# Selection of Optimal Discount of Retail Assortments with Data Mining Approach

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3 Selection of Optimal Discount of Retail Assortments with Data Mining Approach the optimization problem may express that some products should not be sold with discount Estimation of Gross Margins In this sub-module, we analyze the profits due to each frequent d-itemset as generated by the Apriori-TD algorithm and allot these profits to the corresponding d- itemsets. Input a.) The frequent d- itemsets which are outputted from the Apriori TD- Algorithm. b.) The pricing details of the products, which include, purchasing price, selling price and the discount rate. c.) The transaction sales information, including the time period in which it occurred and the quantity of items purchased. Functionality In this function, the margin (profit) generated due to the purchase of the frequent itemsets are calculated. This is based on the transaction data, which helps us determine the intentions of the customer when making the transaction. Output After the execution of this sub-module, we have the profits due to each frequent d-itemset. 4.2 Optimal Discount Selection procedure In this step, the zero-one integer programming methodology is used to find the optimal product discount for maximizing the profits of frequent d-itemsets. The frequent d-itemsets with their corresponding gross margins. In this function, we try to maximize the overall gross margins by making combinations of the various d- itemsets under certain constraints, most important of which is any selected item can have only one discount rate. All possible combinations are considered before the best combination is selected. After execution of this sub-module, we get the final d-itemset combination. The (item, discount) pairs are now extracted from this combination. 5. ARCHITECTURE OF THE SYSTEM The system consists of a front-end provided by the GUI which enables the user to interact better with the system. This is connected to the processing system wherein all the calculations are performed. The user inputs the thresholds and selects to run particular threshold analysis or graphical analysis wherein results for a range of local support threshold values are provided. These inputs are fed to the Apriori-TD algorithm for processing. It accesses the database to mine for frequent itemsets and d-itemsets. Once Apriori- TD completes execution, we need to assign gross margins to the d-itemsets. Here, we need the sales information like cost price and market price along with the exact discount rate at which each item was sold in every transaction. Once this is completed, database access is no longer required. The maximization of gross margins in performed and the results are returned to the GUI which displays the result. Provide Thresholds Use thresholds to process Request data for mining and estimation of gross margins. USER Show results GUI-BASED FRONT-END Return results PROCESSING SYSTEM DATABASE Figure 5.1: System Architecture Return data 5.1. The experiment The product optimal discount selection model was used to analyze market data consisting of 40 transactions for 15 items. The support thresholds were as follows Global support = 0.2 Local support = 0.06 Time support = 3 Effect high threshold = 1.3 Effect low threshold = 0.7 Global Large 1-itemsets BREAD BUTTER SUGAR CHOCOLATE Large local k-d-itemsets Large local 1-d itemsets - {(BREAD, 1)} {(BREAD, 2)} {(BUTTER, 2)} {(BUTTER, 5)} {(SUGAR, 1)} {(SUGAR, 2)} {(CHOCOLATE, 4)} Large local 2-d itemsets -

4 Selection of Optimal Discount of Retail Assortments with Data Mining Approach {(BREAD, 2) (BUTTER, 5)} Gross Margins of itemsets - {(BREAD, 1)}=> Gross Margin: {(BREAD, 2)}=> Gross Margin: 12.5 {(BUTTER, 2)}=> Gross Margin: {(BUTTER, 5)}=> Gross Margin: -7.0 {(SUGAR, 1)}=> Gross Margin: 52.5 {(SUGAR, 2)}=> Gross Margin: 50.0 {(CHOCOLATE, 4)}=> Gross Margin: 85.0 {(BREAD, 2) (BUTTER, 5)}=> Gross Margin: Optimal Product Discount Selection - (BREAD, 1) (BUTTER, 2) (SUGAR, 1) (CHOCOLATE, 4) Overall Gross Margin Profit Analysis - Old Profits New Profits Improved Profits which in % terms is 9.2 Sales Improved Profits with respect to sales The above observations show a spike in net profits of about 9.2 % for the given thresholds. 6. RESULT ANALYSIS The above results indicate that if the merchant had sold the items at the above prescribed discount rates, it would have generated increased profits of units. It also, tells us that the increased profits would have been about 2 % of the total sales, which means you would get 2 units more for every 100 units worth of products sold. The Apriori-TD identifies only 4 items with good global support, namely BREAD, BUTTER, SUGAR and CHOCOLATE. Combinations of these items with their various discount rates are made to get local 1-d itemsets. In this example, the 1-d itemsets total to 7. These are further combined to get local 2-d itemsets. In this example, only 1 local 2-d itemset makes the cut. Next, we estimate the gross margins of each of these large local sets over all the transactions. The result of this step is listed above. Next, we try to maximize the overall gross margins. It can be clearly seen that the derived (item, profit) optimized values give the maximum gross margin. Figure 6.1: GUI Home Screen Figure 6.2: Setting the thresholds Fig 6.3: Result analysis for given data and threshold values

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