An effective way to improve your bots & agents performance
Using conversation AI bot technologies is one of the best ways companies can decrease operational costs and build a long-standing company.
Conversational AI is a branch of machine learning that understands the user’s query and provides responses to resolve their query. However, this AI has not reached a state where it can solve the complex questions that require the skill, intuition, and empathy of a human to resolve. So, Most companies now realize to provide a great customer experience, it’s essential to augment conversational AI based chat or voice bots with the human agents.
Having AI at the forefront and Human agents as fallback decreases the costs by 30%¹ more than just having individual agents.
If we improve the working relationship between Artificial Intelligence and Human-agent we can provide faster resolution of the customer queries, decrease the costs even more & increase the revenues.
Bot and Agents work independently to resolve the customer queries.
Most companies are now familiar with the ‘bots & agents’ setup in resolving customer queries.
A conversational AI-powered bot operates as the first touchpoint, and when it does not understand the query, it will transfer to the human agent. The transfer is usually triggered by the limitations in the AI powering the bot, and sometimes, the business rules are set up to route to the human agent automatically. The business rules usually are set up for the queries that generate high revenues or need multiple systems contexts that AI could not infer. These rules can be much more complex, and companies offer dedicated tools to configure these rules.
Bots can handle an unlimited number of conversations, but a human agent in the chat world typically takes about 6–10 conversations, and in the voice world, handle 1 or 2 at a time. So typically, there is a wait time for the user when the transfer is triggered. As chat interactions are cost-effective, most voice bot-enabled organizations prefer to transfer to the chat-enabled human agents.
Upon transfer, the information collected by the bot is passed for the agent to continue the conversation where it was left off. This information not only comes from user interactions with the bot but also from the channel, profile, and history of user interactions.
As we can see in these interactions, the bot & agent are working independently for the most part. There can be much more cost savings if we bring in a tighter collaboration between the bot & human agent.
Many top Chat & Voice bot providers are experimenting with a tighter collaboration between bots and human agents. Here are a few things most bot providers are working on to bring in better collaboration and save more costs for the entrepreneurs.
These are a way few ways one could bring in a tighter collaboration between Artificial Intelligence and human intelligence.
Bots Assist human agents when they are resolving the user’s query
The companies want human agents to handle the high revenue-generating queries for various reasons. In these situations, Bots can help in assisting the agents in many of these ways.
- Auto-populate the responses
- Show the context based on the conversation history
- Suggest knowledge articles where to find the responses
- Auto tune the text for more empathy etc
For example: When a customer buys a painting, the bot can assist the agent by showing the various discounts applicable for closing the sale.
Human agents disambiguate when the bot is confused
When the bot is confused with the user questions, the agent can disambiguate the specific queries and guide the bot to complete the conversation. For example, when the user types a long sentence and the bot is not trained to handle these long sentences, the human agent can quickly disambiguate this query and put the bot back on track. This human agent disambiguates the question and passes it back to the bot to handle the rest of the conversation has higher cost savings. Also, the user does not have to wait in line for their query resolution.
Bot to handle the mundane or straightforward tasks
After the conversation is transferred to the agent there are many mundane tasks such as collecting payment information, address, basic profile details, etc can be passed over to the bot, and the agent takes over as soon as this information is collected.
This process of collecting mundane information not only helps the agent resolve the customer query faster but also helps auto-store the information so that it can be leveraged later.
Auto tune the agent conversations
One of the possible futuristic solutions is when the agent is resolving the query, depending on the conversation style of the user, the bot can auto-tune the agent response to match this conversation style. This auto-tuning helps provide a personalized experience that could help increase the users’ engagement with the brand.
This is one of the new-age technology that is still in the research stage, and it might take a few years for the Conversational AI based bot providers to adopt this technology.
Summary
Human Intelligence is needed to handle complex queries even though the conversation AI bot tech has improved drastically over the last decade. Most applications have a bot that helps solve user queries transferring to a human agent when the bot does not handle the query. However, the performance of these applications becomes much better when Artificial intelligence and Human intelligence have a tighter collaboration.
[1] Danica Jovic, The Future is Now – 37 Fascinating Chatbot Statistics (2022), Smallbizgenius.net
