HK1128796A - A shopping method and system - Google Patents

Description

Shopping method and system

Technical Field

The present invention relates to a shopping method and system for providing a shopping incentive (indulgent) to a shopper.

Background

It is often seen that marketers offer promotional offers to shoppers in bulk or targeted ways. Typically, the shopper does not accept the promotional offer. One key issue is the opportunity for special offers. It is difficult to get the right time for promotion even in the targeted specialization (for the right buyer); as the special offers may not be synchronized with the purchase needs. For example, if the consumer has purchased a product last week, she is unlikely to accept the promotional offer if the product usage period is two months. Thus, most promotional specials are suboptimal, resulting in inefficiencies in promotional planning, product ordering, and inventory management. For marketers, opportunity costs lie in the associated promotional costs, such as advertising costs, material costs, and promotional inventory costs. For retailers, a more desirable promotion will result in better sales profits. As for the shopper, the promotional benefits cannot be realized. In addition to the timing of the promotion, the shopper not only purchases a single product, but a basket of items. Ideally, the basket should be the most cost effective. The lack of understanding of the purchasing relationships in the baskets results in shoppers not enjoying the benefits of promotional specials, resulting in undesirable benefits and dissatisfaction.

Another consequence of the above is that marketers and retailers bombard shoppers with bulk lists of promotional specials, which leads to greater inefficiency because waste is very high. As a result, shoppers' baskets are rarely good deals; due to under-purchase and depreciation, shoppers may purchase elsewhere, resulting in overall system inefficiencies.

One known approach is to use past purchase history to lock shoppers to minimize waste, for example, to provide special offers to those who have previously purchased. However, the timing is still only guessable at best. Similarly, it is not possible to obtain the most ideal basket value. Thus, the shopper has no willingness to purchase more.

Another known method is to provide membership points, which can be converted into coupons. This approach provides the freedom to reduce the cost of purchasing shoppers by purchasing tickets/credits to them. However, this does not provide the best benefit with marketers, as the shopper's purchases do not match the marketer's goals. Since purchase patterns and habits are not considered in this general value providing method, the total basket value is still low.

Disclosure of Invention

It is an object of the present invention to provide a method and system for solving the above problems and providing an incentive to be more easily accepted by shoppers to increase the value and/or size of a shopping basket in a single purchase.

The present invention provides a shopping method for providing a stimulus to a shopper, comprising:

collecting data relating to products purchased by individual shoppers;

determining a core product category from the collected data and establishing a related product category related to the core product category;

creating a promotional special interest list related to the core product and related products; and

the shopper is provided with an incentive to purchase the product from the list.

By determining the core product and then establishing the relevant product categories, relevant items that meet the needs of the shopper are more properly determined so that special offers can be made based on the likelihood that a person will receive an incentive to purchase multiple products from the core product category and the relevant product categories. This in turn increases the value and/or size of the shopper's shopping basket in a single purchase. This is beneficial from a shopper's perspective, as shoppers gain better value of products purchased due to offered special interest incentives, and from marketers ' perspective, more products are sold, thereby increasing marketers ' profits. This can thus increase consumer satisfaction and marketers only pay for what shoppers accept, thereby reducing the discounts in typical bulk promotions due to premature purchasing of inventory.

Preferably, the method further comprises: determining a frequency of purchase of the shopper in core related products and product categories; and categorize the purchases into a promotional period frequency and recent purchases (repeat of recent) group, and create a promotional special offer list and provide incentives based on the period frequency and recent purchases group.

Preferably, the method further comprises determining the size, quantity and value of the products purchased for each core product category.

The method preferably further comprises collecting data relating to shoppers, including past purchase data and consumer profile data, including geographic demographic data, socioeconomic data, and information.

Preferably, the shopper data is obtained at least in part by requesting the data and information from the shopper as the shopper joins the affiliate program.

The incentive preferably comprises one or more of a coupon, flyer, coupon, promotional sample, point, lottery, and gift.

The present invention also provides a shopping system for providing a shopper with an incentive comprising:

a processor for analyzing data relating to products purchased by individual shoppers and for determining a core product category from the data in the database and a related product category relating to the core product category;

the processor is also configured to create a promotional special interest list associated with the core product and the associated product for presentation to the consumer; and

an output for providing an incentive to the shopper to purchase the associated product.

The output devices may include a card reader and printer that are located at a location for sale of the core product so that the device may be activated to generate an incentive for a shopper to purchase the associated product.

In an alternative embodiment, the output device may comprise only a printer.

In other embodiments, the output may be provided by manually distributing a list to shoppers who purchased products from the core product category, thereby providing an incentive to purchase the associated products.

The processor preferably collects data relating to shoppers, including past purchase data and consumer profile data, wherein the consumer profile data includes geographic demographic data, socioeconomic data, and information.

Preferably, the shopper data is obtained at least in part by requesting the data and information from the shopper as the shopper joins the affiliate program.

The incentive preferably comprises one or more of a coupon, flyer, coupon, promotional sample, point, lottery, and gift.

The processor is also preferably operable to determine the frequency of purchases by the shopper in the core related products and product categories, and categorize these purchases into promotional cycle frequency and recent purchase groupings, and create promotional special offers lists and provide incentives based on the cycle frequency and recent purchase groupings.

The processor also preferably determines the size, quantity and value of the products purchased for each core product category.

The present invention further provides a shopping method for providing a shopper with an incentive comprising:

determining a core product and related products related to the core product by analyzing purchases made by individual shoppers;

creating a promotional special interest list related to the core product and its related products based on the frequency of purchases of the core product by the shopper; and

if the shopper also purchases a product related to the core product, the list is output to the shopper to provide an incentive to the shopper who purchased the core product.

Preferably, the method further comprises analyzing data relating to products purchased by the individual shoppers to determine correlations, and removing from the defined core products and related products categories of related products that are below a predetermined point of correlation.

Preferably, the method further comprises determining a promotional cycle length based on the shopper's purchase cycle of the core product.

Preferably, the method further comprises determining recent purchases based on a period criterion and determining potential sizes or groupings of shoppers likely to purchase the core product within a predetermined period of time, and developing a promotional offer list based on the potential sizes and groupings of shoppers expected to purchase the center product.

The method preferably further comprises collecting data relating to shoppers, including past purchase data and consumer profile data, including geographic demographic data, socioeconomic data, and information.

Preferably, the shopper data is obtained at least in part by requesting the data and information from the shopper as the shopper joins the affiliate program.

The incentive preferably comprises one or more of a coupon, flyer, coupon, promotional sample, point, lottery, and gift.

The present invention further provides a shopping system for providing a shopper with an incentive comprising:

a processor for determining a core product and related products to the core product by analyzing purchases made by individual shoppers;

the processor is also for creating a promotional special interest list related to the core product and its related products based on the frequency of purchases of the core product by the shopper; and

an output that provides the list to the shopper to provide an incentive to the shopper for purchasing the core product if the shopper also purchases a product related to the core product.

The processor preferably analyzes data relating to products purchased by individual shoppers to determine correlations and removes from the defined core and related products categories of related products that are below a predetermined point of correlation.

The processor preferably determines the period length of the promotion based on the purchase period of the shopper's center product.

The processor preferably determines recent purchases based on a periodicity criterion and determines potential sizes or groupings of shoppers who are likely to purchase the core product within a predetermined period of time, and develops the promotional offer list based on the potential sizes and groupings of shoppers who are expected to purchase the center product.

Drawings

Preferred embodiments of the present invention will be described with reference to the accompanying drawings, in which:

FIG. 1 is a schematic diagram illustrating the concept of the present invention;

FIG. 2 is a block diagram of a system in which the present invention is implemented; and

fig. 3 is a flow chart illustrating a method of a preferred embodiment of the present invention.

Detailed Description

Referring to fig. 1, the concept of a preferred embodiment of the present invention is schematically illustrated. The essence of the preferred embodiment of the invention is that: a core product category and a related product category are determined, wherein the core product category is referred to as a center category and the related product category is referred to as a radial category for the purposes of the following description.

Referring to FIG. 1, data relating to products purchased by individual shoppers is collected and analyzed. This data can be collected through a checkout terminal that scans or collects data relating to products purchased by individual shoppers, and in which all products purchased by shoppers can be associated with a particular shopper.

The shopper can register to participate in the incentive program and provide some means for identifying the shopper so that the shopper can be identified when the consumer shops and pays for goods. This may be accomplished by a card containing an identification code or other data associated with the shopper, where the identification code is read by a checkout terminal. In an alternative embodiment, data related to existing affiliate plans can also be used to provide consumer information, such as shopper demographic data, shopping habit data, lifestyle data, and other data related to the shopper, which can serve as a basis for determining products that the shopper may be interested in. We disclose such a scheme in International patent application Nos. PCT/SG2005/000185 and PCT/SG 2005/000224. The contents of both international applications are incorporated into this specification by reference.

The center product and the radial product are determined based on the shopper's actual purchases. The contents of the shopping baskets of the individual shoppers are analyzed and the sum of the baskets of all shoppers will be analyzed to determine the center product and the products in the high correlation category that is used to form the radial category. As described in more detail in fig. 2, this data is obtained in the retailer's database by the retailer's electronic point-of-sale system 14 (fig. 2). This data is passed via electronic data transfer to a central facility where the data is analyzed at a processor 30 (fig. 2) to determine the center and radiation categories, frequency/period of purchases, recent purchases, value, volume, number of items purchased per basket, and each product/category. In particular, when a shopper signs a membership/reward card containing personal data related to the shopper and other socio-economic information, the shopper's profile database 120 (FIG. 3) is established. Thus, the data contained in the personal database 120 will include shopper identifiers, geographic demographics, and other socio-economic data, which can be incorporated into purchase data from electronic point-of-sale systems associated with the same shopper. Thus, the data used to determine the nature of the hub and the radiation is data relating to the actual purchase by the shopper. For example, the center category may be baby diapers. The central category of products will be associated with a plurality of radiation categories, wherein the person who purchased the radiation category also purchased baby diapers. In the embodiment shown in fig. 1, the radiation categories include baby milk, baby food, baby detergent, cereals, coffee, candy, and frozen meat. In another example, although not shown, the center category may be a herbal product such as herbal tea, and multiple radiation categories would be associated with center products such as facial cleanser, sugar, cereals, and natural products. The radiation products can be included even if they do not have a strong correlation with the core products in the actual shopping basket. For example, a new product, i.e. new baby toilet paper introduced into the market, may become a radiological product even if people have not purchased it before because it is a new product. However, most radiation products are strongly associated with the center product.

FIG. 2 is a schematic block diagram of a system for performing the method of the preferred embodiment.

Referring to fig. 2, a block diagram of a system for implementing the present invention is shown. An output print-in and card reader 10 is shown in proximity to a shelf 12, where the shelf 12 contains a center product such as a diaper. Checkout terminals 14 are provided where the shopper pays for the product. Typically, when shoppers purchase products, they will appear at the checkout terminal 14 and their products will be scanned to identify the purchased products. A particular shopper may be identified by entering a code, which may be provided by a card read by a card reader 15 at the terminal 14, or a credit card at the retail store 70, or the like. However, the specific identification of the shopper is not essential to the embodiments of the present invention, and the particular identity of the shopper may not be required to practice the embodiments. What is needed is a retailer data warehouse processor system 60 at a remote retailer central location or back office 80 that collects attributes of products purchased by a particular shopper through terminals 14 at retail stores 70. A single back-end office 80 may be associated with a plurality of different stores of a department store or the like.

Data relating to purchases made by shoppers is collected at the terminals 14 and provided to a store processor 51 and a retail store server 50 maintained at a retail store 70 via a communications link 28 (which may be a LAN or the like). This data is then provided from server 50 to a retailer central location database and processor system 60 comprising database 62 and processor 64. The retailer central location 80 also contains a retailer server and a database system 90 that includes a database 120 and a server 140. The system 90 stores data relating to shoppers for operating a universal membership reward system and promotion, and may incorporate the data with shopping purchase data collected by the system 60 and provide the data to a remote operator central location 100 via a communication link 85, which may be a broadband network, an internet communication link, or any other suitable link.

Accordingly, the purchase data and other data related to the shopper stored in the system 60 is provided to the database 32 at the central location 100 operated by the activity provider.

A processor 30 at the central location analyzes the data in a database 32. Thus, database 32 is a working database used by processor 30 during analysis of purchase data. When the data is received by the database 32, the data is analyzed by the processor to perform a correlation analysis for all products purchased by the shopper in a single basket transaction. The products are ranked according to frequency of purchase and the center category is determined from this data. Typically, each central category is the most frequently purchased product, or the product with high consumer penetration for the intended market. Each center product is then correlated with other categories, and the initial result is a list of correlation scores from strongest to weakest. The weakest correlations can be removed and disregarded. The weakest correlation is determined by reference to a predetermined correlation score or value such that any correlation below that score or value is considered a weak correlation. Thus, all purchases made by the shopper are queried by the processor 30 so that correlations can be made between the products purchased by the shopper to determine the center products and their associated radial products. In this way, center products can be maintained and weakly correlated products removed, wherein center products continue to have a strong correlation with their radiation products.

The processor 30 can then create a favorite list of the center products and related products, and can determine the group of shoppers within a certain period based on purchases made by a particular shopper. A purchase period associated with a particular center product may then be determined and, through that period, the length of a particular promotion may be determined.

The processor 30 also determines a recent purchase and compares it to a period criterion. For example, if the purchase period for a diaper is typically approximately two weeks for an individual shopper, the timing of the promotion may be the same as the purchase period, and the length of the time of the promotion may also be determined. For example, for products purchased on a two week basis, a two week promotion may be offered.

For each central category of product, the potential size of the group of purchasers who may purchase the central product at the next predetermined period, e.g., two weeks, is determined. If the potential size of the purchaser's grouping is large enough, a promotion may be developed for those who purchase a central product that also receives some incentive to purchase the associated radiation product. Thus, the processor 30 can deploy promotional offers for center and radial products and promotional offers for favorite lists for each center and radial category based on offers that marketers are prepared to make. As described above, these incentives may be discounts in price, free additional products, or other incentives such as gifts, samples, lottery tickets, bonus points for later purchases, and the like. The processor 30 forwards the favorite list to the retailer server 50 via the communication link 36.

Alternatively, the processor 30 may forward the list back to the retailer hub location 80 via the communication link 85 so that the EPOS data processor and database system 60 can forward the list to the retail store server 50 via the communication link 35.

The store processor 51 then loads the list into the read printer 10 and the EPOS checks out at the terminal 14.

The favorite list can be loaded from the processor 51 to the printer 10 through the communication link 38 or manually through a flash card storage device that is manually loaded into the printer 10 after it is generated.

Then, when the purchaser appears on the shelf 12 to purchase diapers, the purchaser may choose to activate the printer 10 to receive a favorite list relating to the central product category. The favorite list may indicate that certain stimuli may be provided if the purchaser purchases all of the additional products shown in fig. 1. Different stimuli may be provided if only some of the radiation products are purchased. In an alternative embodiment, rather than providing the printer 10 to generate the favorite list, the person at the shelf 12 hands the favorite list to anyone who purchased diapers.

Thus, when a shopper is present at checkout terminal 14, the product purchased by the shopper can be determined and compared to the stimulus provided by output device 10 so that the shopper receives the stimulus based on what the shopper actually purchases.

A shopper database 120 and server 140 may also be provided for entering data relating to individual shoppers, such as geographic demographic data, socioeconomic data, shopper profile information, past purchase history, and the like. This data can be provided to the processor 30 for use in the favorite list and the promotion specials list to better direct promotions to various shoppers based on their respective likes and dislikes, demographic data, and the center products they actually purchase. This data may be collected by conducting questionnaires to persons applying for participation in an affiliate program that is part of the shopping system or other type of affiliate program.

The structure shown in fig. 2 is merely exemplary. It will be apparent that if the system is provided by a plurality of different retail stores, a single central processor 30 and database 32 may be provided, the database 32 communicating with the printer output devices 10 at each store location. Further, such a printer 10 may be provided in each shop location. Further, the processor 30 may be formed by a central processor which in turn communicates with a store base processor.

Although the embodiment of FIG. 2 relates to an actual store, the store may be a virtual store, such as a store accessed via the Internet or other remote access device, a mall, a food store, and so forth. Further, the category of store may be a living establishment, for example, a place where services such as gyms, entertainment, and the like are provided, rather than a place where a specific product is purchased.

The method of the preferred embodiment of the present invention will be described more fully with reference to the flowchart of fig. 3.

Referring to FIG. 3, at step 301, processor 30 determines the composition of the shopper's basket in the manner noted above to identify the products purchased by the shopper. At step 301, the processor 30 determines whether there is a strong correlation between the center species and the radiation species. Using the example mentioned above, analyzing a large number of shopping baskets from different shoppers, it can be seen that a certain number of people have purchased baby diapers, and among these, some other products. Thus, the central product may be a baby diaper and the radiation categories may be products purchased by a plurality of persons who have purchased baby diapers at the same time.

The processor 30 performs a correlation analysis of the products purchased by the shopper in a single transaction. Other desired analytical tools may also be used. However, in a preferred embodiment, the products are ranked according to frequency of purchase and categories are determined. Each central category is typically the most frequently purchased product, or a product with high consumer penetration for the intended market. Each center category is then associated with other categories. The initial result is a list of correlation scores from strongest to weakest. A correlation point between the strongest and weakest is determined and used to determine a point of separation from weak correlation to strong correlation.

A number of methods are available for determining center and radiation categories, including:

(a) simply related to the highest permeability or frequently purchased species. This makes it possible to include shopping baskets with significant correlation between purchases of various products and exclude those with weak correlation, as in step 304; and

(b) other statistical and analytical tools may be used in conjunction with the correlation analysis.

The method for determining the center and the kind of radiation is chosen according to the end result required by the marketer. For example, it may be necessary only to provide a simple understanding of the center and radial categories, or alternatively, it may be desirable to know how many of the desired center categories are available to operate the method and system of the preferred embodiments most efficiently.

For example, the center categories may be the following categories:

(a) frequently purchased;

(b) a common or base item;

(c) have high shopper penetration;

(d) subject matter is clear or of particular interest, e.g., baby products — thus, baby diapers as a center, healthy food, etc.;

(e) the kind of purpose.

All other categories will be ranked based on the strength of the correlation. For example, if the hub category is baby diapers, the market intent is to increase the basket of the shopper (mother) who purchases the diaper, as the shopper's shopping ability in this regard may not be fully expressed. When the radiation product is purchased together with the center product, the relevant points or values show a strong relationship. Thus, the radiation category may be a predetermined proportion of categories higher than those associated with the purchase of a central category of baby diapers, such as:

(a) infant milk;

(b) baby food

(c) Cereals

(d) Coffee

(e) Frozen meat/breakfast ham

(f) Candy, such as a biscuit;

(g) baby powder

(h) And (3) salt.

The results of the analysis of step 301 are considered in step 302. In case of a strong correlation, the process may proceed to step 303. If weakly correlated, the process proceeds to step 304 and the relatively weakly correlated or completely unrelated instances are disregarded and effectively removed from the system.

At step 303, the center and radiation categories are determined for the promotional specials. Considering the recent promotion, therefore, if baby powder is the subject of the recent promotion, this product may be removed from the proposed promotion during the analysis of the center category and the radiation category at step 303.

At step 303, the analysis determines the center category as a frequently purchased product category or a product with high consumer penetration. For example, many shoppers have tried or purchased these product categories. Typically, the center product will be the primary item in each shopper's basket, representing all of the shopper's purchased top items for a period of time, such as a year. However, specific variants can also be identified, for example further divided into lady's baskets, particularly related to lady's needs, living baskets related to living goods, such as sauces, meats or wines-centered cooking or eating.

At step 305, the initial favorite list is edited based on the analysis in step 303. The potential size of the eligible shopper is determined. For example, the question is whether the potential marketer in the list can participate in a particular promotion? Is the retailer agree that alternate marketers participate in promotions? What promotional strategy will be adopted?

Concurrently with steps 307 through 309, the shopper's grouping and periodicity is determined at step 306.

Based on the grouping of the hub and the category of the radiation, the system now identifies the number of shoppers (and identifies the shopper if necessary-i.e., if a card reader is used at the shelf 12 to identify the person). At step 307, the purchaser's purchase period is analyzed so that the system can determine the length of the promotion period at step 308. If a shopper purchases a center-like baby diaper every two weeks, and assuming that yesterday did not have the shopper purchase, the period length of the promotion should also be two weeks. At step 306, it is also determined whether all radiation species are on a two week cycle, so that if salt is purchased every eight weeks, the radiation species may be terminated.

Thus, at step 305, the initial favorite list is edited based on the data collected at step 301. The initial favorite list is the proper center and radiation categories that were initially seen. It is most important in this step to determine the eligible shoppers for each category and to count their total to estimate the size of a particular group.

At step 306, the shopper's grouping is reduced to shoppers who are likely to make purchases in the average purchase period, and shoppers outside of that period are excluded in order to determine the opportunities for particular promotions and the likely value of offering those promotions.

While at step 307, the purchase patterns of each category are analyzed to determine an average shopping period, which is typically expressed in weeks. Individual shoppers themselves need not be identified, but rather purchase cycle patterns for shoppers of a specified category are analyzed.

At step 308, the cycle length for the particular promotion is determined. The cycle length may be the same as the purchase cycle length of the product. For example, if the promotion is associated with baby diapers that are typically purchased for a two week period, the period of the promotion is two weeks in length. The length of the promotion period at step 308 depends primarily on two factors — the recent purchases of each category by the shopper, and the ability to pay to conduct a particular promotion. Ideally, if the payment capability is not a constraint and the purchase period is two weeks, the period length is two weeks. This includes all shoppers who have made purchases yesterday and are likely to return to purchases the next two weeks. Therefore, a recent purchase is required to determine the divorce point, where a large portion of shoppers will be covered within the divorce point of the promotion period.

At step 309, a recent purchaser of a particular center product is determined. Thus, from the collected data, it can be determined that the shopper purchased the center product only a few days ago, and thus the shopper is unlikely to wish to purchase all of the radial categories of products. However, if the purchase is not recent, such as a week ago, the purchaser may wish to purchase other products identified in FIG. 1. However, the budget constraints of marketers are also taken into account. If budget constraints result in marketers wishing to offer only seven day programs, it is unlikely that a buyer who purchased a week ago will purchase in a 7 day promotional program period. Thus, the potential grouping or size of the shopper to which the promotion is located is determined to estimate the potential business opportunities for the promotion.

In a preferred embodiment of the present invention, it is not necessary to identify a particular shopper to determine recent purchases. All shoppers of a particular category are considered and the extent of the purchase cycle and how many recent purchases are determined to decide whether the promotional cycle can cover these recently purchased shoppers or ignore them. Thus, the initial grouping of shoppers for each hub and radial will be reduced when checked or scanned for shopping periods, recent purchases, and promotion periods.

Thus, at step 310, the potential size or grouping of shoppers likely to be attracted to a particular promotion is determined to consider whether it is worth offering the promotion, or whether the size of the grouping is so small that such promotional specials are unlikely to receive any benefit. If it is determined at step 310 that the potential size of the shopper population likely to be attracted to a particular promotion is sufficiently large, a promotional special offer list is established at step 311 for each center product and radial product. The promotional special listing is a work list that marketers and retailers use to determine the appropriate strategy for each hub or radiation category. The list is agreed to by marketers and supported by retailers.

At step 311, a final form of promotional special lists based on the center categories (baby diapers and the above-mentioned radial categories (a) - (f)) and promotional strategies used by marketers to increase the size of the shopper's shopping baskets in the group is formed for retailer support. It is sent to the retailer to be included in their promotion list in the EPOS terminal 14 so that it can match the special offer and the return can be identified at the terminal 14 when the shopper accepts any incentive.

At step 312, a favorite list of the special buy favorite list for each center and radial category is generated to be provided to the shopper when purchasing the center product. The favorite list contains a special offers list, a set of coupons or similar printed matter used to inform shoppers of available radiation special offers so that radiation products can be purchased at the same time the center category product is purchased.

The list may be printed or provided manually as shown in step 313. The printer is located where the center-type product is sold so that a purchaser who purchases the center-type product can insert a card for identifying the purchaser and print a stimulus to provide to the purchaser of the radiation-type product. For example, the stimulus may be that if a person buys each radiation category product, the person will get a proportional discount off the price of the product. Alternatively, the stimulus may be that the person is given an additional product. Other incentives may be lottery tickets, reward points for other purchases, free gifts, etc., so that the incentive shopper purchases those products he may desire.

Thus, the result at step 314 is that the shopper may purchase products from the relevant radiation category, both because of ongoing incentive specializations, and also because of the fact that the shopper is likely to need these products anyway. Thus, it is likely that the basket size or total value of a shopper's shopping trip on a particular day will increase. This clearly benefits marketers who sell products of the radial variety, and shoppers, as they receive incentives to purchase additional products that shoppers are likely to need anyway. This therefore results in the shopper obtaining the best value from the promotional specials being conducted, thereby increasing consumer satisfaction. Thus, by offering timely and relevant specials to shoppers, the shoppers are likely to purchase more products and obtain the best value of the purchase due to the incentive offered. Step 314 can determine whether the promotion was successful by analyzing the products purchased from the data collected at the terminal 14 to determine the number of hub and radial products actually purchased by the shopper during the promotion.

It is therefore to be understood that references to products in this specification and claims include the purchase of services, such as gymnasium reservations, entertainment such as movies, sporting events, and the like.

Since modifications within the spirit and scope of the invention may readily be effected by persons skilled in the art, it is to be understood that this invention is not limited to the particular embodiments described hereinabove.

In the claims and in the foregoing description of the invention, unless otherwise required by express language or necessary implication, the word "comprise" or variations such as "comprises" or "comprising" is used in an inclusive sense, i.e. to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the invention.

Claims (24)

1. A shopping method for providing a shopper with an incentive comprising:

collecting data relating to products purchased by individual shoppers;

determining a core product category from said collected data and establishing a related product category relating to said core product category;

creating a promotional special interest list related to the core product and related products; and

an incentive is provided to the shopper to purchase the product from the list.

2. The method of claim 1, wherein the method further comprises: determining a frequency of purchases by shoppers in the core related products and product categories and categorizing the purchases into promotional cycle frequencies and recent purchase groupings, and creating a promotional special listing and offering incentives based on the cycle frequencies and recent purchase groupings.

3. The method of claim 1, wherein the method further comprises: the size, quantity and value of the products purchased for each core product category is determined.

4. The method of claim 1, wherein the method further comprises: data relating to shoppers is collected, including past purchase data, as well as consumer profile data, including geographic demographic data, socioeconomic data, and information.

5. The method of claim 4, wherein the shopper data is obtained at least in part by requesting the data and information from the shopper while the shopper is engaged in an affiliate program.

6. The method of claim 1, wherein the stimulus comprises: one or more of a coupon, flyer, coupon, promotional sample, point, lottery, and gift.

7. A shopping system for providing a shopper with an incentive, comprising:

a processor for analyzing data relating to products purchased by individual shoppers and determining a core product category from said data in a database and a related product category relating to said core product category;

the processor is further configured to create a promotional special interest list related to the core product and related products for presentation to the consumer; and

an output for providing an incentive to a shopper to purchase the related product.

8. The system of claim 7 wherein the output devices may include a card reader and printer located at a location for core product sales such that the devices may be activated to generate incentives for the shopper to purchase the associated product.

9. The system of claim 7, wherein the output device comprises a printer.

10. The system of claim 7 wherein the processor is further configured to determine a frequency of purchases by shoppers in the core related products and related product categories and to sort the purchases into promotion period frequency and recent purchase groupings, and to create a promotion special listing and offer incentives based on the period frequency and recent purchase groupings.

11. The system of claim 8, wherein the processor further determines a product size, quantity, and value purchased for each core product category.

12. The system of claim 1 wherein the processor is located at a provider central location and is connected by a communications link to a retail store server at a retail store, the processor also being connected by a communications link to a retailer central location, the central location having a database and a processor system connected by a communications link to the retail store server to receive the purchase data via an EPOS checkout terminal and the store server and to provide the data to the processor located at the provider central location so that the processor can generate the list for provision to the retail store server and loading to the EPOS checkout terminal.

13. A shopping method for providing a shopper with an incentive comprising:

determining a core product and related products related to the core product by analyzing purchases made by individual shoppers;

creating a promotional special interest list with the core product and its related products based on the frequency of purchase of the core product by the shopper; and

if a shopper purchasing a core product also purchases a product related to the core product, the list is output to the shopper, providing an incentive to the shopper.

14. The method of claim 13, further comprising analyzing the data relating to products purchased by individual shoppers to determine relevance, and removing from the defined core products and related products categories of related products below a predetermined relevance point.

15. The method of claim 13, wherein the method further comprises: determining a cycle length for a promotion based on the purchase cycle of the core product by the shopper.

16. The method of claim 13, wherein the method further comprises: recent purchases with a periodicity criterion are determined, and potential sizes or groupings of shoppers who are likely to purchase the core product within a predetermined period are determined, and a promotional offer list is developed based on the potential sizes or groupings of shoppers who desire to purchase the center product.

17. The method of claim 13, wherein the method further comprises: data relating to shoppers is collected, including past purchase data as well as consumption profile data, including geographic demographic data, socioeconomic data, and information.

18. The method of claim 17, wherein said shopper data is obtained at least in part by requesting said data and information from a shopper while the shopper is engaged in an affiliate program.

19. The method of claim 13, wherein the stimulus comprises: one or more of a coupon, flyer, coupon, promotional sample, point, lottery, and gift.

20. A shopping system for providing a shopper with an incentive, comprising:

a processor for determining a core product and related products related to the core product by analyzing purchases made by individual shoppers;

the processor is also configured to create a promotional special interest list related to core products and related products based on the frequency of purchases of core products by shoppers; and

an output for providing the list to a shopper who purchased a core product if the shopper also purchased a product related to the core product, providing an incentive to the shopper.

21. The system of claim 20 wherein said processor is also for analyzing said data relating to products purchased by individual shoppers to determine relevance and removing from said defined core and related products categories of related products below a predetermined point of relevance.

22. The system of claim 21 wherein the processor is also for determining a promotional cycle length based on the purchase cycle of a shopper for a center product.

23. The system of claim 21 wherein said processor is also for determining recent purchases with a periodicity criterion and determining potential sizes or groupings of shoppers likely to purchase core products within a predetermined period of time and developing a promotional special offer list based on said potential sizes or groupings of shoppers desiring to purchase center products.

24. The system of claim 20 wherein the processor is located at a provider central location and is connected by a communications link to a retail store server at a retail store, the processor also being connected by a communications link to a retailer central location, the central location having a database and processor system connected by a communications link to the retail store server to receive the purchase data via an EPOS billing terminal and the store server and to provide the data to the processor located at the provider central location so that the processor can generate the list for provision to the retail store server and loading to the EPOS billing terminal.