Customer service bots have come a long way since 2016’s Facebook-driven frenzy. Developers raced to build applications for the new Messenger platform, with 34,000 hitting the market that same year.
Chatbots may have seemed like a gimmicky marketing trend at first, but they’ve held up since that time.
Not only have they increased in popularity, but chatbots have also shifted our communication preferences. 82% of consumers now expect to receive the instant answers that chatbots provide.
Interactive customer support chatbots are remarkably effective. Yet the standard operation is rather simplistic.
The typical customer service bot operates via rule-based, if-then logic. They detect keywords within a customer query, then search a pre-defined knowledge base for the most relevant answer.
Despite their basic nature, customer service chatbots unlock immeasurable business value.
Why use Customer Service Bots: Business Impact
Chatbots replace human agents at scale, creating a brand that’s open to engagement, 24/7.
Chatbot scenarios can be designed to guide customers to any intended goal. This can be delivering service updates or privately collecting negative feedback before public reviews are posted.
But they do far more than relieve support center workloads.
They create customer-directed interactions which provide deep customer insight and can be tracked across any digital property or channel. They can help create 24×7 touchpoints across the customer journey, starting from when a prospective customer visits your site, continuing as they browse your products and services on your website or app, and during onboarding, purchase, or delivery cycles— depending on the nature of your business.
Inherent Limitations of Rule-based Chatbots
Rule-based customer service bots can only interact within predetermined scenarios and select matching answers.
They can’t discern anything outside the given set of keywords and phrases.
If customers attempt to speak to it outside these parameters, the chatbot is unable to engage. Even minor variances in spelling, jargon, slang, or regional dialects can trip up a rule-based chatbot.
These issues create chatbots that feel stunted, slow, and ineffective, leading to increased customer frustration.
But these challenges only apply to classic rule-based customer service bots. The application of machine learning has rendered many of these issues obsolete.
New artificial intelligence-powered chatbots can engage with customers in a far more natural and fluid manner.
The Rise of the New AI-Based Bot
Artificial intelligence-based customer service chatbots use machine learning and natural language processing (NLP) to understand a question’s intent, then find and provide the right answer.
Customer service AI chatbots simply need to be trained to understand a certain area’s context and work towards a business goal. They then act on their own.
Instead of picking a set answer based on a pre-defined logic path, AI chatbots respond to each question in real-time.
These customer service bots learn from every interaction and situation. They analyze their own performance and get better over time.
AI chatbots still work with a core knowledge database, but this expands as the bot continuously learns.
These chatbots can handle more complex customer service areas that rule-based customer support bots would not.
The AI Chatbot Edge
AI-based customer service chatbots are truly conversational in nature. There are no rote answers or scenario-based restrictions here.
They interact dynamically, responding and adjusting to input in the moment. This gives customers a far more natural feeling, human-like interaction which helps lower frustrations.
AI customer service bots can intuit your customer’s sentiment from their tone, colloquialisms, and other language patterns. This ability brings robot empathy into play.
Customer service AI chatbots interpret a message’s intent and act based on it. This can be as simple as mirroring their speech or shifting into a more appropriate emotional tone.
The AI may realize that the customer needs an entirely different solution offered and act to redirect them. Or decide to escalate situations to human support.
AI-based customer support bots don’t perfectly replicate the human-to-human experience. At least not yet.
The Collaborative Human-AI Customer Support Experience
AI-based customer support chatbots integrate well with human agents. The typical workflow has the chatbot handle all customer messaging until something needs to be escalated.
There are several points in a chatbot interaction that may require human intervention. These escalations can happen when the bot can no longer provide solutions or the customer no longer wishes to interact with it.
Once the bot senses this, it turns the chat over to a human agent, along with the complete message history and any relevant insights.
This hybridized service model does enhance business efficiency by limiting the involvement of human agents. But its most significant impact is in creating a customer experience that offers the best of each.
It begins by providing your customers with immediate attention, completely eliminating service wait times.
And the AI chatbot’s query-processing and response time will generally be faster than a human agent’s. Particularly in support centers where agents juggle several client tickets.
AI isn’t subject to human errors. Your chatbot won’t forget a product detail, take a customer’s tone personally, or have an off day.
Unlike rigid rule-based service bots, AI’s sentiment analysis and language matching skills ensure that service is always provided in an appropriate manner.
Even when AI hits its limit, it still offers your agents insights that can improve their own service.
In fact, AI-human collaboration improves the entire support center’s performance, making it smarter and more efficient, while keeping things personalized.
Implementing it into your customer support operations begins with developing the AI-powered service bot.
How to Build & Integrate Your AI-Customer Service Bot
There are only three steps to developing a customer service AI chatbot.
- Understand your user
- Build a contextual knowledge base
- Develop the bot
Building an AI-powered bot capable of interfacing with an organization’s client base is a straightforward process for experienced developers.
It comes down to one question.
What are your most important customer conversations?
Even the most complex business relationship comes down to a set of conversations. This provides the guiding framework for AI that can be deeply integrated across your entire customer service operations.
Perceptive, adaptive artificial intelligence is here.