Causal AI in Retail & E-Commerce
Hyper-efficient core retail operations and data-driven pricing strategy are more important than ever for retailers and e-commerce businesses. AI in Retail & E-Commerce has the potential to help with business-critical decision-making, but stakeholders must be able to trust their algorithms if they are to drive change. Causal AI is the only AI system retailers can trust.
Priority issues facing our retail clients
- New market entrants
- Changing technology
- Supply chain disruption
- Inadequate AI technology
Solutions
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.
