When people think of AI for customer service, they tend to fall into two camps. The first group pictures a hapless robotic messaging service that rarely understands them and is more trendy than helpful. The second, more optimistic, group imagines a Hollywood-style AI that understands your deepest desires while empathising and learning like a human.
Neither situation is an accurate representation of the technology available to customer experience leaders today. Organisations want to harness the power of AI to intelligently deal with increasing customer service volumes from their frontline teams, but they often end up dealing with less-than-perfect chatbots and annoyed customers. The problem is that rule-based chatbots are rarely able to perform beyond the very simple tasks they are designed for.
So, should companies be investing in scripted chatbots or proper machine learning & AI to power their customer experience? Well, it depends on the problem they’re trying to solve.
What’s the difference between a chatbot and AI?
Though chatbots are often incorrectly characterized as AI, the truth is, they mostly just respond to keywords in a scripted way. They cannot learn over time, and so don’t have actual intelligence. They won’t improve to give better or more accurate answers.
Most chatbots on the market rely on a rule-based system in which someone has pre-scripted a set of rules to understand specific words, patterns, or synonyms. Once they recognize a word or phrase from their complex database, they spit out a pre-determined answer. Let’s use ‘lost luggage’ as an example, if the customer were to text a chatbot about their lost bag, in all likelihood, the chatbot would recognize the term “lost bag” and provide the related response. This response might not address the specific complexities of the distressed traveller’s situation. Instead, it would only offer whatever general information it had been programmed to provide. Not an ideal experience.
Is there a valid place for chatbots? Yes, certainly. There are numerous meaningful applications for which chatbots can be useful. Any time the scope of a customer’s inquiry is highly predictable, a chatbot can serve as an effective tool. Ordering a pizza is a great example. A chatbot could handle ordering pizza quite well because the process need not be complicated. Customers who place an order have a clear objective—getting a pizza—and it’s easy to measure success—delivery of the desired product. The use of specific terms, such as “pepperoni” or “extra cheese,” is consistent and straightforward, so, with few exceptions, orders would be accurate.
Customer Service is delicate. Assist your agents with AI trained on real historical data.
When you give AI logs of historical interactions between service agents and customers, you are giving it a strong baseline of knowledge to start suggesting answers to customer questions, but the real learning happens when the agents start interacting with those suggestions by approving, rejecting, or personalizing them. This joint approach ensures that the AI can be used effectively in the long run and continues improving at a rapid pace.
This ongoing improvement, referred to as continuous learning, is the process through which the algorithm gets stronger and smarter around the topics it interacts with.
The more often a topic comes up, the better equipped the AI becomes at handling it. The agents, simply by doing their job, are helping the AI model learn. The model, in turn, creates higher efficiency for the agents, helping them serve customers faster and more effectively by helping with the more routine, repetitive answers and allowing the agents to focus on more complex responses. When the AI reaches a certain level of confidence for a suggestion, it can automate and respond to repetitive questions without requiring agent approval.
Machine learning improves the experience of both the customer and the agent from the first day it’s implemented. Over time, those improvements compound as the machine learns to respond faster and better to more difficult questions.
AI is a robust and valuable solution, but it’s not a robot that walks into the office and takes away all the jobs. AI in the contact centre isn’t scary, and it isn’t going to take over the world. It’s also not a magic bullet. It will not solve every problem overnight or make your customer service centre disappear. AI is a tool — very powerful and effective, but still a tool. Agents using Service Cloud can avoid mundane and repetitive tasks such as searching for previous answers, classifying cases and typing the same answers again and again. Customer service agents become more effective and consistent, and last but not least, customer satisfaction improves. Most importantly, AI is not coming soon or in the near future. It is already here today.
So… How does DigitalGenius look TODAY in Service Cloud?
If your goal is to supercharge customer satisfaction and reduce time & costs by automating repetitive tasks, there is an app in the appexchange for that
Main features in a nutshell:
– Predictive Case Intelligence: Pre-fill all the case fields, drop downs & automatically route cases
– Human+AI™ Question Answering: AI suggests the best answer. Agent approves. System learns & automates repetitive queries over time
– Language agnostic
– Continuous learning means the AI model improves with each interaction
– Multi-channel, available for email, chat, and mobile messaging