Marketing Executives around the globe have a view to automating customer service using chatbots. 91% of CEOs think about automating customer service, but only 52% can implement, and chatbot effectiveness is still questionable. Chatbots as a trend picked up in 2017 and 2018. 2017 had 84 tech startups whose primary focus area was chatbots. In 2018 the number decreased to 42 and in 2019 to 12. Gartner had predicted that 80% of enterprises would use chatbots in their applications by 2020. The fact in 2020 is that chatbots, in general, have failed to impress executives because of low Return of Investment (ROI) and the end-users on lack of providing relevant service. Most of the available chatbot engines have some rule-based engine combined with Artificial Intelligence (AI) working on the customer data of the enterprise. With the current trend, the best practice for Rules-based chatbot implementation is :
Most of the chatbot companies that currently provide a solution have a rule-based AI Mechanism to handle the responses, and they become efficient when customer data is leveraged. This process is quite time consuming and the reason why chatbots become a controversial topic in executive meetings. With the progress of deep machine learning technologies in the past two years, automated conversational chatbots are leading over rule-based chatbots. These chatbots, instead of being rule based on consumer data, they use the conversational data of entire social media to find the intent of the consumer. Matching with customer data is not required, as in the case with the rule-based chatbot. Think like you are running an image recognization like algorithm on natural conversations on chat to map the primary intent of the customer. The image recognition algorithms match an image rapidly with billions of mapped images to find the type of image. Similarly, a conversational chatbot map outlines a natural conversation to make it contextual based on conversations sets in social media.
Facebook is a pioneer of discussions on social media, and they have recently open-sourced their deep conversational machine learning intelligence. Facebook has pre-trained 9.4 billion neural networks on massive conversational data. It uses 1.5 billion existing open domain natural conversations to train conversations. The conversation seems like human interactions in a very contextual fashion. The algorithm( https://www.parl.ai) can handle a terabyte of conversation data sets quite quickly. The approach to bring your organization's data to work will be to do speech to text conversation of your entire call center historical data set and include that as a training set on top of already available intelligent pre-trained neural network. The algorithm uses skills like knowledge, empathy & personality to engage a consumer in a natural conversation based on billions of trained conversations. It then blends all three skillsets to keep the consumer more engaged in the conversation. This blending is known as blended skill talk. An example of a blended skill talk is as below:
The conversational chatbots can handle conversations like negotiations, chitchats, question and answer, empathetic dialogs, booking reservations, quizzes, and so on. This powerful conversational AI can be easily integrated into Facebook messenger to acquire customers, perform transactions, brand awareness, and excellent customer service.
This technology is easy to implement as compared to the enormous time, effort, and money required for rule-based custom chatbots. The key is to use powerful, open-sourced AI with an already available and integrable chat platform like Facebook messenger. The difference between rule-based chatbots and conversational chatbots is a faster time to market with better customer experience and engaging conversations. Also, Python is the language for this open-sourced framework, which is the preferred language for machine learning, and the skillset is easy to cultivate in your enterprise, large or small.
The library also provides seamless integration with Amazon Mechanical Turk so that human workers can do data collection, training, and evaluation. The human Turks and chatbots can all communicate in the same conversation. The group chat can have humans and bots talking to each other in a single chat thread. The challenges with rule-based bots get addressed by engaging the customers with an intent oriented conversation and engaging humans to perform validated tasks.
If conversational chatbots when used effectively, can
and many more use cases based on your industry.Tweet to @parlai_parley