Automation for Carrier Service Providers

If we look at billing as an example, analysts estimate 10% to 20% of invoices from telecommunication providers are billed in error, which leaves them facing heavy fines and damaged reputations. The fault lies mainly on the complexity of the services and the systems that need to be accessed in order to generate the invoices. Very often, telecommunication companies find themselves having aggregate services from a host of other providers, making it difficult to identify where the discrepancy actually is. RPA (or document processing automation, specifically), automates this process. It ties the systems together and generates a full audit trail, so the discrepancies can be handled faster or avoided entirely and the human workers can be produce a documented audit trail for the auditors.

The network infrastructures consist of multiple service providers and rely on service partners to keep the systems functioning. They are incredibly complicated and it’s inevitable that something will go wrong. When things do go wrong, customers immediately contact the Service Center which can inundate the customer service teams who will, in turn, inundate the operations and network teams. Robotic Processing Automation (RPA) helps with this problem by processing pre-set diagnostic tests, arming the customer service team with more tools to enable them to manage the calls themselves, without having to escalate the issues and create alerts for the network teams. If an escalation is still required, the RPA can still help eliminate many of the root causes and can provide much more detailed information on the service ticket and make it readily available for the Customer Service team, so that the issue can be resolved in a fraction of the time.

RPA can also help to improve customer experiences at the Service Centers. Telecommunication companies rely heavily on the Service Center to deal with customer complaints, but it’s not always an enjoyable experience for the customers. With RPA, it’s a win-win. The customers can have a more enjoyable experience and the the Service Center can be transformed from a traditional cost center into a source of revenue. Using attended robot “helpers,” RPA can automatically aggregate and harmonize customer data from multiple sources into a single 360-degree view of the customer, allowing them to answer questions very quickly, improving the experience for the customer and boosting the productivity of the customer service representatives.

According to Network World, 10% to 20% of telecom charges are billed in error. Not only does this harm the telecom’s reputation, but the financial impact can be significant. Government and auditing bodies who regulate the telecom industry charge hefty fines for overcharging customers. Many CSPs find it challenging to invoice correctly because they are part of a complex infrastructure, serving as aggregators of services, against a variety of Service Level Agreements in different geographies. While the CSPs execute due diligence, it is often difficult to know where the error lies. Is it with the CSP? Is it with a service partner? Or, the customer? Not knowing, or not having the resources to propertly investigate leaves the CSPs at risk of billing incorrectly and under the scrutiny of the regulatory bodies.

In a manual working environment, this process entails transferring customers from one department to the next, or from one partner to the next while a human worker checks the accounting and billing systems to identify the discrepancy. It gets even more complicated if a customer requests a change or cancellation of their services. The request goes through several layers of people and partners and like a game of telephone, the message can get lost in translation, leaving the customer to incur additional costs.

This is a problem that the RPA Robots are extremely suited for. RPA works hand in hand with Optical Character Recognition (OCR) software and Document Processing Automation (DPA) systems that automate the billing and invoice processes. The robots are given specific instructions and they don’t make mistakes so discrepancies are revealed much faster. Furthermore, the robots work 24 hours a day, 7 days a week and they never get tired, so they can process invoices at record speeds. They process transactions between any number of legacy technology systems without having to invest in expensive APIs, ERPs or integration software. They also leave a complete digital footprint of all activities. With this detailed audit trail, the bots can identify which services the customers has subscribed to, any upgrades and changes to their service. The robots take all of that information, consolidate it and send it to a human worker who can intervene if needed, resolve the discrepancy, and serve the customer quickly and accurately.

Maintaining extremely complex networks that evolved through a patchwork of old and new technologies is not an easy task. Without the right tools and resources to diagnose and maintain the networks, the service will not function properly and in such a complex environment, very often things go wrong and disrupt services. That’s the worst case scenario for the telecommunications industry. Irate customers contact the service center and create a backlog of inquiries and even worse, express their dissatisfaction on social media.

This scenario puts pressure on the network and operations teams who are investigating network alerts, which may be false positives but nonetheless, need to be addressed. At the same time, they are receiving inquiries and feedback from the service center who need help diagnosing issues so that they can better manage the customers. They need to know the root cause of the problems, the right department needs to be notified and the customer needs to know when they can expect their service to be restored. The network and operations teams have to prioritize dozens of requests and identify the quickest and most efficient way to avoid further outages and restore exiting outages.

This kind of chaos is where the RPA robots thrive and can help in several different ways. Using attended RPA automation, the robots can receive the alerts and work hand in hand, in real-time, with the network diagnostics software and human team, and based on pre-determined criteria, either disqualify the alert or generate a service ticket. This weeds out the lower priority alerts and moves the more critical alerts to the top of the queue.

Beyond this, RPA can run diagnostic tests using a business rule engine to determine whether a ticket can be eliminated or if it needs to be passed on to the operations team. Combining RPA with AI can help to increase accuracy even further by basing rules on historical patterns so the robots can provide even more accurate next best actions. This means most inquiries can be handled by the call agent, rather than the operations team, which reduces the “time to resolution,” increases quality assurance, optimizes efficiencies and above all, improves customer service, all while minimizing pressure on the operations teams.

RPA platforms with AI capabilities can help planning teams answer specific questions about their environment. The robots can help calculate capacity needs and provide an action plan with next steps. The choice to upgrade a network is a multi billion dollar decision. These pipes will stay in the ground for decades, if not longer. Capacity planning is therefore vital to the future health of any CSP.

With 5G around the corner, network planning is critical to ensure operators get the most favorable ROI. However, data is spread across CSPs’ infrastructure in silos, creating a challenge for the network planners seeking a holistic picture of the results and implications of their decisions. Making these choices without the help of automation is prohibitively time-consuming, extremely complex and an inexact science. Analysts must look at current and historical data on a range of areas such as predicting whether consumption is likely to increase, analyzing customer profiles to determine profitability based on location, assessing current quality of service and predicting whether there could be an increase in market penetration.

Robotic Automation Processing platforms can auto-recommend the shortest, most cost-effective path to lay a cable. Artificial Intelligence (AI) capabilities can also help network planners understand the density of population, so they can accurately predict future opportunities and recommend next best actions.

Customer service is an area that telecommunication and CSPs have traditionally fallen short. Though some automation has been introduced in areas such as self-service, telecommunication companies still rely heavily on traditional methods designed in their Service Centers. Yet customer service representatives aren’t armed with the most effective tools for servicing their customers due to the fact that the data they require is hosted across a multitude of computer systems in different formats, all of which need to be accessible in real-time.

The RPA robots can turn that model on its head and transform the traditional “cost” call center into a potential “revenue” center. Using attended robot ‘helpers’, RPA can automatically aggregate and harmonize customer data from multiple sources into a single 360-degree view of the customer, allowing them to answer a query fast than ever before, improving the customer experience and elevating the productivity of the call center staff.

Because RPA means less complex inquiries can be addressed faster, the customer service workers will have more time to execute up-selling strategies and generate more revenue for the company. This method of aggregating data can also support AI-driven responses. AI can be used to ‘learn’ from customer scenarios and suggest next best action decisions instantly – this improves customer experience, as well as creating cost reductions for businesses.