Autonomous, real-time anomaly detection for telecom analytics

With the advent of 5G and the growing popularity of OTT media services, the telecom industry is witnessing a massive transformation. To cater to customers in today’s highly competitive world, telecom service providers are moving towards an API-first or an E-commerce platform model of business. On one hand, CSPs (Communication Service Providers) must get their infrastructure ready for new technologies while on the other hand, they must manage customer expectations for seamless omnichannel user experience. Considering the massive amount of live data that passes through various telecom channels, autonomous or AI-enabled mechanisms are a necessity today for processing and analyzing streaming telecom data from various sources.

Challenges in the telecom sector


1. Scaling IT platform

Proactive monitoring and remediation are the keys to driving application performance and IT efficiencies. Whether it is to ensure a successful software upgrade or to launch a new feature for facilitating payments, at any given point of time telcos have to analyze live data for billions of events. Tracking issues across various dimensions such as app versions, regions, combinations of systems, etc. is a complex task. Moreover, you need to quickly understand the root cause of issues or glitches for accurate and fast resolution. Telecom operators need a single pane of view for all their IT data in one place, to spot business-critical outliers and patterns in real-time.

2. Need for real-time analysis of customer behavior

In a digital world where customers switch between different channels, it is essential to deliver an integrated seamless experience across all touchpoints. Telecom service providers can no longer rely solely on physical retail stores to drive sales. As per an Accenture report, seven out of ten customers who start their buyer’s journey in a retail store, end up closing the purchase in a different channel. 76% of consumers expect companies to understand their needs. Contextual omnichannel marketing requires a mature and autonomous system to process customer behavior metrics in real-time to drive personalized interactions and marketing campaigns.

3. Preventing telecom fraud

With billions of transactions and data generated on an everyday basis, the telecom sector is highly susceptible to fraudulent activities. Misuse of any telecom products or services can cost operators millions of dollars in revenue. As per the joint report by the European Cybercrime Centre and Trend Micro, telecom fraud costs the world a loss of US$32.7 billion annually. A mobile SIM is associated with the identity of a telecom subscriber. The SIM is linked to IoT devices, critical banking services, and mobile wallets. Hackers are constantly trying to gain access to customer SIM or billing platforms for fraudulent and illegal activities such as theft, money laundering, phishing, and terrorism. Whether SIMs are misused at a retail outlet or compromised through malware or customers are subjected to Wangri fraud, detecting fraud in a sector that has millions of active users is not easy.

4. Managing Network QoE (Quality of Experience)

Network QoE (Quality of Experience) is a measure of how well a network satisfies a subscriber’s requirements. This includes the quality of end-to-end connections and the applications that run on a network. The better the QoE, the happier the subscribers are with a service provider. Globally, there are over two-and-a-half billion digital customers who are under 25 years of age. There is an explosive demand for high-speed data and multimedia capabilities. The network capacity requirements are higher than ever before. To keep up with the needs of today’s digital subscribers who use multiple streaming services, gaming apps, and OTT channels, MNOs (Mobile Network Operators) must ensure high network performance. For this, operators need a granular level of visibility of all activities and performance issues at the cell site level.

5. Revenue assurance

Every business owner needs clear visibility on revenue metrics to know the most important and profitable sources of income. The telecom sector is no different. With multiple services and products spread across different geographies, channeled through various partners, understanding revenue metrics and predicting trends in the telecom business is difficult. Without an autonomous business analytics software, it is challenging to analyze the various metrics that are part of telecom revenue.

How telecom operators can benefit from augmented analytics

With augmented analytics, telecom operators can ingest, monitor, and analyze streaming data from various sources to identify incidents and opportunities in real-time. It also helps in understanding the correlation between events and in faster root cause analysis. Here are ways in which telecom operators can leverage augmented analytics for delivering a superior service quality:

1. AIOps for scaling IT operations and customer service

With early detection and fast root cause analysis, telecom service providers can quickly troubleshoot IT issues and take remedial action to minimize its impact. With fast, near-real-time issue resolution, operators experience improved payment success rate, faster application performance and can deliver better service quality. Moreover, by providing customer care teams with real-time information, MNOs can identify and resolve issues faster, leading to a lesser number of support calls to call centers.

2. Real-time interactions and campaigns

Additionally, real-time anomaly detection also helps operators in understanding customer behavior and in optimizing marketing campaigns across channels to deliver highly contextual offers based on customer preference. Instead of relying on stale data, operators can get real-time insights on customer’s buying behavior allowing them to recommend the right products and services to the right subscribers at the right time.

3. Real-time fraud detection

Augmented analytics helps in detecting hard-to-find anomalies such as identity thefts, data breach, and suspicious transactions. Be it for customer-facing applications or transactions involving partners, AI-enabled anomaly detection enables autonomous monitoring of metrics at a granular level, alerting stakeholders to issues that need immediate attention.

4. Real-time network analytics

AI-enabled analytical capabilities allow network operators to monitor large volumes of network and subscriber level-data in real-time for up-to-date visibility on network performance. This allows operators to prioritize workloads, enhance network efficiency, and improve overall service quality. Further, by predicting the periods of heaviest network usage arising from video streaming, telcos can ease congestion and reduce their planned capital expenditures by as much as 15 percent.

5. Maximize revenue from subscribers and partners

Augmented analytics also helps in granular-level analysis of revenue metrics across dimensions such as cell-sites, products/services, devices, geographies, customer segments, etc, revealing insights to plug potential revenue loss and capitalize on new opportunities to increase income. With a clear understanding of the most profitable partner channels and services, telcos can create highly targeted, revenue-generating offers to increase ARPU (Average revenue per user).

As per McKinsey, telecom companies can reduce customer churn by up to 15% by following an analytics-based approach to base management. Predictive capabilities also enable service providers to increase service quality while lowering network operating center costs by as much as 20%. In many ways, real-time augmented analytics powered by artificial intelligence and machine learning is the key to driving personalization, operational efficiency, application security, and revenue in the telecom sector.

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