Chatbots provide automated, 24/7 self-service solutions to handle customer support issues such as frequently asked questions and basic transactions. Chatbots are typically found on websites, social media channels and mobile apps with the goal of using software to simulate a human conversation. By supporting repetitive customer inquiries with a chatbot, support organizations can significantly increase their efficiency by greatly reducing the volume of calls their staff needs to field.
Customer care is transforming rapidly. Expectations have shifted with the proliferation of mobility and social media. People are no longer willing to wait for an answer—they want their concerns addressed immediately. Because most support interactions start in the digital realm, this critical channel must function flawlessly and resolve as many issues as possible before escalating to human assistance. Making it even more urgent, in the world of social media, people don’t hesitate to share bad service experiences instantly.
Getting this step right is a priority for most businesses.
To ensure their customers remain happy, repeat customers, many companies have turned to chatbots, which can handle basic conversations and transactions—either via phone or online—with customers to resolve their issues. Chatbots have become more common and show promise for improving customer experiences. They can free up support resources and handle repetitive tasks.
But traditional chatbots have a number of significant drawbacks. Put simply—they’re not very smart.
A typical chatbot’s intelligence today is menu-driven and limited to a finite number of fixed paths set up by developers. They’re similar to a densely packed FAQ, but significantly harder to maintain and not optimized to complete transactions. In fact, to keep pace with customer issues, developers must create and maintain complex, time-consuming and error-prone decision trees for each bot conversation—not just anticipating question types but also entire conversations.
Rather than being intelligent and active conversation participants, chatbots have traditionally behaved like answering machines, prompting users to follow an unnatural, pre-defined path to complete a transaction rather than creating an environment that resembles a natural, human interaction. As a result, there are nuances of written and spoken languages that chatbots of this type often have trouble understanding.
For example, if you’re booking a trip, the chatbot might understand that you’re traveling to London. But it could get tripped up on the day and date easily. Should you say the date in numbers or words? Is saying “tomorrow” understandable? Or should you say the actual day? Often, it’s a crapshoot. Some chatbots understand part of what you say, some understand most of it, but it’s rare that they understand everything. And it’s usually the critical points that they miss. In a customer engagement, every second counts, so it’s important to get it right the first time. After encountering this kind of frustration, a customer has already pressed 0 to get a human—or has hung up and called a competitor.
How have these issues cropped up in today’s chatbots? The technology held so much promise.
But that is starting to change.
Using artificial intelligence, cognitive chatbots are quickly transforming customer interactions. Unlike traditional chatbots, these modern implementations are much more transactional and can be trained like people—utilizing a set of goals, examples, and data from existing systems. Rather than employing imperative programming, these new chatbots are built using declarative programming. This kind of code describes what you want to do rather than how you want to do it. This approach enables the replication of tasks, like web and mobile forms. In the process, the chatbot can be trained using existing source materials like FAQ pages.
The declarative approach enables you to describe the information you want to extract from the conversation. Then, a set of cognitive algorithms handle the conversation—going beyond just answering questions to actually conducting transactions.
Companies whose call center operators are at the core of their operations can utilize cognitive chatbots to relieve their operators of mundane activities and repetitive calls by providing customers with an intelligent self-service platform that can handle FAQ and transaction-related tasks.
These cutting-edge cognitive chatbots can have immediate and broad effects on operations, including:
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Progress is helping its customers blaze a new trail with NativeChat—an innovative artificial intelligence-driven platform for creating and deploying purpose-built chatbots for transactional use case. With Progress NativeChat, customer service organizations and ISVs can overcome the challenges that hold back traditional chatbots.
NativeChat is unique because it learns and adapts automatically over time to generate natural-feeling conversations with customers. Companies that rely on call center operators can focus them on what they do best—delighting and retaining customers on tasks that require a human touch.
Unlike traditional chatbots, NativeChat can understand natural conversations in 72 languages.
NativeChat is also simple to get up and running in two weeks—10-15 times faster than using other bot frameworks, which require that developers spend countless hours anticipating then hardcoding steps and responses. This creates an island of fixed information that is only as good as what was entered on a certain date and time. It becomes separate from the daily and hourly dynamics of a modern business. On the contrary, NativeChat is as easy to train as a service agent and it integrates quickly with an enterprise backend—so it remains in the loop as the business adjusts. In contrast to traditional chatbots that require significant developer intervention whenever there is a change, NativeChat adapts and learns without developer support.
Even better, the optional Progress Kinvey backend makes integrating with business logic and middleware straightforward.
In addition to ease of use and implementation, security is critical. Because NativeChat is designed and implemented using a cloud-native, distributed micro-service architecture. Progress employs best practices to protect against intrusions, secure access, scan for viruses, secure the operating systems, provide for safe multi-tenancy, and conduct annual penetration testing.
NativeChat helps companies deliver superior customer experiences while empowering customers to talk just as naturally with chatbots as they do with humans. You can read a technical and security overview of NativeChat at the link below.
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Hristo is responsible for bootstrapping and leading new products at Progress in markets that have strong potential (>$1 billion) and are growing with CAGR 30% and above. Hristo has been with Progress for more than 10 years and is currently leading Progress NativeChat, an artificial intelligence-driven platform for creating and deploying chatbots based on patent pending CognitiveFlow technology that can be trained with goals, examples and data from existing backend systems.
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