Personalized recommendation systems are highly complex, dynamic, and change unpredictably based on various factors. As a result, these models are time-consuming and notoriously challenging to troubleshoot when issues arise.
- Better visualize high-dimensional features such as customer behavior and purchase history to fully understand the impact of each feature on model performance
- Optimize recommendation relevancy by measuring rank-aware metrics
- Improve conversion rates and boost sales by proactively monitoring recommendation performance by ranked groups
Fraud detection models need to continuously adapt to the evolving tactics of fraudsters. However, because they leverage a constant stream of new data, fraud models frequently encounter missing ground truth data, imbalanced class distribution between fraudulent and non-fraudulent transactions, and complex feature generation — all of which makes evaluating model performance difficult.
- Safeguard monetary loss by monitoring drifting dimensions to catch new and evolving fraudulent behaviors
- Detect and investigate suspicious activities in real-time, even if you don’t have ground truth data, to enhance customer satisfaction and minimize financial impact
- Improve model performance with in-depth comparisons between training and production data to easily identify anomalous or underrepresented cohorts
Predictive Sales (Time Series Forecasting)
Predictive sales models generally use historical data. Because of the highly dynamic nature of sales and the volatility of the broader economic environment, this can create misleading or inaccurate predictions. In addition, the nature of highly dynamic sales environments can impact trends quickly, causing misleading or inaccurate predictions.
- Track data quality at scale to improve the data pipeline and proactively catch anomalous patterns
- Monitor drifting sales trends, product demand, and consumer behavior to identify changes before they impact the business
- Optimize inventory, reduce overhead costs, and plan marketing strategies with a clear understanding of model performance in real-time