By working together, data and AI can build a more in-depth, robust customer engagement system for a business.
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December 23, 2020 6 min read
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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 customer experience 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.
Data sources and CX
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.
Higher-grade algorithmic processing
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 (NLP) technology
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. Development costs for integrating this technology into a business website are also significantly less.
Computer vision and customer experience
Customers are always looking for more efficient ways of finding their purchases. Computer vision is simply an input that the system can take to analyze data from a particular source. For example, by collecting traffic data within a store, computer vision can develop a heat map to show where most consumers are spending their time. In turn, this insight can help direct the business to advertising campaigns that are more effective or products that attract more foot traffic. Another excellent example of using computer vision is Pinterest‘s Lens feature. According to Pinterest, Lens allows users to scan something to search for from the world around them using their smartphone camera. Lens show precisely what AI is capable of, with the right impetus.
Deep learning alongside customer experience
Deep learning refers to teaching an AI to think like a human. While we believe it’s easy because we do it naturally, it’s a complex process that needs a lot of processing to figure out. Deep learning algorithms can be priceless in generating leads and creating opportunities for businesses. One of the best examples in the healthcare sector is the usage of scalable real-world data as implemented by Trialbee. Patient engagement during clinical research for vaccine trials stands to benefit immensely. It is focused on offering businesses a participant-engagement solution that utilizes AI to match companies with participants who have taken part in similar studies worldwide. It does this by using criteria to narrow down the pool to a handful of applicants that the company will be sure will be interested in taking part in their study. Using AI in this context offers better returns on investment for pharmaceutical, medtech, biotech and CRO organizations creating a unique customer experience for participants.
Another appropriate use of deep learning is just-in-time interactions. Consumers expect certain things to take place during their business with a customer service representative. A majority of customers value their time, and having to wait for something irks them and may drive them away from the brand. Just-in-time interactions ensure that the AI responds at a precise window. The system utilizes user context and intent to determine what they want to do and react to their real-time actions.
Enabling better customer engagement through smart use of AI
Overall, a company’s bottom line tends to look a lot healthier when it engages its consumers. In the old days of marketing, it was a simple matter of contacting users and understanding their needs. On the other hand, today’s interactions need more energy than even an entire staff of people can provide. If a business wants to remain relevant in this fast-paced modern business environment, it needs to adapt. AI is the best way for companies to move with the times, but AI without data is like a vehicle without fuel. By ensuring that the AI system has enough data to learn and develop from, businesses can leverage the full potential of AI in their customer engagement.