What are the different types of chatbots?

Popular since 2016, chatbots were initially overestimated and then criticized. Today, they settle permanently in the world of customer or collaborating relationship. Zoom on a phenomenon as popular as it is mysterious.

A chatbot, in the broad sense of the term, is a virtual assistant deployed on a website or a messaging application (Messenger, Whatsapp,…), capable of managing a conversation in natural language or at least a guided tree.

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There are two family of chatbots: the linear bots (“dumb bots”) and the “smart” bots (NLP chatbot).

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Dumb bots: linear chatbots

Linear chatbots are automata based on a logic of decision trees. The user experience sequenced in stages, mechanically chaining and allowing navigation in a more or less complex tree. No intelligence, the user experience is similar to browsing in a graphical interface.

Advantages:

  • closed routes, few possible errors,
  • simple to build in POC format

Disadvantages:

  • hardly scalable,
  • unsuitable for “customer service” cases because it is difficult to manage rich knowledge bases,
  • need to rethink the entire course in case of evolution of all or part of the scope,
  • no listening to customer requests or harvesting verbatim customers.

NLP Chatbots or Virtual Wizards

NLP chatbots have an understanding ability. They are the most advanced chatbots and often very suitable for customer service use cases.

Still not widespread, they suffer from an image overtapped by the difficulties of setting up and often half-tone performance.

Since 2016, the date of the emergence of automated natural language processing technologies, the performance and reliability of these technologies have continually improved. At The Chatbot Factory, we embarked on the development of our own NLP technologies with the ambition to simplify their deployment by non-techs and improve their ROI.

NLP chatbots are the best performing virtual agents on the market. Fluid, free and “smart”, AI doped conversational agents are the most similar to humans.

Nevertheless, they can prove to be complex to set up. They are based on machine learning technologies that require a large amount of available and structured data.

In this area, the Tolk platform (developed by The Chatbot Factory teams) innovates by mixing two approaches to automated natural language processing. The semantic approach and the machine learning approach. They complement each other perfectly in order to deliver performance from the first days of the chatbot’s operation, ensuring a boost as the chatbot feeds on the generated conversation data.

Advantages:

  • no decision trees that constrain the experiment,
  • speed of resolution of the request,
  • possibility to make rebounds to other themes or related content,
  • user experience close to a “human” conversation,
  • flexibility in the evolution of pathways and knowledge bases,
  • listen to the customer from the questions asked,
  • climbing to a smart and dynamic agent
  • ability to respond in a customized way,
  • level higher user satisfaction.

Disadvantages:

  • out of scope more frequent,
  • choice of technology (prefer hybrid models),
  • more sharp in performance management.

The Tolk:

  • Hybrid NLP Technologies,
  • Set up in 1 to 2 hours from a FAQ,
  • Native and automatic deambiguization
  • Generation of automatic and dynamic drive datasets,
  • Self-assessment of model performance for continuous optimization

Why launch your chatbot?

Consumer expectations and their uses have evolved sharply to move towards digital environments. Messaging, Chat or other instant messengers have colonized our daily lives. As a personal or in the professional world, exchanges are organized more and more more on these platforms.

The use cases for NLP chatbots are numerous, but most often crystallize around 2 functional verticals:

  • customer service — automatically respond to recurring user questions, automate business processes and manage complex recurring requests (requiring CRM integrations)
  • lead capture — capture and qualify business opportunities, and then intelligently direct them to the right contact for processing.

To maximize the ROI of such projects, it is necessary to have high-performance technology, easy-to-use tools that guarantee the evolution of the bot and finally a team of well-trained and continuously involved business experts to maximize customer satisfaction and virtual agent performance.

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