The Role Of Artificial Intelligence In Transforming Call Centers

A 2020 survey from Interactions LLC discovered that mid-to-high level executives now consider improved customer experience as the most important reason to implement AI. This finding pushes “cost reduction” to second place as a driver of AI implementation thus highlighting the importance of satisfactory customer engagement as a competitive advantage in today’s market.

Companies understand that customer retention is an essential aspect of operations and constantly seek to know and understand their customers' personalities, challenges, and expectations to deliver hyper-personalized solutions to their pain points. This intersection of personalized service delivery and customer satisfaction is the perfect meeting point of Artificial Intelligence and call centers.

Here are some of the ways that the growing influence of Artificial Intelligence is and can further transform traditional call center operations:

 

1. DIGITAL HUMAN AGENTS

This solution is the potential apex of customer/agent call center interactions. Digital human agents, also called virtual humans, are programs or algorithms with the capacity to think and behave in human-like fashion. It is a term that broadly describes applications like chatbots or conversational AI agents (e.g., Siri), virtual tutors, digital twins, etc. A study from analysis firm, Juniper Research, reports that industries with a large volume of human interaction like banking and healthcare are set to benefit from the use of digital human agents leading to a potential $8 billion reduction in business costs. With the live and quick interaction provided by digital human agents, many issues are resolved before the need to even speak with a human agent arises. Companies such as Stallion AI are pushing the boundaries in the field of digital human agents with solutions like their “Digital Artificial Intelligence Twins” where digital versions of both the customer and the agent could potentially interact and resolve service delivery issues with minimal or no involvement from the real-life counterparts. 

 

Stallion AI’s multilingual Digital AI Human Agent’s solution can handle over 90% of customers’ inquiries in different languages, enable financial transactions, and contact the right employee if human intervention is required.

 

2. INTERACTIVE VOICE RESPONSE (IVR)

The customer service sector has always been at the forefront of embracing novel technological solutions. IVR is proof of this. This is the automated voice that customers usually have to first interact with when they dial call centers. The IVR tries to obtain some basic information like the customer’s name, language preference, inquiry nature, etc. Most existing use-cases of this technology are monotonous and frustrating for customers to interact with. However, with the increasing use of machine learning and NLP technology, IVRs will be better equipped to respond to more specific inquiries.


3. CUSTOMER INTERACTION ANALYTICS

Data is the fuel that drives the transformative power of AI. With the explosion in the volume of data being created from customer interactions daily, there is a rich pool of data to mine. Companies can use the insights generated from this analysis to identify trends in the overall customer experience through call duration and frequency, sentiment analysis, and any subsequent customer feedback on the interaction.

 

4. PREDICTIVE CALL ROUTING (PVR)

Firms can further harness the discoveries from customer interaction analytics to drive key solutions like predictive call routing. In PVR, the best-fit customer care agent can be matched to a specific customer. This pairing can be based on the company’s understanding of a customer’s persona and the agent’s expertise in dealing with the particular customer’s personality type and inquiry. PVR allows for a greater degree of personalization of the customer’s experience.


5. SENTIMENT ANALYSIS

The start of many customer-to-agent interactions is a customer pain point. Depending on the customer’s perception of the handling of this pain point, the interaction could either be a pleasant one or a heated exchange. Addressing a customer’s needs requires subject matter expertise and emotional intelligence. The latter can be enhanced by using sentiment analysis where the AI tracks the customer’s tone and language used to determine the customer’s emotional state. The human agent can use the feedback generated to keep the interaction at a satisfactory emotional level for the customer.

The “human touch” will not disappear soon from the customer service engagement business. However, the customer experience cannot be left solely in the hands of a handshake and a smile. AI has many application entry points that key stakeholders must remain aware of and updated on to deliver the best product experience for their customers.




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