Utilizing Robotic Process Automation in Contact Centers Reduces Costs and Increases Operational Efficiencies

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Advances in artificial intelligence (AI), machine learning (ML), big data, and speech technologies have enabled breakthroughs in automating both back and front office processes through the adoption of robotic process automation (RPA). Customer care departments face the competing pressures of keeping costs low, while still providing excellent customer service and improving operational efficiencies. RPA, a potential solution to these challenges, uses software that incorporates technologies such as AI and ML to automate routine, high-volume tasks that are sensitive to human error, thus reducing labor costs, while increasing accuracy. In addition, RPA applications can access data restricted from live agents, helping to avoid security bottlenecks and improve operational efficiency. 

Frost & Sullivan latest analysis, Robotic Process Automation Market Outlook for Customer Care, 2017, explores what’s driving adoption of RPA in contact centers, as well as customer care services in other industries. It also identifies specific focus areas or strategic business initiatives that can be tailored and leveraged by companies seeking to grow in this market.

“Robotic automation processes can dramatically improve the cost effectiveness and efficiency of customer care departments, such as contact centers,” said Frost & Sullivan Digital Transformation Principal Analyst Nancy Jamison. “However, it’s important for organizations to have a cross-organizational plan for automation, as RPA applications can significantly impact people’s roles and adjacent processes. In addition, having such a plan enables organizations to see where automation should and shouldn’t occur.”

The study identifies 22 areas where RPAs can be leveraged, some of which include:

1. Contact Center Automation: guided resolution/next-step actions, biometrics implementation for improved security, repetitive tasks;
2. Cross-organization Process Automation: repetitive data entry, improved consistency and accuracy in data input, IT offload of simple projects;
3. Security & Compliance: security bottleneck reduction, regulatory compliance, fraud pattern detection and alerts;
4. Finance: fraud detection, compliance risk reduction, customer lifecycle management, and regulatory compliance; and
5. Insurance: policy renewals/administration, premium recalculations, underwriting, and data aggregation from disparate sources.

“Organizations considering RPA tools should look for solutions that are scalable and reliable,” said Jamison. “Other key considerations include choosing RPAs that follow a development process that is repeatable, provides access control and auditing, and can be managed similar to live agents with centralized control.”

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