The legacy call centers streamline the customer channels and the workforce using a robust network architecture. Each telephone line was connected to the agents creating a systematic network that worked like a charm. The agents bridged the gap between the brand and its customers with 24/7 telephone-based customer support.
But the rapid rise in AI technologies, smartphones, instant messengers and chatbots have played an instrumental role in shaping the customer psyche. Today, people expect real-time solutions. It has become apparent that customers have become more impatient than they were ten years ago. A new study by Microsoft showed that humans lose concentration after eight seconds (one second less than ill-focused goldfish). This means that businesses have a very short window to captivate and influence the customers. Naturally, there is no window for errors.
Limitations agents face in legacy call centers
1. Constant switching between multiple tabs makes for a hugely laborious experience
2. Inadequate customer insights
3. Large volumes of calls increase the customer waiting time
4. Poor access to information directly affects the first call resolution
Riddled with these complexities, the overall customer satisfaction takes an unfortunate hit.
Considering these challenges, it is fairly unrealistic to expect consistently flawless solutions from the agents. Naturally, an alternative solution to these problems is called for.
Agent assist – intelligent real-time assistance to customer agents
Engagely.ai’s pathbreaking NLP based Agent Assist tackles all the above mentioned challenges with a great ease and efficiency. As the Agent Assist bot performs significant aspects of the operation such as understanding the intent, analyzing and delivering personalized suggestions, it takes a great load off the agent. Hence, the actual training duration of the agents has been reduced enabling them to deliver far better solutions at low operational costs.
Elevate your agent’s efficiency with five amazing features
Natural language processing engine: The NLP engine in Agent Assist enables it to understand the query/request context and recommend apt responses to the agent in real time. It can retain previous contexts freely and ensures the agent deals with complex queries with ease, accuracy and confidence.
Live speech to text transcription: As the voice conversation commences, the speech is translated to text in real-time. This feature helps the agent to understand the query. Furthermore, the transcripts contribute to the customer history and further refine the auditing and analytical processes.
Vast knowledge bank: The Chatbot in the console acta as an enormous knowledge bank. It feeds the agents with information in real-time and helps him resolve the query quickly.
Peer-to-peer chat: A real-time internal communication channel enables the agent to connect with his peers and supervisors to seek real-time assistance in cases of complex queries.
Live sentiment analysis: The Agent Assist captures the keywords and phrases to calibrate an overall sentiment score. The score determines the mood of the customer. On the basis of the score, the Agent Assist prompts apt solutions to the agent.
Unified window console – get a complete view of all the channels on a single dashboard
As the name suggests, Unified Window Console brings all the channels together on a single dashboard. Be it a customer through a web app, or a user asking a query on Whatsapp, the agent gets a complete view of the customer queries through the Unified Window Console.
This feature is a huge addition because it dismisses the tedious tasks of switching tabs and windows. Instead, the agent simply uses the Unifying Window Console and attends the queries with ease.
How does it work?
Let us consider a customer requesting account opening information to a bank agent. As soon as he types, “I want to know about your bank account opening policies.”, the Agent Assist will instantly prompt the agent with the accurate results of the page and forms related to account opening.
As opposed to traditional means of working, where the agent would take some time gathering the right solutions and forms, the Agent Assist helps the agent deliver precise solutions within a minute.
The Agent Assist’s operational genius goes beyond mere assistance with its great capacity to gauge the sentimental aspects of the customer query.
Agent assist’s sentiment driven personalized touch
The NLP based sentiment analysis of Agent Assist empowers agents to assess the mood of the customer. On the basis of the customer mood, it assists the agent with apt solutions. Let us see how this works out in real life situations.
When a customer connects with an insurer to add his newborn to the family plan, the Agent Assist assesses that the customer is happy by gauging the key query phrases. The insurer performs all the necessary tasks without manual data entry and triggers the respective journeys within a span of a few seconds.
Once the task is finished, the Agent Assist, considering the sentiment score, suggests the agent to recommend a Child Plan to the customer and influence an effective upsell.
To sum it up
The traditional call centers fail to meet the growing customer demands. The agents frequently face challenges such as rising volumes of calls, lack of customer insights and limited availability of crucial information. This not only affects the agent’s productivity but eventually leads to poor customer experience.Thankfully, we have the solution to these problems in a simple and remarkable AI solution called Agent Assist.
Engagely.ai powers the Agent Assist with its proprietary NLP engine that enables it to offer real-time smart assistance to the agents. The introduction of AI Agent Assist has drastically simplified the training process and improved agent’s operational efficiency.
As the world is relentlessly headed towards automation, integrating AI Agent Assist is the first resolute step your business can take towards the AI Business ecosystem.
Senior content developer- Marketing
Akshada Benke is a content marketer at engagely.ai with more than twelve years of experience in digital content marketing field. She describes herself as a Philomath. She is confident & professional in developing strong consumer-insights driven goals to build brand and relationships.