Patent Publication Number: US-2021182744-A1

Title: Revenue forecasting method, revenue forecasting system and graphical user interface

Description:
TECHNICAL FIELD 
     The disclosure relates in general to a revenue forecasting method, a revenue forecasting system and a graphical user interface. 
     BACKGROUND 
     The environmental factors that need to be considered during the pricing process of product in the pursuit of maximum profit are very complicated. Under several different circumstances, one forecasting model alone could not provide reasonable sufficient information from which the user could form various information required for making decisions. 
     Traditional sales forecasting needs to consider various factors such as marketing, finance, inventory and logistics. These factors are variable and it is difficult to obtain and analyze data in a real time manner. Unlike weather simulation, business analysis still lacks strong support in terms of expert knowledge and theories and could only use some representative characteristic facts obtained using data driven approach as a basis for simulation. With the development of AIoT, the acquisition of the retailing data of various platforms has been made relatively easier. Therefore, a virtual transaction environment could be established to simulate and test various scenarios, such that the planned marketing strategies could have active forecast function and the strategy failure rate could be reduced. 
     Based on historical records, the researchers could simulate the sales and prices of one single brand using traditional data simulation technology. However, it the data volume is too small, the simulation result may have a low reliability or simulation may fail. Additionally, since traditional data simulation technology does not consider a competition relationship between commodities/brands/channels, the forecasting of the total revenue has a low accuracy. 
     SUMMARY 
     The disclosure is directed to a revenue forecasting method, a revenue forecasting system and a graphical user interface. 
     According to one embodiment of the disclosure, a revenue forecasting method is provided. The revenue forecasting method includes the following steps. A pricing tree, comprising several feature hierarchies, a pricing hierarchy and an order hierarchy, is built by a processing device according to a target product, wherein the pricing hierarchy includes several pricing node, the order hierarchy includes several target historical orders, and each of the target historical orders records a purchaser, a purchase quantity and a discount Several pricing nodes are generalize by the processing device according to several target historical orders in the order hierarchy. A number of pricing paths are generated by the processing device according to several approximate products, wherein each of the pricing paths includes the feature hierarchies, the pricing hierarchy and the order hierarchy. Several simulated historical orders are obtained by the processing device at least according to a correlation between each of the pricing paths and the pricing tree. A total revenue with respect to a reservation price is analyzed by the processing device using a probability model according to target historical orders and the simulated historical orders. 
     According to another embodiment of the disclosure, a revenue forecasting system is provided. The revenue forecasting system includes a storage device and a processing device. The processing device includes a pricing tree establishing unit, a generalizing unit, a path establishing unit, a simulation data establishing unit and an estimating unit. The pricing tree establishing unit is used to build a pricing tree comprising several feature hierarchies, a pricing hierarchy and an order hierarchy according to a target product. The generalizing unit is used to generalize several pricing nodes according to several target historical orders in the order hierarchy. The path establishing unit generates a number of pricing paths according to several approximate products. The simulation data establishing unit is used to obtain several simulated historical orders according to a correlation between each of the pricing paths and the pricing tree. The estimating unit analyzes a total revenue with respect to a reservation price using a probability model. 
     According to an alternative embodiment of the disclosure, a graphical user interface is provided. The graphical user interface includes a pricing tree display window, a generalization button, a simulated historical order increase button, a reservation price input window and a total revenue display window. The pricing tree display window is used to display a pricing tree. The pricing tree, comprising several feature hierarchies, a pricing hierarchy and an order hierarchy, is obtained according to a target product, wherein the pricing hierarchy includes several pricing node, the order hierarchy includes several target historical orders, and each of the target historical orders records a purchaser, a purchase quantity and a discount. The generalization button is used for a user to click and input a generalization command to generalize several pricing nodes according to several target historical orders in the order hierarchy. The simulated historical order increase button is used for the user to click to generalize a number of pricing paths according to several approximate products and to obtain several simulated historical orders at least according to a correlation between each of the pricing paths and the pricing tree, wherein each of the pricing paths includes the feature hierarchies, the pricing hierarchy and the order hierarchy. The reservation price input window is used for the user to input a reservation price. The total revenue display window is used to display a total revenue with respect to the reservation price, wherein the total revenue is analyzed using a probability model according to the target historical orders and the simulated historical orders. 
     The above and other aspects of the invention will become better understood with regard to the following detailed description of the preferred but non-limiting embodiment(s). The following description is made with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic diagram of a revenue forecasting system according to an embodiment. 
         FIG. 2  is a flowchart of a revenue forecasting method according to an embodiment. 
         FIG. 3  is an exemplary diagram of step S 110 . 
         FIG. 4  is an exemplary diagram of step S 120 . 
         FIG. 5  is an exemplary diagram of step S 130 . 
         FIG. 6  is another exemplary diagram of step S 130 . 
         FIG. 7  is an exemplary diagram of step S 140 . 
         FIG. 8  is a schematic diagram of target historical orders and simulated historical orders of an inserted pricing path. 
         FIG. 9  is a schematic diagram of a graphical user interface according to an embodiment. 
     
    
    
     In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the disclosed embodiments. It will be apparent, however, that one or more embodiments may be practiced without these specific details. In other instances, well-known structures and devices are schematically shown in order to simplify the drawing. 
     DETAILED DESCRIPTION 
     Referring to  FIG. 1 , a schematic diagram of a revenue forecasting system  1000  according to an embodiment is shown. The revenue forecasting system  1000  includes a processing device  100  and a storage device  200 . The processing device  100  includes a pricing tree establishing unit  110 , a generalizing unit  120 , a path establishing unit  130 , a simulation data establishing unit  140  and an estimating unit  150 . The pricing tree establishing unit  110 , the generalizing unit  120 , the path establishing unit  130 , the simulation data establishing unit  140  and the estimating unit  150  could be realized by such as a circuit, a chip, a circuit board, several programming codes or a storage device storing programming codes. The storage device  200  could be realized by such as a memory, a hard disc, an optical drive or a clouds data storage center. The pricing tree establishing unit  110  is connected to the storage device  200  and the generalizing unit  120 . The generalizing unit  120  is connected to the pricing tree establishing unit  110 , the simulation data establishing unit  140  and the storage device  200 . The estimating unit  150  is connected to the simulation data establishing unit  140  and the storage device  200 . The path establishing unit  130  is connected to the simulation data establishing unit  140  and the storage device  200 . The storage device  200  is connected to the pricing tree establishing unit  110 , the generalizing unit  120 , the path establishing unit  130  and the estimating unit  150 . The revenue forecasting system  1000  of the present embodiment could generalize historical data using data generalization technology and could partially insert the data according to a competition relationship between approximate commodities/brands/channels to increase the forecasting accuracy of the total revenue. Operations of above elements are disclosed below with a flowchart. 
     Referring to  FIG. 2 , a flowchart of a revenue forecasting method according to an embodiment is shown. Firstly, the method begins at step S 110 , a pricing tree (such as a pricing tree TR 10  of  FIG. 3 ) is built by the pricing tree establishing unit  110  according to a target product. The pricing tree TR 10  includes several feature hierarchies (such as a brand hierarchy BN, a function hierarchy FN and a positioning hierarchy LC, and the disclosure is not limited thereto; the feature hierarchy could be an age hierarchy or a consumer group hierarchy (such as men, women, young girls, and students)), a pricing hierarchy PR and an order hierarchy OD. Referring to  FIG. 3 , an exemplary diagram of step S 110  is shown. The pricing hierarchy PR includes several pricing nodes P 11  to P 15 , such as “80 dollars”, “90 dollars”, “100 dollars”, “110 dollars” and “120 dollars” respectively. The order hierarchy OD includes several target historical orders. For example, the pricing node P 11  does not have any target historical orders, but the pricing node P 13  has five target historical orders O 11  to O 15 . Each of the target historical orders O 11  to O 15  records a purchaser BR, a purchase quantity QT and a discount DC. For example, the purchaser BR, the purchase quantity QT and the discount DC of the target historical order O 11  respectively are “b1”, “3”, “10%”; the purchaser BR, the purchase quantity QT and the discount DC of the target historical order O 12  respectively are “b2”, “5”, “15%”. 
     As indicated in the pricing tree TR 10  of  FIG. 3 , the pricing node P 11  does not have any target historical orders. The pricing node P 11  does not have any historical data, which could be used as a basis for obtaining an approximate simulation order. Therefore, the arrangement of the pricing nodes P 11  to P 15  of the pricing hierarchy PR needs to be adjusted to ensure that each pricing node has a sufficient quantity of target historical orders. 
     Then, the method proceeds to step S 120 , the pricing nodes are generalized by the generalizing unit  120  according to the target historical orders in the order hierarchy OD. As indicated in  FIG. 3 , if the order quantity of one of the pricing nodes P 11  to P 15  is less than a threshold value (such as 2), then some of the pricing nodes are merged. Referring to  FIG. 4 , an exemplary diagram of step S 120  is shown. In the present step, the order quantity in the pricing node P 11  is 0, which is less than 2, therefore the generalizing unit  120  merges the pricing node P 11  and the pricing node P 12  as a pricing node P 21 . Since the order quantity in the pricing node P 14  is 1, which is less than 2, the generalizing unit  120  merges the pricing node P 14  and the pricing node P 15  as a pricing node P 23 . Through data generalization, each of the pricing nodes P 21  to P 23  of the pricing hierarchy PR will have a sufficient quantity of target historical orders. As indicated in  FIGS. 3 to 4 , the pricing nodes P 11  to P 15  of  FIG. 3  are generalized as the pricing nodes P 21  to P 23  of  FIG. 4 . As indicated in  FIG. 4 , the pricing nodes P 21  to P 23  of the pricing tree TR 20  respectively are “low price”, “middle price”, and “high price.” 
     If each of the pricing nodes P 21  to P 23  has a sufficient quantity of orders, data could be partially inserted through the following steps S 120  to S 130 . 
     Then, the method proceeds to step S 130 , a number of pricing paths (such as the pricing paths T 31  to T 37 , etc. of  FIG. 5 ) are generated by the path establishing unit  130  according to several approximate products. Referring to  FIG. 5 , an exemplary diagram of step S 130  is shown. Each of the pricing paths T 31  to T 37 , etc. includes several feature hierarchies (such as the brand hierarchy BN, the function hierarchy FN and the positioning hierarchy LC, but the disclosure is not limited thereto; the feature hierarchy could also be an age hierarchy or a consumer group hierarchy (such as men, women, young girls, and students)), a pricing hierarchy PR and an order hierarchy OD. The brand hierarchy BN includes brand nodes B 31  and B 32 , such as “AA” and “BB” respectively. The function hierarchy FN includes function nodes F 31 , F 32 , etc. The function nodes F 31  and F 32  are such as “moisturizing” and “whitening” respectively. The positioning hierarchy LC includes the positioning nodes L 31  and L 32 , such as “open shelf” and “counter” respectively. As indicated in  FIG. 5 , the pricing paths T 31  to T 37 , etc. could be established in an order of the brand hierarchy BN, the function hierarchy FN and the positioning hierarchy LC, the pricing hierarchy PR and the order hierarchy OD, wherein, the brand node B 31 , the function node F 31  and the positioning node L 31  of the pricing paths T 31  to T 33 , such as “AA”, “moisturizing” and “open shelf” respectively, are identical to the pricing paths T 21  to T 23  of the pricing tree TR 20  of  FIG. 4 . That is, the content of the order hierarchy OD of the pricing paths T 31  to T 33  is identical to that of the order hierarchy OD of the pricing paths T 21  to T 23 . 
     The brand node B 31 , the function node F 31  and the positioning node L 32  of the pricing paths T 34  are “AA”, “moisturizing”, and “counter” respectively. The brand node B 31 , the function node F 31  and the positioning node L 32  of the pricing paths T 35  are “AA”, “moisturizing” and “counter” respectively. The brand node B 32 , the function node F 31  and the positioning node L 31  of the pricing paths T 36  are “BB”, “moisturizing” and “open shelf” respectively. The brand node B 32 , the function node F 32  and the positioning node L 31  of the pricing paths T 37  are “BB”, “whitening” and “open shelf” respectively. The content of the pricing paths T 34  to T 37 , etc. of the feature hierarchy is different from the content of the feature hierarchy of the pricing paths T 21  to T 23  of the pricing tree TR 20  of  FIG. 4 . The pricing paths T 34  to T 37 , etc. represent a competition relationship between commodities/brands/channels. The pricing paths approximate to the pricing paths T 21  to T 23  could be located from the pricing paths T 34  to T 37 , etc. according to the content of the order hierarchy OD. The data of the approximate pricing paths are valuable, and could be added to the pricing tree TR 20  to increase the forecasting accuracy of the total revenue. 
     Various pricing paths could be established according to different arrangement orders of the brand hierarchy BN, the function hierarchy FN and the positioning hierarchy LC. Referring to  FIG. 6 , another exemplary diagram of step S 130  is shown. Other pricing paths T 38 , T 39 , etc. could be obtained according to another arrangement order. The pricing paths T 38 , T 39 , etc. are established according to the arrangement order of the brand hierarchy BN, the positioning hierarchy LC and the function hierarchy FN. Similarly, the pricing paths T 38 , T 39 , etc. represent a competition relationship between commodities/brands/channels. 
     Among the several pricing paths T 34  to T 39 , etc. generated in step S 130 , the arrangement of the feature hierarchies of the pricing paths T 34  to T 39 , etc. are note identical. Moreover, the content of the feature hierarchy of each of the pricing paths T 34  to T 39 , etc. is not identical to that of the feature hierarchy of the pricing tree for the target product. For example, the content of the feature hierarchy of the pricing path T 34  is: “‘AA’, ‘moisturizing’ and ‘counter’”; the content of the feature hierarchy of the pricing path T 36 : “‘BB’, ‘moisturizing’ and ‘open shelf’”; the content of the feature hierarchy of the target product is: “‘AA’, ‘moisturizing’ and ‘open shelf’”. The content of the feature hierarchy of the pricing path T 34  is not identical to that of the feature hierarchy of the pricing tree for the target product; the content of the feature hierarchy of the pricing path T 36  is not identical to that of the feature hierarchy of the pricing tree for the target product. 
     The pricing paths approximate to the pricing paths T 21  to T 23  could be located from the pricing paths T 34  to T 37 , etc. according to the content of the order hierarchy OD. The data of the approximate pricing paths are valuable, and could be added to the pricing tree TR 20  to increase the forecasting accuracy of the total revenue. 
     Then, the method proceeds to step S 140 , several simulated historical orders are obtained by the simulation data establishing unit  140  according to a correlation between each of the pricing paths and the pricing tree (for example, the simulated historical orders O 41  to O 45  of  FIG. 7  could be obtained according to the correlation between the pricing path T 39  of  FIG. 6  and the pricing path T 22  of the pricing tree TR 20  of  FIG. 4 ). Referring to  FIG. 7 , an exemplary diagram of step S 140  is shown. In the present step, the simulation data establishing unit  140  firstly calculates the correlation between the one of the pricing paths T 34  to T 39 , etc. with largest data volume and the pricing path T 21 , the pricing path T 22  or the pricing path T 23  according to the content of the order hierarchy OD of the one of the pricing paths T 34  to T 39 , etc. with largest data volume. If the correlation is higher than a predetermined value, then the content of the order hierarchy could be regarded as simulated historical orders. 
     The correlation between two pricing paths could be represented by the Pearson correlation coefficient, which is calculated according to the frequency at which the commodity on the two pricing paths is purchased. The calculation of correlation is expressed as formula (1). 
     
       
         
           
             
               
                 
                   
                     ρ 
                     
                       X 
                       , 
                       Y 
                     
                   
                   = 
                   
                     
                       
                         Cov 
                          
                         
                           ( 
                           
                             X 
                             , 
                             Y 
                           
                           ) 
                         
                       
                       
                         
                           
                             Var 
                              
                             
                               ( 
                               X 
                               ) 
                             
                           
                           · 
                           
                             Var 
                              
                             
                               ( 
                               Y 
                               ) 
                             
                           
                         
                       
                     
                     = 
                     
                       
                         
                           S 
                           
                             X 
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                             Y 
                           
                         
                         - 
                         
                           
                             S 
                             X 
                           
                            
                           
                             S 
                             Y 
                           
                         
                       
                       
                         
                           
                             
                               S 
                               X 
                             
                              
                             
                               ( 
                               
                                 1 
                                 - 
                                 
                                   S 
                                   X 
                                 
                               
                               ) 
                             
                           
                            
                           
                             
                               S 
                               Y 
                             
                              
                             
                               ( 
                               
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                                   S 
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     Wherein, ρ X,Y  represents the correlation between two pricing paths “X” and “Y”; Cov(X,Y) represents the variance between the pricing path “X” and the pricing path “Y”; Var(X) represents the variance of the pricing path “X”; Var(Y) represents the variance of the pricing path “Y”; S X∪Y  represents the frequency at which the commodity on the pricing path “X” and the commodity on the pricing path “Y” are purchased together; S X  represents the frequency at which the commodity on the pricing path “X” is purchased; S Y  represents the frequency at which the commodity on the pricing path “Y” is purchased. 
     In an embodiment, the commodity on the pricing path T 22  is purchased for 30 times, the commodity on the pricing path T 37  is purchased for 50 times, the two commodities are purchased together for 25 times, and in the database, the total purchase times of commodities is 100 times. Therefore, the correlation between the pricing path T 22  and the pricing path  37  is calculated as: 
     
       
         
           
             
               
                 
                   25 
                   100 
                 
                 - 
                 
                   
                     30 
                     100 
                   
                   × 
                   
                     50 
                     100 
                   
                 
               
               
                 
                   
                     30 
                     100 
                   
                    
                   
                     ( 
                     
                       1 
                       - 
                       
                         30 
                         100 
                       
                     
                     ) 
                   
                    
                   
                     50 
                     100 
                   
                    
                   
                     ( 
                     
                       1 
                       - 
                       
                         50 
                         100 
                       
                     
                     ) 
                   
                 
               
             
             = 
             
               0.436 
               . 
             
           
         
       
     
     In another embodiment, suppose the commodity on the pricing path T 22  is purchased for 40 times, the commodity on the pricing path T 39  is purchased for 50 times, the two commodities are purchased together for 30 times, and in the database, the total purchase times of commodities is 150 times. Therefore, the correlation between the pricing path T 22  and the pricing path T 39  is calculated as: 
     
       
         
           
             
               
                 
                   30 
                   150 
                 
                 - 
                 
                   
                     40 
                     150 
                   
                   × 
                   
                     50 
                     150 
                   
                 
               
               
                 
                   
                     40 
                     150 
                   
                    
                   
                     ( 
                     
                       1 
                       - 
                       
                         40 
                         150 
                       
                     
                     ) 
                   
                    
                   
                     50 
                     150 
                   
                    
                   
                     ( 
                     
                       1 
                       - 
                       
                         50 
                         150 
                       
                     
                     ) 
                   
                 
               
             
             = 
             
               0.532 
               . 
             
           
         
       
     
     The correlation between the pricing path T 22  and the pricing path T 39  is higher than the correlation between the pricing path T 22  and the pricing path  37 . 
     As indicated in  FIG. 7 , the content of the order hierarchy OD of the pricing path T 39  is highly correlated with the pricing path T 22 , therefore the content of the order hierarchy OD of the pricing path T 39  could be regarded as simulated historical orders O 41  to O 45 . The simulated historical orders O 41  to O 45  could be added to the target historical orders O 11  to O 15  of the pricing path T 22  to partially insert the pricing tree TR 20 . Referring to  FIG. 8 , a schematic diagram of target historical orders O 11  to O 15  and simulated historical orders O 41  to O 45  of an inserted pricing path T 22  is shown. 
     After the pricing tree TR 20  is partially inserted in steps S 130  and S 140 , the data volume of the pricing tree TR 20  could be greatly increased to increase the forecasting accuracy of the total revenue. 
     Then, the method proceeds to step S 150 , a total revenue with respect to a reservation price is analyzed by the estimating unit  150  using a probability model according to the target historical orders and the simulated historical orders. For example, the total revenue RV with respect to the reservation price PP is analyzed using the probability model ML of  FIG. 1  according to the target historical orders O 11  to O 15  and the simulated historical orders O 41  to O 45  of  FIG. 8 . 
     For example, when the reservation price PP is 130 dollars, the probability model ML is illustrated in Table 1. When the reservation price PP is much higher than the original pricing node, the purchaser has a lower transfer probability; when the reservation price PP is slightly higher than the original pricing node or the reservation price PP is less than the original pricing node, the purchaser has a higher transfer probability. Under the circumstance of the price difference being the same, different purchasers have different transfer probabilities. The transfer probability could be calculated according to the market ratio of the product or could be determined according to the purchaser&#39;s preference of commodities shown in previous purchase records. 
     
       
         
           
               
               
               
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Purchaser 
                 b1 
                 b2 
                 b3 
                 b4 
                 b5 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Transfer probability for 130 
                 20% 
                 10% 
                 50%  
                 30% 
                 40%  
               
               
                 dollars 
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Target 
                 Purchase 
                 3 
                 5 
                 8 
                 5 
                 4 
               
               
                 historical 
                 quantity QT 
               
               
                 Orders O11 to 
                 Discount DC 
                 10% 
                 15% 
                 0% 
                  0% 
                 5% 
               
               
                 O15 
               
               
                 Simulated 
                 Purchase 
                 2 
                 1 
                 2 
                 4 
                 5 
               
               
                 historical 
                 quantity QT 
               
               
                 orders O41 to 
                 Discount DC 
                  5% 
                 10% 
                 0% 
                 10% 
                 0% 
               
               
                 O45 
               
               
                   
               
            
           
         
       
     
     Based on the probability model ML of Table 1, the total revenue RV with respect to the reservation price of 130 dollars is calculated as: “(3*90%*20%*$130+3*90%*80%*$110)+(5*85%*10%*$130+5*85%*90%*$110)+(8*110%*50%*$130+8*110%*50%*$110)+(5*110%*30%*$130+5*110%*70%*$110)+(4*95%*40%*$130+4*95%*60%*$110)+(2*95%*20%*$130+2*95%*80%*$110)+(1*90%*10%*$130+1*90%*90%*$110)+(2*110%*50%*$130+2*110%*50%*$110)+(4*90%*30%*$130+4*90%*70%*$110)+(5*110%*40%*$130+5*110%*60%*$110)=3967.5” 
     Thus, respective total revenues could be estimated with respect to various reservation prices PP for the decision maker to decide a best reservation price PP. The total revenue is estimated with respect to the reservation price PP. Since the transfer probability is different for each purchaser, the estimated total revenue is different in each time of estimation. After all total revenues are obtained, a mean value could be obtained from the highest and the lowest total revenues. 
     Referring to  FIG. 9 , a schematic diagram of a graphical user interface  900  according to an embodiment is shown. The graphical user interface  900  is such as a screen displayed on a desktop, a smartphone or a tablet. The graphical user interface  900  includes a pricing tree display window  910 , a generalization button  920 , a simulated historical order increase button  930 , a reservation price input window  940  and a total revenue display window  950 . 
     The pricing tree display window  910  is used to display the pricing tree TR 10  disclosed above. The generalization button  920  is used for a user to click and input a generalization command to generalize data. After the data are generalized, the pricing tree TR 20  will be displayed on the pricing tree display window  910 . 
     The simulated historical order increase button  930  is used for the user to click and to obtain several simulated historical orders (such as the simulated historical orders O 41  to O 45  of  FIG. 7 ) according to steps S 130  and S 140 . 
     The reservation price input window  940  is used for the user to input the reservation price (such as 130 dollars). The total revenue display window  950  is used to display the total revenue RV (such as 3967.5 dollars) with respect to the reservation price PP. 
     According to the above embodiments, the revenue forecasting system  1000  could generalize historical data using data generalization technology and could partially insert the data according to a competition relationship between approximate commodities/brands/channels to increase the forecasting accuracy of the total revenue RV. 
     It will be apparent to those skilled in the art that various modifications and variations could be made to the disclosed embodiments. It is intended that the specification and examples be considered as exemplary only, with a true scope of the disclosure being indicated by the following claims and their equivalents.