Every digital channel you offer for customer service is a doorway to helping an essential segment of your customers, but it’s also the source of massive amounts of new messages. Agents are expensive to hire and train; hence, many of those messages go unanswered because it’s almost impossible to have enough agents to respond to everything. Additionally, most of the requests are simple and repetitive, and agents, well, they are human beings; they don’t like having the same simple conversations over and over all day. Burnout is easy to cause and costly to fix.
Moreover, customers today are less loyal because it’s easier than ever to switch from one brand to the other. An ignored message can be all it takes to stop someone from coming back to you. Nobody likes feeling ignored. So you need to find a way to handle all this digital volume at scale. And you can’t keep throwing more agents at the problem.
Some companies try to solve the lack of agents and technology to manage those digital inquiries by directing customers to the channels they invested a lot of money in, such as voice or email. Unfortunately, that’s not viable long-term. Chances are, if a customer contacted you on WhatsApp or on social first, it’s because they don’t want to wait on hold or write an email. They want to use the channels they’re used to and get a quick response. Even worse, from a business perspective, you’re directing people from less expensive channels to the most expensive channels, driving up costs.
The first viable step toward dealing with this volume is to enable guided conversational chatbots that can respond to customers in real-time. These button-based chatbots will allow your customer to get answers to their basic questions. Although they are easy to build, hence a good starting point for any business looking into self-service, they don’t provide the ultimate experience. They simply mimic the process of clicking through a website menu rather than providing human-like conversations.
In order to achieve both scalability and a human-like experience, the only option is to use AI-based conversational chatbots, which enable customers to converse using free text. That said, building successful AI-based chatbots is not easy. A lot of chatbot strategies fail to yield meaningful results because you don’t just need to provide an immediate response; you need to provide the right response. Your bots won’t impress anyone if they guess customers’ intention incorrectly or ask them to repeat themselves.
Using AI to understand customer intent in order to trigger the right dialog responses is important, but to truly handle customers at scale with chatbots, AI has other crucial roles that many strategies leave out.
Before you can start building conversational bots, you need to understand why your customers contact you, so you can make a bot for each intent that matters. That means looking through thousands of customer messages and clustering them by theme, product, issue, or other parameters.
With the volume of messages today, that’s not possible for humans to do manually. So most decisions about which intents to address are made based on intuition, which most of the time leaves a lot of request types unconsidered and, as a result, unserved.
Instead, you should be using unsupervised or semi-supervised AI models to scan all of your historical messages and discover all of the conversation drivers that are happening. Data should drive the decisions about what to automate, not intuition. Building an effective bot takes time and resources, so make sure you’re investing in use cases that will have the most impact.
Once the requests you want to provide self service for are identified, you need to actually design the conversational flow to solve for those requests. This process can be time consuming and guessing the best ways to answer a customer might not provide the optimal solution. The good news is that your agents have already solved those requests multiple times so you could just analyze all your historical conversations to identify which answers drove the best customer experience.
But again, this task is daunting for humans to achieve. Leverage AI models to analyze the entire conversation between customers and agents to provide data-driven recommendations on potential workflows and ensure the chatbot you are building provides the optimal experience.
Building chatbots is an iterative process. When you deploy your first one, it won’t be perfect. You will not contain 80% of conversations from day one.
You should start by creating bots for the most recurring requests. But, over time, you’ll need to add coverage across more use cases to reach your containment targets. Additionally, what was true 6 months ago might not be true anymore. With time, customers will come with some new types of requests that you’ll need to address.
But again, volume is a challenge. Chatbot interactions add up fast after deployment and in order to understand where you are succeeding and failing, you need to analyze all those conversations.
This task is impossible for humans, but it’s ideal for AI. AI can analyze which interactions gave the customer the best satisfaction, which messages resulted in fallback, and so on. It can then spot improvement areas and alert you to the errors that are having the biggest impact. Through regular correction and retraining by humans, your chatbots will get smarter about giving customers what they want more and more consistently.
The shift to modern over traditional channels is great news for so many reasons. Modern channels are cheaper to operate, easier for agents, and no matter how high the volume of messages, they are in formats that are easily analyzable by AI to learn more about your customers and how to make them happier at scale with conversational bots.
Aurélien Caye is a Solution Consultant at Sprinklr, expert on chatbots and Conversational AI, working with the product team to innovate based on customer requirements. After using Sprinklr to optimize engagement on social channels at L’Oréal, he joined Sprinklr, where he implemented multiple chatbots, before joining the solution consulting team. He understands the challenges of building a successful chatbot leveraging AI and aims to assist brands to develop the right self-service strategy.
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