While dashboards provide a centralized view of data, they often demand constant attention and manual checking, making it easy for important events to slip through the cracks. An email alerting system serves as a crucial safeguard against this, ensuring that relevant individuals are notified whenever an unexpected event occurs, a goal is achieved, or at predetermined time intervals.

A Smart alerting system offers an even better solution. By leveraging a smart alerting system, individuals and teams can benefit beyond just basic notifications. By integrating artificial intelligence technologies, smart alerting systems are capable of detecting abnormal data, delivering highly customized and informative alerts, offering suggestions for follow-up actions, and even utilizing reinforcement learning to adapt based on users' responses to previous alerts. In this blog post, we'll talk about the challenges of current dashboard and alerting systems, and explore how AI can be leveraged to effectively overcome these challenges.

Key benefits of Smart email alerts

If you've ever set up email alerts to monitor the metrics in your dashboard and notify you of critical event notifications, you're probably familiar with the following challenges:

Variable Thresholds: The thresholds triggering alerts can vary with time and item. Using a fixed threshold to send out alerts can make them unreliable or inconsistent.

Alert Overload: A deluge of alerts can overwhelm and desensitize users, especially those who are receiving multiple types of alerts. Sometimes, these alerts can even become annoying, further diminishing their effectiveness.

Dull Content: A good alert should lead the user to the next step of solving the problem accordingly. The lack of variety in alert content can lead to users eventually ignoring them due to predictability and lack of relevance.

A smart alerting system empowered by reinforcement learning and adaptable thresholds overcome the first two problems. By dynamically learning from user interactions, it continuously refines its accuracy and elevates the overall user experience. This approach reduces alert overload and ensures that users pay attention to those alerts that really matter. In addition, it sends more informative email alerts that include critical information from multiple data sources and suggested actions that should be taken.

The following real-world example illustrates why this matters and how it works.

Case Study: AI-Driven Inventory Monitoring and Alerting System

We've developed a dashboard with an intelligent alerting system for our client's supply chain department. In their particular use case, a machine learning model was used to predict inventory based on several factors, such as sales velocity, seasonality, lead time for restocking. By setting up several smart alerts to monitor both current and forecasted inventory and logistics data, we aimed to keep your supply chain running smoothly and sustainably, preventing stockouts or underselling.

Initially, we built a regular alerting system. We used a percentage of inventory as the threshold for alerts, but this approach didn't work well. Some items were constantly triggering alerts, even when restocking is not immediately necessary or is already in progress. We soon realized that a fixed criterion wouldn't be suitable since different items have varying tolerance levels for inventory, and these levels could fluctuate over time and depend on various factors.

This was when we brought in a smart alerting system. Powered by reinforcement learning, the alerting system learned from historical data and user behavior, hence dynamically adjusting the threshold for each item and sending out email alerts better fit users' needs. There was still a minimal threshold, ensuring low - inventory won't be overlooked. 

After implementing the new smart alerting system and allowing the model to gather user feedback for three months, we observed significant improvements. Users received an average of 1.3 meaningful alerts per week, and they reported that the alerts and dashboard greatly helped their work, making their lives easier.

Further refinements were made to the alerting system. We integrated with the ERP system and included information from both the ERP system and inventory data in the email alerts. 1-3 suggested actions are provided in the email, which users can click and enter their ERP system to and execute in several clicks, saving time navigating across siloed systems.

As the implementation of the smart email alerting system, the supply chain department experienced a notable decrease in average stockout duration by 0.7 days, and a 20% reduction in the frequency of stockouts. The smart alerting system not only streamlined inventory management but also significantly enhanced overall supply chain workflow efficiency.

Tools for Smart Email Alerting Systems:

To build a smart email alerting system , several tools and platforms stand out:

Code Strategy: 

Python, Java, and JavaScript are commonly used programming languages for developing alerting systems. Adopting a pure code strategy often incurs higher costs but also provides maximum flexibility. You can program complex logic and integrate machine learning models to define alert conditions. It also enables connections to various data sources. The code strategy can also be optimized for performance, enhancing every aspect of the code for speed and efficiency. Such optimizations are particularly crucial in finance, especially in areas like quantitative trading.

Microsoft Power Automate: 

Microsoft Power Automate is an automation tool that assists in building alerting systems. It's a suitable choice if you are already using Microsoft Power BI. Power Automate can run queries against your Power BI datasets, filter data for each recipient, create HTML tables and CSV files, and compose emails. It offers a clean interface and supports numerous active actions. 


Acho is a free low-code BI builder with an exclusive focus on data and automation. If you are building the dashboard and the smart alerting system together, Acho is your best choice. The built-in ETL and data transformation tool simplifies the process of building your dashboard. And you can use automation to build the alerting system within the dashboard. This automation can be seamlessly linked with your existing metrics or represent a separately calculated field. It also offers the flexibility to include custom Python scripts or APIs, enabling you to integrate AI models to make the email alerts "smart".

Ready to build your own smart alerting system? Sign up at Acho now!

Have questions? Find us at contact@acho.io