Industry
Communications, Media and Information Services
Going Beyond: CMI to Focus on Personalized Experiences for Growth
Solution
TCS HOBS™
TCS HOBS™: Engineering experiences with personalization and innovation
Highlights
- While the promises of 5G services are boundless, so are the complexities in safeguarding revenues and costs and enhancing customer experience.
- Business assurance in the 5G era requires a comprehensive and deeper understanding of the complex structures involved to quickly identify and mitigate risks.
- By leveraging real-time or near real-time business assurance controls aided by artificial intelligence (AI) and machine learning (ML), CSPs can maintain Quality of Service (QoS), address anomalies, ensure revenue prevention, and improve customer experience.
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5G – The catalyst
5G technology has emerged as the catalyst for a new wave of innovation and opportunity in the current digital transformation era of unprecedented connectivity. The rollout of 5G networks promises lightning-fast data speeds and low latency and a fundamental shift in how industries and individuals connect, communicate, and collaborate.
A wide range of business scenarios are evolving across industry verticals spanning manufacturing, healthcare, retail, automobile, surveillance, banking, and entertainment. Specific use cases such as remote 3D printing, robotic process automation, robotic surgery, immersive online shopping, autonomous driving, real-time traffic management, AR/VR-enabled gaming, CCTV surveillance, and many more showcase the vast potential of 5G technology.
Service providers are challenged to provide innovative services and monetize them. Additionally, business revenue models relying heavily on subscription and usage are transforming to newer paradigms such as session-based, Quality of service (QoS)-based, performance-based, network slice-based, and more.
To ensure business assurance in 5G, a deep understanding of complex structures is needed to identify and mitigate risks swiftly.
eMBB use case
One potential application of Enhanced Mobile Broadband (eMBB) for retail consumers is augmented reality (AR) and virtual reality (VR) online gaming. AR and VR gaming require high bandwidth, low latency, and seamless coverage to provide immersive and realistic experiences.
With eMBB, gamers can enjoy high-quality graphics, smooth gameplay, and interactive features without lag or interruption. For example, eMBB can enable cloud gaming, where the game is processed on remote servers and streamed to the user's device. This can reduce the user's hardware requirements and costs and enable cross-platform compatibility.
To access eMBB services for AR and VR online gaming, retail consumers will need compatible AR/VR devices and access to cloud-based third-party gaming platforms. Devices will need to be smartphones, tablets, or headsets that support 5G NR (New Radio) standards and frequencies. Consumers must subscribe to third-party partners or utilize pay-per-use services to access gaming platforms.
Some of the key business assurance challenges that CSPs will encounter in providing eMBB services are listed below:
Order provisioning is activating and configuring the service for the customer. Incorrect provisioning of services, such as wrong device ID activation and incorrect VAS bundle configuration can lead to service non-availability, which can impact customer experience, or incorrect charging, which can lead to bill disputes.
Third-party partner services integration is enabling the customers to access other services or platforms that the service provider does not provide. Failures or errors in provisioning workflow or manual activation can lead to service unavailability. Additionally, discrepancies in activation dates between the operator and third-party partners result in differences in the duration billed to customers vis-à-vis the duration considered for partner settlement.
Billing complexities are miscalculating and over/undercharging the user for service usage. This may involve different pricing models, such as subscription-based, pay-per-use, or hybrid. Inaccuracies in the packages or VAS bundles configured result in excess charging, leading to bill disputes, customer dissatisfaction, or short charging, leading to revenue loss.
URLLC use case
One possible use case of ultra-reliable low latency communications (URLLC) is autonomous driving, which requires real-time communication between vehicles, roadside infrastructure, and cloud servers.
To provide a dependable solution for autonomous driving, the network operator must deploy 5G NR base stations supporting URLLC features, such as short transmission time interval (TTI), mini-slot scheduling, grant-free uplink transmission, and hybrid automatic repeat request (HARQ-ACK) feedback. The network must also provide end-to-end QoS guarantees for URLLC traffic, such as low packet loss rate, jitter, and end-to-end delay.
The vehicles need to be equipped with 5G NR user equipment (UE) that can communicate with the 5G NR base stations using URLLC radio resources. The user equipment (UE) must also support multi-connectivity and handover mechanisms to ensure seamless mobility and reliability.
The roadside infrastructure needs to be connected to the 5G NR base stations or the core network using fiber or microwave links. It also needs devices to communicate with vehicles and provide information such as traffic signals, road conditions, and emergency alerts.
The cloud servers must connect to the core network using high-speed links. They also need software that can perform advanced data analysis, artificial intelligence, and decision-making for autonomous driving.
Some of the key business assurance challenges that CSPs will encounter in providing URLLC services are listed below:
Network deficiency impacting QoS: If the network performance is degraded or disrupted, the customers may experience severe consequences, such as accidents, injuries, or losses. This may lead to customer dissatisfaction, churn, and reputational damage for the operator. Therefore, the operator must ensure high availability, reliability, and low latency of the network and the service.
Billing complexity: Since URLLC services require very strict QoS guarantees, the operator needs to monitor the network's performance and the SLAs with the customers. The operator also needs to design a pricing scheme that can reflect the value of the service, such as charging based on the amount of data transmitted, the latency achieved, the reliability achieved, or a combination of these factors. Any deficiencies in the collection and aggregation of SLA data would lead to inaccurate billing and non-payments and could ultimately result in customer churn.
Fraud detection: URLLC services may also attract fraudsters who may try to exploit the network or the service for malicious purposes. For example, fraudsters may try to spoof or jam the signals of the vehicles or infrastructure in autonomous driving scenarios, causing collisions or traffic jams.
mMTC use case
5G Massive Machine-Type Communications (mMTC) revolutionizes smart city services such as Smart Vehicle Parking solutions by enabling a vast network of interconnected devices and sensors. These solutions use various sensor devices embedded in parking spaces, communicating through 5G mMTC technology to inform drivers about the available parking spots in real-time through mobile apps or digital signboards.
Some of the key business assurance challenges that CSPs will encounter in providing mMTC services are as follows:
Metering and data aggregation: Failure to collect, aggregate, and process the enormous volume of transactions generated by connected IoT devices regarding parking space usage could lead to non-charging events, resulting in revenue loss.
Policy management: Complexities associated with implementing different pricing models based on demand, such as peak/non-peak hours and location (proximity to malls, stations, etc.). An incorrectly defined policy could lead to excess or short charging, resulting in customer disputes or revenue leakage scenarios.
Payment gateway integration: API issues/failures encountered with third-party gateways integrated for FASTag-based real-time parking charges could also lead to possible revenue loss scenarios.
Network slicing
Network slicing enables operators to offer customized and differentiated services to various market segments. Some services enabled by network slicing include machine-to-machine/IoT, enhanced mobile broadband, vehicle-to-everything, and ultra-reliable low-latency communications.
To provide these services, operators need to consider several aspects, including:
Network configuration: Creating and activating a network slice for a customer or an application. CSPs handle the requests for network slices, allocate the required resources and functions, configure the parameters and policies, and monitor the slices' performance and usage. While configuring specific parameters relating to latency, bandwidth, etc., for a network slice as per the contractual terms, any inaccuracies causing deviations could severely impact service availability, QoS, reliability, etc.
SIM card configuration: Assigning a network slice to a device or a user. To facilitate this, CSPs must deploy a mechanism to identify a device or user and associate it with the appropriate slice. This is achieved by using a Single Network Slice Selection Assistance Information (S-NSSAI) identifier stored in the SIM card, indicating the type of slice the device or user prefers. Any inaccuracies while configuring the SIM card could lead to non-registration of users in the specific network slice created for a customer, leading to service non-availability or the extension of services not aligned with the customer contract.
Billing process: CSPs charge and invoice customers or applications using the network slices. They need to collect and analyze the usage data of each slice, such as volume, duration, location, QoS, etc. Complexities in configuring the billing plans across different pricing models and schemes for different slices, such as flat rate, pay-per-use, QoS-based, etc., can lead to billing disputes because of incorrect charging or revenue loss scenarios.
Addressing challenges
The cloud-native 5G core architecture brings technological challenges affecting network performance, which can impact services and customer billing and lead to revenue leakage or a compromised customer experience. Some critical challenges include regulatory stipulations like spectrum usage compliance and equipment standards, customer-enforced contractual terms for service levels, and evolving customer expectations to meet or beat the industry benchmark levels.
To address these challenges, a fully automated Business Assurance Operations Center (BAOC) can be implemented. The BAOC will compare the reference data from the business support systems, including contractual terms and experience key performance indicators (KPIs), with the real-time network and service performance data from the next-generation operations support systems (OSS). AI and ML-driven algorithms can then analyze this network data to predict potential QoS issues or abnormalities in the device behavior.
For the exceptions identified, proactive or preventive actions can be suggested to the next-generation OSS, which can process the inputs and decide upon actions to be effected on the network or service platforms. Depending on the exception category and action, BAOC can trigger an alert for human intervention for necessary action.
For example, in the uRLLC use case challenges discussed above, network service deficiency must be monitored continuously. This can be achieved by ingesting QoS-related data feeds from the session management function (SMF) on a near real-time basis to the BAOC. BAOC then raises an alarm upon detection of a breach of QoS values below the committed latency levels for a customer. Further, based on the historical data and trained data models, ML algorithms can predict the possibility of a drop in QoS values at the specific radio network level, calculated based on peak or off-peak hours, day of week, etc. This enables CSPs to dynamically allocate the required network bandwidth to ensure avoiding service penalties and negative customer experience.
ML can also be effectively deployed to identify billing errors and discrepancies, such as incorrect tariff plans or missing/inadequate QoS data required for computing the exact charges as per the contractual terms. By detecting these errors early through the deployment of BAOC, telecom companies can prevent negative customer experiences or revenue leakage and improve their bottom line.
ML can be used to optimize pricing and resource allocation for 5G standalone (SA) services. By analyzing usage patterns and customer behavior, ML algorithms help CSPs identify new pricing models and allocate resources more effectively to increase revenues and margins and enhance the customer experience.
The advent of 5G networks presents a transformative opportunity for the telecommunications industry, but it also comes with innate complexities in service provisioning, billing, device management, partner ecosystem, and service monetization.
To mitigate these risks, CSPs need to fortify their business assurance framework by deploying data analytics, AI-ML, real-time/ near real-time monitoring of KPIs, and process automation tools that enable adaptive error correction and auto-remediation. By embracing these cognitive technological solutions, CSPs can improve the speed and accuracy of business assurance controls, shifting from reactive to proactive/predictive modes, thus ensuring revenue protection, fraud prevention, optimized processes, and enhanced CX.
