Between waiting with a telephone customer service and resolving the query with a few taps on a keyboard, most will choose the second option. It's easy, fast, and according to Gartner, 80% of businesses will move from native mobile apps to messaging by 2025.
Despite knowing this, many of the chatbots we encounter on a daily basis simply don't work. They lag behind, misunderstand simple questions, and most of all, fail to meet the standard of intuitive design that consumers expect.
This is not to say that the chatbot industry has not evolved since its inception. It's come a long way from the clunky and barely functional chatbots of the late '90s and early 'XNUMXs. However, there is room for improvement and innovation. Where is this point of improvement located? In the conversational UX.
What is conversational UX?
Conversational UX has been largely ignored in the bot building process. It's a whole new paradigm in this space, but it's not a whole new hurdle. Every new advance in technology is accompanied by a discussion of how people can interact with technology for better results. Technologists are not only tasked with making sure products work, they must also come up with ways to make the experience functional.
Although chatbots are largely geared towards handling simple customer service tasks, there is an opportunity for improvement in both customer service and business processes.
Conversational UX is an emerging field, but despite the need for more intuitive chatbots, the industry as a whole is not investing the time and effort required to perfect the experience.
Chatbots often function like magnified web forms, without the intuition or seamless integration that new consumer habits expect to see when interacting with "smart" platforms. But the industry has improved dramatically in recent years: modern chatbot platforms have hundreds of pre-built integrations, allowing businesses to connect their existing systems and tools to provide a secure and unified customer experience.
With each new interface, the goal is to improve human-machine interactions and result in a more intuitive user experience. Conversational UX presents a greater challenge due to the nuances involved with human language. It requires careful thought, empathy with the user, and important design considerations to create elegant experiences.
Below are key factors to improve the end user experience.
Omnichannel capabilities
According to a Deloitte study, there are about 25 connected devices in the average American home, including smart TVs, smartphones, tablets, and laptops. This means we are more connected than ever, with endless opportunities for businesses to engage with their customers. Chatbots are often fragmented with different, unrelated touchpoints and unable to query historical data from past customer interactions.
Consumers expect and value continuous and intuitive conversations with brands. Whether it's reminders of items left in carts or questions about orders, continuous communications across multiple devices results in greater convenience and trust in the business. Omnichannel integrations ensure that companies only need to build one bot to deploy across all channels.
voice and intonation
Although the conversation between the customer and the chatbot should be fluid and as human as possible, in order to maintain a level of trust, it should be clear from the start that the chatbot is just that: a bot. Any hesitation a customer experiences in interacting with a bot versus a human employee will be mitigated if the platform is intuitive and straightforward.
The voice or verbal tone of the bot will vary depending on the company. If the product or service is business-oriented, the bot is likely to adopt a more professional tone, while consumer brands are free to have a more cheerful voice. The voice of the chatbot must align well with external messaging and company branding guidelines.
multilingual options
When developing a chatbot, it is key that the chatbot is programmed to understand and respond in multiple languages. This not only helps companies reach global customers, but is also valuable on a national level. For example, more than 20% of US residents speak a different language at work than English on a personal level.
As the world becomes more connected and products and offers reach a larger audience, it's critical that all potential customers feel comfortable and understood. Multilingual natural language understanding can be quickly deployed across geographies, and bots can use self-learning to improve accuracy in every interaction.
Self learning
A primary benefit of text-based communication is that data is regularly collected and stored. Chatbots need to be programmed to regularly assess customer feedback and update it in real time. Any hiccups in the communication process can be used as training data to improve efficiency.
For example, if there is a pattern of customers selecting a specific option when communicating with the chatbot, the bot can automatically move the option up to make it easier for the customer. But if customers rarely select a certain button, it can be moved down or removed from the menu altogether. This data should then be automatically reported back to the organization to identify dropouts and opportunities for improvement.
Adding additional value
Although chatbots are largely intended to handle simple customer service tasks, there is an opportunity to scale both customer service and business communication. Chatbots should be programmed to provide proactive updates on delays, shipping times, and upcoming sales schedules. Also, the same bots can offer sales support for other similar products.
Proactive support and upselling not only helps increase sales and revenue, but also helps build customer confidence by providing value and anticipating customer needs.
communication loops
A common cause of customer frustration when dealing with a chatbot is the seemingly endless loop of miscommunication that can occur when a platform doesn't understand the message. When programming a bot, developers must ensure that the platform can understand and account for common misspellings, abbreviations, and jargon terms. This is where natural language processing can be used to understand customer intent and queries, and respond efficiently with a high degree of accuracy.
Bots should also be programmed with sentiment analysis capabilities to ensure they pick up on all caps, excessive punctuation, or emojis, all of which can represent frustration or anger, at which point the conversation should be passed on to a customer service person.
Increase in human employees
A common misconception about AI and chatbots is that they will take over the jobs of human employees. But the evidence doesn't show that trend: chatbots should be treated as a tool for customer service teams, not a replacement.
The chatbot can provide an agent with most of the information they need before they even have to speak to the customer. Once the customer moves to the agent to discuss the issue, the agent can immediately begin resolving the issue without having to spend time gathering information. The data collected by the chatbot can also help gain deeper insights into the customer journey.
The chatbot market was valued at $17,17 billion and is projected to reach $102,29 billion by 2026. Chatbots are not just a trend. If we want its implementation to have its place in the market, we must ensure that bot developers have the the right insights to improve the customer experience. Mapping all decisions to the end user and their expectations is crucial and beneficial for both parties.
