Alert Detection

Project Details

Summary

My Alert Detection project revolutionized shipment monitoring by statistically comparing recent counts to various historical periods (weekly, monthly, yearly) using paired t-tests. This identifies "Significantly More," "Significantly Less," or "No Significant Difference" changes, triggering automatic alerts for deviations. Frequent data refreshes ensure real-time insights and prompt action. Beyond pinpointing immediate attention cases, the system proactively flags stable or increasing activity, allowing for preparation and resource allocation. These early warnings ultimately reduce churn and promote organizational resilience. Seamless integration with a meticulously designed Power BI dashboard visualizes patterns and alerts, letting users drill down and make informed decisions. This not only boosts efficiency but also fosters a data-driven approach to customer success by providing actionable insights into evolving dynamics.

Tools

• Python: Utilized Python to automate alert detection through paired t-tests, comparing shipment counts over different timeframes and triggering alerts for noteworthy deviations.

• Power Bi: Leveraged Bi to transform alert data into an interactive dashboard, enabling stakeholders to visually pinpoint potential issues in customer shipment trends.