Chatbots answer customer visitor questions or requests. While many rely on command-based functions, the better AI chatbots use artificial intelligence, especially NLP (natural language processing), and sentiment analysis.
Occasionally people refer to these bots as AI assistants, conversational interfaces, conversational agents, or even chatterbots.
AI Chatbots most often appear in the commercial sector, where they answer basic customer queries or alert human customer service agents when they cannot. In this way, businesses can offer 24/7 availability for customers while saving money. The chatbots also enable customer service agents to provide better care when they have more time to focus solely on the most challenging customer concerns.
Chatbots can also hold simultaneous conversations more easily than humans. At the same time, they collect customer behavior, device, and segmentation data for future company use.
Developers can also imbue chatbots with interactive product personalization features which customers enjoy. Sephora’s chatbot is a well-known and very successful example of a gamified, educational AI chatbot.
AI chatbots have also developed to the point where smart devices—especially phones—come equipped with conversational AI interfaces that customers can speak to or even give orders to. Amazon’s Alexa, Microsoft’s Cortana, and Apple’s Siri are the most well-known examples of this technology. These programs also integrate with smart appliances in the home to enable individuals to turn on lights or adjust thermostats via vocal commands.
Another important application of this use case comes from the healthcare industry. AI chatbots support remote medical consults and conversational therapies for vulnerable populations.
Whether you are building your own AI chatbot program or integrating purchased pre-made templates that will integrate with your CRM, you need to provide certain internal company data. These include your customer service guidelines, FAQs, and inventory. Company-specific customer IDs are especially important for customers with large or frequent orders or who face recurring issues.
What you define as “essential” external data depends on your chatbot’s purpose. AI chatbots that you want to use as smart home assistants must have IoT capabilities. E-commerce site chatbots must integrate with your CRM software and will likely need seasonal or event data, as well.
As above, “useful” external data depends on the intended use for the chatbot. As in the example of Sephora’s makeup shopping assistant with advice and instructions, facial recognition capability is highly appreciated. However, you may not need or be able to support such a function.
While there are many open-source or inexpensive rules-based chatbots available, good AI chatbots running NLP or sentiment analysis or both can be expensive. And developing a program on your own may be too difficult.
Additionally, AI programs that learn from customers and adjust their conversational approaches following customer interactions may be too sensitive and responsive. All developers know of the disaster of Microsoft’s Tay AI and, though mindful not to repeat Microsoft’s mistakes, chatbots can still be too suggestive.
Finally, some languages have received less attention from AI chatbot developers, so some customers do not have access to native-seeming conversational interfaces.
The fact that a chatbot is always available is seen as a major bonus. Apart from that, someone sees it as a useful replacement for Googling info themselves. “Nowadays, I often search the internet, but then I end up looking at some really horrible stuff.” A chatbot that has information that has been verified by health authorities can resolve this problem. Despite the positive aspects, people are also cautious. For example, hardly anyone wants to share personal data with a chatbot. “That’s too prone to abuse” is a key argument. People who would like to share data only pass on “strictly necessary data in order to get the best possible answer.”
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