Chatbot performance can effectively be developed to improve customer satisfaction. We already know much about chatbots that convert better, but what about the ultimate KPI – customer satisfaction?
Importance of escalation paths
Chatbots are quick to answer routine questions and they offer first-rate value to any enterprise. However, the option to escalate an interaction to a human employee should always be available in the event that the questions are too complex for the bot to respond to.
If necessary, the chat should switch to a call so that the customer is not left feeling that the query was not handled adequately. Escalations need adequate resources, minimum waiting times, and service implementation.
Ensuring an escalation path is critical for customer satisfaction and retention.
Implementing and operating chatbots require time-consuming commitment. They need to continuously be provided with sufficient data so that their knowledge can grow. This information needs to be real-time relevant and up-to-date. If these duties are neglected chatbots become useless, much like a website that is never updated.
Chatbots constantly need new information otherwise they won’t offer customer satisfaction.
Injecting empathy into chatbots
Chatbots need to be made empathetic so that the customers using them can feel their understanding so that they can also in turn, express their emotions. This can be done through training the chatbot before production.
Chatbots need to be given the ability to capture sentiment through the course of conversations, without asking (implicit measure); adapt the conversation flow and the response to these sentiments; ask customers at the end of the conversation how they feel (explicit measure); and compare implicit and explicit measures to improve sentiment capture over time.
By covering various emotional situations and emotions and weaving them into the reporting process on real conversations, contact center teams can fine tune chatbots to perform better.
Customers who feel the empathy are more willing to use chatbots and report having a better customer experience.
As I said recently to our senior engineers: “The most important reason why customers abandon a brand, even if it has been one of their favorites in the past, is often because of just a single bad experience. Providing a friendly, seamless, consistent and personal experience is easier with a digital strategy that effectively employs bots.”
Collectors of information
Bots can be added to the customer satisfaction mix after existing live chats or social media applications have been introduced.
Once bots are thrown in they can be used to automate the repetitive information collecting task at the start of a dialogue and before the user is directed elsewhere.
When implemented correctly, bots are successful at improving customer satisfaction.
Natural Language Processing (NLP) creates an interactive customer experience
Chatbots can be friendlier, non-intrusive and also can adopt an appropriate tone that will please customers. This is thanks to NLP, which learns to recognize real language through software that uses data to provide personalized customer information and tone. The chatbot can then interact and engage conversationally.
The conversational tone of the software creates an interactive customer experience, much like they would with a real person. Transcripts of conversations can be analyzed and businesses can understand what types of customers are visiting their website. Most importantly, the can understand what their users are looking for and what issues need resolving, especially recurring ones.
Customers are more inclined to converse with a chatbot that does not sound robotic.
Chatbots need to be trained to communicate with consumers in a conversational tone, much like they do on the phone, rather than the way they do with text messages.
Chatbots can be coached to adopt this style and they should have access to analysis of every chatbot interaction. This is necessary to ensure the preferred approaches on different channels.
Interaction analytics allow chatbot adaptations that can avoid setting off negative reactions.
Machine learning and bot intelligence
Organizations need to continue learning from consumer interactions so that chatbots can be informed on how to react to new interactions. This can be done with machine learning and neural networks, which make use of data; learn from it and inform catboats.
These technologies allow employees more time to deal with other complex issues by reducing their workload.
The human touch
With training, bots can achieve satisfactory levels of friendliness and empathy, but certain situations may arise where complex queries and issues still need the human touch.
Chatbots are at their most efficient when they work in collaboration with advisors. They are highly effective in dealing with information requests, package tracking and other common problems, but they also need to be empowered to seamlessly hand complex issues to human advisors for greater customer satisfaction.