Data analytics to extract insights from large output data sets may become a daunting task for humans. ML is useful to better understand the impact of design parameters on automotive parts response to crash.
In the automotive market, customer experience is the new competitive battlefield and a primary differentiator. For this purpose, chatbots are a way to reach customers 24/7. Chatbots that implement AI and ML support deep analysis of the voice-of-the-customer, deliver the right message to the right customer at the right time, and systematically transport the desired marketing message to the customer.
But what is an “intelligent” chatbot? Think of the user interface as the tip of the iceberg: what is under water is Natural Language Processing (NLP), Understanding (NLU), and Generation (NLG). The bot also learns from data including customers conversations. And it talks to legacy IT systems, like CRM.
This enables on the fly context relevant product information and optimized leads assessement.
KIA Motors uses a chatbot to improve conversions, such as number of scheduled test drives, of credit applications, and ultimately of sales leads.
How would you implement a chatbot in your business? This is a make or buy decision. You could use IBM Watson and develop the chatbot by yourself. Or approach one of the many startups such as botwiser.com or pigro.ai. Whichever way, you will need a plan to maintain the bots, to blend human intervention for un-answered questions, to have a data quality assurance in place, to ensure secure interoperability across platforms, and to integrate across various channels for connecting with existing analytics.