CSPs Can Use AI to Simplify Operations

Automated operations are table stakes for communications service providers (CSPs) looking to enhance operations, grow revenue, and improve customer experience. However, to drive impactful outcomes , automation needs to be driven by actionable insights drawn from CSPs’ data sets—artificial intelligence (AI) provides this capability.

COVID-19 highlights the need for intelligent automation and AI

The COVID-19 pandemic has intensified the need for CSPs to make their operations more agile. Digital engagement is more important than ever for both enterprise customers and consumers. At the 2020 Digital Transformation World event, Dialog Axiata’s chief customer officer, Sandra De Zoysa, noted that digital channel engagement with consumers increased significantly at the beginning of the pandemic. Enterprises have made similar shifts as well. According to Omdia’s 2020 SME Insights Survey, 32% of small and medium-sized enterprises (SMEs) are now purchasing communication services online. We expect the shift to digital engagement to continue to grow as customers already enjoy fast, flexible, and frictionless experiences provided by tech giants, such as Amazon, Google, and Facebook.

As a result, CSPs face growing pressure to deliver similar experiences—hence the need to transform their operations. Improving operations by leveraging intelligent automation and AI has become a priority for CSPs. According to Omdia’s 2021 ICT Enterprise Insights survey, almost 70% of CSPs think that the COVID-19 lockdown has made intelligent automation and AI more or significantly more important in 2021. By investing in intelligent automation, CSPs can not only reduce costs but also drive the speed and agility required to deliver excellent customer experience while improving employee productivity.

The role of AI in driving intelligent automation

AI provides actionable insights to drive intelligent automation. With AI, models are trained to automatically analyze large volumes of disparate data sets. Events of interest are detected or predicted and presented to business users with recommended actions to meet their objectives. Sales and customer service are just two examples of operations where AI can boost productivity.

Using AI in CSP sales operations

To capture revenues and meet sales quotas, CSP sales agents need to quickly identify customer needs and offer them relevant services. This is extremely important in the SME market where our research indicates that 35% of SMEs say that understanding their digital business requirements and processes is a top criterion when selecting CSPs. Offering the best price point and terms and ensuring that business customers receive the correct services are key to successful sales operations. Sales agents also need to understand the state of their pipeline to predict how close they are to meeting their sales targets.

Performing and tracking these functions over a large customer base requires analysis of multiple data sets, which can be challenging when done manually. Platforms with embedded AI can facilitate CSP sales functions, providing insight to support the daily activities of sales agents. Customer data can be analyzed to provide recommendations on which services to offer to new leads or existing customers. They can also track, score, and prioritize leads. Potential results include reduced churn, increased revenues, greater customer lifetime value, and faster time to meeting sales quota.

Using AI in CSP service operations

Omdia’s Digital Consumer Insights survey indicates that good customer service is one of the top three most important features that consumers expect from their broadband service providers. Good customer service is quick to detect customer needs, addressing them with optimal recommendations. CSPs are not generally recognized as delivering optimal customer service. Among other things, average call handling times (AHT) are often higher than for other industries.

To improve customer service, AI-based tools can analyze data from customer and network domains to provide agents with deep insights into the customer’s past and current engagements. Upon receiving a customer’s call, the agent is armed with relevant data to anticipate the customer’s sentiment and needs. They can also identify events impacting their experiences and determine how to resolve them. Consequently, customer service agents can reduce AHT, increase customer satisfaction, reduce costs, and improve margins.

Build the right foundations today to make the most of AI tomorrow

Successful implementation of AI will depend on several factors including how CSPs recruit and retain employees with the data science skills and provide access to high-quality data sets. The telecoms industry is not the most attractive to AI talent as CSP operations are less digitally mature compared to the tech giants. Processes are driven by legacy systems running in silos; with limitations to sharing data with other functions. Access to data is hindered and so stifles the functions of data scientists. Reluctance of current employees to adopt new AI tools is another challenge to address.

By investing in operational systems with embedded AI capabilities, CSPs can avoid the need to hire full time or large data science teams. These tools, however, need to be supported by an effective enterprise-wide data management infrastructure that provides and secures access to data. They also need to provide features that enable CSP users to update AI models as the business evolves. Providing AI tools that are transparent with respect to their operations will be critical to getting employees to trust and use them.

— Adaora Okeleke, Principal Analyst, Service Provider Operations and IT, [email protected]

This content is sponsored by Salesforce.

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