This post is the third in a series on real-time analytics. It is an excerpt from Real-time analytics, a definitive guide which can be read in full here.
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Here are several examples of real-time analytics use cases that can improve customer experiences, unlock new business value, and optimize systems:
Sports betting and gaming. Real-time analytics can help sports betting and gaming companies reduce time-to-first-bet, improve the customer experience through personalization, maintain leaderboards in real-time based on in-game events, segment users in real-time for personalized marketing campaigns, and reduce the risk of fraud.
Inventory and stock management. Real-time analytics can help online retailers optimize their fulfillment centers and retail location inventory to reduce costs, provide a modern customer experience with real-time inventory and availability, improve operational efficiency by streamlining supply chains, and make better decisions using real-time data and insights into trends and customer behavior.
Website analytics. Real-time analytics can help website owners monitor user behavior as it happens, enabling them to make data-driven decisions that can improve user engagement and conversion rates even during active sessions.
Personalization. Real-time analytics can help companies personalize user experiences as a customer is using a product or service, based on up-to-the-second user behavior, preferences, history, cohort analysis, and much more.
In-product analytics. Real-time analytics can give product owners the power to inform their end users with up-to-date and relevant metrics related to product usage and adoption, which can help users understand the value of the product and reduce churn.
Operational intelligence. Real-time analytics can help companies monitor and optimize operational performance, enabling them to detect and remediate issues the moment they happen and improve overall efficiency.
Anomaly detection and alerts. Real-time analytics can be used to detect real-time anomalies, for example from Internet of Things (IoT) sensors, and not only trigger alerts but build self-healing infrastructure.
Software log analytics. Real-time analytics can help software developers build solutions over application logs, enabling them to increase their development velocity, identify issues, and remediate them before they impact end users.
Trend forecasting. Across broad industry categories, real-time analytics can be used as predictive analytics to forecast trends based on the most recent data available.
Usage-based pricing. Real-time analytics can help companies implement usage-based pricing models, enabling them to offer personalized pricing based on real-time usage data.
Logistics management. Real-time analytics can help logistics companies optimize routing and scheduling, enabling them to improve delivery times and reduce costs.
Security information and event management. Real-time analytics can help companies detect security threats and trigger automated responses, enabling them to mitigate risk and protect sensitive data.
Financial services. Real-time analytics can be used for fraud detection. Fraudulent transactions can be compared to historical trends so that such transactions can be stopped before they go through.
Customer 360s. Real-time analytics can help companies build a comprehensive and up-to-date view of their customers, enabling them to offer personalized experiences and improve customer satisfaction.
Artificial intelligence and machine learning. Real-time analytics can help AI and ML models learn from accurate, fresh data, enabling them to improve accuracy and predictive performance in data science projects over time.
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