E-commerce & Retail

E-commerce return rates average 20-30%, with fashion reaching 40%. Each return costs $10-20 in processing, restocking, and potential markdown. Poor product descriptions and sizing issues drive preventable returns that eat into margins.

AI predicts return likelihood at the point of purchase, enabling proactive interventions like better size recommendations or enhanced product visuals. Post-purchase, automated triage routes returns efficiently, and predictive analytics optimize restocking decisions.

Digital advertising costs have skyrocketed, with CAC increasing 60% over five years. Competing for attention on saturated channels yields diminishing returns, and cookie deprecation threatens targeting effectiveness.

AI-powered lookalike modeling identifies high-value prospects more efficiently. Predictive lifetime value scoring ensures ad spend targets customers with genuine purchase intent. Personalization increases conversion rates, making each acquired customer more valuable.

Global disruptions have exposed fragile supply chains. Demand volatility makes forecasting difficult, and inventory imbalances result in both stockouts (lost sales) and overstock (margin erosion). Real-time visibility remains a challenge.

Machine learning models incorporate external signals—weather, economic indicators, social trends—for more accurate demand forecasting. Dynamic safety stock algorithms adapt to supply variability. Real-time inventory optimization across locations minimizes both stockouts and excess.

Online fraud costs retailers $48 billion annually. Sophisticated fraud rings exploit system gaps, while aggressive fraud rules reject legitimate customers—false declines cost 3x more than actual fraud. Manual review is costly and slow.

Our AI evaluates hundreds of signals in milliseconds—device fingerprints, behavioral patterns, transaction velocity, network analysis—to accurately distinguish fraud from legitimate purchases. Adaptive models stay ahead of evolving fraud tactics while minimizing false positives.

International e-commerce requires navigating multiple currencies, languages, tax regulations, shipping logistics, and cultural preferences. One-size-fits-all approaches fail, while true localization seems prohibitively complex and expensive.

AI enables automated localization at scale—dynamic currency conversion, intelligent language translation, localized product recommendations, and region-specific pricing strategies. Machine learning optimizes cross-border logistics and predicts regulatory compliance requirements.