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The Pricing Analytics solution uses your transactional history data to show you how the demand for your products responds to the prices you offer, to recommend pricing changes and to allow you to simulate how changes in price would affect your demand, at a fine granularity.
The solution provides a dashboard where you can see optimal pricing recommendations, item elasticities at an item-site-channel-segment level, estimates of related-product effects such as “cannibalisation”, forecasts given the current process, and model perfomance metrics.
Direct interaction with the pricing model in Excel lets you simply paste your sales data there and analyse your prices without the need to integrate the data into the solution database first, simulate promotions and plot demand curves (showing demand response to price), and access dashboard data in numerical form.
The rich functionality is not confined to Excel. It is driven by web services that you, or your implementation partner, can call directly from your business applications, integrating price analysis into your business applications.
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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.
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.
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.