AI Quality in Retail and Brands
Evaluate, test, monitor, and debug ML models to improve AI Quality
Retailers and brands face challenges building high quality AI and delivering consistent ROI on AI Initiatives
Artificial Intelligence and Machine Learning (AI/ML) have the potential for far-reaching impacts on retailers and brands. High quality ML systems have been proven to deliver real ROI in use cases such as marketing propensity, search, recommendations, forecasting, customer experience, supply chain management, fraud, and more. However, today’s ML Teams have limited access to the state of the art technologies necessary to effectively manage the quality and performance of ML systems and realize this ROI.
Key challenges teams face include:
- limited tools to monitor model inputs and outputs
- unreliable ad-hoc debugging methods
- inadequate testing/validation tools
- limited model explainability
- and brute force model retraining approaches.
TruEra’s AI Quality solutions provide state of the art technology to monitor, evaluate, test, explain and debug AI/ML models – to address these challenges and ensure quality, build trust, and enable AI adoption at scale.
TruEra helps retailers and brands capture real business value from AI and ML at scale
AI/ML models across Retail and Brands need to be tested, explained, and monitored
- Track how propensity for products and brands changes over time (e.g. seasonality, drift) overall and for important segments
- Understand root causes of propensity drift and seasonality to inform retraining, feature engineering and stakeholder collaboration
- Automatically test, evaluate, explain and debug large numbers of models to save time and improve performance
- Track forecasting errors (e.g., MAPE) overall and at a segment level across model runs
- Report and debug error drift by understanding segment level errors and automatically determining how much features contributing to drift
- Inform retraining strategies with advanced evaluation, explainability, and RCA. Automatically test retrained models
Customer Experience and Churn
- Monitor and debug overall and segment score, accuracy drift from changes in customer behavior, competition, etc.
- Analyze and improve customer segment accuracy for segments like usage, profitability, tenure, etc.
- Ensure models do not display significant unfair bias
- Explain drivers of churn predictions to inform customer actions
- Monitor and understand the root causes of false positives and negatives. Accurately explain to stakeholders (investigators, CSRs, etc.) and reduce errors
- Systematically iterate on model accuracy and adversarial response with monitoring, RCA, improved retraining strategies, and automated testing of retrained models
- Analyze and improve segment (e.g., source of data, products, customer type, etc.) performance
- Supply chain management
- Natural Language Processing (NLP)
- And more…