Customer engagement is among one of the hallmarks of successful businesses in the twenty-first century. As Hubspot notes, customer engagement creates interaction with consumers over several channels to strengthen the company's relationship with them. Thanks to advances in social media, customer engagement is at its highest point. However, as businesses scaled up, they started to realize that there was simply no way they could deal with fielding hundreds or even thousands of relevant comments, questions, and feedback from consumers. Some businesses try to hire staff to cope with this flood early on, but they quickly realize they're fighting a losing battle.
Artificial Intelligence is the immediate thought when considering large amounts of data. MIT mentions that Big Data, when powered by AI, can lead to exciting and vital insights for businesses today. However, when we speak about Artificial Intelligence, the term covers a broad range of emerging technologies. Not all of them are applicable in the sense of and engagement. Here, we'll explore how data and AI can work together to help build a more in-depth, more robust customer engagement system for a business.
If you've used Google for a search recently, you'd realize that the engine is now tending to push users towards what they think you're searching for. Relevance is vital in what the engine presents to you, and it's no different for a business utilizing data to fuel its AI. Companies can collect data from cookies or mobile applications, train their AI, and develop a unique customer experience. Since machine learning algorithms allow retraining based on new information, the AI responses will always be relevant based on the latest data collected on that user's account. The legalities of collecting user data do vary, however, and if businesses intend to do this, they must ensure they are compliant across all their platforms.
Wired notes that algorithms are a series of steps for a particular calculation; it's a mathematical term in its simplest form. Still, it becomes more nuanced as we apply it to computer science. Algorithmic learning is at the heart of AI since it teaches the system what it should pick up from new data. While most algorithms today are supervised (they are watched over by an administrator and corrected if errors occur), eventually, they'll be able to run on their own. Machine learning can pick up on the nuances of consumer behavior and profile the psychological aspects of a buyer. The data it generates can help locate relevant items for customers.
Natural Language Processing attempts to make AI respond using human parameters, rather than what you'd expect from a computer. NLP changes the way a brand interacts with its buyers. It makes dealing with AI much more accessible from the consumer's perspective since they don't have to learn complicated interfaces. The system simply speaks to them in plain English and collects their feedback, adding it to the data stores that already exist. Chatbots also come with easy integration systems allowing them to be embedded into a company's website.
