U.S. patent application number 14/075773 was filed with the patent office on 2014-10-23 for system and method for providing relative price point incentives based upon prior customer purchase behavior.
This patent application is currently assigned to Catalina Marketing Corporation. The applicant listed for this patent is Catalina Marketing Corporation. Invention is credited to Ryan Carr, Gary M. Katz, Angela Clemens Kimes.
Application Number | 20140316874 14/075773 |
Document ID | / |
Family ID | 28038565 |
Filed Date | 2014-10-23 |
United States Patent
Application |
20140316874 |
Kind Code |
A1 |
Katz; Gary M. ; et
al. |
October 23, 2014 |
SYSTEM AND METHOD FOR PROVIDING RELATIVE PRICE POINT INCENTIVES
BASED UPON PRIOR CUSTOMER PURCHASE BEHAVIOR
Abstract
The invention provides a system, computer program, and method
for generating price point based incentives comprising determining
a category specific price point (620) associated with a dominant
competitive brand and a client brand; and generating an incentive
(630) for said client brand based upon said price point and an
anticipated price differential (640).
Inventors: |
Katz; Gary M.; (Northbrook,
IL) ; Carr; Ryan; (South Elgin, IL) ; Kimes;
Angela Clemens; (St. Louis, MO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Catalina Marketing Corporation |
St. Petersburg |
FL |
US |
|
|
Assignee: |
Catalina Marketing
Corporation
St. Petersburg
FL
|
Family ID: |
28038565 |
Appl. No.: |
14/075773 |
Filed: |
November 8, 2013 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
10498003 |
Jun 14, 2004 |
8583474 |
|
|
PCT/US02/06861 |
Mar 7, 2002 |
|
|
|
14075773 |
|
|
|
|
Current U.S.
Class: |
705/14.23 |
Current CPC
Class: |
G06Q 30/0207 20130101;
G06Q 30/0222 20130101; G06Q 30/0283 20130101; G06Q 30/0226
20130101; G06Q 30/02 20130101; G06Q 30/0238 20130101; G06Q 30/0273
20130101 |
Class at
Publication: |
705/14.23 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A computer implemented method of generating price point based
incentives comprising: determining a category specific price point
associated with a dominant competitive brand and a client brand;
and generating an incentive for said client brand based upon said
price point.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] This invention relates to the field of marketing. More
specifically, this invention relates to the field of marketing
consumer goods.
[0003] 2. Discussion of the Background
[0004] Point of sale (POS) computer systems function to account for
transactions at POS terminals. POS systems typically include a
database management system including a product price look-up table
which is accessed by the POS terminal during a transaction. POS
systems retrieve to the POS terminal data defining the prices of
items for which a consumer requests purchase. POS systems total the
costs for all of those items. POS systems log the purchase of the
items. Some POS systems log the purchase of items in transaction
records also including a unique customer identification (CID)
associating that CID with the items purchased, the price of the
items purchased, and the quantity of each product item purchased,
the date of purchase, and the lane (POS terminal identification) in
which the purchase occurred.
[0005] The present inventors recognize that the data stored in some
POS computer systems can beneficially be used to determine purchase
incentives that would induce customers to purchase certain
products, as indicated below.
SUMMARY OF THE INVENTION
[0006] It is an object of this invention to determine purchase
incentives sufficient to induce customers to purchase products upon
which the incentives are offered.
[0007] It is another object of this invention to provide those
purchase incentives to the customers.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] These and other objects of the invention are explained in
more detail below with reference to the following figures.
[0009] FIG. 1 is a schematic showing a network computer system for
performing the present invention;
[0010] FIG. 2A-2C each schematically show a data structure for
records in a central database of a central computer system of the
invention;
[0011] FIG. 3 is a high level flowchart showing high level steps of
the invention;
[0012] FIG. 4 is a high level flowchart showing steps providing
incentives to customers;
[0013] FIG. 5 is a medium level flowchart showing steps for
analyzing transaction data for step 330 in FIG. 3; and
[0014] FIG. 6 is a medium level flowchart showing steps for
generating incentive data for step 340 in FIG. 3.
DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION
[0015] FIG. 1 shows a computer network system 1. System 1
preferably includes central computer system 11, central database
12, Internet 13, retailer1 computer system 14, retailer1 database
15, retailer2 computer system 16, retailer2 database 17, retailer3
computer system 18, and retailer3 database 19.
[0016] In addition, system 1 may include customer computer 5,
manufacturer computer system 6, and manufacturer database 7.
[0017] While shown with one central computer system and three
retailer computer systems, the present invention may also function
with either a single computer system performing all of the
functions that are specified below or the single central computer
system 11 and a single retailer computer system (one of 14, 16,
18).
[0018] Each of the noted computer systems 6, 11, 14, 16, and 18
preferably include hardware and software enabling them to exchange
data via the Internet 13. Each of the aforementioned computer
Systems is indicated as connected to Internet 13 via a
communication line. The communication lines may be electrical,
optical, or wireless based lines. The data communication need not
be over the Internet.
[0019] Each of the databases 7, 12, 15, 17, and 19 preferably are
relational computer database systems in which data is stored in
sets of fields associated with one another, referred to as records,
and in which data of the same type, e.g., field, is stored in a
common format and in association with a field name. Field names are
optional. Each set of records and fields having the same types of
associations is called a table. Each of the aforementioned
databases may have a plurality of tables. Each of those tables may
include one or more fields the concatenation of which provides a
unique identification of that record in that table. Those unique
identifications are referred to as primary keys. Each of the tables
may include a field which is a primary key in a different table,
which field is referred to in the subject table as a foreign key.
Preferably, each of the database management systems includes
associated software enabling a plurality of software functions to
be performed on the records and tables in the database, including
sorting, summing, and selecting, preferably based upon the
structured query language (SQL) standard database language.
[0020] Each of the aforementioned computer systems preferably
includes at least one digital processor and hardware for inputting
and outputting data and inputting control signals.
[0021] Retailer databases 15, 17, and 19 are each representative of
databases of a retailers POS systems. Each one of those databases
preferably includes transaction records for transactions recorded
by the POS terminals (lanes), in the corresponding retail store or
stores. In this regard, each of the retailer computer systems 14,
16, 18, may represent the computer POS system for a single store or
a plurality of associated stores.
[0022] Central database 12 stores transaction data from various
ones of the retailers. The transaction data stored in central
database 12 corresponds to transaction data stored in each one of
the retailer databases 15, 17, 19.
[0023] FIGS. 2A-2C illustrate alternative data structures of
transaction data records stored in the central database 12.
[0024] FIG. 2A shows record format 200 including records
illustrated by rows including field name record 210, data record
211, and data record 212. Data structure 200A also shows columns
illustrating data fields including the store ID field 213, the
customer ID field 214, the transaction date field 215, the lane ID
field 216, the UPC1 field 217A, the UPC1 price field 217B, the UPC2
field 218A, and the UPC2 price field 218B. In addition, the data
structure 200A would include additional pairs of UPC and price
fields for all UPCs corresponding to products sold in the
corresponding store or stores controlled by one of the retailer
computer systems 14, 16, 18. The UPC1 field stores the number of
product items having UPC code UPC1 in the transaction associated
with the data record. The UPC1 price field stores a price
associated with the UPC1 product items purchased in the
transaction; either the per unit price, the average per unit price,
or the total price for all product items having UPC code UPC1.
[0025] FIG. 2B shows data structure 200B including the same columns
213, 214, 215, and 216 as in FIG. 2A. The data structure in 200B
differs from the data structure 200A in the manner in which the
product transaction data is stored. Specifically, field 217C stores
in a single field a UPC code of a first product purchased, the
number of units of that product purchase, and an associated price
for the purchase of the units of that product. Field 218C stores
UPC code, number of units purchases, and associated price for the
next product contained in the same transaction record. Additional
fields would contain UPC codes, number of units purchased, and
associated price for each of the remaining products contained in
the transaction record Elements 210B, 211B, and 212B, correspond to
elements 210, 211, and 212, differing only in the naming of the
field headings 217C, 218C, and corresponding data for product items
contained in the transactions.
[0026] FIG. 2C shows data structure 200C presenting yet another
means in which the same data shown in the data structures 200A and
200B can be stored. In data structure 200C all data for products
purchased, the number of units of the products purchased, and
associated prices are stored in a data delimited form in the single
data field 217A. In this format, the triplet of product UPC code,
number of units purchased, and associated price are separated from
the next triplet by a field delimiter, shown here to be a semi
colon, and each member of the triplet is separated from one another
by another delimiter, shown here to be a comma.
[0027] Data records 200A-200C illustrate alternative data
structures in which transaction data can be stored. One skilled in
data base design will recognize that there are other data
structures that may be used to store the same data. In addition,
the TransDate field may contain both date and time of day data.
[0028] Embodiments of the method of the invention are specified in
connection with FIGS. 3-6.
[0029] The following definitions are useful in specifying the
methods of the invention.
[0030] A product or service category as used herein means a group
of products or services that have a similar set of characteristics
such that they may be considered to provide consumers
interchangeable utility. Examples of categories of products are
tomato sauce, cold cereal, canned beans, and deodorants.
[0031] A purchase cycle is defined herein to mean the average or
medium period of time between purchases of products associated with
a CID, or a store D and a CID. That is, the average time between
purchases of goods associated with either the same consumer or
consumers that use the same CID, such as members of a household or
family.
[0032] A category specific purchase cycle is defined herein to mean
the average, median, or range of time centered about either the
average or median times between purchase of goods in a specified
category in association with a CID, or a store ID and a CID. The
category specific purchase cycle is a prediction of the time
between when a consumer purchases products from the specified
category.
[0033] A category specific price point is defined herein to mean a
difference in price between two brands of products in the same
category at which purchases (by consumers) associated with a CID,
or with a store ID and a CID are statistically equally likely to be
for either of the two brands of products.
[0034] FIG. 3 shows steps involved in recording and analyzing
transaction data.
[0035] In step 310, a retailer's POS system records transaction
data. The transaction data typically includes a customer ID, a
transaction date and time, a lane specification, and the UPC codes,
number of units of that UPC code that are contained in the
trasaction, and the associated price, for each product item in a
transaction. Preferably, the transaction record includes a CID.
[0036] This invention relates to those transaction records which do
include a CID. The transaction record may include a store ID.
However, store IDs may be associated with records received by the
central computer system 11 when the central computer system
receives records from a specified retailer computer system, such as
retailer computer systems 14, 16, or 18.
[0037] In step 320, a retailer computer system transmits
transaction data to the central computer system 11. The central
computer system 11 stores that transaction data in the central data
base 12.
[0038] In step 330, the central computer system 11 analyzes the
transaction data. Results of that analysis include records which
contain either a CID or a store ID and a CID. Each record also
includes data indicating at least one category and an associated
category specific price point. Each record preferably also includes
at least one of data indicating a purchase cycle and a category
specific purchase cycle for the specified category.
[0039] In step 340, the central computer system 11 generates
incentive data. The incentive data includes data associated with a
CID, and preferably data specifying discounts contingent upon the
purchase of specified products. Typically, the incentive data is
also stored in association with at least one of a store ID and a
retailer chain ID.
[0040] In step 350, the retail computer system transmits the
incentive data relating to transaction data from a specified retail
store or association of stores to the corresponding retailer
computer system 14, 16, 18 either for one or a plurality of CIDs.
Preferably, the corresponding retail computer system stores the
incentive data in the corresponding retailer database 15, 17, or
19. However, if the CID relates to a transaction in process, the
data may used by the CPU of the retailer computer system or the
CPU, if any, of a smart POS terminal, in processing that
transaction. Thus, in some embodiments, the incentive data need not
be stored in the database 15, 17, 19.
[0041] FIG. 4 shows steps involved in providing incentives to the
customer during a transaction in a retail store. Alternatively, the
incentives could be provided via postal mail, via email, or via any
other communication medium.
[0042] In step 400, the retail computer system 14, 16, or 18,
records a CID, preferably at a POS terminal. That recording may
occur during a customer's transaction in which the customer is
purchasing products. However, the retail computer system may record
the customers' CID at any time. For example, the retailer's
computer system may record the CID in response to receipt of that
CID transmitted from the customer computer 5 over the Internet 13
to the retailer computer system. In addition, one or more CIDs may
be transmitted by the manufacturer computer system 6 to any one of
the retailer computer systems 14, 16, 18.
[0043] Furthermore, either the manufacturer computer system 6 or
the customer computer 5 may transmit one or more CIDs and one or
more retailer computer system identifications to the central system
11. In response, the central computer system 1 may generate
incentive data and transmit the incentive data to the corresponding
retailer computer system, or to the manufacturer computer system 6.
In addition, the central computer system 11 may transmit the
incentive data for a specific consumer (as indicated for example by
a CD associated with a network address for the customer's computer
5) to that consumer's customer computer 5. The incentive data may
specify, or whichever computer system to which that data is sent
may contain means for, printing that data in either or both of
machine readable and human readable form. That is, the incentive
data may be printed or stored in the form of vouchers or coupons
providing discounts to the specified CID for purchases of one or
more specified products.
[0044] In step 420, the retailer computer system controlling the
POS terminal at which the CID has been recorded, typically but not
necessarily in association with a purchase transaction at the POS
terminal, determines whether the CID qualifies for incentives.
[0045] In step 421, assuming the answer to the determination in
step 420 was yes, the retailer computer system provides incentive
to the customer. The POS terminal or an associated device generates
the incentive so that it can be provided to the person holding the
CID. It may be the central computer system 11 instead of a retailer
computer system which performs step 420 during the customer's
transaction.
[0046] In step 422, the retailer computer system completes the
customer's transaction at the point of sale terminal and awaits the
next transaction. This step involves the storing of the customer's
transaction record for the current transaction.
[0047] FIG. 5 shows steps involved in analyzing data. In overview,
FIG. 5 shows a flowchart including four nested loops. The outermost
loop involves retrieving from memory the next customer ID. The
intermediate loop involves retrieving from memory the next
category. The two inner loops involve determining whether data
associated with a CID indicates that the CID corresponds to
category switcher purchase behavior and, if category switcher
behavior exists, whether the purchase data meets filters indicating
that the incentives for the associated CID should be price
based.
[0048] In step 510, the central computer system 11 retrieves the
next customer ID.
[0049] In step 520, the central computer system 11 receives the
next product category.
[0050] In step 530, the central computer system determines whether
the CID's transaction for products in the current category
retrieved in step 520 meets category switcher criteria. If the
CID's transaction data for that category does not meet category
switcher criteria, the processing loops back to step 520 and
retrieves the next category. If the CID's transaction data for that
category does meet switcher criteria for that category, processing
continues to step 540.
[0051] A client brand is defined herein to mean a brand of a
manufacturer associated with an incentive program for execution by
the central computer system 11.
[0052] A competitive brand of a specified category is defined
herein to mean a brand of a product associated with that category
made by other than the manufacturer of the client brand. Typically,
the specified manufacturer is an entity requesting services from
the entity owning the central computer system 11 disclosed herein.
For example, the specific manufacturer may be a manufacturer
requesting the owner of the central computer system 11 to perform a
customer category specific price point marking program as disclosed
in this application.
[0053] A dominant competitive brand in a specified category is
defined herein to mean a brand, other than the client brand, for
which there are associated with the current CID the most purchases
(as measured either in number of units purchased or dollar value of
purchases or number of times a shopper goes to a store and buys in
the specified category, referred to herein as category trips) in
the product category over a specified period of time. Preferably,
that specified period of time is at least two months, more
preferably at least six months and more preferably at least about
one year. Preferably, that specified period of time extends up to
the present time, or to within about one, two, or three weeks of
the present time.
[0054] Step 530 involves the sub-steps of (1) determining the
dominant competitive brand in the specified category for the
current CID and (2) determining whether the CID's purchase behavior
with respect to the dominant competitive brand and the client brand
or brands meets category switcher criteria. The client brand may be
specified. The central database 12 may store a table or file
listing the product brands for each one of a plurality of product
categories.
[0055] Preferably, the dominant competitive brand for the specified
category is defined to be the brand of product other than the
client brand for which either the largest number of units or the
largest number of dollars of purchases exists in the transaction
records associated with the current customer ID being analyzed in
step 530. While the dominant competitive brand and the client brand
definitions refer specifically to step 530, the method of analyzing
the data need not be limited to algorithm specifically shown and
discussed with respect to FIG. 5. For example, the loops retrieving
CIDs and categories can be inverted without affecting the results
of the analysis shown in FIG. 5.
[0056] In step 540, central computer system 11 determines whether
the CID's transaction data meets certain filter criteria. If the
CID's transaction data for the specified category does meet the
filter criteria, processing returns to step 520 and the next
category is retrieved.
[0057] Filter criteria are criteria indicating that providing to a
customer holding a card storing the CID incentives based upon price
point data would be ineffective, either because of lack of price
point sensitivity or because the incentives would interfere with a
sponsoring manufacturer's anticipated sales. Thus, one filter is
criteria indicating lack of price sensitivity between the dominant
competitive and client brands. Another filter is criteria
indicating that the dominant competitive and client brands for the
specified category are both made by the same manufacturer. That is,
customer loyalty to a manufacturer for a specified category is a
filter which returns processing from step 540 back to step 520 to
retrieve the next category. Another filter is data showing the
customer's tendency to purchase different brands in the same
product category during the same purchase. This purchase behavior
shows a lack of price point sensitivity. Purchasing in multiple
brands for products in the same category during the same purchase
transaction is a filter which returns processing from step 540 back
to step 520 to retrieve the next category.
[0058] In step 540, if no filter criteria are met, processing
proceeds to step 550.
[0059] In step 550, central computer system 11 determines the
category specific price points between the dominant competitive and
client brands for the CID and specified category. For example, the
CID's transaction data in the hand soap category may indicate that
the dominant competitive brand is Dial brand soap. The client brand
may be Ivory brand soap. The CID's transaction data for the hand
soap category may indicate that the customer purchases Dial soap
whenever Dial soap is no more than 20 cents more expensive than
Ivory soap, per 4 ounce bar of soap. In such an example, the price
point is 20 cents favoring Dial over Ivory. Preferably, the period
of time extends from the current time back from between a few
months, preferably greater than six months, and preferably up to
one year.
[0060] Price point determinations are preferably based upon price
data for the dominant competitive and client brands in the
specified category in a specified store at the time of each
transaction being analyzed in step 530. Price data for all products
is preferably obtained from the transaction records transmitted
from the retail store to the central computer system 11. The data
for the price of both the dominant competitive and the client
brands in a specified store at a specified time may be determined
by the central computer system 11 by reviewing data for additional
customer records corresponding to purchases made in the
corresponding retail store for both the dominant competitive and
client brands. Alternatively, price data for all product brands
sold in a store may be periodically or intermittently transmitted
from each retailer computer system 14, 16, 18 to the central
computer system 11. In addition, the central computer system 11 may
use any of the foregoing sources of product price data at various
times to predict product brand price variations and future prices
in a specific retail store as a function of time.
[0061] In step 560, the central computer system 11 determines the
category specific purchase cycle. For example, the central computer
system 11 might determine that the CID associated with a specified
customer and a specified retail store purchases hand bar soap most
probably in the range of two to four weeks after the most recent
prior purchase. In this example, the purchase cycle in the hand bar
soap category for this specified customer ID would be set at
between two and four weeks, preferably at three weeks.
[0062] FIG. 6 illustrates steps involved in the central computer
system 11 in generating incentive data.
[0063] In step 610, the central computer system retrieves the next
pair of CID and category.
[0064] In step 620, the central computer system 11 determines for
the retrieved CID and category whether price point and purchase
cycle data are present. If no price point and purchase cycle data
is present, processing returns to step 610 to retrieve the next
CID) and category combination. If price point and purchase cycle
data exist for the current CID and product category, processing
proceeds to step 630.
[0065] In step 630, the central computer system 11 anticipates the
price differential between the dominant competitive and client
brand in the specified category at a time indicated by the purchase
cycle data when the customer associated with the current customer
ID is likely to next purchase products in the specified category.
The anticipation may of course be based upon the current or recent
past price differential. The anticipation may be based upon
interpolating using annual cyclical price variations, or by
extrapolating based upon recent trends (less than one year) in
prices. Extrapolation for example may employ linear regression
analysis.
[0066] In step 640, the central computer system 11 determines an
incentive value based upon the price differential anticipated in
step 630 and the price point determined in step 550. The incentive
is preferably a discount contingent upon purchase of the client
brand during a specified period of time. In addition, in step 640,
the central computer system may also determine when or during what
period of time to offer the customer associated with the current
specified CID the foregoing incentive. The price point based
incentives for a category may be offered from the current time
until the end of the calculated purchase cycle, at a time
corresponding to a few days around the mid point of the category
specific purchase cycle, or near the end of a category specific
purchase cycle.
[0067] The incentive determined in step 640 may be offered to the
customer either via transmission over the Internet to an address
associated with the CID, such as the customer's email address,
often associated with the customer computer 5, to a personal web
page (i.e., a web site address for which the file associated
therewith is programmed to display graphics or information
preselected by the person associated with the CID) associated with
a CID, to the retailer computer system 14, 16, or 18 associated
with the retailer ID and customer ID, or to the manufacturer
computer system 6. If the central computer system 11 transmits the
incentive data to one of the retailer computer systems 14, 16, 18,
then the retailer computer systems can provide the corresponding
incentive to the customer associated with the CD when the customer
next presents the CID at a POS terminal.
[0068] The invention also comprises a computer program product
storing programming to implement the steps of the invention.
[0069] Obviously, numerous modifications and variations of the
present invention are possible in light of the above teachings. It
is therefore to be understood that within the scope of the appended
claims, the invention may be practiced otherwise than as
specifically described herein.
* * * * *