AI and customer service continue to be priorities for customer-centric businesses that want to stay top of mind. With so many chatbots in a chaotic market, however, bot fundamentals can often get lost in the "AI hype".
That's why we've brought it back to the basics, asking three important questions:
- what is an AI-powered chatbot for customer service?
- at a high level, how does AI actually work? How does it empower CX chatbots?
- what are analyst-approved strategies for maximizing the return on an AI-powered chatbot?
What is an AI-powered chatbot for customer service?
Putting it simply, an AI-powered chatbot is a customer service software that allows users to chat online with an artificially intelligent “bot” the way they would chat with a live agent. The bot, depending on its AI and software, is able to both answer basic questions, like “what are your store’s hours?”, and complex questions, like “can I make a payment on last month’s phone bill?” Businesses can add chatbots to their websites, mobile apps, Facebook pages, and any other digital channel they use to communicate with customers.
Chatbots allow businesses to provide 24/7 customer service, freeing up agents’ time to spend on complex problems, and lowering support costs. Many bots, including Ada, generate customized responses for every individual customer, creating an automatic yet personal customer experience.
Businesses across industries are realizing the benefits of chatbot technology, and you’ll notice that everyone from Shopify stores to Facebook pages to major telecommunications companies now offer self-service options in the form of a chatbot.
At a high level, how does AI actually work? How does it empower CX chatbots?
AI is a vast universe of technology, software, research, and products. There are AI-powered shopping carts. AI-powered legal drafting programs. AI-powered marketing platforms. AI-powered code reviews. Machine learning is a type of AI. Deep learning is, also, a type of AI.
So: anything can be AI-powered, and AI-powered can mean anything. On its own, AI isn’t a selling point.
Generally, AI is a tool that takes in data, makes predictions, then creates a feedback loop that improves those predictions automatically. It’s a sophisticated process, and a new tool for the tech industry, but it doesn’t mean anything without the right techniques, the right data, and the right (human) team guiding the AI to useful outputs.
In customer service, AI-powered chatbots harness data to deliver customized responses to individual customers. At Ada, we use the feedback and best practices generated from our client base to improve our AI, so its outputs get more accurate and more personalized, creating a better customer experience. Learn more about our AI here.
What are analyst-approved strategies for maximizing the return of an AI-powered chatbot?
You’ve learned that AI-powered chatbots for CX are digital self-service tools that allow customers to handle simple and complex inquiries by talking to a “bot” the same way they would talk to a live agent. You’ve learned that customer service chatbots use AI to create a type of feedback loop that leverages user data to automatically create personalized customer experiences.
But…we’ve brushed over the real tech specs.
According to Gartner, there are 3 main ways that vendors use AI for CX chatbots.
Either a chatbot company will use an AI architecture based in linguistics, based in data science, or based in human knowledge.
Gartner’s Competitive Landscape: Virtual Assistant Platforms, Worldwide, report expands on these three categories, known as “linguistic extraction,” “machine learning,” and “intent mapping,” respectively:
- “One segment of vendors favors extraction and tagging of linguistic descriptors to form an abstracted view of the language input.
- A second segment leverages data science methodologies to create a machine learning model initially trained by supervised learning and subsequently moving to unsupervised training.
- A third segment of vendors favors human resources to program the rules, relationships, vocabulary and knowledge that is germane to a certain industry or even individual company.”
Gartner’s analysts elaborate that each of these architectures has benefits and drawbacks depending on your business’ needs. The report highlights the benefits using a model with a smaller machine learning footprint, due to its ability to scale and integrate into existing systems. Gartner points to Ada as one example: “Ada’s competitive thrust is designed to deliver positive outcomes as to transactional speed and compact size of the data model to make their architecture deployable at the edge, in a device or hybrid architecture.”
Businesses should use Gartner’s research as a reminder to choose the right chatbot for their unique department’s needs, whether it’s choosing the AI architecture that allows for a go-to-market solution the fastest, or that is best equipped for vertical-specific terminology, or one that will scale and provide flexibility over time.
These are just some of the numerous ways AI and automation technologies can improve CX. Reach out to learn more about how our Ada’s superior AI and automation-first approach can empower your support team.