Apply breakthrough solutions from other industries to telecom (Reader Forum)
Today, businesses are transforming to meet the increasing demand from consumers and users who want instant, accurate results for everything from ATM transactions to video analytics for online gaming. Businesses that meet the demands of today’s economy by transforming their infrastructure will thrive now and into the future.
Telcos taking a financial hit in the transition to 5G can unlock new revenue streams that other industries are experiencing by meeting the demands of the current economy. For example, let’s look at potential value-added applications such as fraud detection and prevention, image and video analytics, online gaming, and product recommendations in mobile commerce.
In particular, as mobile operators migrate from 4G to 5G, the resulting network capacity and quality improvement will come at a huge cost due to the monies being spent on spectrum and densifying and upgrading network infrastructure. 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.
For this reason, it makes sense for telcos to provide some of the value-added applications themselves, rather than acting as a “dumb pipe” — a bandwidth provider that just transfers bits and bytes between the customer’s device and the internet at large.
Several non-telco applications can pay big dividends. Among them:
Fraud Detection and Prevention
It is estimated that businesses in sectors such as retail, travel, hospitality and entertainment lose an average of $4.5 million each year 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 go unnoticed. The advantages are apparent.
Multinational universal banks can and will benefit from ultra-low latency, along with seamless scalability 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 to telcos? Typical areas of fraud in the telecom context are identity theft and identity theft, enrolling stolen devices in a Bring Your Own Device (BYOD) scheme and mobile payment applications. In each area, telcos can benefit from massively scalable, highly consistent, super-fast data solutions on the back-end, coupled with appropriate front-end applications.
According to Mordor Intelligence, the facial recognition market was valued at $3.72 billion in 2020, but this value is expected to grow to $11.62 billion in 2026.
For example, companies use data platforms to power facial recognition solutions for the banking industry. Anyone entering a branch with the solution is subject to facial recognition to verify identity. A data platform plays a key role by not only storing the metadata of the contour of human faces, but most importantly, delivering this data to the AI engine in real-time to facilitate a quick and seamless decision.
Image and video analytics are key growth areas for telecom companies. The edge infrastructure can aggregate the field data captured by CCTV cameras or other input devices and then forward it in the desired format to a central location where the System of Record (SoR) database resides. Ideally, a data platform that can be used on both edge and SoR systems works in this context. In addition, Cross-Datacenter Replication (XDR) technology can work wonders behind the scenes 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 available to the AI engine to generate insights without compromising speed and relevance.
Again, what’s at stake for telcos? Telecoms physical locations and buildings (e.g., signal access points and cable/telecom hubs) in metropolitan areas can facilitate edge deployments and aggregate field data for the core SoR database at more central locations. This program enables the corporations, corporations, and local and federal governments to offer custom-designed services based on image and video analytics.
In 2020, the global online PC gaming market was worth US$42.2 billion and is expected to reach US$46.7 billion by 2025. However, the online mobile games market is expected to grow much faster at a double-digit CAGR.
The technology should meet key priorities by being able to store, manage and retrieve vast amounts of player and game information; provide instant response time and rapid, iterative development; deliver high availability; and deliver a distributed database system running on inexpensive off-the-shelf servers.
For example, India-based Dream11 faced operational issues in 2020. It solved this problem using a real-time data platform that allowed them to manage more than 100 million sports fan users to grow 30% and reach one million transactions per second under 15 milliseconds latency.
Telcos have a distinct advantage here as they own the end-user relationship, including direct visibility of device usage patterns. The key opportunity here is to stay on top of their in-game interactions, gain insights, analyze data on the backend, and then upsell or cross-sell offers while users are still on their mobile devices are. From a database perspective, the requirement is to provide real-time subscriber profile information to both front-end applications and the back-end analytics engine.
Product recommendation in mobile commerce
The pandemic caused an explosion in online shopping for goods and services, with US e-commerce growing by around $105 billion in 2020. Consumers also paid attention to what other people were buying. One study found that product recommendations can increase product sales by 11%. It’s not new that within the larger e-commerce space, mobile commerce (m-commerce) is a crucial subset.
Real-time data platforms can process data at the edge and combine it with the SoR to power recommendation engines. The best can also efficiently run petabyte-scale AI/ML inference models to support e-commerce and retail systems of engagement applications for real-time decision-making with a fraction of the servers other technologies require.
While many companies have struggled in recent years to expand their online e-commerce products and services to meet consumer demand, Wayfair, a leading online furniture retailer, has already been at the forefront of e-commerce. Years ago, Wayfair wanted to make its environment highly scalable, greatly increase the flexibility of its data architecture, and significantly reduce server requirements. Wayfair now uses the platform for customer rating and segmentation, online event tracking, onsite ads and recommendation engines. It has reduced its ad-tech server footprint to an eighth of its previous size and leverages both cloud and local storage to scale up or down as needed to control costs.
As with the online gaming use case, telecom providers are in a unique position to track app usage by their subscribers. Telcos can leverage insights from cross-app analytics and offer these individual apps (in this case, ecommerce/mcommerce apps) to shorten the sales cycle and increase the shopping cart. In some developing markets, it is not uncommon for telcos to offer virtual malls and act as an integrated platform for e-commerce/m-commerce apps – a “super app of apps”.
It is in the best interests of organizations to use a real-time data platform that is vertically independent to leverage the tremendous potential of cross-referencing use cases across different industries. We look forward to seeing telcos take market-leading non-telecom use cases and adopt them to serve new audiences at the height of the current economy.