U.S. patent application number 16/406145 was filed with the patent office on 2019-08-29 for dynamic re-pricing of items on electronic marketplaces and/or online stores.
The applicant listed for this patent is FEEDVISOR LTD.. Invention is credited to Eyal LANXNER, Victor ROSENMAN.
Application Number | 20190266627 16/406145 |
Document ID | / |
Family ID | 51532121 |
Filed Date | 2019-08-29 |
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United States Patent
Application |
20190266627 |
Kind Code |
A1 |
LANXNER; Eyal ; et
al. |
August 29, 2019 |
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.
Inventors: |
LANXNER; Eyal; (Modiln,
IL) ; ROSENMAN; Victor; (Tel-Aviv, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
FEEDVISOR LTD. |
Tel-Aviv |
|
IL |
|
|
Family ID: |
51532121 |
Appl. No.: |
16/406145 |
Filed: |
May 8, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14207909 |
Mar 13, 2014 |
10332139 |
|
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16406145 |
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61783397 |
Mar 14, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0206
20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
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.
Description
RELATED APPLICATION
[0001] This application claims the benefit of priority under 35 USC
.sctn. 119(e) of U.S. Provisional Patent Application No. 61/783,397
filed Mar. 14, 2013, the contents of which are incorporated herein
by reference in their entirety.
BACKGROUND
[0002] The present invention, in some embodiments thereof, relates
to management of prices of goods and services in electronic
marketplaces and/or online stores and, more specifically, but not
exclusively, to dynamically re-pricing in real time the goods and
services offered on electronic marketplaces and/or online
stores.
[0003] Electronic commerce (e-Commerce) in general and electronic
marketplaces in particular serves as a platform through which
third-party merchants (sellers and/or vendors) may offer products
and/or services to consumers. The electronic marketplaces may be
utilized through on-line systems available to the sellers and the
consumers through a plurality of interfaces, for example, client
application and/or web browser based service that execute on a one
or more of a plurality of client terminals, for example,
Smartphone, tablet, work station, desktop computer and/or laptop
computer. The electronic marketplaces, for example, Amazon
Marketplace, eBay Marketplace and/or Sears Marketplace, provide
sellers with access to large consumer traffic for a fee and/or a
percentage of the sales made through the electronic marketplace.
The marketplace may offer the sellers additional services, for
example, billing, shipping and/or advertising.
[0004] The electronic marketplaces are highly competitive arenas in
which many sellers operate and offer the same item (product and/or
service). In case the same item is offered by multiple sellers, the
electronic marketplace system automatically orders the presentation
of offers from sellers to a potential consumer in a prioritized
manner, the example according to the rank of the sellers. Highest
ranking offers get to appear higher in a list and therefore get
more exposure from offers from other sellers. This exposure
increases the chance of winning the deal. In some electronic
marketplaces, for example, Amazon Marketplace, the highest ranking
offer gets to be selected as the default seller. The default
seller, for example, the buy box winner on the Amazon Marketplace,
gets to be the one to close the deal when the consumer selects to
make a purchase of the offered item, for example, through the "add
to cart" option and/or through the "buy it now" option. It is
therefore, desired for the sellers to get high ranking for their
offer in order to get best exposure which may result in winning
many sale transactions and increasing sales and profit.
[0005] The set of rules by which the electronic market place system
ranks the offers made by the sellers relies on a set of criteria
which may be unpublished and/or unknown to the public. The set of
criteria may include a plurality of criterion for the product, for
example, price, availability, shipping details and/or number of
reviews, and/or a plurality of criterion for the seller, for
example, sales history, consumers' rating, credibility and/or
number of returned items. The criterions may be weighted so as to
have different influence of each of the criteria on the ranking of
the offers.
[0006] The sellers may manipulate the prices of the items they
offer for sale in order to increase their sales and/or profits.
Reducing the price may result in getting high ranking and higher
volume of sales but may also result in loss in profitability.
Increasing the price may result in the seller dropping in ranking
and probably winning less deals. The optimal price may be set
according to a plurality of attributes of the items offered for
sale and/or attributes of the sellers. However, the price of the
item(s) will typically have the highest immediate impact on the
ranking of the offer made by a seller. As the electronic
marketplace may be a dynamic place, the prices of an item offered
by multiple sellers may vary.
[0007] Dynamic pricing of items on electronic marketplaces may be
done manually by a seller who is continuously following the trade
activity of the item on the electronic marketplace and adjusts the
price accordingly. Some solutions may be available in which a
seller may define a set of rules by which the price of an item may
be adjusted over time. However these solutions usually employ a
static set of rules which do not adapt to the changing conditions
on the trade of the item. The rules defined in these sets of rules
are also usually independent of each other and may not be able to
serve a comprehensive sales strategy. Furthermore, some of the
electronic marketplaces provide limited access to pricing
information of competitor sellers, thus reducing the effectiveness
of the static set of rules. In addition the system for ranking the
offers of the sellers may not be fully deterministic in order to
provide equal opportunity to several sellers by selecting a
different default seller for a specific item during consecutive
views of the item by consumers,
SUMMARY
[0008] According to some embodiments of the present invention,
there are provided systems and methods for dynamically re-pricing
items by receiving from a seller a sale policy for one or more
items offered for sale by one or more vendors, creating a state
machine to execute the sale policy by adjusting the price of the
one or more items, 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, dynamically adjusting a plurality of price
setting rules according to analysis of the commerce information and
executing the state machine to select one or more of the plurality
of to price setting rules and modifying the price according to the
one or more selected price setting rules.
[0009] Optionally, the one or more items are offered for sale by
the one or more vendors on an electronic marketplace.
[0010] Optionally, the one or more items are offered for sale by
the seller on an online store exclusive to the seller.
[0011] Optionally, the sale policy includes a traffic strategy to
increase overall traffic from one or more traffic generators to an
online store hosting the one or more items.
[0012] Optionally, the sale policy is translated to a sale strategy
which is implemented through the plurality of price setting rules,
wherein execution of one or more selected setting rules fulfills
the goals of the sale policy.
[0013] Optionally, the sale strategy resolving conflicts between
the plurality of price setting rules.
[0014] Optionally, modification to the price is performed in a
plurality of re-pricing iterations, during each of the re-pricing
iteration the commerce information is monitored and analyzed in
order to adjust the plurality of price setting rules and execute
the one or more selected price setting rules.
[0015] Optionally, the commerce information includes previous
commerce information of the one or more items collected in the
past.
[0016] Optionally, the commerce information includes at least one
member of a group consisting of: sale transactions of the one or
more items, the one or more vendors, prices set by one or more
vendors, views of the one or more items, ranking of offer made by
the seller, ranking of offers made by the one or more vendors,
inventory level, shipping information of the one or more vendors,
terms of payment of the one or more vendors, consumers rating of
the seller and consumers rating of the one or more vendors.
[0017] Optionally, the commerce information includes traffic
generated by each of the respective traffic generators, to an
online store hosting the one or more items.
[0018] Optionally, analysis of the commerce information includes
determining a contribution of each of a plurality of traffic
generators in producing orders for the one or more items, and
wherein dynamically adjusting the plurality of price setting rules
comprises dynamically adjusting the plurality of price setting
rules to increase overall traffic from the plurality of traffic
generators.
[0019] Optionally, the sale policy includes a plurality of sale
parameters which are defined using a range of values in order to
allow flexibility in adjusting the price.
[0020] Optionally, the sale policy defines an aggressiveness level
which dictates the state machine rapidity in adjusting the price to
achieve the goals of the sale policy.
[0021] Optionally, the aggressiveness level dictates the extent of
statistical analysis required for adjusting the plurality of price
setting rules for adjusting the price.
[0022] Optionally, the aggressiveness level dictates the amount in
units when adjusting the price.
[0023] Optionally, the aggressiveness level is adjusted
automatically for a limited period to achieve the goals of the sale
policy.
[0024] Optionally, the commerce info ion is presented to the seller
to enable the seller to analyze the commerce information of the one
or more items.
[0025] Optionally, the seller manually adjusts one or more of a
plurality of price setting rules.
[0026] Optionally, extrapolation is performed over a plurality of
price levels points adjacent to the price in order to enhance
statistical information used by the analysis.
[0027] Optionally, priority is set between two or more items
offered for sale by the seller. The priority defines the frequency
in which the price is set for the two or more items.
[0028] Optionally, the analysis includes trade off analysis to
evaluate two or more pricing alternatives in order to select a
pricing alternative that best achieves goals set by the sale
policy.
[0029] Optionally, the sale policy is a long term policy, while
executing the long term policy one or more intermediate goals is
set and fulfilled in order to fulfill the goals of the sale
policy.
[0030] Optionally, the method further comprises predicting the sale
policy, and wherein dynamically adjusting the plurality of price
setting rules comprises dynamically adjusting the plurality of
price setting rules according to analysis of the prediction of the
sale policy.
[0031] Optionally, predicting the sale policy is calculated based
on a correlation between at least one intermediate metric and the
sale policy.
[0032] Optionally, the method further comprises calculating one or
more intermediate metrics for the one more items for a selected
current or previous time period to estimate a baseline level of the
one or more intermediate metrics.
[0033] Optionally, the one or more intermediate metrics includes at
least one member of a group consisting of: competition for the one
or more items, top rank rate of the seller for the one or more
items, price of the one or more items, demand denoting popularity
and purchase levels of the one or more items, traffic for the one
or more items from a respective traffic generator, conversion rates
for the one or more items from the respective traffic
generator.
[0034] Optionally, the method further comprises predicting at least
one intermediate metric for the at least one item for a selected
future time period.
[0035] Optionally, one or more intermediate metrics are calculated
for the one or more items offered for sale on a preselected
electronic marketplace.
[0036] Optionally, the method further comprises calculating a
statistical significance level reflecting a probability of
predicting the one or more intermediate metrics and/or a
probability of the predicting the sale policy.
[0037] Optionally, predicting the sale policy comprises predicting
one or more metrics associated with the sale policy. Optionally,
the one or more metrics associated with the sale policy includes at
least one member of a group consisting of: revenue, profit, and
margin.
[0038] According to some embodiments of the present invention,
there are provided systems for dynamically re-pricing items, the
system includes an input module which receives from a seller a sale
policy for one or more items offered for sale by one or more
vendors, a monitor module which collects commerce information by
monitoring in real time a plurality of prices given to the one or
more items by the one or more vendors, an analysis module which
dynamically adjusts a plurality of price setting rules according to
analysis of the commerce information and a state machine module
which selects one or more of the plurality of price setting rules
to adjust a price of the one or more items.
[0039] Optionally, the re-pricing system includes an output module
which presents the commerce information to the seller for analysis.
The commerce information is analyzed by the analysis module to
adjust the plurality of price setting rules.
[0040] Optionally, the output module presents the plurality of
price setting rules to the seller to allow the seller to adjust at
least one of the plurality of price setting rules.
[0041] Optionally, the input module communicates with the seller
having a client terminal executing a client application.
[0042] Optionally, the client application is implemented through a
web based service which is accessible through the client
terminal.
[0043] Optionally, the analysis module is further programmed to
dynamically adjust a plurality of price setting rules according to
analysis of a prediction of the sale policy for the one or more
items based on the commerce information. Optionally, prediction of
the sale policy is based on correlation with a prediction of one or
more intermediate metrics, the intermediate metric includes one or
more members of a group consisting of: competition for the one or
more items, top rank rate of the seller for the one or more items,
price of the one or more items, and demand denoting popularity and
purchase levels of the one or more items.
[0044] Unless otherwise defined, all technical and/or scientific
terms used herein have the same meaning as commonly understood by
one of ordinary skill in the art to which the invention pertains.
Although methods and materials similar or equivalent to those
described herein can be used in the practice or testing of
embodiments of the invention, exemplary methods and/or materials
are described below. In case of conflict, the patent specification,
including definitions, will control. In addition, the materials,
methods, and examples are illustrative only and are not intended to
be necessarily limiting.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0045] The patent or application file contains at least one drawing
executed in color. Copies of this patent or patent application
publication with color drawing(s) will be provided by the Office
upon request and payment of the necessary fee.
[0046] Some embodiments of the invention are herein described, by
way of example only, with reference to the accompanying drawings.
With specific reference now to the drawings in detail, it is
stressed that the particulars shown are by way of example and for
purposes of illustrative discussion of embodiments of the
invention. In this regard, the description taken with the drawings
makes apparent to those skilled in the art how embodiments of the
invention may be practiced.
[0047] In the drawings;
[0048] FIG. 1 is a schematic illustration of an exemplary dynamic
re-pricing system for dynamically re-pricing one or more items
offered for sale on an electronic marketplace, according to some
embodiments of the present invention;
[0049] FIG. 2 is a flowchart of an exemplary process of dynamically
re-pricing one or more items offered for sale on an electronic
marketplace, according to some embodiments of the present
invention;
[0050] FIG. 3 is a schematic illustration of exemplary software
modules executed by an exemplary re-pricing system, according to
some embodiments of the present invention;
[0051] FIG. 4 is a schematic illustration of an exemplary
distributed re-pricing system, according to some embodiments of the
present invention;
[0052] FIG. 5 is a screen capture of an exemplary overview screen
of item sales as presented by an exemplary user interface of an
exemplary re-pricing system, according to some embodiment of the
present invention;
[0053] FIG. 6 is a screen capture of an status screen of an item
with respect to competition as presented by an exemplary user
interface of an exemplary re-pricing system, according to some
embodiment of the present invention;
[0054] FIG. 7 is a screen capture of an exemplary trends overview
as presented by an exemplary user interface of an exemplary
re-pricing system, according to some embodiment of the present
invention;
[0055] FIG. 8 is a screen capture of an exemplary trend summary of
an item buy box share as presented by an exemplary user interface
of an exemplary re-pricing system, according to some embodiment of
the present invention;
[0056] FIG. 9 is a screen capture o exemplary product trend summary
of an item buy box price as presented by an exemplary user
interface of an exemplary re-pricing system, according to some
embodiment of the present invention;
[0057] FIG. 10 is a screen capture of an exemplary alerts summary
as presented by an exemplary user interface of an exemplary
re-pricing system, according to some embodiment of the present
invention;
[0058] FIG. 11 is a screen capture of an exemplary commerce
information summary of item sales on a selected channel as
presented by an exemplary user interface of an exemplary re-pricing
system, according to some embodiment of the present invention;
[0059] FIG. 12 is a screen capture of continuation of an exemplary
commerce information summary items sales on a selected channel as
presented by an exemplary user interface of an exemplary re-pricing
system, according to some embodiment of the present invention;
[0060] FIG. 13 is a screen capture of an exemplary performance
overview of item as presented by an exemplary user interface of an
exemplary re-pricing system, according to some embodiment of the
present invention;
[0061] FIG. 14 is a screen capture of price and buy box tab of an
exemplary product analysis summary as presented by an exemplary
user interface of an exemplary re-pricing system, according to some
embodiment of the present invention;
[0062] FIG. 15 is a screen capture of sales and orders tab of an
exemplary product analysis summary as presented by an exemplary
user interface of an exemplary re-pricing system, according to some
embodiment of the present invention;
[0063] FIG. 16 is a screen capture of product contribution tab of
an exemplary product analysis summary as presented by an exemplary
user interface of an exemplary re-pricing system, according to some
embodiment of the present invention;
[0064] FIG. 17 is a screen capture of an exemplary editing screen
of an exemplary user interface for inserting an item to an
exemplary re-pricing system, according to some embodiment of the
present invention; and
[0065] FIG. 18 is a block diagram of traffic generators for
generating traffic to an online store and/or electronic
marketplace, in accordance with some embodiments of the present
invention.
DETAILED DESCRIPTION
[0066] The present invention, in some embodiments thereof, relates
to management of prices of goods and services on electronic
marketplaces and/or online stores and, more specifically, but not
exclusively, to dynamically re-pricing the goods and services in
real-time.
[0067] According to some embodiments of the present invention,
there are provided systems and methods for dynamically re-pricing
items offered for sale on an electronic marketplace and/or online
stores, and/or other types of electronic commerce arenas, for
example, a website hosting the items offered for sale. The
electronic marketplace may host items for sale by different
sellers, for example, a website that presents a user looking for an
item with different offers of sale for the item by different
sellers. The online store and/or website may be operated by the
seller, for example, exclusively offering items for sale by the
seller such as an online store owned and operate by the seller. The
terms electronic marketplace and/or online stores and/or hosting
website are not necessarily limiting, and are meant to cover
different models of on-line electronic commerce. For example, a
website exclusive to a seller, but offering other different items
for sale by different vendors. As described herein, the terms
electronic marketplace, online store and website hosting the item
are sometimes interchangeable, for example, depending on the
context. Alternatively, as described herein, sometimes the terms
electronic marketplace, online store and website hosting the item
are sometimes not interchangeable. For example, based on the
context, the term electronic marketplace may refer to an online
arena offering items from different sellers. For example, based on
the context, the terms online store and/or website may refer to an
online arena offering items from an exclusive seller.
[0068] A sale policy which defines sales goals for one or more
items offered on the electronic marketplace is received from the
seller (user). A state machine is created to execute a plurality of
offer adjusting actions which are selected from dynamically adapted
rules. The rules are created and/or modified to adjust the price of
the one or more items in order to fulfill the goals of the sale
policy. The rules are optionally adapted in real time according to
offers pertaining to the one or more items offered by different
sellers in the electronic marketplace arena, for example, reduce
the price of the one or more items compared with one or more offers
made by the competitor sellers in order to make the offer more
attractive and increase sales volume. The rules are optionally
adapted in real time according to a prediction of future values
related to the items offered for sale, optionally metrics
associated with the sales goals, for example, metrics related to
revenue, profits, margins and/or other metrics and/or other goals.
Optionally, the goals of the sale policy are achieved by adjusting
one or more characteristics of the one or more items other than the
price, for example, terms of payment, expedited delivery and/or
discount for purchase of large quantity of the one or more
items.
[0069] Optionally, the optimal price and/or dynamic re-pricing is
selected and/or performed based on one or more of: per seller
basis, per product basis, per electronic marketplace basis, per
period of time, and/or for other factors.
[0070] The seller also provides item information, for example,
product cost, inventory and/or shipping costs. The item information
may include additional characteristic of the one or more items, for
example, cost structure, fee structure and/or profit structure. The
cost structure may include, for example, direct and indirect costs
of the one or more items. The fee structure may include, for
example, a commission transferred to the electronic market place
for a sale. The profit structure may include, for example, minimum
profit, maximum profit, gain margin and/or markup.
[0071] The sale policy may be a high level user defined policy
which specifies sale goals, for example, pricing within a
pre-defined range, increase in volume of sales, increase in profit,
increase in profit margin, maximize profit while maintaining sales
volume, maximize volume while within a pre-defined profit margin
range, liquidate inventory while minimizing loss, maximize
perception (to improve ranking) and/or increase visibility and/or
impression on consumers on the electronic marketplace. The scope of
the sale policy may be defined, for example, for one or more items,
for a line of items, for a category of items and/or for a portfolio
of the seller. By defining the high level sale policy the seller is
relieved from continuously following the trade and manually
adjusting the price of the item(s) the seller offers for sale. The
seller may also avoid the need to specify low level pricing rules
which may be static and as such may need to be continuously
modified to adapt to the changing trade on the electronic
marketplace. Moreover, it is impossible to represent high-level
user defined policies by low level pricing rules, as the criteria
for ranking the offers may not be available from the operator of
the electronic marketplace, and as price changes generated by such
pricing rules may trigger an unknown response by one or more
competitors.
[0072] The sale policy is translated by the system to a strategy to
be followed in order to achieve the goals set by the sale policy
and/or maintains a current state. The strategy is implemented
through a set of price setting rules for adjusting the price of the
one or more items. The set of price setting rules may include a
plurality of rules, each rule may define low level threshold(s),
for example a requirement to reduce profit margin to no more than a
certain level and/or a requirement to increase sales volume to no
less than another certain level. The rules may interact and/or
impact each other as the objective of one of the rules may
interfere with the objective of another rule. The system may
resolve the mutual interferences between the rules to create a
coherent strategy to be followed in order to meet the goals of the
sale policy. The system evaluates the goals of the sale policy with
respect to the commerce information representing the trade activity
e electronic marketplace to identify the best alternative for
adjusting the price of the one or more items and creates a set of
price setting rules to carry out this alternative.
[0073] The sale policy may include (or be translated into) a
traffic strategy to increase overall traffic from one or more
traffic generators to the online store, website and/or electronic
marketplace hosting the item for sale by the seller. Traffic
generators may present products and prices of different merchants,
by crawling to the merchant's online store and/or by receiving
structured data, for example, via xml. Examples of traffic
generators include pricing engines and/or aggregators, for example,
Google.TM. Shopping, shoppingdotcom.RTM., and/or other websites.
Traffic generators may be electronic marketplaces, for example,
pop-ups or links within different pages of the electronic market
directing traffic to specific items, and/or the electronic
marketplace itself being a traffic generator directing traffic to
online stores stored on different servers. The traffic strategy may
increase qualitative traffic to the site hosting the seller's items
for sale. The traffic may achieve the goals set by the sales
policy.
[0074] The system may determine the optimal price for a given item
(optionally at a given period of time) that generates overall
traffic from multiple traffic generators. The overall traffic may
be optimal, for example, achieving the goals of the sales policy.
Optionally, the system determines the contribution of each of the
different traffic generators in producing orders for the item. One
or more variables described herein may be adjusted to increase the
overall traffic based on the contribution from each of the
different traffic generators, for example, the price setting rules
may be adjusted.
[0075] The commerce information includes for example, traffic
generated by each traffic generator to the online store hosting the
item for sale, consumer traffic volume, competitor sellers, prices
set by competitor sellers, number of sale transactions, number of
consumer views of the item, ranking of the offer of the seller,
ranking of the offers of the competitor sellers, inventory level
and/or other characteristics of the seller, of the competitor
sellers and/or the item dynamics on the marketplace. Other
characteristics of the competitor sellers may include for example,
shipping time, terms of payment and/or consumers rating. In some
cases the commerce information received from the electronic
marketplace may not provide the granularity of single competitor
sellers but rather one or more bulks of information relating to
part and/or all of the competitor sellers. In some electronic
marketplaces, for example Amazon Marketplace, the operator of the
electronic market-place may also be a vendor offering items for
sale.
[0076] The system includes a feedback loop for continuous
adjustment of the price of the offer made by the seller according
to real time analysis made on the commerce information collected
during trade of the one or more items. For example, current ranking
of the offer made by the seller compared to offers made by the
competitor sellers is evaluated, a price adjustment is applied to
the offer of the seller and commerce information is collected for
another analysis which may result in another price adjustment. The
analysis may include statistical analysis over the collected
commerce information.
[0077] Optionally, the sale policy includes a plurality of
operation mode parameters, for example, sale parameters, sale
parameters flexibility level and/or aggressiveness level. The sale
parameters, for example, minimum sales volume, minimum profit,
minimum profit margin and/or inventory level define low level sale
objectives which may be considered and/or evaluated while executing
the sale policy. Since the sale policy defines high level goals
rather than low level rules and as such the system may need some
flexibility in the sale parameters received from the seller in
order to create an efficient strategy to achieve the sale goals.
The required flexibility may be achieved by allowing the seller to
specify non-deterministic values for the sale parameters, for
example, setting a range, setting a maximum level and/or setting a
minimum level. The seller may specify a range for the sale
parameters, for example, a scale of 0% through 100%, to indicate
how closely the system should follow the sale parameters to give
the system a degree of freedom in order to achieve the sale goals
defined by the sale policy.
[0078] The aggressiveness level may be used to tune the system as
to how aggressive it is allowed to be with respect to the
competitor sellers in re-pricing the one or more items, where
aggressiveness level translates to the rapidity in achieving the
sale goals defined by the sale policy. Rapidity refers to the
period required to meet the sales goals and is characterized
through, for example the time period allocated for collecting
commerce information, the amount of commerce information required
for statistical analysis, the scope of the statistical analysis for
adjusting the price setting rules (significance level) and/or the
size of steps (in price units) taken for adjusting the price of the
one or more items. For instance, a higher aggressiveness level is
indicative of a shorter period and/or to the probability of
achieving the sales goal defined by the sales policy in cases where
the marketplace uses a non-deterministic ranking system. A high
aggressiveness level may imply, for example that larger steps are
taken in adjusting the price, less statistical significance is
required over the collected commerce information and/or less time
is allocated for learning the mechanism the system of the
electronic marketplace employs for ranking the offers of the
sellers. A low aggressiveness level sult in reaching the optimal
price level by allocating more time for collecting more commerce
information to allow for more accurate statistical analysis of the
trade activity and/or taking smaller step in re-pricing the one or
more items. The optimal price level may therefore be reached in a
longer time but the profit margins may not be compromised. The
aggressiveness level therefore may present a tradeoff between the
time needed to find the optimal price level and optimization of the
profit margins.
[0079] Optionally, the aggressiveness level is set automatically by
the system. For instance, the system may automatically modify the
aggressiveness level in order to accommodate the high level goals
as stated by the sale policy, for example, the aggressiveness level
may be reduced in order to maintain a profit margin level that is
derived from the sale policy. The aggressiveness level may be
temporarily modified for a specific period and then brought back to
the original level.
[0080] Optionally, the system predicts one or more goal metrics,
for example, revenue, sales, profits, margin and/or other goals.
The goal metrics are optionally defined in the sales goals of the
sales policy. Optionally, the prediction of the goal metrics is
based on prediction of one or more intermediate metrics. The goal
metrics may be correlated with the intermediate metrics to generate
the prediction of the goal metrics based on the prediction of the
intermediate metric, for example, based on machine learning
methods. Examples of the intermediate metrics include: top rank
rate of the seller, price of the items for sale, competition of the
items for sale, demand for the items for sale, traffic from a
respective traffic generator, conversion rates from a respective
traffic generator (e.g., items bought, profit, sales, or other
sales measures divided by number of visits), and/or other
intermediate metrics.
[0081] Optionally, the predicted goal metrics are used to
dynamically adapt the sales policy, for example, the sales goals,
the rules, the strategy, operation mode parameters, and/or other
policies, goals and/or rules. Alternatively or additionally, the
predicted goal metrics are used to adjust the price of the item.
Alternatively or additionally, the price setting rules are
dynamically adjusted according to an analysis of the prediction of
the sale policy. For example, the aggressiveness level may be
increased if increased competition is predicted.
[0082] Optionally, the prediction is performed continuously and/or
periodically, for example, based on new prediction data.
Optionally, the feedback loop for continuously adjustment of the
item is based on the dynamic predictions.
[0083] The prediction may allow the system to more accurately
and/or efficiently achieve the seller's goals. The prediction may
allow the system to anticipate events and adjust before the events
occur, rather than reacting to events after they occurred. For
example, the system may predict increased competition and/or a
reduction in demand, even though the current item enjoys little
competition and/or high demand. The prediction may allow the system
to adjust parameters (e.g., price) to maintain and/or achieve the
seller's goals in view of the prediction, rather than waiting until
the item has succumbed to the competition and loss in demand, and
then trying to recover the previous position.
[0084] Optionally, the sale policy defines long term goals in which
one or more intermediate goals may be set to achieve the final
goals as specified by the sale policy. The intermediate goals may
be predicted. Re-predictions may take place when the intermediate
time periods are reached, and/or before the final and/or
intermediate time periods. For example, the system executes one or
more actions to maximize sales volume of one or more items while
the sales volume is below a certain level and then execute
different one or more actions to maximize the profit margin after
the sales volume of the one or more items reaches the certain
level. Another example may be, the system executes one or more
actions achieve higher market penetration and/or improve the
ranking of the seller's offer. Higher market penetration may
increase market share which may translate to increase in sales
volume and/or profit. During the phase of market penetration the
system may take actions that may result in temporary drop in profit
margin and/or loss of profit.
[0085] Before explaining at least one embodiment of the invention
in detail, it is to be understood that the invention is not
necessarily limited in its application to the details of
construction and the arrangement of the components and/or methods
set forth in the following description and/or illustrated in the
drawings and/or the Examples. The invention is capable of other
embodiments or of being practiced or carried out in various
ways.
[0086] As will be appreciated by one skilled in the art, aspects of
the present invention may be embodied as a system, method or
computer program product. Accordingly, aspects of the present
invention may take the form of an entirely hardware embodiment, an
entirely software embodiment (including firmware, resident
software, micro-code, etc.) or an embodiment combining software and
hardware aspects that may all generally be referred to herein as a
"circuit," "module" or "system." Furthermore, aspects of the
present invention may take the form of a computer program product
embodied in one or more computer readable medium(s) having computer
readable program code embodied thereon.
[0087] Any combination of one or more computer readable medium(s)
may be utilized. The computer readable medium may be a computer
readable signal medium or a computer readable storage medium. A
computer readable storage medium may be, for example, but not
limited to, an electronic, magnetic, optical, electromagnetic,
infrared, or semiconductor system, apparatus, or device, or any
suitable combination of the foregoing. More specific examples (a
non-exhaustive list) of the computer readable storage medium would
include the following: an electrical connection having one or more
wires, a portable computer diskette, a hard disk, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable
read-only memory (EPROM or Flash memory), an optical fiber, a
portable compact disc read-only memory (CD-ROM), an optical storage
device, a magnetic storage device, or any suitable combination of
the foregoing. In the context of this document, a computer readable
storage medium may be any tangible medium that can contain, or
store a program for use by or in connection with an instruction
execution system, apparatus, or device.
[0088] A computer readable signal medium may include a propagated
data signal with computer readable program code embodied therein,
for example, in baseband or as part of a carrier wave. Such a
propagated signal may take any of a variety of forms, including,
but not limited to, electro-magnetic, optical, or any suitable
combination thereof. A computer readable signal medium may be any
computer readable medium that is not a computer readable storage
medium and that can communicate, propagate, or transport a program
for use by or in connection with an instruction execution system,
apparatus, or device.
[0089] Program code embodied on a computer readable medium may be
transmitted using any appropriate medium, including but not limited
to wireless, wireline, optical fiber cable, RF, etc., or any
suitable combination of the foregoing.
[0090] Computer program code for carrying out operations for
aspects of the present invention may be written in any combination
of one or more programming languages, including an object oriented
programming language such as Java, Smalltalk, C++ or the like and
conventional procedural programming languages, such as the "C"
programming language or similar programming languages. The program
code may execute entirely on the user's computer, partly on the
user's computer, as a stand-alone software package, partly on the
user's computer and partly on a remote computer or entirely on the
remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider).
[0091] Aspects of the present invention are described below with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems and computer program products according
to embodiments of the invention. It will be understood that each
block of the flowchart illustrations and/or block diagrams, and
combinations of blocks in the flowchart illustrations and/or block
diagrams, can be implemented by computer program instructions.
These computer program instructions may be provided to a processor
of a general purpose computer, special purpose computer, or other
programmable data processing apparatus to produce a machine, such
that the instructions, which execute via the processor of the
computer or other programmable data processing apparatus, create
means for implementing the functions/acts specified in the
flowchart and/or block diagram block or blocks.
[0092] These computer program instructions may also be stored in a
computer readable medium that can direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable medium produce an article of manufacture
including instructions which implement the function/act specified
in the flowchart and/or block diagram block or blocks.
[0093] The computer program instructions may also be loaded onto a
computer, other programmable data processing apparatus, or other
devices to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other devices to
produce a computer implemented process such that the instructions
which execute on the computer or other programmable apparatus
provide processes for implementing the functions/acts specified in
the flowchart and/or block diagram block or blocks.
[0094] Reference is now made to FIG. 1 which is a schematic
illustration of an exemplary dynamic re-pricing system, according
to some embodiments of the present invention. A re-pricing system
100 includes a processing unit that executes one or more software
modules for re-pricing one or more items offered for sale on an
electronic marketplace 102 and/or hosting seller online store, for
example, personal computer, server, and/or a distributed processing
system that includes a plurality of processing nodes. The
re-pricing system 100 interacts with one or more of the plurality
of sellers 101 using a plurality of client terminals, for example,
Smartphone, tablet, desktop computer and/or laptop computer over
one or more of a plurality of networks, for example, cellular
and/or internet. For brevity, a client terminal used by a seller
101 may be referred to herein as a seller 101. The re-pricing
system 100 receives from the seller 101 the sale policy for one or
more items offered for sale on the electronic market 102. The sale
policy defines sales goals for the one or more items, interaction
with one or more of the sellers 101 may be implemented, for example
through a client application executed on the client terminal and/or
through a web based service accessible using a web browser and/or
equivalent application executed on the client terminal of the
seller 101.
[0095] Optionally, the system 100 automatically defines the sale
policy. The system 100 may automatically set the goals of the sale
policy through analysis of past commerce information collected
during previous trade transactions of the one or more items and/or
similar items. The goals of the sales policy may be dynamically
adjusted by the system over time to according to analysis of the
collected commerce information in order to adapt to the changes in
the trade of the one or more items and maximize revenues.
[0096] The re-pricing system translates the sale policy to a
plurality of price setting rules which are used by the re-pricing
system 100 to adjust the price of the one or more items on the
electronic marketplace 102. The re-pricing system 100 communicates
with the electronic marketplace 102 over the one or more networks
and dynamically adjusts the price of the one or more items in order
to meet the goals set by the sales policy of the seller 101.
[0097] The re-pricing system 100 monitors the trade activity
relating to the one or more items offered for sale and collects the
commerce information of the one or more items.
[0098] The re-pricing system 100 may predict variables related to
the one or more items offered for sale. The re-pricing system 100
may predict one or more intermediate metrics associated for the
items offered for sale. The re-pricing system 100 may predict
metrics associated with the sale policy of the item offered for
sale.
[0099] The re-pricing system 100 may be a learning system which
analyzes the received commerce information and/or predicted values
in order to define and/or modify the set of recommended price
setting rules for adjusting the price of the one or more items. The
re-pricing system 100 may continuously evolve, for example through
a support vector machine (SVM) to identify the mechanisms the
system of the electronic marketplace 102 employs for ranking the
offers of the sellers. The re-pricing system 100 then takes one or
more actions for adjusting the price of the one or more items
according to the set of price setting rules.
[0100] Optionally, the re-pricing system 100 provides output
interfaces for example, visual interface, textual interface and/or
audio interface through which the seller 101 is presented with
commerce information through which the seller 101 may track the
trade activity at the electronic marketplace 102 concerning the one
or more items the seller 101 offers for sale.
[0101] Optionally, the user 101 adjusts one or more of the price
setting rules which dictate the course of action taken by the
re-pricing system 100. The rules adjusted by the seller 101 may
have precedence over the recommended rules that are automatically
crated and/or adjusted by the re-pricing system 100.
[0102] Reference is now made to FIG. 2 which is a flowchart of an
exemplary process of dynamic re-pricing one or more items offered
for sale on an electronic marketplace, according to some
embodiments of the present invention. As shown at 201, a process
200 which may be utilized through the exemplary re-pricing system
100 starts with receiving a sale policy form the seller 101. In
addition the seller 101 inserts the information of the one or more
items that be offers for sale at the electronic marketplace
102.
[0103] As shown in 202, a state machine is created for executing
the sale policy that is received from the seller 101. The state
machine employs a learning analysis mechanism (algorithm) which
analyzes the trade activity of the one or more items offered by the
seller 101 for sale on the electronic marketplace 102 and creates a
set of price setting rules that are executed by the state machine.
The analysis mechanism operates within the range of the parameters
specified by the sale policy. The set of price setting rules may be
modified, for example, to promote the offer of the seller 101 to
get high ranking so that he may get the maximum exposure and/or be
selected as the default seller by the electronic marketplace 102.
Another approach for executing the sale policy may be, for example,
improving terms of payment and/or acquiring sponsored adverts.
[0104] As shown at 203, the state machine may set top price and
bottom price boundaries for the one or more items that are used
throughout the re-pricing process 100 and sets an initial optimal
price.
[0105] As shown at 204, the trade activity of the one or more items
at the electronic marketplace 102 is continuously monitored to
collect.
[0106] As shown at 205, the collected commerce information is
analyzed to dynamically adjust in real time the recommended rules
for setting the optimal price of the one or more items. The
analysis includes, for example, identifying the trade activity of
the one or more items, identifying the ranking of the offer of the
seller 101 and checking the ranking performance of the offer of the
seller 101 compared to the expectations and/or assumptions made
while setting the optimal price. The recommended rules are taken to
maintain operation within the parameters specified by the sale
policy, for example, price boundaries, minimum profit margin,
maximum profit margin and/or aggressiveness level. The optimal
price is determined by analyzing real time commerce information
with full, little and/or no previous commerce information. The
optimal price is set by also considering the top rank rate, i.e.
what is the share of the offer of the seller 101 in the top ranking
position. Analysis of the commerce information may include
statistical analysis.
[0107] Optionally, metrics associated with the seller's goals are
predicted, for example, revenue, profit, margin and/or other
metrics. Alternatively or additionally, the sales policy and/or
components thereof are predicted. The seller's goals may be
components of the sales policy.
[0108] The metrics associated with the seller's goals and/or sales
policy may be predicted based on a prediction of one or more
intermediate metrics. Machine learning methods may be applied to
correlate the predicted intermediate metrics with the predicted
goal and/or sales policy metrics, for example, accompanied with the
corresponding weights generated by the correlation. Prediction of
the intermediate metrics arid then correlating to predict the sales
policy may be more accurate than direct prediction of the sales
policy.
[0109] The intermediate metrics may include: competition, top rank,
price, demand, traffic, conversion rates, and/or other metrics. The
intermediate metrics may be functions. The intermediate metrics may
be calculated for a selected current or previous time period. The
calculated current or previous intermediate metric may serve as a
baseline level for prediction of the intermediate metric. The
intermediate metric may be calculated for a selected item. The
intermediate metric may be calculated for a selected time period
(e.g., one day). The intermediate metric may be calculated for a
selected electronic marketplace.
[0110] The competition metric denotes the probability of the seller
achieving top rank in the context of a selected item. The
competition metric may be calculated, for example, as a weighted
average of the following parameters: average number of sellers,
average number of top ranked sellers, price volatility (e.g., may
imply price wars). The competition may be predicted.
[0111] The top rank metric denotes the top rank rate that the
seller will gain for a given item. The top rank rate that the
seller may gain may be predicted.
[0112] The price metric may denote price level of the selected
item. The price level may be calculated, for example, as a weighted
average of the following parameters: average top rank price during
the selected time period, average seller's minimum and/or maximum
prices during the selected time period. The price level may be
predicted.
[0113] The demand metric may denote the popularity and/or purchase
level for the item. The demand may be calculated, for example, as a
weighted average of the following parameters: number of product
items ordered from the seller (may be extrapolated to overall
number of items ordered in the marketplace, optionally assuming the
seller's top rank>preselected threshold (the order levels may be
somewhat representative of the marker's order levels)); product's
objective ranking provided by the marketplace (e.g., Sales Rank
provided by Amazon.RTM.). The demand for the item may be
predicted.
[0114] The traffic metric may denote the traffic generated to the
hosting online store for a respective traffic generator. The
traffic generated by each traffic generator may be predicted.
[0115] The conversion metric may denote the conversion rate for the
hosting online store, optionally the conversion rate generated for
a respective traffic generator. The conversion rate may be
predicted.
[0116] Other intermediate metrics may be calculated and/or
predicted.
[0117] The parameters used to calculate the intermediate values
described above are optionally normalized and/or weighted before
being summed up into the final measure of the intermediate
value.
[0118] Optionally, the intermediate value is predicted, for
example, by applying trend analysis, for example, using linear
and/or logistic regression, and/or other extrapolation methods
and/or other machine learning methods.
[0119] Optionally, the prediction is performed for a selected item.
Alternatively or additionally, the prediction is performed for a
selected future time period. Alternatively or additionally, the
prediction is performed for a selected electronic marketplace.
Optionally, a statistical significance denoting the probability of
the predicted intermediate value is calculated. A statistical
significance may be calculated for the metrics of the seller's
goals and/or sales policy.
[0120] Predictions of the intermediate variables may be calculated
based on correlations with measured metrics, measured past and/or
current intermediate variables, correlations with predicted
intermediate variables, and/or based on other methods. When
correlation is high (e.g., above about 70%, or above about 80%, or
over about 90%, or over about 95%, or other values), a relationship
between the predicted intermediate variable and the intermediate
variable to be predicted may be calculated, for example, by
generation of a transformation function.
[0121] Optionally, the top rank is predicted by determining the
correlation between the competition function (e.g., as described
above) and the seller's actual top rank rate over a period of time.
When the correlation value is high, a transformation function may
be generated from the competition prediction function o the top
rank prediction function.
[0122] Optionally, the prediction of the goal metric and/or
intermediate metric is used in adjusting one or more system
variables, for example, the sales policy, sales goals, rules,
strategy, item price, operation mode parameters, aggressiveness
level, and/or other parameters as described herein. Optionally, the
prediction provides automatic feedback for the system and/or manual
feedback to the seller. Based on the prediction, the system and/or
seller may consider different alternatives and select the best
one.
[0123] Optionally, the performance of the system is continuously
and/or periodically reassessed based on the prediction, for
example, the selling status of a given item in the market place.
Optionally, actions are performed based on the prediction, for
example, the aggressiveness level is increased.
[0124] As shown at 206, the state machine employs one or more of
the recommended rules and modifies the price of the one or more
item to the optimal level.
[0125] Optionally, the process 200 is iterative and is performed in
one or more iterations. The trade activity at any price point may
be monitored to collect commerce information which may be analyzed
to take further action in order to promote the seller 101 and
achieve the goals set by the sale policy. The period allocated for
analyzing the commerce information in order to learn the mechanism
of the system of the electronic marketplace 102 during each
iteration as well as the increase and/or decrease in the optimal
price may be dictated by the aggressiveness level (if specified by
the sale policy). The increase and/or decrease in the optimal price
may also depend on the performance of the offer of the seller 101
in ranking, for example, in case the offer of the seller 101
continuously gets top rating, the increases in the optimal price
will grow linearly or non-linearly from one price point to the
next.
[0126] Optionally, setting the initial price is done by identifying
one or more groups of competitor sellers offers and setting a price
which emphasizes the advantage the offer of the seller 101 has over
the offers of competitor sellers. The one or more groups may be
created with respect to a static characteristic of the item and/or
the seller, for example shipping time, terms of payment and/or
seller rating. Each group may include one or more offers. The
initial price is set by analyzing the static to competitive
advantage the offers of sellers in each group have over the offer
of the seller 101 so as to eliminate the competitive advantage
through an attractive price. Analysis may focus on groups
consisting of offers by competitor sellers which continuously
and/or repeatedly receive top ranking by the system of the
electronic marketplace 102, so that by over performing them the
offer of the seller 101 receives top ranking.
[0127] The following is an exemplary algorithm for determining the
price of the item based on traffic generated to the hosting online
store and/or electronic marketplace 102 by traffic generators. The
algorithm analyzes the traffic generated by each respective traffic
generator, and adjusts the price of the offer of the seller 101
accordingly. The traffic algorithm may be executed, for example,
during block 205 of FIG. 2, and/or by analysis module 304 of FIG.
4. The traffic algorithm may be integrated with the state machine,
for example, the state machine adjusts the price setting rules
and/or modifies the price based on the analysis of the traffic
algorithm.
[0128] To help understand the algorithm (and other references to
traffic generators as described herein) reference is made to FIG.
18, which is a block diagram of multiple traffic generators 1802
generating traffic to an online store 1804, in accordance with some
embodiments of the present invention. Traffic may be generated by
buyers 1808 accessing traffic generators 1802. Online store 1804
may be an electronic marketplace (e.g., with multiple vendors), an
exclusive website offering items from the exclusive seller, or
other models of electronic commerce.
[0129] An analysis module 1806 analyzes traffic generated by
traffic generators 1802 to online store 1804. Analysis module 1806
may be, for example, as described herein with reference to analysis
module 304 of FIG.3, block 205 of FIG. 2, or other traffic analysis
methods and/or systems.
[0130] Optionally, analysis module 1806 executes the exemplary
traffic algorithm. The traffic algorithm may determine reference
prices at traffic generators, for example, major selling website
channels such as Amazon.RTM. and/or Ebay.TM.. Prices may be
determined for the item at a selected time frame. The reference
prices may be generated based on the selected strategy, for
example, to maximize sales, profits and/or margins.
[0131] Traffic generator 1802 and online store 1804 may be
independent, separate and/or distinct web site, for example,
residing on different servers, and/or operated by different
entities. Alternatively, traffic generator 1802 and online store
1804 are part of a single unit. For example, traffic to a specific
item or webpage (i.e., store 1804) may be generated from other
parts of the website, electronic marketplace, and/or online store.
For example, a customer browsing for a slow cooker on a certain
page of a certain website may be presented with a link to a book of
slow cooker recipes for sale on a different page of the same
website. The link to the book may be traffic generator 1802, and
the page of the book may be online store 1804.
[0132] The traffic algorithm collects data and analyzes the data
over a period of time. The data is collected for each traffic
generator, for the selected item(s). The data may be analyzed to
determine a correlation between each reference price for the item
and an optimal price or each respective traffic generator. For
example, the price that generates the optimal traffic through the
respective traffic generator. The optimal traffic may be the
traffic that best meets the selected strategy. Different prices may
be set at different points in time. The reference prices may be
used as a baseline for setting the different prices. The different
prices may be selected based on the prediction.
[0133] The traffic algorithm assigns weights in the context of each
item, for each reference price, for each traffic generator. For
example, the system may determine that the optimal price for an
item in shoppingdotcom.RTM. is 0.78*Amazon's reference price for
the product +0.35*Ebay's reference price for the product. The
traffic algorithm may occasionally recalibrate the weights.
[0134] For the item, the traffic algorithm may determine the
importance and/or potential of each traffic generator. A weight may
be assigned based on the importance. The weight may reflect the
contribution of the respective traffic generator in producing
orders. Traffic generators may be evaluated based on quality and/or
quality of traffic. The highest weights may be assigned based on
the number of generated orders and/or based on the profitability of
the generated orders. The traffic algorithm may occasionally
recalibrate the weights.
[0135] The traffic algorithm may occasionally apply the determined
weights to the reference prices. The traffic algorithm may operate
in a feedback loop.
[0136] Optionally, the following exemplary algorithm and supporting
API are used for setting the initial price of the one or more items
offered for sale by the seller 101 on the electronic marketplace
102. Presentation of the exemplary algorithm is done with reference
to buy box which relates to the top ranking offer (or default
offer) as may be done in some electronic marketplaces for example,
Amazon Marketplace. The algorithm analyzes the commerce information
and adjusts the price of the offer of the seller 101 accordingly.
The algorithm is expressed in pseudo code using some functions of
the supporting API.
The Following Definitions may be Used Throughout the Algorithm:
[0137] 1) The API function attr_pref(attribute, value 1, value 2)
which is an ordinal relationship, may determine the preference of
one group attribute value over another group, for example, shipping
time, consumer rating, shipping coverage. For instance, a seller
who ships within of maximum 2 days may be preferred over another
seller who ships in up to 3 days. The function takes into account
only one attribute at a time and may avoid all other attributes.
The function returns the following output values: <, >, =.
[0138] 2) The API function grp_pref (group 1, group 2) which is an
ordinal relationship, may determine the preference of one group
over another group. The function returns the following output
values: <, >, =, UNKNOWN. [0139] It may be presumed, for
example, that if group 2 is preferred over group 1, sellers in
group 2 may have better chance to win the buy box over sellers in
group 1, given that they offer the same price. [0140] 3) The API
function num_of_groups_by_price(price) returns number of groups,
for which lowest offer price matches the given price. [0141] 4) The
API function num_of_merchants_by_group(group) returns the number of
merchants (sellers) that were considered for that group.
The Algorithm may Receive the Following Input:
[0141] [0142] 1) Current lowest offers for the product (item).
[0143] 2) Current buy box price for the product (item). [0144] IF
num_of_groups_by_price(buy box price)=1 THEN buy box group is the
group with the matching price. [0145] ELSE [0146] stay empty.
[0147] IF num_of_groups_by_price(merchant price)>0 AND one of
the matching groups is proven to indeed include the offer of the
merchant (via the second API call) then merchant group is the group
with the matching price. [0148] ELSE [0149] stay empty.
[0150] The algorithm may execute as follows:
[0151] The algorithm is designated for determining the new
suggested price for a given merchant and product. It handles the
various scenarios, where main division is whether the user
currently holds buy box or not. The fact that suggested price may
be in the range of [floor price, ceiling price] is since this is
obvious.
Scenario in which the Merchant (Seller 101) Does not Hold the Buy
Box:
[0152] IF merchant group are known--then [0153] IF merchant price
isn't lowest in his group THEN [0154] Merchant new price=lowest
price in the group-PricePortion/* where PricePortion denotes the
amount (step) in which the price may be adjusted by the system*//*
Note: this line may not be reached since the merchant's group is
determined by the fact that the merchant has the lowest price
within the group, [0155] ELSE [0156] Remove all lowest offer
listings belonging to groups that are lower than the merchant group
{ leave the merchant's group}. New merchant price=lowest price of
the remaining groups-PricePortion
[0157] /* Note: This may be because the existing prices weren't low
enough to obtain buy box*/I
[0158] IF previous merchant price--new merchant
price<PricePortion THEN New merchant price=previous merchant
price-PricePortion /* Note: if buy box group<merchant group,
existing prices of groups>=merchant group were not sufficient
for buy box, therefore the lowest price within them may be used as
anchor. If buy box group>=merchant group, only one (by
definition) of the groups>=merchant groups, gained the buy box,
but all others failed-so a more competitive offer compared to these
groups may be suggested.
[0159] Nevertheless, scenarios may be encountered in which the
price of the buy box group is very low, thus it may take prolonged
time to reach its surroundings. */
Scenario in which the Buy Box Group is Known and Merchant Group is
Unknown: [0160] Remove all lowest offer listings belonging to
groups that are lower than the buy box group or that their price is
lower than the buy box price.
[0161] New merchant price=lowest price of the remaining
groups-PricePortion /* Note: this may be because the existing
prices weren't low enough to obtain buy box, it may be assumed the
merchant (seller 101) is within those groups. */
[0162] IF previous merchant price-new merchant
price<PricePortion THEN New merchant price=previous merchant
price-PricePortion
Scenario in which the Buy Box Group is Unknown and Merchant Group
is Unknown:
[0163] New merchant price=buy box price-PricePortion
[0164] IF previous merchant price-new merchant
price<PricePortion THEN New merchant price =previous merchant
price-PricePortion
Scenario in which the Merchant (Seller 101) Holds the Buy Box:
[0165] IF buy box group is known (=merchant group is known) THEN
[0166] IF num_of_merchants_by_group(merchant group)<=1 THEN
Remove all lowest offer listings belonging to groups that are lower
than the merchant group, as well as the merchant's group. New
merchant price=MAX(lowest price of the remaining
groups-PricePortion, buy box price+PricePortion) [0167] ELSE [0168]
New merchant price=buy box price /* Note: Adding PricePortion may
be considered, but probably should not since it is believed that
Amazon, for example, tends to take lowest in the group. */ Scenario
in Which Box Broup is not Known (i.e. Merchant Group is not
Known)
[0169] Remove all lowest offer listings that their price is lower
than the buy box price /* Note: it is assumed that these offers did
not secure the buy box probably due to their grouping and lack of
competitive pricing. The price may be raised to verify this.
[0170] */New merchant price=MAX(lowest price of the remaining
groups-PricePortion, buy box price).
[0171] /* Note: it may be considered to add PricePortion to the
later component in the MAX function, but it probably should not be
done since it is believed that Amazon, for example, tends to take
lowest in the group. */
[0172] Optionally, the algorithm makes the following assumptions:
[0173] 1) It is assumed that the highest ranking offer having the
lowest price belongs to a certain group when the certain group is
the only group which includes an offer having a price which equals
the price of the highest ranking offer which is the lowest price
offer. [0174] 2) It is assumed that there are offers having lower
price than the price of the offer of the seller 101 when the offer
of the seller 101 is not the lowest price offer in one or more
groups. [0175] 3) It is assumed that even when the price of the
offer of the seller 101 is the lowest price offer in his group
there may be other one or more offers having the same price in
other one or more groups. [0176] To determine which group the
seller 101 is included in, the algorithm is executed again with the
seller 101 excluded from the analysis (ExcludeMe flag is set to
TRUE).
[0177] Optionally, the commerce information includes past
information on previous sales, past trade activity and/or dynamics
and/or interaction of the sellers and the consumers with respect to
the one or more items. The past commerce information may be used to
educate the learning analysis mechanism and is analyzed to improve
prediction, to support setting the initial price and/or to support
the dynamic adjustments to the prices of the one or more items.
Analyzing the past commerce information may be useful for a
plurality of ends, for example, provide more accurate pricing
compared to previous pricing of one or more competitor sellers,
identify trade patterns and/or make predictions for future trade
patterns. Analysis of the past commerce information may also be
used by the learning analysis mechanism to understand the criteria
and way the system of the electronic marketplace 102 ranks the
sellers on the arena.
[0178] Optionally, analysis of real time and/or past commerce
information may also be used to overcome incomplete commerce
information that is received from the electronic marketplace 102,
for example, missing one or more characteristics of the price
and/or sellers and/or there is no full correlation between specific
competitor sellers and the items they offer for sale.
[0179] Optionally, in case aggressiveness level is specified by the
sale policy, the aggressiveness level dictates the rapidity in
achieving the goals of the sale policy. The rapidity in achieving
the goals of the sale policy may impact the execution of the
re-pricing iterations, for example, the time allocated for
collecting the commerce information and analyzing it during the
iteration, the amount (in units) in price adjustment and/or the
need for generating statistical information. While employing the
aggressiveness level while re-pricing the one or more items
additional parameters may be required, for example, recent price
range of the one or more items for the top ranking competitor
sellers' offers and/or recent price range of the one or more items
for all competitor sellers' offers. The additional parameters may
be weighted so as to have some more influential than others. The
aggressiveness level may be employed in a linear or non-linear
transformation function for transforming the aggressiveness level
into metrics for re-pricing the one or more items. Expression 1
below provides an example to such a transformation function.
f ( AggressivnessLevel , X 1 , X 2 ) = 1 2 ( W 1 .times. X 1 + W 2
.times. X 2 ) 100 .times. AggressivnessLevel Expression 1
##EQU00001##
Where:
[0180] X.sub.1 denotes a recent price range of the one or more
items for top ranking competitor sellers' offers. [0181] X.sub.2
denotes a recent price range of the one or more items for all
competitor sellers' offers. [0182] W.sub.1 denotes a weight given
to the offers of the top ranking competitor sellers. [0183] W.sub.2
denotes a weight given to the offers of all competitor sellers.
[0184] AggressivenessLevel denotes the aggressiveness level as
derived from the sale policy and provided on a scale of 0 through
100.
[0185] In an exemplary re-pricing process, the following values are
given:
X 1 = $10 , X 2 = $20 , W 1 = 1.0 , W 2 = 0.75 ,
AggressivenessLevel = 50 ##EQU00002## f ( AggressivnessLevel , X 1
, X 2 ) = 1 2 ( 1 .times. 10 + 0.75 .times. 20 ) 100 .times. 50 =
6.25 ##EQU00002.2##
Taking the above values into consideration, during the next
re-pricing cycle the price of the one or more items is set to $6.25
less than the current price of the one or more items offered by the
seller 101.
[0186] Optionally, the analysis made by the learning analysis
mechanism includes trade off analysis. Trade off analysis may
identify scenarios in which, for example, a small decrease in price
may increase dramatically the ranking of the offer of the seller
101 and/or a little decrease in ranking may increase dramatically
the price and/or profit. As part of the trade off analysis an
optimal constellation is selected to provide the best tradeoff
within the sale policy and the parameters defined by the sale
policy.
[0187] Optionally, the re-pricing process 200 identifies scenarios
of unfulfilled potential and adjusts the price of the one or more
items to extend the profits. An unfulfilled potential scenario may
be, for example, the one or more items has high profit margin and
high sales potential that is not reached due to low ranking of the
offer. The re-pricing system 100 monitors the commerce information
and identifies the offer of the seller 101 is continuously ranked
low by the system of the electronic marketplace 102. The price of
the one or more items may be adjusted to reduce the price at the
expense of reducing the profit margin. However, the increase in
volume may provide higher profit for the portfolio of the seller
101. Another example may be, a scenario in which the offer of the
seller 101 gets high ranking but has low sales volume. In order to
increase the sales volume, the re-pricing system 100 may reduce the
price of the one or more items on the assumption that more
consumers may be interested in purchasing the one or more items at
the reduced price. The re-pricing system 100 may repeat this
process while analyzing the commerce information during each
re-pricing iteration (price point). The analysis may result in
further adjustments (increases or decreases) to the price to
achieve higher sales volume.
[0188] Optionally, the seller 101 has access to the commerce
information collected during the process 200. The collected
information may include current commerce information and/or past
commerce information. The commerce information may be presented to
the seller 101 using a plurality of means, for example, textual,
audible and/or visual means. The commerce information may include a
plurality data items, for example, tracking performance indicators,
such as sales, orders, profit and/or Buy Box share. Commerce
information may be further processed to provide statistical data on
the trade, for example, revenue, sales, profit, profit margin,
orders and/or average price. Trade statistics may be provided on a
plurality of views, for example per one or more items, per items
line, per items category and/or per portfolio. This information may
enable the seller 101 to evaluate the performance of the re-pricing
system 100, identify market trends, identify best selling items
and/or modify the sale policy and/or the sale goals.
[0189] Optionally, the recommended rules created and/or modified by
the learning analysis mechanism are available to the seller 101.
The seller 101 may analyze the commerce information presented by
the re-pricing system 100 and manually intervene in the automatic
re-pricing process executed by the re-pricing system 100 to alter
the rules and/or recommendations for re-pricing the one or more
items.
[0190] Optionally, alerts are generated to the seller 101 to
indicate of a plurality of events that occur during the trade of
the one or more items. Defining conditional events for triggering
alerts may be created automatically by the re-pricing system 100
and/or set manually by the seller 101. Alerts may be generated for
a plurality of events, for example, drop in sales volume, drop in
profit, drop in profit margin, low inventory, high inventory,
and/or inability to meet the sale policy goals. The alerts may be
associated with recommended actions to be taken by the seller 101,
for example, adjust price setting rules, modify policy goals and/or
replenish the inventory for the one or more items.
[0191] Optionally, in the event there are items offered for sale by
the seller 101, prioritization is made between two or more of the
items in order to control the frequency of the re-pricing
iterations to avoid overloading the re-pricing system 100 and/or
avoid performing unnecessary re-pricing adjustments. Some items may
require more frequent tracking and re-pricing than other items due
to intensive trade activity, aggressive competition and/or rapid
changes in ranking of the offers of the sellers. The items
requiring more frequent tracking and re-pricing receive higher
priority from the re-pricing system 100 that manages the re-pricing
process 200.
[0192] Optionally, extrapolation is performed during the analysis
phase to overcome lack of commerce information at the price point
set during the current re-pricing iteration. Commerce information
that is collected in real time and short term past may lack
sufficient data points to provide a statistically significant data
set, from which reliable conclusions may be derived. To overcome
this, commerce information may be collected for one or more
adjacent price points and deducted to other price points to create
a sufficient data set for the target optimal price point.
[0193] Optionally, the inventory level that is displayed to the
consumers for the one or more items represents a partial inventory
level replenished as inventory goes down. Presenting the consumer
with the partial inventory level to imply upcoming possible
shortage in the one or more items may encourage consumers who are
hesitating to make the order for the one or more items.
[0194] Reference is now made to FIG. 3 which is a schematic
illustration of exemplary software modules executed by an exemplary
re-pricing system, according to some embodiments of the present
invention. A re-pricing system such as the re-pricing system 100
includes a user interface module 301, a state machine module 302, a
monitor module 303 and an analysis module 304. The re-pricing
system 100 is executed on a processing unit which is capable of
communicating with the electronic marketplace 102 over the one or
more networks. The re-pricing system 100 receives the sale policy
from the seller 101 through the user interface module 301. The sale
policy defines sales goals for the one or more items the seller 101
offers for sale on an electronic marketplace 102.
[0195] The user interface module 301 may be utilized, for example,
through a web based service accessed using a web browser and/or
through a client application. The web browser and/or the client
application may be executed on one or more of a plurality of client
terminals, for example, Smartphone, tablet, work station, desktop
computer and/or laptop computer. The user interface 101
communicates with the re-pricing system 100 to transfer the sale
policy to the re-pricing system 100. The communication between the
user interface 101 and the re-pricing system 100 may be local in
case the user interface 101 executes on the same processing unit as
the re-pricing system 100. In case the user interface 101 executes
on client terminal that is to remote from the processing unit
hosting the re-pricing system 100 the user interface 101 may
communicate with the re-pricing system 100 over the one or more of
a plurality of networks.
[0196] Once the sale policy is received at the re-pricing system
100, the state machine module 302 is created to execute one or more
actions to achieve the goals defined by the sale policy. The state
machine module 302 interacts with the analysis module 304 to
receive a plurality of price adjusting rules for adjusting the
price of the one or more items. The state machine module 302 may
communicate with the system of the electronic marketplace 102
through the one or more networks to adjust the price of the one or
more items. The state machine 102 may interact with the system of
the electronic marketplace 102 using an application programming
interface (API) available from the operator of the electronic
marketplace 102. The API may define system calls, a function set
and/or libraries allowing interaction with external systems in
order to transfer data.
[0197] The monitoring module 303 continuously monitors the trade
activity of the one or more items the seller 101 offered for sale
to collect commerce information. The monitoring module 303 may
communicate with the system of the electronic marketplace 102
through the API available from the operator of the electronic
marketplace 102.
[0198] The monitored commerce information received through the
monitoring module 303 is forwarded to the analysis module 304. The
analysis module 304 includes or connected to a learning module
analyzing the commerce information in order to define and/or modify
the set of price setting rules for adjusting the price of the one
or more items. The analysis module includes or is connected to a
prediction module for prediction of one or more metrics associated
with the sale policy and/or prediction of one or more intermediate
metrics. The prediction of the sale policy may be based on
correlation with the prediction of the intermediate metric.
[0199] The analysis module 304 may continuously evolve to identify
the mechanisms and/or parameters and/or weights by which the system
of the electronic marketplace 102 ranks the offers of the sellers.
The set of price setting rules is then transferred to the state
machine module 302 which performs one or more actions according to
the set of price setting rules to adjust the price of the one or
more items in order to meet the goals set by the sale policy of the
seller 101.
[0200] Optionally, the user interface module 301 provides output
interfaces for example, visual interface, textual interface and/or
audio interface through which the seller 101 may track the trade
activity and/or adjust one or more of the rules which dictate the
course of action taken by the re-pricing system 100.
[0201] Reference is now made to FIG. 4 which is a schematic
illustration of an exemplary distributed re-pricing system for
dynamically re-pricing one or more items offered for sale on an
electronic marketplace, according to some embodiments of the
present invention. A distributed re-pricing system 400 includes a
central unit 401, for example, server, desktop computer and/or
laptop computer which communicates over a network 410, for example,
cellular and/or interact with a plurality of sellers 101 having a
plurality of client terminals 402, for example, Smartphone, tablet,
desktop computer and/or laptop computer. The system 100 may be
distributed among one or more processing units, for example the
central unit 401 and/or the one or more client terminals 402. The
central unit 401 has a processing unit which is capable of
executing software program instructions executes a software program
comprising for example of the state machine module 302, the monitor
module 303 and/or the analysis module 304. The central unit 401
executing the re-pricing software application communicates with the
electronic marketplace 102 over the network 410 to monitor the
price of the one or m ore items and/or to monitor commerce
information relating to the one or more items. The seller 101 using
the client terminal 402 interacts with the re-pricing software
application executed on the central unit 401 through a user
interface, for example the user interface module 301 which is
executed on the client terminal 402. The user interface module 301
executed on the client terminal 402 may be implemented through a
software application program executed on the client terminal 402
and/or through a web based service which is accessed from the
client terminal 402 using a web browser and/or a similar web access
application.
[0202] Optionally, the re-pricing software application is executed
on the client terminal 402 which has access to the electronic
marketplace 102 over the network 410 without going through the
central unit 401. In this case the entire re-pricing system is
executed on the client terminal 402.
[0203] Some embodiments of the present invention, are presented
herein by means of an example, however the use of this example does
not limit the scope of the present invention in any way.
[0204] A numeric example for a re-pricing process such as the
process 200 performed by a re-pricing system such as the system 100
is presented herein. The seller 101 (denoted S) is offering an item
(denoted P) for sale on an online marketplace, such as the
electronic marketplace 102 where additional sellers (S1, S2, S3)
are listed for the same product, each listing having a price.
Current Prices and Top Rank Rates of Each Offer of the Sellers are
as Follows:
[0205] S: $11.50, 15%
[0206] S1: $10, 35%
[0207] S2: $11, 50%
[0208] S3: $11, 0%
Objectives & Settings:
[0209] Floor price of item P by the seller 101 S=10$
[0210] Ceiling price of item P by the seller 101 S=20$
[0211] Target top rank rate=50%
[0212] Aggressiveness level=50% [0213] Goal: Reach 50% top rank
rate, with pricing as high as possible--but within the
floor/ceiling, price boundaries, as fast as possible considering
aggressiveness level of 50%.
Algorithm Execution:
[0213] [0214] 1) System collects data points regarding the current
pricing and ranking for item P. [0215] 2) System organizes sellers
into groups as follows: [0216] G1={S} [0217] G2={S1, S2} [0218]
G3={S3} [0219] After grouping the sellers and analyzing the lowest
pricings of the groups, system suggests an initial price of $10.85.
[0220] Setting the initial price of the item is performed, for
example, as presented by the exemplary algorithm for setting the
initial price of an item. [0221] 3) At this stage, no further
adjustment is made to the price, since historical pricing/ranking
information is insufficient. System sets the initial price ($10.85)
for the item on the electronic marketplace 102. [0222] 4) The
system repeatedly samples the electronic marketplace 102 and
collects commerce information, including, for example, prices of
offers and/or ranking of offers. The system continuously tracks the
differences in price between the seller 101 S compared with the
competitor sellers S1, S2, S3 and also compared to other groups.
The resulting top rank rate of the seller 101 S in accordance to
the price differences is also monitored. [0223] 5) During the trade
the system concludes that setting the price of the item P to $10.85
will result in 20% of top rank rate. Setting the price of the item
P to $10.85 means maintaining price differences of $0 between the
seller 101 S and G1, $0.85 between the seller 101 S and G2 and
+$0.15 between the seller 101 S and G3. [0224] 6) As the objective
of the process is top rank rate of 50%, the system will continue
reducing the price until the objective is reached. In case the
objective is not reached and the price exceeds the bottom price
boundary, the number of samples required for determining the impact
of the change, before applying further changes, depends on the
aggressiveness level. For this example, the aggressiveness level is
set to 50%, so 4 samples are required. Additionally, the price
amount unit (step) to be inducted or deducted from the price is a
function of the competitor sellers S1, S2, S3 pricings and the
aggressiveness level. In this case, the price amount unit is set to
$0.05. [0225] 7) Eventually, after several re-pricing iterations it
is discovered that the price of $10.75 brings the seller 101 S to
the desired top rank ratio (50%). [0226] 8) If during the trade,
conditions change, for example, the sellers S1, S2 and/or S3 adjust
their offers pricing, top rank rate of the seller 101 S changes
and/or new offers are made by additional competitor sellers, the
system will repeat the above process to find a new optimal price
that will meet the objectives of the seller 101 S.
[0227] Exemplary user interface such as the user interface 101 of
an exemplary system such as the re-pricing system 100 are provided.
The example provides screen captures of the user interface such as
the user interface 101 of a re-pricing system such as the
re-pricing system 100. Through the user interface 101 the seller
101 may be presented with commerce information and/or adjust the
recommended rules for re-pricing the one or more items. The screen
captures are presented in a user friendly graphical manner for
simple of use and comprehension.
[0228] Reference is now made to FIG. 5 which is a screen capture of
an exemplary overview screen of item sales as presented by an
exemplary user interface of an exemplary re-pricing system,
according to some embodiment of the present invention. The screen
capture 500 presents to the seller 101 an overview of the commerce
information relating to the one or more items over the past 24
hours, for example, overall sale transactions, overall sales value,
overall profit and/or overall profit margin. The overview may be
set by the seller 101 to present commerce information at a
plurality of levels, for example, one or more items, product line,
product category and/or portfolio. Selection of the level of
products to be presented is done through a selection box available
by the user interface module.
[0229] Reference is now made to FIG. 6 which is a screen capture of
an exemplary status screen of an item with respect to competition
as presented by an exemplary user interface of an exemplary
re-pricing system, according to some embodiment of the present
invention. The screen capture 600 presents to the seller 101 an
overview of an exemplary buy box distribution for the one or more
items offered on the Amazon Marketplace. The buy box represents the
default seller that is selected by the system of the marketplace
102. The default seller is the seller 101 whose offer for the one
or more items received the highest ranking. The presented
information relates to the offer of the one or more items and may
include, for example, the share the offer took in the overall buy
box transactions, trends identified during trade activity,
inventory shortage and/or inventory risk with respect to sales
volume. In addition the screen capture 400 may include additional
information, for example, information relating to non-competitive
and/or non-selling offers and/or alerts generated during the trade
activity to inform the seller 101 of specific events.
[0230] Reference is now made to FIG. 7 which is a screen capture of
an exemplary trends overview as presented by an exemplary user
interface of an exemplary re-pricing system, according to some
embodiment of the present invention. The screen capture 700
presents to the seller 101 overview of trends of a plurality of
items organized as a table with multiple entries, each entry
presents a different item. The table describes a plurality of
commerce information items, for example, item identifier, item
name, buy box price range, buy box share range and/or number of
sale transactions.
[0231] Reference is now made to FIG. 8 which is a screen capture of
an exemplary trend summary of an item buy box share as presented by
an exemplary user interface of an exemplary re-pricing system,
according to some embodiment of the present invention. The screen
capture 800 presents to the seller 101 an overview of a trend of
buy box share won by an offer of the seller 101 on the electronic
marketplace 102 over a pre-define period. The time period over
which the trend information is presented may be adjusted by the
seller 101.
[0232] Reference is now made to FIG. 9 which is a screen capture of
an exemplary product trend summary of an item buy box price as
presented by an exemplary user interface of an exemplary re-pricing
system, according to some embodiment of the present invention. The
screen capture 900 presents to the seller 101 an overview of a
trend of an item buy box price on the electronic marketplace 102
over a pre-define period. The time period over which the trend
information is presented may be adjusted by the seller 101.
[0233] Reference is now made to FIG. 10 which is a screen capture
of an exemplary alerts summary screen as presented by an exemplary
user interface of an exemplary re-pricing system, according to some
embodiment of the present invention. The screen capture 1000
presents to the seller 101 an overview of alerts generated in
response to a plurality of pre-defined events with respect to
offers on the electronic marketplace 102. The events may be set
through default settings of the re-pricing system 100 and/or the
alerts may be set by the seller 101. The alerts are associated with
an item and may be prioritized in a severity level and may include
additional information, for example, type of alert event, number of
transactions made with respect to the item, inventory level and/or
recommendation for actions in response to the alert. The seller 101
may click on one or more of the presented items to receive
additional information relating to the alert event. The seller 101
may adjust the number of alerts to be presented on screen.
[0234] Reference is now made to FIG. 11 which is a screen capture
of an exemplary commerce information summary of items sales on a
selected channel as presented by an exemplary user interface of an
exemplary re-pricing system, according to some embodiment of the
present invention. The screen capture 1100 presents to the seller
101 an overview of items sales on a selected channel (electronic
marketplace 102), for example, Amazon Marketplace and/or eBay. The
commerce information may include, for example, commerce information
graphs, profit information graphs, profit margin information graph,
overall sales, overall profit, average profit margins and/or
overall orders made to the item. The seller 101 may adjust the
number of items to be presented on screen. In addition top products
are presented from various perspectives, for example sales
perspective and/or profit margin perspective. The Seller may set
the time period for which the information is presented.
[0235] Reference is now made to FIG. 12 which is a screen capture
of continuation of an exemplary commerce information summary of
items sales on a selected channel as presented by an exemplary user
interface of an exemplary re-pricing system, according to some
embodiment of the present invention. The screen capture 1100 is a
continuation of screen capture 1200. The screen capture 1010 may
include, for example, overall number of orders, percentage of
orders made to the seller 101 out of all orders made for the items
and/or the gross value of the merchandise. In addition top products
are presented from various perspectives, for example, orders
perspective and/or gross merchandise volume (GMV) perspective. The
Seller may set the time period for which the information is
presented.
[0236] Reference is now made to FIG. 13 which is a screen capture
of an exemplary performance overview of item as presented by an
exemplary user interface of an exemplary re-pricing system,
according to some embodiment of the present invention. The screen
capture 1300 presents to the seller 101 the performance of a
plurality of items offered for sale on the electronic marketplace
102. Performance information may include, for example, number of
orders, sales volume, profit value and/or profit margin. The seller
101 may set the time period over which the performance of the items
is presented.
[0237] Reference is now made to FIG. 14 which is a screen capture
of price and buy box tab of an exemplary product analysis summary
as presented by an exemplary user interface of an exemplary
re-pricing system, according to some embodiment of the present
invention. The screen capture 1400 presents to the seller 101, for
example, average buy box price for a specific item and/or average
price in which the specific item was offered by the seller 101. The
seller 101 may set the time period over which the performance of
the items is presented.
[0238] Reference is now made to FIG. 15 which is a screen capture
of sales and orders tab of an exemplary product analysis summary as
presented by an exemplary user interface of an exemplary re-pricing
system, according to some embodiment of the present invention. The
screen capture 1500 presents to the seller 101 commerce information
as distributed over a period, for example, sales distribution over
a time period, profit distribution over a time period and/or orders
made over a time period. The seller 101 may set the time period
over which the performance of the items is presented.
[0239] Reference is now made to FIG. 16 which is a screen capture
of product contribution tab of an exemplary product analysis
summary as presented by an exemplary user interface of an exemplary
re-pricing system, according to some embodiment of the present
invention. The screen capture 1600 presents to the seller 101 the
contribution of a specific item to the overall revenues of the
portfolio of the seller 101, for example, total sales value, total
profit value, percentage of the specific item orders out of the
portfolio orders and/or percentage of revenues of the specific item
out of the GMV of the seller 101. The seller 101 may set the time
period over which the performance of the items is presented.
[0240] Reference is now made to FIG. 17 which is a screen capture
of an exemplary editing screen of an exemplary user interface for
inserting an item to an exemplary re-pricing system, according to
some embodiment of the present invention. The screen capture 1700
is used by the seller 101 to edit an item offered for sale on the
electronic marketplace 102 using the re-pricing system 100. Editing
may include inserting a new item offered for sale into the
re-pricing system 100. The screen provides the seller 101 a
plurality of options for characterizing the item and/or the arena
in which the item is offered, for example, item costs, inventory
level, shipping costs, desired electronic marketplace, electronic
marketplace fee, minimum profit (in percents), minimum profit (in
currency) and/or price boundaries (floor price and/or ceiling
price). The price boundaries are typically set by the re-pricing
system 100, however the seller 101 may override them.
[0241] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of code, which comprises one or more
executable instructions for implementing the specified logical
function(s). It should also be noted that, in some alternative
implementations, the functions noted in the block may occur out of
the order noted in the figures. For example, two blocks shown in
succession may, in fact, be executed substantially concurrently, or
the blocks may sometimes be executed in the reverse order,
depending upon the functionality involved. It will also be noted
that each block of the block diagrams and/or flowchart
illustration, and combinations of blocks in the block diagrams
and/or flowchart illustration, can be implemented by special
purpose hardware-based systems that perform the specified functions
or acts, or combinations of special purpose hardware and computer
instructions.
[0242] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
[0243] It is expected that during the life of a patent maturing
from this application many relevant systems, methods and computer
programs will be developed and the scope of the term commerce
information and price is intended to include all such new
technologies a priori.
[0244] As used herein the term "about" refers to .+-.10%.
[0245] The terms "comprises", "comprising", "includes",
"including", "having" and their conjugates mean "including but not
limited to". This term encompasses the terms "consisting of" and
"consisting essentially of".
[0246] The phrase "consisting essentially of" means that the
composition or method may include additional ingredients and/or
steps, but only if the additional ingredients and/or steps do not
materially alter the basic and novel characteristics of the claimed
composition or method.
[0247] As used herein, the singular form "a", "an" and "the"
include plural references unless the context clearly dictates
otherwise. For example, the term "a compound" or "at least one
compound" may include a plurality of compounds, including mixtures
thereof.
[0248] The word "exemplary" is used herein to mean "serving as an
example, instance or illustration". Any embodiment described as
"exemplary" is not necessarily to be construed as preferred or
advantageous over other embodiments and/or to exclude the
incorporation of features from other embodiments.
[0249] The word "optionally" is used herein to mean "is provided in
some embodiments and not provided in other embodiments". Any
particular embodiment of the invention may include a plurality of
"optional" features unless such features conflict.
[0250] Throughout this application, various embodiments of this
invention may be presented in a range format. It should be
understood that the description in range format is merely for
convenience arid brevity and should not be construed as an
inflexible limitation on the scope of the invention. Accordingly,
the description of a range should be considered to have
specifically disclosed all the possible subranges as well as
individual numerical values within that range. For example,
description of a range such as from 1 to 6 should be considered to
have specifically disclosed subranges such as from 1 to 3, from 1
to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as
well as individual numbers within that range, for example, 1, 2, 3,
4, 5, and 6. This applies regardless of the breadth of the
range.
[0251] Whenever a numerical range is indicated herein, it is meant
to include any cited numeral (fractional or integral) within the
indicated range. The phrases "ranging/ranges between" a first
indicate number and a second indicate number and "ranging/ranges
from" a first indicate number "to" a second indicate number are
used herein interchangeably and are meant to include the first and
second indicated numbers and all the fractional and integral
numerals therebetween.
[0252] It is appreciated that certain features of the invention,
which are, for clarity, to described in the context of separate
embodiments, may also be provided in combination in a single
embodiment. Conversely, various features of the invention, which
are, for brevity, described in the context of a single embodiment,
may also be provided separately or in any suitable subcombination
or as suitable in any other described embodiment of the invention.
Certain features described in the context of various embodiments
are not to be considered essential features of those embodiments,
unless the embodiment is inoperative without those elements.
[0253] Although the invention has been described in conjunction
with specific embodiments thereof, it is evident that many
alternatives, modifications and variations will be apparent to
those skilled in the art. Accordingly, it is intended to embrace
all such alternatives, modifications and variations that fall
within the spirit and broad scope of the appended claims.
[0254] All publications, patents and patent applications mentioned
in this specification are herein incorporated in their entirety by
reference into the specification, to the same extent as if each
individual publication, patent or patent application was
specifically and individually indicated to be incorporated herein
by reference. In addition, citation or identification of any
reference in this application shall not be construed as an
admission that such reference is available as prior art to the
present invention. To the extent that section headings are used,
they should not be construed as necessarily limiting.
* * * * *