Patent Publication Number: US-2021182889-A1

Title: System and method for price optimization of a retail portfolio

Description:
PRIORITY STATEMENT 
     The present application hereby claims priority to Indian patent application number 201941052025 filed on 16 Dec. 2019, the entire contents of which are hereby incorporated herein by reference. 
     BACKGROUND 
     Embodiments of the description generally relate to system and method for price optimization of a retail portfolio, and more particularly to system and method for generating automated pricing plan for retail portfolio based on a given sales target. 
     Pricing is one of the major strategic elements of marketing and has evolved over time. Pricing directly affects the marketing mix elements such as product features, business decisions, and promotions. The way pricing strategies are utilized will have a direct effect on purchasing decisions and thus on the success of any business. In recent years, pricing of products and services being sold online has become one of the most exciting and complex aspects in e-commerce. E-retailers are provided an unprecedented visibility into customer purchase behavior and an environment in which prices can be updated quickly and economically in response to changing market conditions. Such dynamic pricing strategies are widely used for maximizing revenue in an Internet retail channel by actively learning customers&#39; demand response to price (price elasticity) and thus providing a rich framework for pricing projects. However, such broader level insights might not lead to correct assumptions, especially in the fashion industries. For example, categorizing retail items with same elasticity in one group without considering other aspects at the granular level might not always yield the correct results. Moreover, it may be desirable to develop pricing strategy at an individual retail item/style level for an entire portfolio/catalogue while maximizing on a given sales target for the portfolio/catalogue. 
     Thus, there is a need for systems and methods that provide for price optimization at a retail item/style level for an entire portfolio/catalogue while maximizing on a given sales target for the portfolio/catalogue. 
     SUMMARY 
     The following summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, example embodiments, and features described, further aspects, example embodiments, and features will become apparent by reference to the drawings and the following detailed description. Example embodiments provide systems and methods to 
     Briefly, according to an example embodiment, a system for optimizing prices of a plurality of retail items in a portfolio is presented. The system includes a demand estimator and a price optimizer including a return computation module, a price configuration generator and a price configuration selector. The demand estimator is configured to estimate a set of demand values for the plurality of retail items at a plurality of discount levels. The return computation module is configured to compute return on investment (ROI) values for the plurality of retail items, based on the estimated set of demand values and price attributes of the plurality of retail items. The price configuration generator is configured to generate a plurality of price configurations for the plurality of retail items, wherein a price of one or more retail items in the plurality of price configurations is selected based on the computed ROI values. The price configuration selector is configured to select an optimum price configuration from the plurality of price configurations based on a sales target for the portfolio. 
     According to another example embodiment, a method for optimizing prices of a plurality of retail items in a portfolio is presented. The method includes estimating a set of demand values for the plurality of retail items at a plurality of discount levels. The method further includes computing return on investment (ROI) values for the plurality of retail items, based on the estimated set of demand values and price attributes of the plurality of retail items. The method furthermore includes generating a plurality of price configurations for the plurality of retail items, wherein a price of one or more retail item in the plurality of price configurations is selected based on the estimated ROI values. Moreover, the method includes selecting an optimum price configuration from the plurality of price configurations based on a sales target for the portfolio. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       These and other features, aspects, and advantages of the example embodiments will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: 
         FIG. 1  is a block diagram illustrating a system for optimizing prices of a plurality of retail items in a portfolio, according to some aspects of the present description, 
         FIG. 2  is a is a block diagram illustrating a system for optimizing prices of a plurality of retail items in a portfolio, according to some aspects of the present description, 
         FIG. 3  is a is a block diagram illustrating a system for optimizing prices of a plurality of retail items in a portfolio, according to some aspects of the present description, 
         FIG. 4  is a flow chart illustrating a method for optimizing prices of a plurality of retail items in a portfolio, according to some aspects of the present description, 
         FIG. 5  shows the data flow for a method step illustrated in  FIG. 4 , according to some aspects of the present description, 
         FIG. 6  shows the data flow for a method step illustrated in  FIG. 4 , according to some aspects of the present description, 
         FIG. 7  shows the data flow for a method step illustrated in  FIG. 4 , according to some aspects of the present description, and 
         FIG. 8  is a flow chart illustrating a method step shown in  FIG. 4 , according to some aspects of the present description. 
     
    
    
     DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS 
     Various example embodiments will now be described more fully with reference to the accompanying drawings in which only some example embodiments are shown. Specific structural and functional details disclosed herein are merely representative for purposes of describing example embodiments. Example embodiments, however, may be embodied in many alternate forms and should not be construed as limited to only the example embodiments set forth herein. 
     The drawings are to be regarded as being schematic representations and elements illustrated in the drawings are not necessarily shown to scale. Rather, the various elements are represented such that their function and general purpose become apparent to a person skilled in the art. Any connection or coupling between functional blocks, devices, components, or other physical or functional units shown in the drawings or described herein may also be implemented by an indirect connection or coupling. A coupling between components may also be established over a wireless connection. Functional blocks may be implemented in hardware, firmware, software, or a combination thereof. 
     Before discussing example embodiments in more detail, it is noted that some example embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations as sequential processes, many of the operations may be performed in parallel, concurrently or simultaneously. In addition, the order of operations may be re-arranged. The processes may be terminated when their operations are completed, but may also have additional steps not included in the figures. It should also be noted that in some alternative implementations, the functions/acts/steps noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed substantially concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved. 
     The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. As used herein, the singular forms “a,” “an,” and “the,” are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the terms “and/or” and “at least one of” include any and all combinations of one or more of the associated listed items. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     Example embodiments of the present description present systems and methods for price optimization of a portfolio given a particular sales target. 
       FIG. 1  is a block diagram of a system  100  for optimizing prices of a plurality of retail items in a portfolio. The system  100  includes a demand estimator  102  and a price optimizer  104  operatively coupled to the demand estimator  102 . The price optimizer  104  further includes a return computation module  106 , a price configuration generator  108 , and a price configuration selector  110 . The demand estimator  102  and the components of the price optimizer  104  are described in further detail below. 
     The term “portfolio” as used herein refers to a defined collection of retail items. Non-limiting examples of retail items include fashion retail items, furniture items, decorative items, linen, furnishing (carpets, cushions, curtains), lamps, tableware, and the like. In one embodiment, the portfolio is a collection of fashion retail items. Non-limiting examples of fashion retail items include garments (such as top wear, bottom wear, and the like), accessories (such as scarves, belts, socks, sunglasses, bags), jewellery, foot wear and the like. For the purpose of this description, the following embodiments are described with respect to an online fashion retail platform. However, it must be understood that embodiments described herein can be implemented on any e-commerce platform having a portfolio of retail items. 
     The portfolio may be defined based on metrics and/or organizational structure of the retailer. For example, the portfolio may be defined based on individual departments within the retail organization. In some example embodiments, the portfolio may be segregated based on the gender and categories of the fashion retail items. For example, in an example embodiment, the portfolio may include all men&#39;s top wear. In another example, the portfolio may include all women&#39;s western wear (including both top wear and bottom wear). In another example embodiment, the portfolio may include all the retail items being sold by the retailer, e.g., the entire catalogue on an online fashion retail platform. 
     It should be noted that the term “retail item” as used herein refers to a particular “style” of the “retail item” within the portfolio. For example, for a portfolio including all men&#39;s shirts, the term “plurality of retail items” refers to the different style of shirts (varying by brand, design, color etc.) available in the portfolio. As each retail item (e.g., a shirt) will be available at different sizes, the term “retail item” encompasses all the sizes for a particular style (e.g., shirt of a particular brand with a particular design and color). Similarly, for a portfolio including all women&#39;s wear, the term “plurality of retail items” refers to the different products, such as, bottom wear and top wear (varying by style, brand, design, color etc.) available in the portfolio. 
     Referring again to  FIG. 1 , the demand estimator  102  is configured to estimate a set of demand values  12  for the plurality of retail items at a plurality of discount levels. The term “demand value” as used herein refers to the demand of each retail item in the portfolio at a particular price point or the discount level. The demand estimator  102  includes a suitable demand prediction model, and is configured to estimate the set of demand values  12  from the demand prediction model based on input data  10  presented to the demand prediction model. In one example embodiment, the demand prediction model is a gradient boosted decision tree. The demand estimator  102  may be further configured to train the demand prediction model based on historical data. 
     In one embodiment, as shown in  FIG. 2 , the demand estimator  102  is configured to estimate the set of demand values  12  based on one or more historical sales attributes  24 , changes in one or more historical sales attributes  26 , one or more historical inventory attributes  28 , one or more competitive features  30 , and one or more current features  32  of the plurality of retail items. Non-limiting examples of historical sales attributes  24  include historical data on quantities sold, average selling price, or average input discounts for the plurality of retail items. Similarly, non-limiting examples of changes in one or more historical sales attributes  26  include step change in one or more of quantities sold, average selling price, or average input discounts for the plurality of retail items. Non-limiting examples of historical inventory attributes  28  include live stock keeping unit (sku) count, non-live sku count, and the like. Competitive features  30  may include features of competing styles (e.g., aggregation on visibility, quantities sold, maximum retail price (mrp)) based on brand, day of sale, article and gender based, or relative value based. Current features  30  may include inventory and price-based features (e.g., average selling price, average input, mrp, and the like). In some embodiments, the demand estimator  102  may be further configured to estimate the set of demand values based on additional features such as traffic-based variable, age of the style, week day, and the like. 
     The demand estimator  102  is configured to estimate the set of demand values at a plurality of discount levels. The discount levels may be pre-defined by the retailer or an individual business unit of the retailer. In one embodiment, the demand estimator  102  is configured to estimate the set of demand values at three discount levels, such as, a default discount level (d), a first discount level (d1) that is greater than the default discount level, and a second default level (d2) that is lower than the default discount level. The term “default discount level” as used herein refers to the discount that would have been given on a particular retail item before implementation of the price optimization as described herein. In an example embodiment, the plurality of discount levels include d+5% (d1), d, and d−5% (d2). 
     The demand estimator  102  is operationally coupled to the return computation module  106  of the price optimizer. The return computation module  106  is configured to receive the estimated set of demand values  12  from the demand estimator  102  and further configured to compute return on investment (ROI) values  16  for the plurality of retail items, based on the estimated set of demand values  12  and price attributes  14  of the plurality of retail items. Price attributes may include the mrp and the discount level for each retail item of the plurality of retail items. 
     The term “return on investment value” refers to the ratio of change in revenue to the change in discount for each retail item. The return on investment (ROI) value for each retail item may be calculated using the following equation: 
       ROI=change in revenue/change in discount  (I)
 
     The return computation module  104  is configured to compute a set of first ROI values (ROI d−d1 ) for the plurality of retail items for a change in discount level from the default discount level (d) to the first discount level (d1). The return computation module  104  is also configured to compute a set of second ROI values (ROI d2−d ) for the plurality of retail items for a change in discount level from the second default level (d2) to the default discount level (d). 
     The price configuration generator  108  is configured to receive the computed ROI values (ROI d−d1  and ROI d−d2 )  16  and the estimated demand values  12  from the return computation module  106  and the demand estimator  102 , respectively. The price configuration generator  108  is further configured to generate a plurality of price configurations  18  for the plurality of retail items, wherein a price of one or more retail items in the plurality of price configurations  18  is selected based on the computed ROI values (ROI d−d1  and ROI d2−d )  16 . 
     The computed ROI values (ROI d−d1  and ROI d−d2 )  16  are used by the price configuration generator  108  to generate the plurality of price configurations  18  while limiting the number of possible configurations to a defined heuristic number. In one embodiment, the number of possible configurations  18  is limited to less than 2000. In one example embodiment, the number of possible configurations  18  is limited to 1600. 
     In one embodiment, the price configuration generator  108  is configured to generate a price configuration of the plurality of price configurations by: (i) selecting a price of a first set of retail items of the plurality of retail items at the first discount level (d1) if the computed first ROI value (ROI d−d1 ) for the first set of retail items is greater than a first ROI threshold value (ROI t1 ), (ii) selecting a price of a second set of retail items of the plurality of retail items at the default discount level (d) if the computed second ROI value (ROI d−d2 ) for the second set of retail items is greater than a second ROI threshold value (ROI t2 ), (iii) selecting a price of a third set of retail items of the plurality of retail items at the default discount level (d) if the estimated demand value is zero, and (iv) selecting a price of the remaining retail items of the plurality of retail items at the second discount level (d2). The price configuration generator  108  is further configured to generate the plurality of price configurations  18  by repeating steps (i) to (iv) at different first ROI threshold (ROI t1 ) and second ROI threshold (ROI t2 ) values. As mentioned earlier, the number of possible configurations is limited to a defined heuristic number, and therefore, the first ROI threshold (ROI t1 ) and second ROI threshold (ROI t2 ) values are varied until the number of desired configurations is reached. 
     With continued reference to  FIG. 1 , the price configuration selector  110  is configured to select an optimum price configuration  20  from the plurality of price configurations  18  based on a sales target  22  for the portfolio. The sales target  22  may be provided by the retailer or an individual business unit of the retailer. The sales target  22  for the portfolio may include a revenue target for the portfolio, a gain margin target for the portfolio, or both. 
     In one embodiment, the price configuration selector  110  is configured to select a price configuration as the optimum price configuration  20  if an estimated revenue for the selected price configuration is equal to or greater than the revenue target for the portfolio and an estimated gain margin for the selected price configuration is equal to or greater than the gain margin target for the portfolio. 
     Referring now to  FIG. 3 , the system  100  may further include a business constraint module  112  configured to provide one or more business constraints  34  as inputs to the price configuration generator  108 . Non-limiting examples of business constraints include inventory-based constraints, constraints on discount levels for certain brands, or constraints on discount levels for certain retail items/styles. 
     With continued reference to  FIG. 3 , the system  100  may further include a demand sensitivity module  114  configured to estimate a demand sensitivity  36  for the plurality of retail items based on the selected optimum price configuration  20 . The demand sensitivity  36  may include demand elasticity, which is a measure of the change in quantity demanded in related to its price change. The demand elasticity may be estimated as the ratio of % change in quantity demanded to the % change in price. 
     The manner of implementation of the system  100  of  FIGS. 1-3  is described below in  FIGS. 4-8 . 
       FIG. 4  is a flowchart illustrating a method  200  for optimizing prices of a plurality of retail items in a portfolio. The method  200  may be implemented using the systems of  FIGS. 1-3 , according to some aspects of the present description. Each step of the method  200  is described in detail below. 
     The method  200  includes, at step  202 , estimating a set of demand values for the plurality of retail items at a plurality of discount levels. The set of demand values are estimated using a suitable demand prediction model. The method  200  may further include training the demand prediction model based on historical data. In one embodiment, the method  200  may further include generating simulated data, based on historical data and appropriate scaling factors, before providing the input data to the demand prediction model. 
     In one embodiment, the set of demand values  12  may be estimated based on one or more historical sales attributes  24 , changes in one or more historical sales attributes  26 , one or more historical inventory attributes  28 , one or more competitive features  30 , and one or more current features  32  of the plurality of retail items. Non-limiting examples of historical sales attributes  24  include historical data on quantities sold, average selling price, or average input discounts for the plurality of retail items. Similarly, non-limiting examples of changes in one or more historical sales attributes  26  include step change in one or more of quantities sold, average selling price, or average input discounts for the plurality of retail items. Non-limiting examples of historical inventory attributes  28  include live sku count, non-live sku count, and the like. Competitive features  30  may include features of competing styles (e.g., aggregation on visibility, quantities sold, maximum retail price (mrp)) based on brand, day of sale, article and gender based, or relative value based. Current features  30  may include inventory and price-based features (e.g., average selling price, average input, mrp, and the like). In some embodiments, the method  200  may further include estimating the set of demand values  12  based on additional features such as traffic-based variable, age of the style, week day, and the like. 
     The discount levels may be pre-defined by the retailer or an individual business unit of the retailer. In one embodiment, the method  200  includes estimating the set of demand values at three discount levels, such as, a default discount level (d), a first discount level (d1) that is greater than the default discount level, and a second default level (d) that is lower than the default discount level. In an example embodiment, the plurality of discount levels include d+5%, d, and d−5%, that is, the default discount level and ±5% of the default discount level. 
       FIG. 5  illustrates the data flow, for step  202  of  FIG. 4 , according to an example embodiment of the present description. As shown in  FIG. 5 , for a retail item identified by ID  501 , the method first includes simulating data at three different discount levels d1, d, and d2 vis-á-vis different features, as described earlier, to generate input data  10 . The input data  10  may be generated based on historical data  8 . In the example embodiment illustrated in  FIG. 5 , d1 is d+5% and d2 is d−5%. It should be noted that the number of features is limited to five for illustration purposes only, and the actual number of features will vary depending on the demand prediction model used. In some embodiments, the number of features may be greater than  90 . The input data may be simulated for all the retail items in the portfolio. Further, the input data  10  is presented to a demand prediction model (e.g., in a demand estimator  102  of  FIG. 1 ) to estimate the set of demand values  12  at the three demand levels d1, d, and d2.  FIG. 5  shows the estimated demand values for four retails items ( 501 ,  502 ,  503  and  504 ) at the three discount levels as an example embodiment. Similarly, the demand values  12  may be estimated for the rest of the retail items in the portfolio. 
     Referring back to  FIG. 4 , the method  200  further includes, at step  204 , computing return on investment (ROI) values for the plurality of retail items, based on the estimated set of demand values  12  and price attributes  14  of the plurality of retail items. Price attributes  14  may include the mrp and the discount level for each retail item of the plurality of retail items. As noted earlier, the ROI value for each retail item may be calculated using the following equation: 
       ROI=change in revenue/change in discount  (I)
 
     Step  204  includes computing a set of first ROI values (ROI d−d1 ) for the plurality of retail items for a change in discount level from the default discount level (d) to the first discount level (d1). Step  204  further includes computing a set of second ROI values (ROI d2−d ) for the plurality of retail items for a change in discount level from the second default level (d2) to the default discount level (d).  FIG. 6  shows the data flow in step  204 , based on the data generated in step  202  ( FIG. 5 ). As shown in  FIG. 6 , for the four retail items  501 - 504 , the two sets of ROI values  16  are computed based on the estimated demand values  12  and the price attributes  14 . Similarly, the ROI values  16  may be computed for the rest of the retail items in the portfolio. 
     The method  200  further includes, at step  206 , generate a plurality of price configurations  18  for the plurality of retail items, wherein a price of one or more retail items in the plurality of price configurations  18  is selected based on the computed ROI values (ROI d−d1  and ROI d2−d )  16 . The term “price configuration” as used herein refers to the selection of discount level and the corresponding selling price for each retail item in the portfolio. The price configuration may further include additional attributes of the retail items, such as, the mrp and the estimated demand value for the selected discount level. As will be apparent to one of ordinary skill in the art, for a portfolio of large number of retail items, the number of such possible configurations would be infinite. 
     The method  200 , in accordance with embodiments of the present description, provides for a methodology to limit the number of possible configurations to a manageable number, which is a defined heuristic number. In one embodiment, the number of possible configurations  18  is limited to less than  2000 . In one example embodiment, the number of possible configurations  18  is limited to  1600 . 
     According to embodiments of the present description, the number of possible configurations is limited to a defined heuristic number by moving from the highest revenue point (by increasing discount for all retail items) to the highest margin point (by decreasing discount for all retail items). This is further illustrated in  FIG. 8 . As shown in  FIG. 8 , the step  206  includes generating a price configuration of the plurality of price configurations by: (i) selecting a price of a first set of retail items of the plurality of retail items at the first discount level (d1) if the computed first ROI value (ROI d−d1 ) for the first set of retail items is greater than a first ROI threshold value (ROI t1 ) (step  302 ), (ii) selecting a price of a second set of retail items of the plurality of retail items at the default discount level (d) if the computed second ROI value (ROI d2−d ) for the second set of retail items is greater than a second ROI threshold value (ROI t2 ) (step  304 ), (iii) selecting a price of a third set of retail items of the plurality of retail items at the default discount level (d) if the estimated demand value is zero (step  306 ), and (iv) selecting a price of the remaining retail items of the plurality of retail items at the second discount level (d2) (step  308 ). 
       FIG. 7  illustrates the methodology for generating a price configuration  18  based on the ROI values computed in  FIG. 6 , according to an example embodiment of the present description. In the example embodiment illustrated in  FIG. 7 , the first ROI threshold value (ROM) is defined as 10 and the second ROI threshold value (ROI t2 ) is defined as 20. As mentioned earlier, the first and second ROI threshold values may be predefined and may be varied to generate the plurality of price configurations. 
     As shown in  FIG. 7 , according to step  302  of  FIG. 8 , only the price of retail item  501  is selected at the first discount level d1, as only the ROI d−d1  value for the retail item  501  is greater than 10, which is the defined RO t1 . The ROI d−d1  values for the remaining retail items  502 - 504  are all less than 10. Further, according to step  304  of  FIG. 8 , out of the remaining retail items  502 - 504 , the price of retail item  502  is selected at the default discount level d, as only the ROI d−d2  value for the retail item  502  is greater than 20, which is ROI t2 . Moreover, the price of the retail item  503  is also selected as default discount level d, as the estimated demand value is 0 (step  306  of  FIG. 8 ). Finally, the price of the remaining retail item  504  is selected as the second discount level d2 (step  308  of  FIG. 8 ). Therefore, a price configuration  18  including the selected discount levels, the corresponding demand values, and the mrp is generated for the plurality of retail items. 
     The step  206  further includes repeating steps  302 - 308  as shown in  FIG. 8 , at different first ROI threshold (ROI t1 ) and second ROI threshold (ROI t2 ) values. As mentioned earlier, the number of possible configurations is limited to a defined heuristic number, and therefore, the first ROI threshold (ROI t1 ) and second ROI threshold (ROI t2 ) values are varied until the number of desired configurations is reached. 
     In some embodiments, step  206  may further include factoring in one or more business constraints before generating the plurality of price configurations  18 . Non-limiting examples of business constraints include inventory-based constraints, constraints on discount levels for certain brands, or constraints on discount levels for certain retail items. 
     Referring back to  FIG. 4 , the method  200  further includes, at step  208 , selecting an optimum price configuration  20  from the plurality of price configurations  18  based on a sales target  22  for the portfolio. The sales target  22  may be provided by the retailer or an individual business unit of the retailer. The sales target  22  for the portfolio may include a revenue target for the portfolio, a gain margin target for the portfolio, or both. 
     In one embodiment, step  208  includes selecting a price configuration as the optimum price configuration  20  if an estimated revenue for the selected price configuration is equal to or greater than the revenue target for the portfolio and an estimated gain margin for the selected price configuration is equal to or greater than the gain margin target for the portfolio. In such embodiments, step  208  includes calculating the revenue and gain margin for each price configuration of the plurality of price configurations until the revenue and gain margin targets are met. The configuration at which the targets are met is selected as the optimum price configuration, thereby providing the optimum price point for each retail item in the portfolio while meeting the revenue and gain margin targets. 
     In some embodiments, the method  200  may further include estimating the demand sensitivity of the plurality of retail items based on the optimized price configuration. The demand sensitivity may include demand elasticity, which is a measure of the change in quantity demanded in related to its price change. For example, in the optimized price configuration, the retail items with higher discounts would be highly elastic. The highly elastic retail items would have a higher ROI and would drive higher revenue. Similarly, the retail items with lower discounts would be highly inelastic meaning and not discount sensitive, i.e., their demand would not change by a lot even when their prices have been increased. The highly inelastic retail items would drive higher margins. 
     The system(s), described herein, may be realized by hardware elements, software elements and/or combinations thereof. For example, the modules and components illustrated in the example embodiments may be implemented in one or more general-use computers or special-purpose computers, such as a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), a programmable logic unit (PLU), a microprocessor or any device which may execute instructions and respond. A central processing unit may implement an operating system (OS) or one or more software applications running on the OS. Further, the processing unit may access, store, manipulate, process and generate data in response to execution of software. It will be understood by those skilled in the art that although a single processing unit may be illustrated for convenience of understanding, the processing unit may include a plurality of processing elements and/or a plurality of types of processing elements. For example, the central processing unit may include a plurality of processors or one processor and one controller. Also, the processing unit may have a different processing configuration, such as a parallel processor. 
     Embodiments of the present description provide for systems and methods for generating automated pricing plan for an entire portfolio based on historical sales data while optimizing sales target, such as revenue, margin, or both. Further, the systems and methods of the present description provide optimized price, revenue and margin estimates at a retail item/style level. Accordingly, revenue and margin goals can be targeted on a day to day basis, giving better control over meeting revenue and margin targets. The systems and methods in accordance with some embodiments of present description also factor in the effect of competitive styles as the price change for one style could affect the demand and price of all other competing styles. Moreover, systems and methods according to embodiments of the present description may further provide detailed understanding of price-demand elasticity at a style level. Therefore, it may be easy to identify non performing styles and their demand could be estimated at different price points. Hence stock clearance and date of holding could be optimized. 
     While only certain features of several embodiments have been illustrated, and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the scope of the invention and the appended claims.