Telecommunication companies keep people connected with global networks and hence they are expected to offer cutting-edge services. With advanced technologies like Internet of Things (IoT) and 5G there is a significant increase in number of users and connected devices globally.
Deloitte’s 2019 Connectivity and Mobile Trends (CMT) survey shows that the average household has 11 connected devices, including seven different smart screens on which to view content. Twenty-eight percent of consumers use smart home devices such as connected thermostats, cameras, and lighting. Whether their connected devices are for entertainment, control, or security, consumers want better home internet for all of them.
With increased customer expectation and competition, telecom companies need reliable, efficient and quicker operations. Robotic Process Automation (RPA) can provide the telecom companies with a superior level of service. It can play a crucial role in steering transformation, bringing greater efficiency to telecom functions – from supply chain and operations to enterprise management and customer care.
Challenges in Telecom Industry
The current situation in telecom industries is critical as the expectations increase. Some top challenges faced by them are:
- High operational cost: Telcos have to invest upfront in back-office, technology and support centers, advertisements and similar structures. This increases the cost of operations but reduces their margins.
- Wafer-thin margins: Telecoms face intense competition from over-the-top players, VoIP services and other messaging apps and services. To be able to match up, they may face a steep drop in revenues forcing them to reduce margins, and this makes it a very price-sensitive industry.
- Higher customer service expectations: To provide superior quality with lesser TAT, telecom industries often have to invest in workforce and infrastructure. They are expected to meet consumer’s demand for better network stability and service. If they fail to do so, they become prone to the risk of losing credibility.
- Heterogeneous systems and applications: Telecom industries have multiple application systems for different products and services, but not all of them are integrated together, creating data silos and complex processes.
- Up-gradation and offerings: To meet the next-gen demands, telcos must evolve and upgrade to newer technologies and offerings—at a faster pace and scale.
Benefits of RPA in Telecom
RPA can help reduce manual error and turn-around-time in processes like customer support, document verification, billing, invoice processing and so on.
The telecommunications industry has some of the highest adoption rates of RPA technology (together with healthcare, retail, IT, BFSI). A Market Research Report includes a forecast for the RPA market until 2024, during which period, telecom and IT appear to be leading the automation trend in business processes, with a phenomenal growth rate (CAGR) of 60%.
Some benefits of RPA in telecom can be summarized as:
- Improvised customer experience: Machine Learning (ML) & Natural Language Processing (NLP) capabilities enable RPA to understand the customer query and provide appropriate solutions, thus reducing the wait time. RPA can also help in self-allocation of queries to concerned teams avoiding customer dissatisfaction.
- Effective data management: With OCR technology and ABBYY integration, RPA can help automate non-standard formats of back office data entry process, which help in reducing the error rate and TAT.
- Invoice processing: RPA, when integrated, can process and check thousands of invoices and purchase orders and capture the data into the systems. This can save time and avoid errors to up to 90% of those that occur due to manual processing.
- Better network management: RPA can predict and detect possible network issues when connected to different systems and improve customer experience. This helps support team to resolve the incident and stay updated in real-time to provide effective diagnostics management.
- Improved accuracy: Since telcos receive reports from disparate systems in different format, the data needs to be integrated and analyzed to enable prediction and decision making. RPA can integrate with different systems and process the data with utmost accuracy.
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