When people think of bots, they don’t normally think about the teams they need to support them! Some might think that setting up a bot is as simple as waving a magic wand, but there are several layers that have to be taken into consideration to set up an effective Einstein Bot. In order to successfully implement an Einstein Bot, you need to assemble a team with a superb skill set. The following roles are crucial to an intelligently designed Einstein Bot that effectively accomplishes the goals you set for it and provides a 24/7 personalized customer experience.
NeuraFash‘s bot team is staffed with UX/UI designers, data scientists, developers, and AI experts, but at the center of it all are the natural language experts, also known as Conversational Designers. As was mentioned, the conversational designer has linguistic expertise. They think through what customers would be looking for when they start a chat with a bot, such as order status, or return inquiries. They help establish a greeting strategy for the beginning of a chat which greets the user and provides the user with the top 3 or 4 intents set up. This allows the consumer to understand that they are speaking to a bot and what the bot can help them accomplish.
Overall, conversational designers come well equipped with the knowledge and expertise that will help you easily transfer your thoughts on paper to a digital bot format and they will also provide you with best practices and data-driven recommendations regarding the design and the story. NeuraFlash will provide the essential guidance that will facilitate that seamless interactive strategy between your brand and your customers.
As one of the only partners in the Salesforce ecosystem that has a dedicated bot team, Neuraflash makes sure customers are using a chatbot that has a human-like quality with the goal of creating a simplified and desired customer experience. “All too often, there is still a substantial amount of people who carry the common sentiment that when they have a conversation with a bot, it doesn’t understand what they’re saying,” says Noah Girgis, NeuraFlash’s NLP expert.
This is where Natural Language Processing comes into play. Natural language processing is defined as follows:
“A branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable.”
NLP works by taking data, applying meaning, also known as intents, and mapping those intents to dialogs. In our experience building bots, Natural Language Processing needs to take a broad perspective to be effective. To create a great experience, the bot needs to understand what the user is saying, how the user is saying it, and what the best response is in return. The data scientist looks at data to understand what customers are asking and trains NLP models to allow the bot to understand the various ways that customers express those concepts. The conversational designer trains the bot to provide the best response when it recognizes one of those requests.
A well-programmed NLP based chatbot can give the end-users on the other side of the screen the feeling that they’re having a conversation, as opposed to the frustration of choosing through a limited set of menus that doesn’t answer their question. NLP functions with data-driven models that take in what people are saying and creates its dialogue intents based off of that data. Within the right context for the right applications, NLP can pave the way for an easier-to-use interface to features and services.
A considerable amount of thought and planning goes into creating the NLP of the chatbot. Natural Language Processing is understanding what people are saying and translating it into a different language. Some of the most important features considered when defining the bot context, are giving personality to the bot, and designing the conversation. These features are broken down even further into the core language of the bot which includes pattern matching and normalization (proper syntax and grammar) as well as creating an algorithm that will allow the system to gather large amounts of data, classify it, and learn it.
By understanding what kind of data should go into the bot, and acquiring the appropriate amount, Neuraflash’s NLP experts like Noah can program a bot to understand what phrases (also known as utterances) are used in a bot interaction. Neuraflash bots are programmed to separate or combine intents or categories a person may ask, so as to clarify with them what exactly they are asking. This is crucial in avoiding confusion and frustration from the customer's end, such as when a customer is asking about a return. Noah will program the bot to separate related key terms such as return and refund and clarify which one the customer specifically wants. This way the bot doesn’t just assume one or the other and cause a misunderstanding.
As dialogue evolves overtime, the NLP evolves alongside it as people come up with new questions. NLP experts have to tweak the dialogue of a bot and refine it with new improvements. The expert will continue to add to the chatbots capabilities to further improve its self-service rates over time. At NeuraFlash, doing so is crucial in keeping our bots relevant and updated. “Pruning out certain intents is crucial because some questions become less popular, so if you don’t change the phrasing to match, customers become bothered, and confused and turn away from using the bot,” says Noah.
The structure of the bot begins with defining its labels or intents. This is done by first figuring out its scope, and processing data from previous chats to see how users interact with contact centers. NLP experts will look at logs of what a customer says, and analyze the back and forth interaction. Once the NLP expert understands a customer's dialogue and how they ask questions, an expert on the subject matter will determine what the right sets of intents are for the bot to ask. The key is to cover multiple topics under one intent. NeuraFlash programs our bots to match together 2 or 3 big key words at a time in their NLP dialogue, becoming more effective, and powerful.
Creating an effective NLP may seem tedious and time consuming, but in the end is worthwhile to obtain significant results. Royal Bank of Scotland is an example of this. They use NLP experts for their chatbots to enhance the customer experience through text analysis to interpret the trends from the customer feedback in multiple forms such as surveys, call center discussions, or emails. It helps them identify the cause of the customer’s dissatisfaction and help them improve their services.
Overall, NeuraFlash implemented Einstein Bots that can help enhance your business processes and elevate the customer experience to the next level while also increasing overall company growth and profits. Let NeuraFlash help keep your company a competitive player on the market, while ultimately saving time and costs that lead to increased customer satisfaction and engagement!