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Demand Forecasting and Price Optimization

Pricing is recognized as a pivotal determinant of success in many industries and can be one of the most challenging tasks. Companies often struggle with several aspects of the pricing process, including accurately forecasting the financial impact of potential tactics, taking reasonable consideration of core business constraints, and fairly validating the executed pricing decisions. Expanding product offerings add further computational requirements to make real-time pricing decisions, compounding the difficulty of this already overwhelming task.

This solution addresses the challenges raised above by utilizing historical transaction data to train a demand forecasting model. Pricing of products in a competing group is also incorporated to predict cross-product impacts such as cannibalization. A price optimization algorithm then employs the model to forecast demand at various candidate price points and takes into account business constraints to maximize profit. The solution can be customized to analyze various pricing scenarios as long as the general data science approach remains similar.

The process described above is operationalized and deployed in the Cortana Intelligence Suite. This solution will enable companies to ingest historical transaction data, predict future demand, and obtain optimal pricing recommendations on a regular basis. As a result, the solution drives opportunities for improved profitability and reductions in time and effort allocated to pricing tasks.

Demand Forecasting and Price OptimizationPricing is recognized as a pivotal determinant of success in many industries and can be one of the most challenging tasks. Companies often struggle with several aspects of the pricing process, including accurately forecasting the financial impact of potential tactics, taking reasonable consideration of core business constraints, and fairly validating the executed pricing decisions. Expanding product offerings add further computational requirements to make real-time pricing decisions, compounding the difficulty of this already overwhelming task.

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Demand Forecasting and Price OptimizationPricing is recognized as a pivotal determinant of success in many industries and can be one of the most challenging tasks. Companies often struggle with several aspects of the pricing process, including accurately forecasting the financial impact of potential tactics, taking reasonable consideration of core business constraints, and fairly validating the executed pricing decisions. Expanding product offerings add further computational requirements to make real-time pricing decisions, compounding the difficulty of this already overwhelming task.

Related solution architectures

Demand ForecastingAccurately forecasting spikes in demand for products and services can give a company a competitive advantage. This solution focuses on demand forecasting within the energy sector.

Demand Forecasting

Accurately forecasting spikes in demand for products and services can give a company a competitive advantage. This solution focuses on demand forecasting within the energy sector.

Demand Forecasting for Shipping and DistributionThe Demand Forecasting for Shipping and Distribution Solution uses historical demand data to forecast demand in future periods across varios customers, products and destinations. For instance, a shipping or delivery company wants to predict the quantities of the different products its customers want delivered at different locations at future times. A company can use these forecasts as input to an allocation tool that optimizes operations, such as delivery vehicles routing, or to plan capacity in the longer term.

Demand Forecasting for Shipping and Distribution

The Demand Forecasting for Shipping and Distribution Solution uses historical demand data to forecast demand in future periods across varios customers, products and destinations. For instance, a shipping or delivery company wants to predict the quantities of the different products its customers want delivered at different locations at future times. A company can use these forecasts as input to an allocation tool that optimizes operations, such as delivery vehicles routing, or to plan capacity in the longer term.