Fueling 5G revenue growth with big data, machine learning & AI-driven analytics
One industry that’s always been at the cutting edge of technology is telecommunications. If we consider some of the breakthroughs made in human achievements, the most significant involve communication technology – from wired telegraph relays crossing vast continents to the invention of the telephone, to the world-crossing internet and the ubiquitous mobile phone.
The need to communicate being at the heart of human behavior means that modern communications service providers (CSPs) operate in a highly competitive market; everyone needs to connect. Unless companies can find differentiation from one another, the technology that underpins the many services (like landlines, internet and mobile) is interchangeable, for businesses and consumers alike. After all, changing one’s cellphone provider can be as simple as flicking a software toggle switch in a phone’s settings to use SIM B, not SIM A.
Retaining profitability is essential for CSPs as it is in every other vertical, of course, and with a saturated market that’s based on technology, the challenges in the sector are very specific. Naturally, they come down to a reduction of costs, especially operating costs (more of this below), and increasing revenues.
With operational costs providing only around 20 percent of potential positive effects on a company’s bottom line, finding ways to increase revenues is pressing and needs prioritization. One basic differentiator, that of price, in most telco operator offerings is not something that can be used – razor-thin margins or even loss-leaders predominate, especially in established product lines.
As far as revenue growth goes, telcos are sitting on an often-underused asset – that of data. As the “new oil”, information about usage patterns, interests and preferences, purchase and customer experience histories, demographic and socio-economic data, and the like present new opportunities and openings for companies.
The new black gold is made of silicon
Unfortunately, the technological basis of modern communications spells its own disadvantage: often, there’s simply too much data, from the point of view of gaining meaningful insights, and that situation is only going to get worse with 5G, the fifth-generation cellular network technology. The terabytes gathered daily by even a small telco contain rich veins of virtual gold – and one issue is locating it.
Sometimes the value is not hidden by the mere quantity of information, but rather the crucial findings from data present themselves only after specialized processing methods, formulated especially for “big data” situations.
The challenge represented by big data is, therefore, also an opportunity for telcos to increase revenues. The advent of 5G as part of the offering portfolio has the potential to alter the makeup of current market shares significantly. In addition to the up-selling or cross-selling opportunities that might present themselves as data is properly utilized, 5G and its associated uses present massive opportunities.
Assigning hundreds of new hires to comb through the information on prospects and existing customers is not viable for any company. Therefore, using technologies like machine learning (ML) and artificial intelligence (AI), algorithms that teach themselves, can be thought of as the mainstay of monetizing data. Thankfully, there are suppliers in the market that can help any forward-looking organization leverage this highly complex technology.
From its birth in theoretical computing abstracts in academic institutions at the end of the twentieth century, AI code is available to use from companies like IBM, which are perhaps best known for Watson. Younger, more agile companies are racing to exploit breakthroughs in machine intelligence, including Guavus, which specializes in Big Data analytics, and is applying advanced AI/ML techniques to real-time behavioral analysis of mobile subscribers so that CSPs can increase customer value while ensuring quality-of-experience (QoE).
The future in technology
There are plenty of new technologies on the horizon and starting to be implemented, like 5G, and what they all have in common is the preponderance and importance of data. Big data processing, therefore, is getting especially pressing. But the default state for data is to exist in silos, often of proprietary design. While that doesn’t necessarily mean the information is rendered valueless, its utility can easily be multiplied. By joining the different data silos (the computing phrase is “data normalization”) across a CSP, in areas such as customer experience management, customer care and field operations, network and service operations, product management, sales and marketing, and finance (to name just a few examples), the whole information picture can be analyzed.
Insights drawn by these cutting-edge technologies help telcos find and develop new market spaces and develop key differentiators. In customer-facing functions, ML-derived data is used to uncover opportunities to cross-sell or up-sell new and existing products, services, and packages. For marketing departments, the otherwise hidden trends, interests, preferences and behavior of customers can yield new opportunities for product exposure.
Mobile operators are keen to grow revenues by increasing the number of high-value subscribers – the ones who buy higher tier services and consume more services than others. The overarching objective is to serve these subscribers well and to provide incentives for lower tier subscribers to climb the value ladder. Guavus powers real-time insights and right-time decisions so that CSPs can classify and target subscribers with personalized marketing campaigns in real-time, automatically.
Increasing revenues by becoming more conversant with the resources that many CSPs are acquiring every day in the form of data is perhaps the most promising ground on which to build the future. And it should be noted that the astute, technologically conversant telecom professional can also use those same resources to elevate operational efficiency and lower operating costs, as well as improve QoE and reduce subscriber loss – sadly, there isn’t enough room in this article to fully explore the possibilities.
To see a useful infographic and download more reading specifically on addressing 5G operational complexity and reducing OPEX for CSPs, click here.
20 September 2019
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