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Price Optimization with SAP Predictive Analytics

Price Optimization with SAP Predictive Analytics

Would you like to understand how to optimize pricing and triple your profit in just a few minutes?

Price Optimization 1

Of course you would! Keep reading to learn how one organization has built an R custom component that allows business users to access a powerful optimization algorithm quickly and easily.

Input Data
For this particular use case, the pricing department for our manufacturing company would like to know how to best price each of our products for next year. For this simple case, we’ll use 9 products, and include the product name (or SKU identifier), the current annual volume sold, current price of the product, and current profit margin.

Price Optimization 2

Some of the products are currently unprofitable (as evidenced by the negative profit margin), so management would like to re-set prices for next year to ensure that:

1.) No product has a price increase or decrease of over 10%
2.) Any product that is currently unprofitable has a minimum price that is break-even for profit
3.) Overall, given the same volumes as this year, total revenue will increase by 2-4%

After importing the dataset we can build some basic visualizations to help understand our current product mix. A few examples are below, showing that Wallets, Necklaces, Hats, and Jackets account for the highest volumes of product sold, while Pants and Earrings are the highest priced (per unit). The stacked bar charts show how much revenue and profit each product accounts for – consistent with the profit margins listed in the original data, several products account for negative profit.

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 Hilary BlissAbout Hillary Bliss
Hillary Bliss is a Senior ETL Consultant at Decision First Technologies, and  specializes in data warehouse design, ETL development, statistical analysis, and  predictive modeling. She works with clients and vendors to integrate business  analysis and predictive modeling solutions into the organizational data  warehouse and business intelligence environments based on their specific operational and strategic business needs. She has a master’s degree in statistics and an MBA from Georgia Tech.


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