for real-time decisions with a fraction of the servers that other technologies require.
While many companies have scrambled to amp up their online e-commerce products and services to meet consumer demand during the last couple of years, Wayfair, a leading online furniture retailer, was already on the leading edge of e-commerce. Years ago, Wayfair was looking to make their environment highly scalable, substantially increase the flexibility of their data architecture and significantly reduce server footprint. Wayfair now leverages the platform for customer scoring and segmentation, tracking online events, onsite ads and recommendation engines. It has cut its server footprint for ad tech to one-eighth of its previous size and uses both the cloud and on-premises storage to scale up or down as needed in order to control costs.
Like the online gaming use case, telcos are also in a unique position to track app usage by their subscribers. Telcos can bring in insights from cross-app analytics and offer that to individual apps (e-commerce/m-commerce apps in this case) to shorten the sales cycle and enlarge the shopping cart. In some developing markets, it’s not uncommon to see telcos offering virtual malls and acting as an integrated platform of e-commerce/m-commerce apps — a “super app of apps.”
It is in the best interest of companies to use a real-time data platform that is vertical-agnostic to leverage the huge potential in cross-referencing use cases among different industries. We look forward to seeing how telcos take market-leading, non-telecom use cases and adopt them to serve new audiences at the height of the right-now economy.
