Hello, everyone. My name is Natalia. I'm a product owner with a passion for using technology to solve real world problems. Over the years, I've led innovative projects such as building a serum system for FinTech company, and creating AI driven platform for social media monitoring. One of the most impactful projects I worked on was a social media monitoring platform for brand reputational management. This project taught me how AI can turn unstructured data into actionable insights. But today I want to explore how we can take this further. Imagine how using IoT devices, not just to monitor behavior, But to actively collect and analyze client feedback in real time together, let's dive into how Internet of Things and artificial intelligence can bring the gap between data collection and client understanding. Here's what I'll cover in today's session. First, I'll share lessons from the social media monitoring and analysis project I worked on, where AI played a pivotal role in transforming unstructured data into actionable insights. Next, we'll talk about the challenges of collecting feedback using AI alone, what works, what doesn't, and where the gaps are. Then I will explain how Internet of Things can enhance feedback systems by combining explicit feedback, like verbal responses, and implicit feedback, such as behavioral data. Finally, I will dive into practical applications of Internet of Things and feedback collection, explore the challenges and opportunities that come with it, and look ahead at future trends. So let's take a closer look at the key question, how can Internet of Things redefine feedback collection? Thank you Specifically, how can it complement traditional methods like surveys or interviews and address the limitations of AI only solutions? Let's begin with the major challenge I faced when developing the AI powered social media monitoring platform. Traditional methods of gathering public opinion like surveys or focus groups are slow, expensive, and often lack the scale needed to capture the full picture. Social media offered us an alternative. A continuous stream of opinions, but this game with its own set of challenges. The data was massive, fragmented, and unstructured. Think about millions of tweets, posts, and comments in various languages, tones and contexts. How do you make sense of all of that in real time? We were overwhelmed by the volume of data. Social media doesn't stop. It's constant, 24 7. AI struggles with nuances, especially sarcasm or cultural differences. For example, a sarcastic comment like, Oh great, another amazing update. could be misinterpreted as positive feedback. Another challenge is scalability. The more channels we monitor, the harder it became to scale the system while maintaining accuracy. We created an AI driven platform that collected data from multiple sources, including Twitter, Facebook, and forums. We used natural language processing to analyze sentiment and detect trends. We developed a formula to calculate reputational risks in monetary terms. This way, we increased monitoring coverage from 10 percent to 70, providing a clear and more comprehensive view of public sentiment. The response time to critical incidents improved by over 60%. We created a formula to calculate reputational risks in monetary terms. Stakeholders This project showed us the potential of artificial intelligence, but it also highlighted its limitations. AI alone can analyze, but can't interact. This is where Internet of Things steps in, offering physical and interactive components to collect and act on feedback in real time. Before we explore Internet of Things solutions, let's sum up why AI alone isn't always enough for feedback collection. AI can struggle with emotions and tone. As I mentioned earlier, feedback this is amazing, could be sarcastic, but AI might flag it as positive. AI system typically analyze data passively. They don't ask follow up questions or clearly ambiguous feedback. While AI process data quickly, it's still analyzing what's already happened. It doesn't collect data proactively, but Which means opportunities to address issues in real time are missed. Why Internet of Things for feedback collection? Traditional feedback systems are reactive. Internet of Things enable proactive, continuous feedback collection. Now let's define two key types of feedback Internet of Things can help to collect. Explicit and implicit. Explicit feedback. This is when a customer directly tells you what they think. For example, selecting a rating on a feedback screen after the transaction. Explicit feedback is clear and direct, but it's often limited. Not all customers take the time to give it. Implicit feedback comes with observing customer behavior. Examples include how long a customer interacts with a product or screen, repeatedly pressing a button on the screen, the tone of the voice when saying, this ATM is great, which could indicate sarcasm. Implicit feedback is valuable because it captures data that customers might not express explicitly. Let's bring this concept to life with the practical examples of how Internet of Things and Artificial Intelligence can work together to enhance client feedback collection. Imagine a customer using a smart ATM. The machine fails to dispense cash and they mutter, This ATM is useless. With IoT enabled ATM, microphones detect verbal frustration, sensors monitor repeated button presses or prolonged interactions. Using this information, banks can schedule maintenance or software updates for specific ATMs. Insights can also inform design improvements, like simplifying the interface. Potential impacts include customer frustration decreases, improving satisfaction. Banks can see reduced churn and increased trust in their services. Now let's look at another example. In a smart bank office, IoT enabled kiosks or screens assist customers with transactions, account inquiries, or loans applications. If a customer finds a confusing interface or feels the service is slow, they might say, Why this takes so long or show frustrations through aggressive tapping on the screen? IoT devices track voice, gestures, and touch patterns using pressure sensitive displays and sensors. AI detects frustration or dissatisfaction in tone or interaction style. It links the feedback to specific processes, such as loan application delays. Artificial intelligence and Internet of Things together provide instant suggestions or fixes via the IoT enabled kiosk. Bank managers use aggregated insights to redesign flows or improve customer support strategies. The potential impact of implementing such a system is significant. It reduces customer churn by addressing frustrations in real time, making customers feel heard and valued. It increases trust in bank services as customers see issues being resolved promptly. It improves overall operational efficiency by highlighting bottlenecks and enabling targeted improvements. Here's why Internet of Things is a game changer. It captures organic, unfiltered feedback during the service experience and allows for immediate responses, improving client experience. It goes beyond verbal feedback to include touch patterns, interaction times and gesture analysis, offering deeper insights into customer behavior. It identifies trends before they escalate into larger problems and helps allocate resources effectively. This reduces churn and builds trust. Let's take a closer look at the challenges and opportunities that come with implemented IoT driven feedback system. First challenge is data integration. Combining IoT and AI data with existing systems, like traditional feedback collection tools or CRMs, can be technically complex. Each system may use different formats or protocols, making Seamless integration, a challenge. Next, there are privacy concerns. Customers might feel uneasy about being recorded or monitored by IoT devices. Transparency is essential. Businesses need to clearly communicate what data is being collected, how it's used, and ensure compliance with privacy regulations. Finally, device adoption. Encouraging customers to interact with IoT devices requires thoughtful design. If the device is too complicated, customers may avoid using it. The key is to make the experience intuitive, non intrusive, and valuable for the customer. Now let's talk about the opportunities that IoT driven feedback systems bring to the table. First, IoT allows for a continuous feedback process. Unlike traditional methods, which often rely on periodic surveys, IoT devices can provide ongoing real time insights. This makes feedback more accurate and actionable. Second, IoT enables closing the feedback loop. Systems can respond instantly to feedback, whether it's addressing the issue on the spot or making adjustments in real time. This not only solves problems faster, but also builds a trust with customers.