How Data and AI Will Engage Your Customers

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Customer engagement is a hallmark of 21st century businesses. According to Hubspot, customer engagement involves interacting with customers via multiple channels. Customer engagement is at an all-time high thanks to social media. As businesses grew, they realized they couldn’t handle hundreds or even thousands of relevant consumer comments, questions, and feedback. Some businesses try to hire staff to deal with the flood early on, but they lose.

When faced with large amounts of data, artificial intelligence comes to mind. According to MIT, Big Data combined with AI can yield exciting and vital business insights. But when we talk about AI, we mean a wide range of new technologies. Not all are relevant to customer experience and engagement. Here, we’ll look at how data and AI can help a business build a more robust customer engagement system.

Data sources and CX

Google now tends to push users towards what they think you’re searching for, if you’ve used the search engine recently. Relevance is key in the engine’s recommendations, and it’s the same for businesses using data to power AI. Then they can train their AI and create a unique customer experience. Because machine learning algorithms allow for retraining based on new data, AI responses will always be current. The laws governing the collection of user data vary, so businesses must ensure they are compliant across all platforms.

Advanced Algorithm

Algorithms, according to Wired, are a set of steps for performing a calculation. In computer science, it becomes more nuanced. Fundamental to AI, algorithmic learning instructs the system on what to learn from new data. While most algorithms today are supervised (an administrator monitors them and corrects errors), they will eventually be self-running. Machine learning can profile a buyer’s psychological traits based on their behavior. Its data can help customers find relevant items.

Co-learning deep learning and CX

Learning to think like a human is deep learning. This is a complex process that takes a lot of processing to figure out. Deep learning algorithms can help businesses generate leads and opportunities. Trialbee’s use of scalable real-world data is one of the best examples in healthcare. Patient involvement in vaccine trials will greatly benefit. Its goal is to connect companies with people who have participated in similar studies all over the world. It does so by using criteria to narrow down the pool of applicants to a few who are sure to be interested in participating in the study. A unique customer experience for participants is created by using AI in this context.

Just-in-time interactions are another use of deep learning. Consumers expect certain things from customer service representatives. Waiting for something irritates most customers and may drive them away from a brand. So the AI responds precisely when needed. The system uses user context and intent to determine what actions to take in real-time.

Better customer engagement with AI

Engaging consumers tends to improve a company’s bottom line. Marketing used to be as simple as contacting customers and understanding their needs. However, today’s interactions require more energy than a whole team can provide. A company must adapt to stay relevant in today’s fast-paced business environment. Companies need AI to keep up with the times, but AI without data is like a car without gas. Businesses can fully utilize AI in customer engagement by ensuring that the AI system has enough data to learn and develop from.

Computer vision and the customer

Customers are always looking for faster ways to shop. Computer vision is simply an input that a system can use to analyze data. For example, computer vision can create a heat map of a store based on traffic data. This knowledge can help businesses create more effective advertising campaigns or products that attract more customers. Pinterest’s Lens feature is another great example of computer vision. According to Pinterest, Lens allows users to scan objects with their smartphone’s camera. Lens exemplify what AI can do when given the right push.

NLP (Natural Language Processing)

Natural Language Processing tries to make AI respond like a human rather than a computer. NLP alters a brand’s relationship with its customers. It makes dealing with AI easier for consumers because they don’t have to learn complex interfaces. In plain English, the system simply asks for feedback and adds it to existing data stores. Chatbots can also be easily integrated into a company’s website. The cost of integrating this technology into a business website is also reduced.

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