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Accenture Introduces Enhancements To Accenture CAS Software To Help Consumer Goods Companies Build Efficient Sales Processes And Operations

To help consumer goods companies take the guesswork out of store promotions, Accenture (NYSE:ACN) has enhanced its Accenture CAS software with better support for promotional planning, sales processes and operations. Leveraging its flexible architecture, Accenture CAS release 10.1 includes new baseline and promotional planning capabilities to create an integrated sales platform that can result in more successful promotions and boosted revenue. Additional updates include new mobile functionality that can help maximize efficiency and additional options for distributor management.

Accenture CAS release 10.1 can help account managers quickly and easily plan for promotions with confidence. A new single screen baseline planning process simplifies baseline predictions and refinements with an automated quality check that quickly gives users an analysis of data quality so that they can filter out inaccurate or duplicate entries. Accenture CAS release 10.1 includes new features such as built-in analytics capabilities to help clients predict what, when and how much consumers will buy; a new alert system that reviews historical data of similar promotions; and an automated Trade Promotion Optimization (TPO) check to gauge the feasibility of a promotion based on factors such as price and product availability, creating a “playground” where users can refine and adjust baseline prices to see effects on the sales cycle.

Business-driven data patterns – such as new items or discontinued products – and long data gaps can present difficulties in promotional planning. Accenture CAS release 10.1 addresses this through the automated data check as well as a statistical model toolbox for predictive modeling. This consists of template baseline models that are used for both consistently promoted products and regular-behaving products, and three uplift models for measuring the incremental impact of a promotion. Improved algorithms allow the best-fitting model combination to be automatically applied to the data for each product, enabling more efficient planning.

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