Exacting customer expectations, narrow margins and technological disruption are creating headwinds and opportunities for retailers and e-commerce businesses. Hyper-efficient core retail operations and precise pricing strategy are more important than ever. But current machine learning systems fail to predict supply-and-demand dynamics and price elasticities.
Causal AI identifies the drivers of consumer demand, understands consumer preferences, and shapes buying behaviour, enabling retailers to supercharge processes and optimize revenue outcomes.
E-commerce and retail are in a constant state of disruption by new market entrants and changing technologies across the supply chain. Businesses look to Machine Learning to adapt, but current AI techniques are often inadequate:
Forecasts based on standard machine learning techniques only extrapolate from historical trends, making them fragile to new market entrants, product line changes and evolving consumer behaviours such as demand for omni-channel fulfillment. causaLens uncovers the true structure behind the supply chain to create predictive causal models for any supply chain variable. These models autonomously adapt up to three times faster than current state-of-the-art machine learning under regime shifts. CausalNet, causaLens’ proprietary inference engine, delivers actionable insights that not only evaluate the factors impacting fulfillment performance down to individual SKU and node level, but go beyond predictions to provide dynamically updated optimal warehouse layouts. With causaLens, retailers can prioritize slow-moving or obsolete store inventory, and make use of it at the most profitable price point.
Data-driven approaches can optimize the advertising ROI and increase the customer conversion & profitability. causaLens enables marketing teams to autonomously discover the causal drivers of marketing performance. Distinguishing between statistical correlations and true causal drivers translates to vastly improved models and, as a result, optimized marketing spend and product recommendations. It also allows business users to automatically answer what-if questions and assess market interventions that happened before without the traditional, costly trial-and-error process
Current approaches rely purely on historical data and they fail to efficiently model market interventions as well as competitors’ behaviour, leading to suboptimal results. causaLens allows you to autonomously discover the most profitable pricing points based on true causal drivers and market interventions. It also allows you to conduct virtual A/B testing by modelling novel scenarios without relying on traditional, costly trial-and-error experimentation.