Automate processes and achieve faster ROI with full-featured RPA

Employing staff is the biggest operating expense for service providers in the Connectedness industry. Employees are also their greatest asset. Automation technology advances enable service providers with new ways to maximize employee productivity, revenue, and customer satisfaction while minimizing human error. One of those advances is robotic process automation (RPA), a broad, deep category of tools for automating business, network, and operational processes.

RPA can automate mundane processes which are tiring and boring for a human to do all day long – the kind of fatigue that results in mistakes and expensive turnover. By providing what customers seek faster than a live agent can, RPA eliminates one of their major complaints: wasting minutes on hold to speak with an agent or hours or days for a work order to be processed manually. The big advantages delivered by RPA are customer satisfaction, employee satisfaction, greater revenues and profits, security, and reliability. But the biggest obstacle to achieving these ends through RPA misunderstands how and where it can be deployed. This insight, sponsored by Prodapt and published by TM Forum, elaborates on how to embrace RPA across the business processes to achieve greater efficiencies, improved customer experience, and faster ROI.

Leverage AI/ML to enhance the efficiency of your day-to-day business operations and improve customer experience

Today’s digital-savvy customers demand advanced and ultra-rich experiences. Therefore, factors like service availability, turnaround time, and quality of service (QoS) are becoming increasingly important. But the question remains how can Service Providers bridge the huge gap that currently exists between customer demands and their fulfillment?

Service providers’ current infrastructure and service delivery approach are unable to match up with customer expectations. There is a dire need to enhance their key operational areas such as customer service, service assurance and network automation.

Artificial Intelligence/Machine Learning (AI/ML) technology, with its recent advancements, is fast becoming the choice of service providers to bridge this gap and improve operational efficiency. AI/ML systems, together with Bigdata, can process huge amounts of historical and real-time data from various systems such as CRM, billing systems, NMS/EMS, and product catalogues to provide actionable insights and predictions.

Service providers must adopt AI/ML solutions in their infrastructure to offer next-generation services and experiences.

• Intelligent software-defined approach for operations and delivery of services (virtualization, self-healing, and self-learning networks)
• Automation of customer service and customer experience improvement (chatbots, virtual assistants)
• Predictive maintenance and agile operations (automated problem detection, troubleshooting, and optimization of networks)
• Innovation in subscriber profiling, usage analysis, and personalized offers

Use proven tools and techniques to improve the efficiency of Technical Assistance Center (TAC), reduce inbound repeat calls and customer trouble tickets

Today, telecommunication is no longer limited to voice. In the past few years, the industry has rapidly grown to accommodate multiple services. In this era of multi-play, service providers in the connectedness industry are adding various services to their catalog, such as voice, messaging, broadband, IPTV, DTH, VAS etc. And to support these services, service providers have technical assistance centers (TACs) to help customers resolve issues related to specific services.

However, service providers have been facing challenges in maintaining and improving the efficiency and productivity of TACs as much of their efforts go into non-value-adding (NVA) tasks causing resource wastage. NVA tasks can be classified into following categories:

Figure : NVA Waste Classification

Further analysis of NVAs (over-processing, rework, waiting, etc.) shows that managing a high volume of inbound repeat calls and tickets and operating with distributed tools are the major challenges of a TAC.

To mitigate these challenges, service providers must explore innovative and field-proven tools and techniques, including robot-assisted screening, Proactive Network Analyzer etc. By implementing these techniques, service providers can easily realize a 30- 40% reduction in inbound repeat calls and customer trouble tickets.

How fiber operators could build a lean and scalable operating model to deliver with speed, keeping an eye on cost

Deep fiberization remains a strategic objective for global fiber operators to meet the data consumption demands. As per the EY report, global fiber deployment will double during CY2018-2026, majorly led by 5G. The global fiber optic cable market is expected to reach USD 20.8B by 2026 at a projected CAGR of 14.5% between 2020-2026. To ride on this fiberization wave, fiber operators must act quickly, keep an eye on cost and check data accuracy before making planning decisions.

Fiber operators need to rethink their business operations to overcome the challenges in their journey towards accelerating fiber rollouts. The three key domains of a fiber operator and the associated challenges are listed below:

• Plan & Build Massive coverage targets, high cost to build, shortage of skilled labor
• Service Delivery Longer cycle time, siloed and disconnected customer journeys, repeat visits and rework
• Service Assurance Operational Efficiency due to lack of automation and standardization, reactive approach in network management

Fiber operators must build a lean and scalable operating model to overcome all the above challenges and achieve their fiberization goals efficiently. Here’s a proven 4-step approach to building a lean and scalable Target Operating Model by transforming your business capabilities:

• Step 1: Perform due diligence and discovery of as-is fiber journeys
• Step 2: Benchmark capabilities using Capability Maturity Assessment
• Step 3: Collaborate and identify change initiatives
• Step 4: Plan implementation and define a roadmap

Successful implementation of these steps can help fiber operators to reduce their operational expenses by 53% (in just 3 years) and create a lean and scalable organization.

Leverage AI-powered capacity planning to modernize field services

Most service providers face challenges in planning and allocating field technicians based on the demand vs capacity. According to Gartner, “Balancing available resources against the demand for those resources is essential to successful initiative completion“. Inefficient capacity planning often leads to over-staffing or under-staffing of field technicians. This further results in order fallouts and dissatisfied customers. The most common sources of dysfunction are:

• Unavailability of tools to estimate capacity in real-time
• Lack of strategy to identify the key influencing factors that impact the capacity planning process
• Lack of mechanisms to assign the right technician for the right service
• No end-to-end visibility into field service capacity

To overcome these challenges and handle the diverse field data, service providers in the connectedness industry should move towards intelligent capacity planning, which helps in the real-time mapping of dispatches and the optimal usage of resources. Leveraging an AI-powered capacity planning framework helps the service providers to reduce resource wastage by 20% and improve the effectiveness of service response and customer satisfaction. Enterprise AI can, over time, improve the prediction of field technician work hours by considering the key factors such as weather, season and maintenance data.

Fig: Leverage AI-powered capacity planning framework for real-time field tech resource management