Chatbots, powered by artificial intelligence (AI), are becoming a popular and essential application in contact centers by swiftly, accurately, and cost-effectively handling, and helping agents to handle, many customer issues.
But like any particularly rapidly evolving technology chatbots have their issues. To find out more, and how to solve them in order to obtain value from these investments, we recently had a conversation with Christoph Börner, Senior Director, Digital, Cyara.
Q. What are the top issues with chatbots in contact centers?
While businesses increasingly rely on chatbots to save costs and alleviate call center strain, some reports suggest consumers are far less enthusiastic about the shift to automated customer support.
According to new research from Forrester Consulting, chatbots are failing to live up to their potential – leaving 50% of customers feeling frustrated and impacting their overall opinion of the brand.
One of the most common and arguably the most critical areas where chatbots fail is establishing customer intent, which is often due to a lack of sufficient AI training and testing.
Q. What are the causes and the consequences of these problems?
The world is home to 7,100 languages–not to mention countless dialects and all kinds of slang, phrasing, and other styles of writing and speech.
Because of the complexity and diversity of ways in which customer intent can be expressed, even humans sometimes have difficulty understanding it. So, you can imagine how hard it must be to ingrain such skills in chatbots.
For example, a customer wanting to return a pair of shoes could type the request to a chatbot in a number of ways:
- How can I get a refund on my shoes?
- Shoes are too big and want $ back.
- I need a refund on these shoes.
Even though none of these requests use the word “return”, the chatbot must understand that the customer intent is to return an item.
This is where chatbots often stumble, particularly as the need becomes more complex or emotionally-driven. This is why consumers are commonly disappointed by their chatbot experiences. Despite the existence of quite capable technology, the bots don’t always fulfill their intended purpose.
Chatbots typically use natural language understanding, a branch of AI, to interpret typed customer inputs.
But chatbots are only as good as the knowledge that powers them. And due to the complexity and variety of ways that humans can convey their intent, it takes large volumes of data to effectively train a chatbot to recognize the wide range of inputs they must interpret.
Such training data is a set of examples expressing each intent that a chatbot uses to turn into a model for recognizing each intent. By emphasizing target use cases, the training data can teach chatbots to successfully recognize and handle each one.
ChatGPT And Chatbots
ChatGPT (GPT stands for generative pre-trained transformer) is a new chatbot technology developed by OpenAI. First released in November 2022, it has gone through a few iterations since then; at presstime it is on GPT-4.
We asked Christoph Börner of Cyara “What are the impacts and implications of ChatGPT, including the new GPT-4, on contact center chatbots?”
Here is his reply:
“ChatGPT has created one of these rare disruptive moments in technology that can change everything. Technically, it is indeed a significant improvement in natural language processing.
“ChatGPT’s ability to understand complex user inquiries, as well as its power to make human-computer interactions feel more human-like, is pointing the way for other players. Together, these things could make it substantial for all kinds of business communication and create a new era of how companies engage with their customers.
“More than that, Generative AI, with ChatGPT, could also be leveraged to assist human agents in contact centers. For example, summarizing conversations with clients, personalizing content, or doing a more intelligent routing.”
Q. Please outline the solutions
Human communication is constantly evolving, requiring continuous learning and continuous adapting for the chatbot to assure quality. The only way to accomplish this at scale is to automate the ongoing testing process.
...it takes large volumes of data to effectively train a chatbot to recognize the wide range of inputs they must interpret.
Effective QA throughout a bot’s lifecycle is essential to delivering exceptional customer experiences (CXs). Here are the key testing considerations for organizations seeking to improve the quality and efficiency of their chatbots:
- Create training data from sources such as your call center, or third-party sources.
- Test target use cases, as well as non-target use cases.
- Test the chatbot in the context of the whole omnichannel customer journey.
- Test the chatbot’s understanding.
- Test chatbot escalations to a live chat agent.
- Test chatbot and live chat agent performance under peak load conditions.
- Test the security of the chatbot.
- Monitor the chatbot in production.
Q. What steps can contact centers take to prevent future issues and if they do occur, to resolve faster and more effectively than before?
Companies must train their chatbots how to understand customer intent and continuously test the technology to ensure that, if issues occur, they can catch and resolve them before their customers experience them.
Having an automated and continuous chatbot testing approach, like those outlined in my answer to the last question, improves the quality of the chatbot, thereby ensuring it performs as expected while reducing the efforts of enterprise IT teams.
Organizations should train their chatbots with various phrases in advance so that they can recognize the correct intents.
Effective chatbot testing examines the way a chatbot understands customer intent, and it will do so continuously as the chatbot evolves and consumer input changes. This testing can also be done at scale to ensure the chatbot can perform even at high volumes.
Chatbot testing must evaluate how the bot performs in the various channels it resides, whether that is on the website, in a mobile app, within an IVR, or on Facebook Messenger or other channels.
This comprehensive testing provides value in every phase of the bot development lifecycle, enabling companies to:
- Deliver on their business goals of improving customer satisfaction and reducing costs.
- Mitigate the risk of chatbot fails and negative brand impact.
- Accelerate their chatbot development cycle.
- Increase agent efficiency and improve churn by equipping them to properly direct customer inquiries that can be handled by the chatbot.
Q. Finally, how can contact centers best maximize the benefits of chatbots?
A successful chatbot is defined not only by the technology that powers it, but by a well-thought-out conversational design.
Every chatbot behaves differently, depending on its purpose, topic coverage, and target user. However, there are general best practices to follow when building a chatbot that can improve its quality and lead to a better CX:
- Pre-train your Conversational AI. When a chatbot comes to life, it usually only has a small amount of training data. Organizations should train their chatbots with various phrases in advance so that they can recognize the correct intents.
- Use a fallback strategy. It’s impossible for a chatbot to answer every single question. This is either because the AI is not yet fully trained or the chatbot is being asked questions that it is not designed to answer.
- There are several ways chatbots can handle these fallbacks. For example, a chatbot can capture the contact details of the customer and forward them to a live agent who can then assist the customer.
- Get feedback. Ask users to provide feedback on the chatbot and recommendations on how it can be improved upon. Receiving negative feedback can help identify where there is room for improvement. This can provide a wealth of suggestions and ideas to further improve the chatbot.
- Think about the user experience (UX). When designing a chatbot window, focus on including elements that follow the company’s branding, such as typography or color. Use interactions like buttons, quick replies and cards to give the user predefined options to choose.
- Using such elements can enhance UX because users often don’t know how to write the question or what information the chatbot needs.
Retail Chatbots
Summer is around the corner. And that means new clothes, garden supplies, lawn care, home renovation supplies and tools, and recreation and sports gear. And just ahead is fall, winter, and yes, holiday shopping.
Which also means retail sales and support including online: where chatbots can help customers both directly and indirectly with contact center agents.
Here’s what we asked Christoph Börner of Cyara about this application of chatbots:
What are the prime functions and reasons why chatbots would be used in retail?
With a recession looming, more retailers are deploying chatbots to not only improve their customers’ experiences, but also to keep productivity up and costs down.
As a result, chatbots are becoming a matter of necessity. Here are the reasons why:
- Save time and money. Chatbots often follow a question-and-answer pattern that mimics the back and forth of a real human conversation. Thus, they are perfect for answering frequently asked questions from existing and prospective customers.
- This gives human agents more time to focus on complex inquiries instead of spending their time answering simple questions that can be handled by a chatbot.
- Generate more qualified leads and higher revenues by creating a better user experience (UX). One of the worst things a business can do is allow visitors to leave their site because they couldn’t find something they indeed offer. Chatbots solve this problem by guiding users to their desired product or service.
- Chatbots are also great at providing a personalized shopping experience for retail customers. For example, Sephora’s chatbot connects customers to a beauty expert that helps them pick out the best product for their specific needs and supports them through their purchase decisions.
- By creating a better UX, chatbots are generating more upselling and cross-selling opportunities for retailers, transforming customer assistance channels from cost centers to profit centers.
- Provide immediate help, 24/7. It’s a 24/7 world. Accordingly, people expect to be able to contact businesses at any hour of the day. Chatbots make it possible for retail organizations to meet these expectations.
- Increase customer satisfaction. Chatbots provide customers with a convenient self-service experience. Rather than navigating a website through drop down menus and search bars, chatbots provide conversation-based interfaces, which simplifies the customer’s overall experience.
- Chatbots can also help companies improve their CX for a wider audience. For example, younger customers who might not want to take the time to navigate a retailer’s website may respond better to a chatbot.
Chatbots are also great at providing a personalized shopping experience for retail customers.
Conversely, what challenges does the installation and use of chatbots pose in retail?
While chatbots continue to experience tremendous growth driven by the meaningful benefits they deliver to businesses and customers alike, they’re also commonly plagued with challenges that include misinterpreted customer intent and delayed or disrupted hand-offs to live agents.
Chatbots in contact centers are usually text- and voice-enabled. In addition to the challenges that retailers face with text input, voice adds a few more layers of complexity because the number of possible user inputs are limitless.
When using a voice-enabled chatbot, there are more variables to take into account, such as different accents, voices, languages, poor bandwidth, or phone quality and background noises.
When a chatbot cannot understand a customer’s intent, it often fails to provide a route to human escalation. It’s crucial for these handovers to happen seamlessly for the customer, which includes relaying all the background information captured by the bot during its interaction with the customer.
Any recommendations specific to retail?
Contact centers maximize the benefits of chatbots, and every chatbot behaves differently depending on its purpose, topic coverage, and target user.
As mentioned in the article, there are several general best practices to follow such as pre-training Conversational AI, fallback strategies, obtaining feedback, and having a UX focus when building a chatbot.