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Most of us can remember at least one conversation with a chatbot that didn’t lead to any resolution due to the inability of the chatbot to take any action to resolve the query, or to even understand it altogether. This clearly beats the purpose of deploying chatbots to improve and speed up customer service. Thanks to Artificial Intelligence (AI) and cognitive learning, a new generation of chatbots can handle complex tasks and provide a personalised cognitive and conversational experience to customers.
Here we take a look at how a cognitive chatbot is different from its predecessor and discuss five factors that businesses need to look out for when deploying a next generation chatbot.
Not all chatbots are born equal
Chatbots for customer engagement come with the great promise of providing a 24/7 self-service solution to address a range of customer support requirements. Customer self-service has emerged as the channel of choice for customers seeking support, as simple queries can get answered in real time instead of waiting in a call queue. This is the ideal scenario that sounds appealing to customers and businesses alike.
In practice, however, customer conversations with chatbots often lead to bad customer experiences and even frustration as traditional chatbot solutions might not understand a customer query or recognise accents. This means that chatbot conversations can quickly escalate to human assistance, leading to disappointment and discouraging customers from wanting to engage with a chatbot.
This is because the intelligence of traditional chatbot solutions is limited by predefined paths and decision trees that are fundamentally complex and error-prone. This imperative programming approach forces chatbots to behave more like answering machines, requiring users to follow an unnatural, predefined path that can only manage certain commands, languages and tasks.
Cognitive chatbots are really ‘chatty’
While it is clear from the above that traditional chatbots don’t quite cut it when it comes to customer engagement, the solution is a cognitive and AI-powered chatbot that can be deployed to any channel - website, social or mobile, covering all customer touch points. Technologies like machine learning enables this type of bots to become much more autonomous and transactional, as they are developed to learn from each conversation, meaning that the more they are used, the smarter they become.
Below we look at five factors that we need to consider when developing a chatbot strategy:
· The secret lies in programming:
Organisations need to be clear from the outset about what they want to get out of chatbot deployment. This will inform the programming approach of choice. An imperative approach will result in chatbots that only have limited intelligence and autonomy, and can only field requests pretty much like an answering machine. With a declarative programming approach, on the other hand, the developer can describe what information should be extracted from the conversation and lets a set of cognitive algorithms handle the conversation. The result is a transactional cognitive chatbot that can perform tasks and resolve queries.
· Make conversation as human like as possible:
As humans we are used to interacting in a natural way in our language of choice. Anything different could alienate customers and damage customer experience. A conversational user interface (UI) allows chatbots to hold human like conversations that feel natural to customers. In addition, multilingual chatbots that are able to communicate in the customer’s preferred language can not only overcome linguistic and geographical constraints but also deliver a much more enjoyable user experience.
· Keep things simple and deploy fast:
A chatbot should not be built to do everything – that would be a lengthy and complicated solution that would most likely be below average at everything. A chatbot should support between 4 to 6 processes, which means a business might need to deploy more than one chatbot over time, depending on its customer use cases. A cognitive chatbot solution will take goals and examples from existing data systems and be deployed much faster than chatbots requiring hardcoding steps and responses.
· Integrate with backend system for a ‘smart’ chatbot:
Organisations hold a wealth of data on their customer base. By integrating the chatbot with backend systems, the chatbot can be equipped with the intelligence to prepopulate and understand customer information like the customer’s name, account number, prior inquiries and requests so the conversation does not have to start from scratch in every customer interaction. Integration with backend systems is complex which is why some chatbot implementations often avoid or limit the extent of it. However, severless backend-as-a-service platforms have simplified these processes and spared organisations the need to write server-side logic.
· Keep the chatbot up to date:
To deliver quality customer service, the chatbot needs to be consistently up to date with the latest product, service, or support-related questions, transactions and processes. Whilst this is difficult for traditional chatbots with predefined decision trees, cognitive bots can adapt and learn on their own without costly developer intervention. Cognitive chatbots support dynamic training on top of existing enterprise data resources, so the key is to ensure that your data is up-to-date.
The future of customer support is ‘smart’
Customer support drives brand reputation and loyalty and ultimately plays a key role in shaping a business’ bottom line. Deploying chatbot technology is a great step towards the right direction but not all solutions will deliver the same results. AI-driven chatbots are a quick and cost-effective solution that can improve customer engagement and bring innovation to the business’ front line.