How can Real-Time Visibility and Predicting End-User Experience Go Together
Delivering a superior end-user experience has become the sine-qua-non for the success of any software application. In addition, real-time digital experience monitoring can assist businesses in staying ahead of the competition. The practice of tracking user behavior while the latter interacts with a website, mobile, or web application can yield valuable data for stakeholders to understand the former’s behavior. Monitoring the digital customer experience is considered a key capability of an end-to-end application performance management solution.
In fact, business enterprises can identify application glitches/issues and minimize any mean time to repair (MTTR) by tracking every customer transaction in real-time. Today, modern applications speedily deliver a host of end-user services, including the popular voice technology. The humongous data generated therein can overwhelm the IT systems. This is why businesses should ensure the reliability and performance of distributed systems across multi-cloud and on-premise environments.
Why is real-time visibility into the end-user experience important?
Any real-time visibility into customer behavior garners critical data about the browsing and buying patterns of customers. This can help enterprises tweak the quality of their products, improve the supply chain, and track their assets better. Also, it enables businesses to get real-time insights into data-driven predictive customer decisions and augment capabilities. These capabilities may include customer branding, personalized product recommendations, intelligent and secure communication, driving efficiency, customer scheduling, and improving capacity utilization, among others. By providing an omnichannel customer experience and real-time visibility into the deliverables, you can help build customer confidence in the product or service.
Digital experience monitoring for individual end-user transactions can help determine the performance of a website, web, or mobile application. Teams, using performance monitoring tools, can simulate customer interactions and record, test, and monitor test transactions in an omnichannel environment. By using a digital experience assurance tool, teams can monitor the performance of digital platforms across channels, devices, and geographic locations and come to know of any performance issues in quick time. This preempts the possibility of such issues turning into serious outages.
By monitoring the performance of a digital customer experience platform using tools, businesses can evaluate site performance, eliminate errors, save time, and improve the efficiency of processes. It can also offer a centralized view of key performance indicators and business-critical site performance metrics that are easy to understand. Monitoring any digital experience platform can deliver instant visibility into the end-users’ interactions with the platform and help stakeholders understand their behavior.
In manufacturing or retail, for example, offering real-time visibility into the inventory data right from the point of distribution to the point of sales can achieve a host of benefits. These include maximizing operational output, optimizing stocking, and driving better financial outcomes. An insight into the inventory data can significantly reduce shopping cart abandonment given that customers are willing to abandon their cart if some items are not in stock. Transparency in retail stock positions can motivate customers to purchase products if they see the availability of limited stock of items.
How to provide real-time visibility into the end-user experience
Predicting customer buying patterns can be the touchstone to improving the delivery of service, augmenting inventory stocks in real-time, and eliminating supply chain bottlenecks. This is where digital tools such as Artificial Intelligence (AI) and Machine Learning (ML) can help enable supply chain competencies, make them resilient, and improve agility and predictability. AI and ML systems can learn continuously from data generated in the supply chain and dynamically update the automated systems. These enable the management of supply chains to become more efficient and predictable and help match supply to demand.
Another methodology to transform supply chains is hyper-automation. Here, Robotic Process Automation (RPA) combines with AI and ML to streamline supply chains and make them more resilient. Since RPA automates various repetitive tasks in the supply chain, it helps to reduce errors and frees employees to execute more complex tasks. Both RPA and AI can complement each other to predict risks, analyze data, and improve decision making. The future of supply chains will be powered by real-time data to deliver accurate forecasts.
Conclusion
Business enterprises need to deliver a seamless customer experience for success. Any level of customer experience assurance can only be achieved if performance issues are monitored in real-time using specific tools. These tools provide in-depth insights into the customers’ journey and key performance metrics to help prevent outages and bottlenecks.