Pricing is recognised 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 adds further computational requirements to making real-time pricing decisions, thereby compounding the difficulty of this already overwhelming task.
This solution addresses the challenges raised above by utilising 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 cannibalisation. A price-optimisation algorithm then employs the model to forecast demand at various candidate price points and takes into account business constraints to maximise profit. The solution can be customised to analyse various pricing scenarios as long as the general data science approach remains similar.
The process described above is operationalised 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.
The Demand Forecasting for Shipping and Distribution Solution uses historical demand data to forecast demand in future periods across various 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 inputs into an allocation tool that optimises operations, e.g. in the routing of delivery vehicles, or to plan capacity in the longer term.
In today’s highly competitive and connected environment, modern businesses can no longer survive with generic, static online content. Furthermore, marketing strategies using traditional tools are often expensive, hard to implement, and do not produce the desired return on investment. These systems often fail to take full advantage of the data collected to create a more personalised experience for the user.