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Organizations do make use of the accessibility of a powerful optimization algorithm rather quickly as well as easily. SAP analytics have an important role to play in business operations and price optimization is indeed a major aspect of business growth and development.
Input Data
The focus is on the best price each of the respective products.The aspects taken into consideration are the product name (or SKU identifier), the current annual volume that has been sold and current existing price of the product as well as the current profit margin.
Some of the products are indeed presently unprofitable (as per evidence by the negative profit margin), so management would like to re-set prices for next year to ensure that:
1.) No product has, in fact, a price increase or decrease of over 10%
2.) Any product that is rather currently unprofitable has a minimum price that would break-even for profit
3.) Overall, given the same volumes as this year, total revenue will increase by 2-4%
Visualizations
After importing the dataset one can build some of the basic visualizations in order to help understand the current product mix. The stacked bar charts to show how much revenue as well as profit each product accounts for and that which are consistent with the given profit margins that have been listed in the original data as several products account for negative profit.
At the very beginning of a customer call, one has to always try to understand what sort of problems the customer would like to solve and often they do come back with an optimization scenario. Be it be price optimization or inventory optimization the issue that comes into focus is when it comes to solving problems with predictive analytics, with many customers equating optimization with prediction.
Although it is extremely convenient for a single tool to build up predictive models and also find those optimal sweets spots, most tools do one (prediction) or the other (optimization). Many customers simply do not have the required historical data in order to build up predictive models for doing optimization.
For customers who do have historical data around sales for various prices, inventory levels, and so on, it is possible to perform simple optimization use of predictive modeling algorithms and an apply-in dataset that does contain a set of pricing scenarios.
In the pricing optimization, there will indeed be a special spot where the price is not that high that it would prevent customers from buying.
Conclusion
It is but obvious the price optimization is essential in SAP analytics and much focus is required upon it by business operators in order to maximize their profits. Price optimization is given much importance in SAP applications and the net result is higher profits and better business output. Businessmen have to study price operations and see how they impact their respective growth. Price fluctuations are there and the monetary returns of business set up would depend much upon these fluctuations.
