The Best AI Chatbot (and most advanced one)
8min read - iAdvize
Providing top-notch customer service isn't always easy--especially in today's digital world. As consumer thirst for convenience and speed has grown, many brands have turned to chatbots. Simplistic rules-based bots are everywhere, and they have some value for handling routine queries. But many brands are looking beyond basic bots to understand the best AI chatbot for digital retail applications.
The advent of conversational bots was a significant step forward for digital customer service. Using natural language processing (NLP) and machine learning, conversational bots did a far better job of understanding user intent and providing relevant responses.
Recent chatbot advances have led to a breakthrough solution, the augmented intelligence AI chatbot. Combining machine learning (ML), NLP, and human guidance, this next-generation chatbot is continually learning about the variances and nuances of human language. The result is a powerful capability to detect user intent and provide shoppers with the direction and answers they need.
How Can Brands Choose the Best AI Chatbot for Their Needs?
Which chatbot is right for your brand? All bots deliver some value, and which you use depends on your level of customer experience and conversational maturity. As data from Statista explains, organizations rely on bots for different purposes, with self-service and information-gathering as two top uses for bots:
Your organizational goals should drive your chatbot strategy. Your best bet is to learn about how each type of bot works and the value it delivers to make an informed decision for your company.
What Are Today's Chatbot Alternatives?
Today, brands can choose from three primary chatbot alternatives and may ultimately use a combination of all three on their websites. The first style is a keyword-based bot, which relies on manual programming to operate. Conversational chatbots that use NLP are far more advanced and can learn through conversations with site visitors.
Recently, a more advanced form of chatbot has taken center stage: the augmented intelligence chatbot. This bot combines machine learning and NLP with human input to gain a better grasp of human conversation.
Although the augmented intelligence chatbot is the most advanced option in the marketplace, brands can benefit from both traditional and conversational bots. For brands to reach the highest levels of conversational maturity, they need to deliver truly human-centered experiences, which means using augmented intelligence bots is a necessity.
The chart below outlines key features of today's chatbot alternatives.
How Traditional Rules-Based Chatbots Work
If you want to understand how rules-based chatbots work, imagine a flow chart. Each step leads to a discrete set of potential, pre-defined next steps. With a rules-based bot, each user comment or question leads to a defined next step instead of opening up a broad range of potential responses.
Why is that the case? Rules-based chatbots depend on the input of the teams that program questions and answers. Teams define keywords that relate to visitor queries and identify related responses. Each answer is automated and leads to a next step, which may be another information-gathering question or a link to a web page or help content.
Imagine a visitor coming to a website to check on the status of a shipped order. If that user engages with a rules-based bot, the bot may start by asking what the user needs to do. The bot may accept open-ended input or provide a small set of options to help guide user responses.
If the visitor indicates he or she is checking on an order, the bot will most likely offer a login link or ask if the visitor needs a user ID or password reminder. If the user has forgotten the account password, the bot may provide an opportunity to recover the password by text or email. Depending on the user response, the bot will offer a specific next action.
In this scenario, the rules-based bot may be able to satisfy the visitor's needs. The situation is straightforward and may not require any human intervention.
However, if the reason the visitor is checking on an order is that the order appears to have been delivered according to tracking information but not received, that is a much more complicated issue. Directing the visitor to account login and offering account recovery isn't going to solve the problem. The visitor most likely needs human input and will grow upset if the bot only provides a limited set of options without the opportunity to connect with a live representative.
The bottom line is that rules-based chatbots only work well for a narrow range of simple tasks. These bots can only respond in ways that their programming teams have identified and addressed. If a visitor's question doesn't match the bot's programmed set of queries, it will not understand customer intent. As a result, visitors can grow frustrated and may develop a bad impression of the brand.
Unsophisticated Chatbots Can Create Customer Frustration
Most online shoppers have encountered a rules-based bot and had a poor experience that has tarnished their perceptions of chatbots. In fact, one Forrester study found that more than half (54%) of online consumers in the US feel that interacting with a chatbot has a negative impact on their life.
Unfortunately, many shoppers may have only had subpar experiences with rules-based bots and may assume that engaging with a bot isn't a good use of their time. Forrester also found that two-thirds of consumers don't believe that chatbots can provide the same quality of experience as a human service agent.
What does this mean for brands? Deploying only rules-based bots can actually diminish the service you deliver to shoppers. On the surface, it may seem like rules-based bots can help you scale digital service and deflect inbound customer service contacts. But consumers' frustration with bots may motivate them to avoid bots altogether. Instead, they may reach out to customer service representatives and cause service costs to rise. Or, they may not seek the answers they need and not pursue the purchases they were considering--and that means missed revenue for you.
How Conversational Chatbots Work
The good news is many brands are well aware of the limitations of rules-based chatbots. They have recognized that they can only rely on rules-based bots for a narrow set of shopper inquiries. That is why more companies have started to turn to conversational chatbots.
What makes a conversational chatbot different from a rules-based bot? Instead of being solely dependent on pre-programmed queries and responses, conversational bots use NLP and machine learning to understand user intent.
For example, in the scenario where a visitor is checking on order status, he or she could type several phases, such as "I need to check on an order" or "My order never arrived." A conversational bot could understand both of these phrases and know where to direct the visitor. Also, conversational bots can understand misspellings, so if the visitor typed "check my odrer," the bot could realize the visitor was asking about an order.
Another strong point of conversational AI is that it learns through interaction with visitors. Over time, a conversational bot's understanding of language improves. That learning helps the bot deliver better answers and assistance to shoppers.
Understanding Machine Learning and Natural Language Processing
To understand how conversational chatbots work, you should have a baseline understanding of machine learning and NLP.
- Machine Learning - a branch of artificial intelligence (AI), machine learning focuses on applications that learn from data and improve their responses over time without needing to be programmed. As applications get more input, the AI platform machine identifies patterns and uses them to make predictions and generate responses.
- Natural Language Processing - a subfield of AI focused on analyzing language to help computers and applications understand text and spoken words. With NLP, applications can interpret documents, emails, written and spoken feedback, and other forms of human input. NLP lets organizations analyze and derive meaning from large volumes of unstructured human-generated content.
There are four steps in natural language processing. A conversational chatbot works through each of these steps to collect input and deliver a response:
- Input generation: Users provide voice or text input through an app or a website.
- Input analysis: The conversational AI uses natural language understanding (NLU) to determine the meaning of text input and assess its intention. For speech input, the AI will use both automatic speech recognition (ASR) and NLU for its analysis.
- Dialogue management: In this step, the conversational bot uses natural language generation (NLG), an aspect of NLP, to generate a response to the user.
- Reinforcement learning: The bot's machine learning algorithms use the input from each interaction to learn more about language and user intents to refine its understanding and deliver more accurate responses over time.
Entering the Next-Generation with Augmented Intelligence Chatbots
Conversational chatbots have made great strides in providing better customer service, but they still had limitations. Human language is highly complex and constantly evolving. Even the most sophisticated bots can't decipher user intent for every interaction.
However, shoppers' desire to engage and transact online has only accelerated. Digital momentum was strong before 2020, but the global COVID-19 pandemic drove even more people to explore online shopping options. At iAdvize, we witnessed a major surge in conversations on our platform, as evidenced by an 82% increase in chat volumes related to consumer products.
Clearly, consumers want more digital interaction with companies--and the brands that respond can position themselves as service leaders in the next era. Meeting those shopper demands requires us to reinvent the way chatbots work, with augmented intelligence as the way forward.
What is augmented intelligence? It's a solution that combines the machine learning and NLP used by conversational bots with the human input of rules-based bots. The result is a next-generation chatbot that constantly learns through shopper interactions while receiving training and guidance from human experts.
When shoppers engage with an augmented intelligence bot, the bot asks a question to prompt a user answer. The bot uses artificial intelligence to process the response and detect the specific intent in the user's input. Over time, the bot uses inputs to do a better job of matching user intents to outcomes.
For example, imagine a user tells the bot that he wants to return the order he placed yesterday. Unlike a rules-based bot that may focus on the word order, a more advanced bot will notice the word "yesterday," which is essential if the customer has multiple orders. That insight should give the bot a better ability to help the customer.
But sometimes, a bot may fail to understand what the visitor is seeking. With augmented intelligence, the bot can identify that failure and compare it with other failures to create a logical grouping of responses where it needs input to determine intent. The bot can then present the situation to a human reviewer to clarify user intent. Brand experts who converse with customers can also note frequently asked questions and suggest new intents for the AI.
In addition, augmented intelligence uses gamification to present phrases to brand experts to help refine understanding of user intent. Augmented intelligence relies on input from external experts who are passionate about the brand and who engage in conversations with shoppers. This vantage point gives these experts a unique ability to review chatbot input and coach the bot to grow its knowledge of human communication.
To extend the capabilities of augmented intelligence, the solution is integrating in-chat feedback from site visitors. Users will have the option to identify whether the bot understood their intent and provided a relevant response.
Another benefit of augmented intelligence is that it is remarkably easy to implement. Brands can launch augmented intelligence in minutes by deploying intent libraries with thousands of visitor sentences tailored to their industries. Once augmented intelligence is up and running, the bot can continuously learn from interaction and receive real-world guidance and coaching to extend its relevance further.
Deploy the Best AI Chatbot and Elevate Your Digital Presence
Although consumers have had mixed reactions to chatbots, there is no doubt that bots will remain a force in digital retail for the foreseeable future. But you can't expect that the same unsophisticated chatbot strategies will meet shoppers' ever-increasing needs.
The truth is, most of us have had less than stellar encounters with chatbots. According to a Statista study, half of the respondents (50.7%) said they felt that chatbots prevented them from reaching a live person when they needed one. And 47.5% of people affirmed that chatbots frustrated them by providing too many unhelpful responses.
Today's consumers expect simplicity and transparency with every business they encounter. They also expect to be treated as human beings, whose needs, questions, and time matter. Getting stuck in an endless loop of repeated chatbot responses isn't going to make any website visitor happy and is almost sure to drive a shopper away from your website.
As Cap Gemini explains, the pandemic era has caused us all to desire more humanity and more personal experiences from the brands we choose to support with our business:
As we emerge into a new chapter, it's time for your brand to rethink how you meet this need for personal connection--and that means revisiting your chatbot approach. Instead of looking at simplistic chatbots as a quick way to lower incoming contact volumes, you need to consider the experience you deliver to customers.
With augmented intelligence, you can be one of the rare brands that impress shoppers with bots that understand their needs, provide assistance when possible, and connect shoppers with humans for personal conversations.
We live in a new era shaped by the upheaval of an unexpected pandemic that transformed all of our lives. Today's brands are in the unique position of being able to restore some of the human connection that was lost during a time when socializing less and keeping a distance became the norm. Bots will never replace humans, but bots can learn from us. We can instill our empathy and intelligence to create technology that humanizes digital experiences and creates a truly connected world.