Use AI in Customer Service to Make your Service Agents Super Agents

With inputs by Nipun Sharma, Lead Service Cloud Specialist

80% of service decision-makers say emerging technology is transforming customers’ expectations of their service organisation. Customers now expect omnichannel support, personalised communication, and end-to-end resolution of their issues in a short period of time.

Businesses, for their part, do not simply consider customer support as a cost centre. They now look uponsuperior customer service as an opportunity to increase revenue and wincustomer loyalty. In fact, 71% of service agents believe their role is more strategic than it was two years ago.

But for customer service agents to deliver against these evolving expectations, they must be empowered with the right tools. 75% of customer service organisations using AI (tools) have seen increased agent morale, and 79% have seen increased CSAT or NPS scores.

Empowering customer service agents with artificial intelligence (AI)-driventools and solutions can help them deliver round-the-clock customer support, enable seamless handoffs and contextual responses, and above all, aid in effective decision-making.

Here’s how AI is revolutionising customer service byenabling service agents to contribute more actively to business growth:

1. Case classification and routing

Service agents classify service requests or cases based on the customer’s specific query.Case classification is crucial for routing service requests to the right service agents. Thisimproves not only the customer experience but also the efficiency of customer service agents. And this is often done manually.

But, with the use of AI in case classification and routing, customer information is collected and qualified by AI-enabled chatbots, such as Einstein Bot. These botsleverage machine learning to engage with customers at the front line, and only if a case needs a service rep to intervene, dothe bots route the query to an agent for further resolution. Einstein Case Routing automatically applies predictions to the case and routes it to the right agent. This lowers the Average Handle Time (AHT), case transfers or escalation, and the time spent in manually triaging and routing cases.

2. Operational insights

AI tools can have a great impact on the analysis of performance and productivity of service agents. Using AI, managers can track operational metrics such as the number of queries handled by an agent, time spent on resolution of queries, and the customer waiting time, while also parsing the customer feedback. These key metrics can inform decisions on the training of service staff and knowledge management. They also help optimise an agent’s performance by identifying where the gaps are and where efficiencies need to be built.

For example, if a particular nature of queries is taking more time than usual to resolve, an enterprise can dive into the data and check where the issue lies. Such data can then be used to enhance employee training and achieve operational excellence.

AI-enabled customer service tools, such as Service Cloud Einstein, along with the inbuilt smart reporting capability of Service Analytics, can help companies analyse the predominant nature of service requests, complaints or queries raised by their customers, and also centralise their key KPIs. Such information can help enterprises to fine-tune their marketing and sales engagement, and share insights with product designers as well.

3. Recommendations for next best action

Machine learning models can scan past work orders and similar jobs to predict the most likely cause for a service request. These predictions, when fed into AI tools, enable them to make relevant recommendations to service agents. For instance, the Einstein Recommendations Builder recommends the next best action by leveraging prior knowledge of similar service requests; such as recommending to a field service agent the right parts to be carried for a job.

Einstein Reply Recommendations, on the other hand, supports service agents with standardised chat responses. These ready-to-send messages help save the time an agent spends in thinking and drafting appropriate responses during a customer chat.

4. Prioritisation of issues

For enterprises that handle a large volume of queries, it can be challenging to prioritise urgent issues. This can adversely affect customer satisfaction. AI algorithms, such as those built into Service Cloud Einstein, canuse sentiment analysisto predict the urgency of issues and put them in front of the queue.

The use of AI in customer service allows agents to rise above the mechanical and scripted approach to customer service. It allows service agents to focus on customer engagement and business growth. Empowered by AI tools, service agents can make prompt and effective decisions and personalise service to win customer trust, further creating opportunities for cross-sell and up-sell.

With Salesforce Service Cloud’s in-built AI capabilities, businesses can conveniently leverage the power of AI across all their service channels. With its simple, ready-to-integrate setup, the platform allows businesses to deploy AI without getting into the hassle of coding their own solutions.