Dynamic re-pricing of items on electronic marketplaces and/or online stores

Abstract

A method of dynamically re-pricing items, comprising: a) Receiving from a seller a sale policy for one or more items offered for sale by one or more plurality of vendors. b) Creating a state machine to execute the sale policy by adjustinga price of the one or more items. c) Collecting commerce information by monitoring in real time a plurality of prices given to the one or more items by the one or more vendors. d) Dynamically adjusting a plurality of price setting rules according to analysis of said commerce information. e) Executing the state machine to select one or more of the plurality of price setting rules and modifying the price according to one or more selected price setting rule.

1 - 38 . (canceled) 
     
     
         39 . A system for dynamically adjusting price of an item offered for sale by a seller in an electronic marketplace, an online store, or website, the system comprising at least one processor configured and operable to communicate data with said electronic marketplace, online store, or website, over a data network, and one or more computer-readable media storing computer-executable modules comprising:
 an input module configured and operable to communicate with a client terminal and receive therefrom a sale policy for said item, said sale policy defining at least sales goal for said item;   a monitor module configured and operable to monitor commerce data associated with said item, the commerce data including ranking information indicative of automatically generated ranking of the seller, of the item offered for sale, of at least one other seller, or of the item offered for sale by at least one other seller;   an analysis module configured and operable to analyze the commerce data from the monitor module, including the ranking information, and dynamically produce a plurality of price setting rules based at least in part on said ranking information, said price setting rules configured to achieve the sales goals defined by said sale policy at least in part by affecting the automatically generated ranking; and   a state machine module configured and operable to select at least one of said plurality of price setting rules and use it to determine a new price for said item in said electronic marketplace, online store, or website, for achieving the sale goals defined by said sale policy.   
     
     
         40 . The system of  claim 39  wherein the at least one sales goal defined by the sales policy received at the user interface module comprises at least one member of a group consisting of: minimum sales for the item, minimum profit for the item, minimal profit margin for the item, minimum number of orders of the item made over a time period, maximum number of orders of the item made over a time period, minimum impressions for the item, cost structure for the item, and inventory levels of the item, and adjusting at least one of the price setting rules based thereon. 
     
     
         41 . The system of  claim 39  comprising an output module configured to present to the user at least one of the following: the commerce data; the plurality of price setting rules; or recommendations from the analysis module for adjusting the sale goals of the sale policy. 
     
     
         42 . The system of  claim 39  wherein the commerce data further comprises at least one member of a group consisting of: sale transactions of said item; at least one other seller associated with the item; prices set by the seller; prices set by said at least one other seller; impressions of said item; sales ranking of said item; consumer rating of said item; inventory level of said seller; inventory level of said at least one other seller; shipping information of the seller; shipping information of said at least one other seller; terms of payment of said seller; terms of payment of said at least one other seller; consumers rating of said seller; or consumers rating of said at least one other seller. 
     
     
         43 . The system of  claim 39  wherein the sales goals comprise at least one member of a group consisting of: a pricing range for the new price; a target sales for the item; a target profit for the item; a target profit margin for the item; a target number of orders for the item made over a time period; a target impressions for the item; a target increase or decrease in sales for the item; a target increase or decrease in profit for the item; a target increase or decrease in profit margin for the item; a target increase or decrease in the number of orders for the item made over a time period; a target increase or decrease in impressions for the item; maximize sales; maximize profit. 
     
     
         44 . The system of  claim 39  wherein the analysis module is configured to dynamically adjust the sale goals of the sale policy according to analysis of the commerce data accumulated over time. 
     
     
         45 . The system of  claim 39  wherein the sales goals define one or more goal metrics associated with the item and configured to fulfill one or more of the sales goals, said goal metrics comprise at least one member of a group consisting of: sales; profit; profit margin; a number of orders for the item made over a time period; or impressions. 
     
     
         46 . The system of  claim 45  comprising a prediction module configured to predict at least one of the goal metrics for the at least one item for a selected future time period, and wherein the system configured to adjust at least one of the price setting rules based on said predicted at least one goal metric. 
     
     
         47 . The system of  claim 45  wherein the analysis module is configured and operable to calculate, based on the analysis of the commerce data from the monitor module, including the ranking, one or more intermediate metrics associated with the item offered for sale, and dynamically produces the plurality of price setting rules based at least in part on the one or more intermediate metrics. 
     
     
         48 . The system of  claim 47  wherein the intermediate metrics include at least one member of a group consisting of: competition level for said item; ranking rate of said seller for said item; price of said seller for said item; price position of said seller for said item; demand denoting popularity and purchase levels of said item; potential profit of said item; traffic for said item from a respective traffic generator; conversion rates for said item from traffic generator or advertisement. 
     
     
         49 . The system of  claim 48  wherein the demand metric is calculated as an extrapolation of one or more of the following parameters: number of product items ordered from the seller; and product's objective ranking provided by the marketplace. 
     
     
         50 . The system of  claim 48  comprising a prediction module configured to predict at least one of the intermediate metrics for the item for a selected future time period. 
     
     
         51 . The system of  claim 50  wherein the prediction module is configured to use machine learning procedures in the prediction. 
     
     
         52 . The system of  claim 50  wherein the prediction module is configured to predict one or more of the goal metrics based on the predicted at least one intermediate metric. 
     
     
         53 . The system of  claim 50  configured to adjust at least one of the price setting rules based on at least one of the current or predicted intermediate metrics predicted by the prediction module. 
     
     
         54 . The system of  claim 50  wherein the prediction module is configured to identify correlation between one or more of the goal metrics and one or more of the predicted intermediate metrics. 
     
     
         55 . The system of  claim 39  comprising a learning module configured to analyze past and current commerce data from the monitor module, determine a ranking mechanism used by the electronic marketplace for ranking the sellers, and to determine based thereon probability or incidence of the seller achieving at least a predetermined rank in the context of the item offered for sale. 
     
     
         56 . The system of  claim 55  wherein the learning module is configured to determine the ranking mechanism utilizing machine learning techniques. 
     
     
         57 . The system of  claim 39  wherein the analysis module is configured to adjust at least one of the price setting rules based on the analysis of the commerce data for adjusting the ranking of the seller in the electronic marketplace, online store, or website. 
     
     
         58 . The system of  claim 39  to wherein the analysis module is configured to focus the analysis on at least one of the other sellers that continuously or repeatedly receives high ranking by the electronic marketplace, online store, or website, and to adjust at least one of the price setting rules according to the said analysis so as to promote the seller to achieve the sales goals defined by the sale policy. 
     
     
         59 . The system of  claim 48  wherein the analysis module is configured to adjust at least one of the price setting rules based on at least one of the conversion rates associated with an advertising of the item. 
     
     
         60 . The system of  claim 39  wherein the sale policy defines a traffic strategy, and wherein the system comprises a traffic analysis module configured to determine contribution of traffic from at least one traffic generator in producing orders for the item in the electronic marketplace, online store, or website, for adjusting at least one of the price setting rules in order to optimize said traffic from said at least one traffic generators. 
     
     
         61 . A computer-implemented method of dynamically re-pricing an item offered for sale on an electronic marketplace, an online store, or website, the method comprising providing at least one processing unit and one or more software modules configured and operable to perform the re-pricing of the item, as follows:
 at a user interface module, communicating data with a client terminal for receiving therefrom a sale policy for said item, said sale policy defining at least sales goals for said item;   at a monitor module, communicating data with said electronic marketplace, online store, or website, for monitoring commerce data associated with said item, the commerce data including ranking information indicative of automatically generated ranking of the seller, of the item offered for sale, of at least one other seller, or of the item offered for sale by at least one other seller;   analyzing, at an analysis module, the commerce data, including the ranking information;   dynamically producing, at the analysis module, a plurality of price setting rules based at least in part on said ranking information, said price setting rules configured to achieve the sales goals defined by said sale policy at least in part by affecting the automatically generated ranking; and   selecting, at a state machine module, at least one of said plurality of price setting rules for determining a new price for said item in said electronic marketplace, online store, or website, for achieving the sale goals defined by said sale policy.   
     
     
         62 . The method of  claim 61  wherein the receiving of the sale policy at the user interface module comprises defining at least one sales goal being a member of a group consisting of: at least one of minimum sales for the item, minimum profit for the item, minimal profit margin for the item, minimum number of orders of the item made over a time period, maximum number of orders of the item made over a time period, minimum impressions for the item, cost structure for the item, and inventory levels of the item, and adjusting at least one of the price setting rules based thereon. 
     
     
         63 . The method of  claim 61  wherein the commerce data further comprises at least one member of a group consisting of: sale transactions of said item; at least one other seller associated with the item; prices set by the seller; prices set by said at least one other seller; impressions of said item; sales ranking of said item; consumer rating of said item; inventory level of said seller; inventory level of said at least one other seller; shipping information of the seller; shipping information of said at least one other seller; terms of payment of said seller; terms of payment of said at least one other seller; consumers rating of said seller; or consumers rating of said at least one other seller. 
     
     
         64 . The method of  claim 61  wherein the sales goals comprise at least one member of a group consisting of: a pricing range for the new price; a target sales for the item; a target profit for the item; a target profit margin for the item; a target number of orders for the item made over a time period; a target impressions for the item; a target increase or decrease in sales for the item; a target increase or decrease in profit for the item; a target increase or decrease in profit margin for the item; a target increase or decrease in the number of orders for the item made over a time period; a target increase or decrease in impressions for the item; maximize sales; maximize profit. 
     
     
         65 . The method of  claim 61  comprising dynamically adjusting the sale goals of the sale policy according to the analysis of the commerce data accumulated over time. 
     
     
         66 . The method of  claim 61  comprising defining based on the sales goals one or more goal metrics associated with the item and configured to fulfill one or more of the sales goals, said goal metrics comprise at least one member of a group consisting of: sales; profit; profit margin; a number of orders for the item made over a time period; or impressions. 
     
     
         67 . The method of  claim 66  comprising predicting at a prediction module at least one of the goal metrics for the at least one item for a selected future time period, and adjusting at least one of the price setting rules based on said predicted at least one goal metric. 
     
     
         68 . The method of  claim 66  comprising calculating at the analysis module, based on the analysis of the commerce data from the monitor module, including the ranking, one or more intermediate metrics associated with the item offered for sale, and dynamically producing the plurality of price setting rules based at least in part on the calculated one or more intermediate metrics. 
     
     
         69 . The method of  claim 68  wherein the intermediate metrics include at least one member of a group consisting of: competition level for said item; ranking rate of said seller for said item; price of said seller for said item; price position of said seller for said item; demand denoting popularity and purchase levels of said item; potential profit of said item; traffic for said item from a respective traffic generator; conversion rates for said item from traffic generator or advertisement. 
     
     
         70 . The method of  claim 69  comprising calculating the demand metric by extrapolation of one or more of the following parameters: number of product items ordered from the seller; and product's objective ranking provided by the marketplace. 
     
     
         71 . The method of  claim 68  comprising predicting at a prediction module at least one of the intermediate metrics for the item for a selected future time period. 
     
     
         72 . The method of  claim 71  wherein the predicting comprises using machine learning procedures. 
     
     
         73 . The method of  claim 71  wherein the predicting comprises predicting one or more of the goal metrics based on the predicted at least one intermediate metric. 
     
     
         74 . The method of  claim 71  comprising adjusting at least one of the price setting rules based on at least one of the current or predicted intermediate metrics. 
     
     
         75 . The method of  claim 71  wherein the predicting comprises correlating at the prediction module one or more of the goal metrics with the predicted intermediate metrics, and predicting one or more of the goal metrics based on the correlation. 
     
     
         76 . The method of  claim 61  comprising analyzing at a learning module past and current commerce data from the monitor module, utilizing machine learning techniques for determining a ranking mechanism used by the electronic marketplace for ranking the sellers, and determining based thereon probability or incidence of the seller achieving at least a predetermined rank in the context of the item offered for sale. 
     
     
         77 . The method of  claim 61  comprising adjusting at least one of the price setting rules based on the analysis of the commerce data for adjusting the ranking of the seller in the electronic marketplace, online store, or website. 
     
     
         78 . The method of  claim 61  comprising focusing the analysis to at least one of the other sellers that continuously or repeatedly receives top ranking by the electronic marketplace, online store, or website, and adjusting at least one of the price setting rules according to the said analysis so as to promote the seller to achieve the sales goals defined by the sale policy. 
     
     
         79 . The method of  claim 68  comprising adjusting at the analysis module at least one of the price setting rules based on at least one of the conversion rates associated with an advertising of the item. 
     
     
         80 . The method of  claim 61  wherein the sale policy defines an aggressiveness level, and wherein the method comprises at least one of the following: controlling a rate of the price adjustment to achieve the sales goals; deriving a time period from the aggressiveness level for the monitoring of the commerce data, determining based on the aggressiveness level at least one of a time period allocated for collecting the commerce data, an amount of commerce data analyzed, an amount of commerce data required for statistical analysis, and a scope of statistical analysis. 
     
     
         81 . The method of  claim 80  comprising automatically adjusting the aggressiveness level based on at least one of sales policy and the commerce data and carrying out at least one of the following: a plurality of repricing iterations, each of which is conducted within a time period and according to at least one price setting rule derived from the automatically adjusted aggressiveness level; generating and analyzing in each repricing iteration performed within its respective derived time period new instances of the commerce data for automatically adjusting at least one of the plurality of price setting rules and said aggressiveness level based on said new instances of said commerce data.