Customer service representatives (CSRs) are the face of your brand. In the age of hyper-personalization and rising consumer expectations, agents can’t do it alone, and need a new best friend to help keep them afloat: Artificial Intelligence (AI). In our last piece, we examined the potential of AI in call centers; today, we’re digging deeper into five ways that AI can augment the CSR’s relentless pursuit to deliver exceptional cognitive care.
1. Contextual AI search
Given the rapid pace of product changes and high turnover, agents routinely have to put the customer on hold while they research topics. This typically involves the agent frantically searching through knowledge bases, FAQs, and forums, and skimming through multiple web pages, PDFs, documents, and spreadsheets. This inevitably results in longer Average Handle Time (AHT), a critical agent metric.
Most traditional keyword-based enterprise searches are frustrating since results are generic and lack context of what the customer asked, resulting in less relevant results. Tools such as IBM’s Watson Discovery can deliver an AI-powered insights engine that understands the context and serves answers from complex business content via simple natural language search. It continuously learns from agent interactions and unstructured content to instantly surface the right passages from relevant documents, helping agents reduce 10-20% AHT and deliver a delightful customer experience.
2. Solution advisor
When an irritated customer calls their wireless provider to complain about a big spike in their bill, they demand instant answers. They expect the agent to be prepared, explain the rationale and offer the best resolution. With no context of incoming call, agents are at a disadvantage; they need to isolate the issue, review account history, evaluate potential options and resolve the issue.
Tailored AI solutions can tap into a customer’s interaction history, analyze usage and transactions, look for anomalies, and evaluate potential resolutions in a manner of seconds. So, when the customer calls in, the agent’s screen automatically shows a summary of findings, contextual information and ranked recommendations — letting agents deliver focused discussions and exceptional experiences. IBM has also helped clients turn this AI to outbound proactive resolution even before the customer gets concerned and reaches out.
3. The agent’s AI assistant
AI virtual assistants and basic chatbots have traditionally been customer facing for simple FAQs. Today, we can create sophisticated agent assistants to provide immediate contextual help for CSRs. The AI assistant is trained on in-depth operating procedures, understands where to look for the right information, and can automate mundane tasks for the agent.
Consider the complex landscape of healthcare. One large health insurance firm deployed an agent AI assistant to help CSRs with claims research. When a health plan member calls in about a denied claim, the AI assistant pops up on the agent’s screen with contextual information about that specific claim number and walks the agent through a guided workflow of claim research. It automates some steps by calling APIs from multiple systems, such as checking if the physician was in or out of network. AI recommends personalized verbiage that the agents can use with customers and the rationale behind it.
By delivering consistent and accurate responses, it also solves another major issue: 76% customers get conflicting answers from different agents. In addition to accelerating the research process, AI also tracks and summarizes findings that agents can use in post-call notes.
4. Chat AI co-pilot
As digital messaging increasingly becomes the preferred channel to engage with brands 24/7, 90 percent of consumers expect an immediate response. CSRs use messaging platforms to address those questions, often handling multiple conversations in parallel. Instead of switching between multiple applications, searching for answers, and copying and pasting information, AI can be seamlessly embedded into the messaging platform to provide contextual answers to assist agents, speeding up the workflow and delivering instant customer satisfaction.
For example, if a customer is chatting with an agent about a credit card foreign transaction fee, AI can read the interaction, understand the context and customer profile, retrieve the fee information from a PDF or knowledge base, and suggest a response. It could also mine prior chats by more seasoned agents on that topic, which had high CSAT, and recommend that response.
Instead of searching for veritable needles in data stacks, CSRs simply need to click “send response to customer”. And if customers need help with issues that AI can confidently handle — such as password resets or make a payment — the AI co-pilot can suggest to join the chat conversation, freeing up the agent for more complex tasks, driving 2-3 times more productivity.
5. Active listening
For coaching and quality control, call center managers often listen in to agent conversations, but they are limited to only a select few and one at a time. In the past few years, massive improvements in the quality and accuracy of speech-to-text algorithms and computing power means that AI can listen in on millions on live conversations in parallel, transcribe them, understand intent and conversational context, formulate smart queries, and proactively present insights on the agent’s screen in real-time. In addition, by leveraging tone and sentiment analysis on every customer utterance and agent response, it creates real-time scorecards to alert managers to calls that require supervisor assistance and real-time trends across the call center.
Agent Experience = Customer Experience
CSRs are doing the best they can with what they have — but they can’t meet evolving customer expectations on their own. It is no wonder that the call center industry sees high attrition rates between 25 and 40 percent. Cognitive care, powered by AI, can help bridge gaps by augmenting agent abilities to deliver an exceptional customer experience.