Fuelling a Smarter Pricing Strategy with Causal AI

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Presented by

Jerry Stephens, GM, causaLens

About this talk

Organizational investment in pricing and promotions continues to grow, yet 70% of promotions are delivering a negative return. In today’s ever-changing landscape, companies must enable a robust understanding of the causal drivers of business, including pricing and promotional changes. Organizations are seeking to answer questions like the following: What is the potential impact of changing macro, market, and competitive landscape? How should I change my seasonal strategy, win the season with a more informed approach? How can I balance growth with profitability with predictable results? What do I need to do to inform smoother inventory flow, in and out of promotional windows? Now is the time when organizations are turning to causal ai to inform an understanding of the causal drivers of their business enabling a clear picture of the impact of the pricing and promotion execution. After this talk you will be able to answer: How does Causal AI enable models to deliver more transparent, trusted and explainable insights? How can teams leverage Causal AI models to inform smarter scenario planning and counterfactual analysis? How does Causal AI enable a better understanding of other business drivers to support a more complete and integrated plan?

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causaLens is the pioneer of Causal AI—a giant leap in machine intelligence. Today’s machine learning algorithms extract correlations from data and predict outcomes based on patterns in past data. Correlations are useful for making predictions, but they’re of little use for decisions. Causal AI goes beyond predictions by understanding the actual causes behind an outcome and quantifying the impact of different interventions. It is the only form of machine intelligence that can answer “Why?”. causaLens builds Causal AI-powered products that empower all users to make superior decisions and drive business value. Leading organisations across a wide range of industries trust causaLens with their most important decisions.