Real-time analytics for better customer engagement Customer behaviors are rapidly evolving, supply chains are reorganizing, and employees are working in new ways. Businesses need to provide more personalized customer experiences, react more quickly to market trends, and
detect and prevent potential problems. But few can respond to changes in data minute by minute or second by second. With MongoDB, businesses can analyze any data in place and deliver insights in real time. That gives organizations new capabilities, including: By combining data from real-time events with historic and reference datasets, organizations can optimize queries to quickly deliver actionable results. This translates to better insights — and better customer engagement.
From personalized offers on a retail website to your banking app alerting you that there has been fraudulent activity in your account, real-time analytics power applications in ways big and small. Often surfaced as a microservice within another application, real-time analytics are most commonly
presented in four ways: Personalization: Real-time analytics can be used to evaluate user behavior, present profile information, and call up historical interactions to better tailor and enhance customers’ experiences or help with a decision in real time. Fraud and error prevention: Real-time analytics can help identify fraudulent activity and clerical errors by matching existing information with the current situation. Because of the immediate nature of
real-time information, instantaneous action can be taken to prevent deceptive practices. Performance optimization: Real-time analytics can help you make just-in-time adjustments to processes and activities to optimize for better performance and resource allocation. Preemptive maintenance: Real-time analytics can aid in optimizing systems and machines, improving performance and productivity along the way to reduce the chance of costly downtime and loss of
productivity.
Capture data from multiple sources Real-time data reflects what is happening now. It includes event-driven and streaming data — for example, user activity on a retail site or within a banking app, or sensor data within an IoT application. Historical data reflects events or inputs that happened in the past — for example, customer profiles, purchase history, or shipments. There’s a good chance that you offload historical data into a data warehouse or
cloud storage, such as an Amazon S3 bucket. With MongoDB, real-time analytics can be derived from multiple data sources — from basic aggregations to machine learning and AI — and stored separately. The analysis can be done on fresh data at scale and with high integrity. MongoDB’s capabilities include: Whether you’re preventing fraud or sending personalized offers, timeliness is crucial to the success of your app and, ultimately, your business. Insights must be delivered as they happen. Configuring and developing real-time analytics with high productivity — meaning less time wasted mapping data tables or coding single-use data pipelines — means you’re making your data a
competitive advantage. MongoDB:
Combine, enrich, and analyze data
Deliver action-driven insights
Get the most out of Atlas
Power more data-driven experiences and insights with the rest of our application data platform.
Start using the Query API today
Get started in seconds. Use preloaded sample data sets to familiarize yourself with the Query API — and the MongoDB application data platform.
GET STARTED FOR FREE WITH:
- CRUD
- Aggregation pipeline
- Change streams
- Geospatial
- Full-text search
- Language drivers