Source: http://www.google.de/patents/US20120016727
Timestamp: 2017-11-24 07:08:27
Document Index: 797806166

Matched Legal Cases: ['§120', '§120', 'art 220', 'art 222', 'art 224', 'art 224']

Patent US20120016727 - Commerce System and Method of Controlling The Commerce System Using ... - Google Patentsuche
A commerce system has a plurality of members. A maximum discounted offer is determined for a product in the commerce system. A discounted offer is generated less than the maximum discounted offer for the product. Members of the commerce system are assigned to a control group and offer group. A control...http://www.google.de/patents/US20120016727?utm_source=gb-gplus-sharePatent US20120016727 - Commerce System and Method of Controlling The Commerce System Using Performance Based Pricing, Promotion and Personalized Offer Management
Veröffentlichungsnummer US20120016727 A1
Anmeldenummer US 13/171,262
Auch veröffentlicht unter WO2013003064A1
Veröffentlichungsnummer 13171262, 171262, US 2012/0016727 A1, US 2012/016727 A1, US 20120016727 A1, US 20120016727A1, US 2012016727 A1, US 2012016727A1, US-A1-20120016727, US-A1-2012016727, US2012/0016727A1, US2012/016727A1, US20120016727 A1, US20120016727A1, US2012016727 A1, US2012016727A1
Ursprünglich Bevollmächtigter Myworld, Inc.
Patentzitate (5), Referenziert von (2), Klassifizierungen (6), Juristische Ereignisse (1)
US 20120016727 A1
providing a maximum discounted offer for a product;
generating a discounted offer less than the maximum discounted offer for the product;
providing the discounted offer to a member of the commerce system to assist with purchasing decisions;
recording a sale of the product using the discounted offer;
determining an incremental revenue or profit as a difference between the maximum discounted offer and the discounted offer; and
controlling activities within the commerce system by distributing the incremental revenue or profit between members of the commerce system.
assigning a first member of the commerce system to a control group;
providing a control discounted offer to the control group;
assigning a second member of the commerce system to an offer group;
providing the discounted offer to the offer group; and
distributing the incremental revenue or profit based on purchasing decisions of the control group with the control discounted offer and purchasing decisions of the offer group with the discounted offer.
3. The method of claim 2, wherein the control discounted offer is no discounted offer.
4. The method of claim 1, wherein the discounted offer includes an individualized discounted offer.
5. The method of claim 4, further including assigning an identifier to the discounted offer.
6. The method of claim 1, further including distributing the incremental revenue or profit by setting a sharing percentage of the incremental revenue or profit for members of the commerce system.
generating a discounted offer for a product;
determining an incremental revenue or profit as a difference between the discounted offer and a predetermined value; and
9. The method of claim 8, wherein the control discounted offer is no discounted offer.
10. The method of claim 8, further including determining a statistical correlation between the discounted offer and the purchasing decisions of the offer group based on the purchasing decisions of the control group with the control discounted offer.
11. The method of claim 7, wherein the discounted offer includes an individualized discounted offer.
12. The method of claim 7, wherein the discounted offer is based on geographic location or demographics of members of the commerce system.
13. The method of claim 7, further including distributing the incremental revenue or profit by setting a sharing percentage of the incremental revenue or profit for members of the commerce system.
determining an incremental revenue or profit as a difference between a discounted offer and a predetermined value; and
16. The method of claim 15, wherein the control discounted offer is no discounted offer.
17. The method of claim 15, further including determining a statistical correlation between the discounted offer and the purchasing decisions of the offer group based on the purchasing decisions of the control group with the control discounted offer.
18. The method of claim 14, wherein the discounted offer includes an individualized discounted offer.
19. The method of claim 14, further including distributing the incremental revenue or profit by setting a sharing percentage of the incremental revenue or profit for members of the commerce system.
20. A computer program product usable with a programmable computer processor having a computer readable program code embodied in a computer usable medium for controlling a commerce system, comprising:
22. The computer program product of claim 21, wherein the control discounted offer is no discounted offer.
23. The computer program product of claim 21, further including determining a statistical correlation between the discounted offer and the purchasing decisions of the offer group based on the purchasing decisions of the control group with the control discounted offer.
24. The computer program product of claim 20, wherein the discounted offer includes an individualized discounted offer.
25. The computer program product of claim 20, further including distributing the incremental revenue or profit by setting a sharing percentage of the incremental revenue or profit for members of the commerce system.
The present application is a continuation-in-part of U.S. patent application Ser. No. 12/806,951, filed Aug. 24, 2010, which is a continuation-in-part of U.S. application Ser. No. 12/804,272, filed Jul. 15, 2010, and claims priority to the above applications pursuant to 35 U.S.C. §120. The present application is further a continuation-in-part of U.S. patent application Ser. No. 13/079,561, filed Apr. 4, 2011, and claims priority to the above applications pursuant to 35 U.S.C. §120.
The present invention relates in general to consumer purchasing and, more particularly, to a commerce system and method of controlling the commerce system using performance based pricing, promotion, and personalized offer management.
In its basic form, the economic model can be viewed as a predicted or anticipated outcome of a system defined by a mathematical expression and driven by a given set of input data and assumptions. The mathematical expression is formulated or derived from principles of probability and statistics, often by analyzing historical data and corresponding known outcomes, to achieve a best fit of the expected behavior of the system to other jets of data. In other words, the model should be able to predict the outcome or response of the system to a specific set of data being considered or proposed, within a level of confidence, or an acceptable level of uncertainty.
Even though the retailer expends large amounts of time and money into marketing campaigns, there is little or no feedback as to the success or performance of the particular marketing strategy. The retailer often cannot determine how many consumers actually made a purchase decision as a direct result of responding to the advertisement. The consumer may have selected the item for purchase with no prior knowledge of the advertisement, i.e., the published advertisement was not the catalyst for bringing the consumer into the retailer. Alternatively, the consumer might have purchased the item without a discount. The consumer will of course accept the discounted price, but would have paid regular price. In some cases, the retailer is unnecessarily foregoing profit by discounting the product to the general public.
Retailers have used a variety of techniques to understand the success or performance of a particular marketing strategy. For example, a marketing agency may charge the retailer based on how many people viewed the advertisement, e.g., clicked on the advertisement or promotion on a website. If a consumer views or clicks on the advertisement or promotion, the retailer is charged for that event. However, there is no correlation to an actual consumer purchase. The retailer is charged for the consumer merely coming into contact with the advertisement, even if the consumer does not purchase the product. Moreover, even if the consumer does purchase the product, the marketing evaluation does not take into account whether the consumer would have purchased the product without a promotion. The promotion is accepted by the consumer, but marketing dollars are wasted and potential profit is lost because the promotion was not the controlling factor in making the purchasing decision. The consumer would have purchased the product without a promotion. Alternatively, the promotion could have caused the consumer to purchase the advertised product at a lower profit margin at the expense of cannibalizing sales of another product having a higher profit margin sold by the same retailer.
A need exists to evaluate the effectiveness and performance of a marketing promotion. Accordingly, in one embodiment, the present invention is a method of controlling a commerce system comprising the steps of providing a maximum discounted offer for a product, generating a discounted offer less than the maximum discounted offer for the product, providing the discounted offer to a member of the commerce system to assist with purchasing decisions, recording a sale of the product using the discounted offer, determining an incremental revenue or profit as a difference between the maximum discounted offer and the discounted offer, and controlling activities within the commerce system by distributing the incremental revenue or profit between members of the commerce system.
In another embodiment, the present invention is a method of controlling a commerce system comprising the steps of generating a discounted offer for a product, recording a sale of the product using the discounted offer, determining an incremental revenue or profit as a difference between the discounted offer and a predetermined value, and controlling activities within the commerce system by distributing the incremental revenue or profit between members of the commerce system.
In another embodiment, the present invention is a method of controlling a commerce system comprising the steps of determining an incremental revenue or profit as a difference between a discounted offer and a predetermined value, and controlling activities within the commerce system by distributing the incremental revenue or profit between members of the commerce system.
In another embodiment, the present invention is a computer program product usable with a programmable computer processor having a computer readable program code embodied in a computer usable medium for controlling a commerce system comprising the steps of generating a discounted offer for a product, recording a sale of the product using the discounted offer, determining an incremental revenue or profit as a difference between the discounted offer and a predetermined value, and controlling activities within the commerce system by distributing the incremental revenue or profit between members of the commerce system.
FIG. 15 illustrates an evaluation of the effectiveness of discounted offers toward incremental profits;
FIG. 16 illustrates an evaluation of the effectiveness of discounted offers toward incremental profits using a control group and offer group;
FIG. 17 illustrates consumers assigned to the control group and offer group for a promotional product;
FIG. 18 illustrates consumers assigned to the control group and offer group for a promotional time period;
FIG. 19 illustrates consumers assigned to the control group and offer group making purchasing decisions; and
FIG. 20 illustrates the process of controlling activities within the commerce system by distributing the incremental revenue or profit between members of the commerce system.
With the retailer product information collected and stored in central database 146, personal assistant engine 74 generates optimized shopping list 144 by considering each line item of the consumer's shopping list from webpage 138 and reviewing retailer product information in the central database to determine how to best align each item to be purchased with the available products from the retailers. In addition, personal assistant engine 74 determines the individualized “one-to-one”discounted offer, if any, that will be associated with each line item in shopping list 170, as shown in FIG. 12. For example, a particular consumer 62 wants to purchase bread and has provided shopping list 170 with preference levels for weighted product attributes for bread that are important to his or her purchasing decision. Central database 146 contains bread product descriptions, bread product attributes, and pricing for each retailer 66-70. Personal assistant engine 74 reviews the product attributes of each bread product offered by each retailer 66-70, as stored in central database 146.
The “Final Price” column shows the final price (FP) offered to the consumer, i.e., regular price less the default discount from retailer 66 ($4.00−1.00=3.00). The “Net Value” column is the net value or normalized value (NV) of the BB1 product to consumer 62. In one embodiment, the net value is the consumer value normalized by the final price, i.e., NV=CV/FP. Alternatively, the net value is determined by NV=(CV-FP)/CV. Using the first normalizing definition, NV=2.50/3.00=0.83. The consumer value CV is less than the final price FP offered by retailer 66, including the default discount. The net value NV to consumer NV 62 is less than one so the BB1 product will not be a good choice for the consumer. Using the second normalizing definition, NV=(2.50-3.00)/2.50=−0.20. The net value NV to consumer 62 is negative so the BB1 product will not be a good choice for the consumer. Consumer 62 is unlikely to buy the BB1 product because the product attributes do not align or match well with the consumer weighted attributes, taking into account the individualized discounted offer. A net value NV less than one or negative indicates that retailer 66 would likely not receive a positive purchasing decision from consumer 62. Personal assistant engine 74 should not recommend the BB1 product to consumer 62 in optimized shopping list 144.
Bread brand BB3 from retailer 70 is shown with BB3 product attributes, e.g., small loaf, whole grain, 1 day freshness, and pricing of $2.30 (regular price of $3.20 less 0.90 discounted offer from retailer 70). The BB3 product gets attributes points AP9 for small loaf, attributes points AP10 for whole grain, attributes points AP11 for 1 day freshness, and attributes points AP12 for the $2.40 price. The consumer value is AP9*0.7+AP10*0.6+.AP11*0.8+AP12*0.3. Assume that the BB3 product gets CV of $3.40 USD. The final price FP is the regular price less the default discount ($3.20−0.90=2.30). Using the first normalizing definition, NV=3.40/2.30=1.48. The net value NV to consumer 62 is greater than one so the BB3 product is a possible choice for consumer 62. Using the second normalizing definition, NV=(3.40−2.30)/3.40=+0.32. The net value NV to consumer 62 is positive so the BB3 product is a possible choice for the consumer. In fact, based on the default discounted offer from retailers 66-70, the net value of the BB3 product (NV=1.48) is higher than the net value of the BB2 product (NV=1.19) or BB1 product (NV=0.83). The BB3 product is placed on optimized shopping list 144. The BB3 product is the optimal choice for consumer 62 in that if the consumer needs to purchase a bread product, then BB3 is the product most closely aligned with the consumer weighted attributes, i.e. highest net value NV, and would likely receive a positive purchasing decision from consumer 62.
Retailers 66-70 will want to show up as the recommended source for as many products as possible on optimized shopping list 144. Primarily, a particular retailer will be the optimized product source when the combination of the individualized discounted price and product attributes offered by the retailer aligns with, or provides maximum net value for the consumer in accordance with, the consumer's profile and shopping list with weighted preferences. Retailers 66-70 can enhance their relative position and provide support for consumer service provider 72 by making T-LOG data 46 available to consumer service provider 72. One way to get a high score when comparing retailer product attributes to the consumer-defined weighted product attributes is to ensure that personal assistant engine 74 has access to the most accurate and up-to-date retailer product attributes via central database 146. Even though a given retailer may have a product with desirable attributes, personal assistant engine 74 cannot record a high score if it does not have complete information about the retailer's products. By giving consumer service provider 72 direct access to T-LOG data 46, the retailer makes the product information readily available to personal assistant engine 74 which will hopefully increase its score and provide more occurrences of the retailer being the recommended source for as many products as possible on optimized shopping list 144. While the use of webcrawlers in FIG. 11 is effective in gathering product information from retailer websites 152-156, direct access to retailer T-LOG data 46 will further aid the consumers in generating optimized shopping list 144.
The purchasing decisions actually made by consumers 62-64 while patronizing retailers 66-70 can be reported back to consumer service provider 72. Upon completing the check-out process, the consumer is provided with an electronic receipt of the purchases made. The electronic receipt is stored in cell phone 116, downloaded to personal assistant engine 74, and stored in central database 146 for comparison to optimized shopping list 144. The actual purchasing decisions made when patronizing retailers 66-70 may or may not coincide with the preference levels or weighted attributes assigned by the consumer when constructing the original shopping list. For example, in choosing the canned soup, consumer 62 may have decided at the time of making the purchasing decision that one product attribute, e.g., product ingredients, was more important than another product attribute, e.g., brand. Consumer 62 made the decision to deviate from optimized shopping list 144, based on product ingredients, to choose a different product than the one recommended on the optimized shopping list. Personal assistant engine 74 can prompt consumer 62 for an explanation of the deviation from optimized shopping list 144, i.e., what product attribute became the overriding factor at the moment of making the purchasing decision. Personal assistant engine 74 learns from the actual purchasing decisions made by consumer 62 and can update the preference levels of the consumer weighted product attributes. The preference level for product ingredients can be increased and/or the preference level for brand can be decreased. The revised preference levels for the consumer weighted product attributes will improve the accuracy of subsequent optimized shopping lists. The pricing and other product information uploaded from cell phone 116 after consumer check-out to personal assistant engine 74 can also be used to modify the product information, e.g., pricing, in central database 146.
FIG. 15 illustrates an approach to evaluating the effectiveness of the individualized discounted offers made available to consumers 62 and 64. The evaluation also provides a process of assessing the fee paid to consumer service provider 72 based on an objective performance of individualized discounted offers. The performance based fee paid to consumer service provider 72 is determined in accordance with demonstrable incremental revenue or profits generated for retailers 66-70 arising from consumers 62 and 64 actually making a purchasing decision to buy the product as a direct result of receiving the individualized discount offers.
Consumer service provider 72 makes an individualized discounted offer 180 available to each of consumers 62 and 64 for product P1 with authorization and funding from retailers 66-70. Personal assistant engine 74 will determine the least individualized discounted offer 180 that will result in a positive purchasing decision for product P1 by the consumer. That is, personal assistant engine 74 must find the consumer purchase tipping point in terms of the individualized discounted offer. Consumers 62 and 64 each get an individualized discounted offer 180 for product P1, which may be the same or may be different depending on the shopping list and weighted product attributes as determined for each consumer.
In the present example, consumer service provider 72 transmits an individualized discounted offer of $1.25 to consumer 62 for product P1. In block 182, consumer 62 patronizes retailer 66-60 and purchases product P1 using individualized discounted offer 180. The purchase of product P1 by consumer 62 is recorded in T-LOG data 20. In block 184, an evaluation is made of the purchase of product P1 using individualized discounted offer 180, as well as other objective metrics described below, to determine the incremental revenue or profit to retailer 66-70.
When distributing individualized discounted offers 180 to consumers 62-64, personal assistant engine 74 can measure incremental profitability associated with the various individualized discounts for product P1 that can be offered to the consumer. Assume that the maximum retailer acceptable discounted offer for product P1 is set to a predetermined value of $2.00. Based on its business plan and profit margin, retailers 66-70 cannot profitably sell product P1 with any greater discount. The retailer authorizes personal assistant engine 74 to offer the consumer an individualized discounted offer 180 no greater than the $2.00 maximum discount for product P1. If consumer 62 or 64 purchases product P1 with individualized discounted offer 180 less than the maximum discount, then an incremental revenue or profit is realized because the consumer purchased product P1 for a higher price (regular price−individualized discounted offer) than would have been earned with the maximum discount (regular price−maximum retailer acceptable discount). The difference between the maximum discounted offer authorized by retailers 66-70 and the amount of the individualized discounted offer 180 made to consumers 62 and 64 is the incremental profit. Consumer service provider 72 is paid a performance based fee 186 from the incremental revenue or profit, e.g., a share or percentage of the incremental revenue or profit for product P1.
For example, if the retailer has authorized a maximum discounted offer of $2.00 and consumer 62 is offered an individualized discounted offer of $1.25, then the incremental profit is $0.75 for product P1. That is, the retailer was willing to offer a maximum discount of $2.00, but consumer service provider 72 had determined that consumer 62 would likely purchase product P1 for $1.25 discount. The regular price, individualized discounted offer 180, and actual purchase of product P1 is recorded in T-LOG data 20, as described in FIG. 1 and Table 1. T-LOG data 20 shows that consumer 62 did indeed purchase product P1 with the individualized discounted offer of $1.25. The retailer realized $0.75 more revenue or profit than would have been earned if consumer 62 had received a maximum discount of $2.00. The incremental profit for the transaction involving the sale of product P1 to consumer 62 is $0.75. Based on a sharing percentage of 30%, consumer service provider 72 receives a performance based fee of $0.75*0.30=$0.225 for the purchase of product P1 by consumer 62.
In another transaction, consumer service provider 72 determines that consumer 64 would likely purchase product P1 for a $0.50 discount. Consumer service provider 72 transmits an individualized discounted offer of $0.50 to consumer 64 for product P1. In block 182, consumer 64 patronizes retailer 66-70 and purchases product P1 using the individualized discounted offer 180. The purchase of product P1 by consumer 64 is recorded in T-LOG data 20. In evaluation block 184, T-LOG data 20 shows that consumer 64 did indeed purchase product P1 with the individualized discounted offer of $0.50. The retailer realized $1.50 more profit than would have been earned if consumer 64 had received the maximum retailer acceptable discount of $2.00. The incremental profit for the transaction involving the sale of product P1 to consumer 64 is $1.50. Based on a sharing percentage of 30% in block 186, consumer service provider 72 receives a performance based fee of $1.50*0.30=$0.45 for the purchase of product P1 by consumer 64.
Retailers 66-70 can monitor the incremental revenue or profit in block 184 and provide assurances to their management that the marketing budget is being well spent via individualized discounted offers 180. T-LOG data 46 shows that the consumer purchased the product with an individualized discounted offer 180 that is less than the maximum retailer acceptable discount. The promotional campaign achieved its goal in that the consumer actually redeemed the discounted offer. The retailer made a sale and received more profit than would have been realized with the maximum retailer acceptable discount. Retailers 66-70 benefit because they pay consumer service provider 72 only if an incremental profit is realized. If the consumer does not redeem the discounted offer, then there is no incremental profit. The retailer does not have to pay consumer service provider 72 for generating a non-redeemed discounted offer. In addition, retailers 66-70 receive the remainder of the incremental profit after distributing a share to consumer service provider 72. If the incremental profit is small, then the portion paid to consumer service provider 72 is proportionately small. If the incremental profit is large, then both retailers 66-70 and consumer service provider 72 benefit by their relative proportions of the incremental revenue or profit. The retailer can rely on effective utilization of the marketing budget because the compensation to consumer service provider 72 is based on objective, positive results. The performance based pricing, promotion, and personalized offer management is effective and useful for consumers 62 and 64, retailers 66-70, and consumer service provider 72.
The discounted offers made to consumers 62 and 64 can be other than individualized discounted offers 180. Consumer service provider 72 can make a discounted offer that is less than the maximum discounted offer authorized by retailers 66-70 to a targeted segment of the consumer populace. For example, one or more retailers 66-70 may make a promotional offer for product P1 with maximum discount of $2.00. Consumer service provider 72 transmits a discounted offer of $1.25 to all consumers who have identified product P1 as being a frequently used product from optimized shopping list 144 or by considering each line item of the consumer's shopping list from webpage 138. Alternatively, consumer service provider 72 transmits a discounted offer of $1.25 to a group of consumers within a geographic region or with similar consumer demographics based on consumer profiles, see FIG. 6. All consumers in the targeted segment receive the same $1.25 discounted offer for product P1.
FIG. 16 illustrates another embodiment of evaluating the effectiveness of the individualized discounted offers made available to consumers, including an analysis of the motivation for the purchasing decision, i.e., whether the individualized discounted offer was a primary catalyst for inducing the sales transaction for the consumer. A control group 190 is established to represent a group of consumers that receive a control discounted offer 208. The control discounted offer 208 can be any value between no discounted offer and the maximum discounted offer authorized by retailers 66-70. Control group 190 includes consumers 192, 194, and 196 known to consumer service provider 72 by the profiles created in FIG. 6. An offer group 200 is established to represent a group of consumers that receive a discounted offer less than the maximum retailer acceptable discount. Offer group 200 includes consumers 202, 204, and 206 known to consumer service provider 72 by the profiles created in FIG. 6. Retailers 66-70 can also assist with determining members of control group 190 and offer group 200 based on shopper loyalty cards or other T-LOG data 20.
A consumer assigned to control group 190 for one promotional product or group of promotional products can be assigned to offer group 200 for a different promotional product or different group of promotional products. FIG. 17 illustrates a chart 220 of consumers assigned to control group 190 and offer group 200 based on the promotional product. Consumer 192 is assigned to control group 190 for promotional product P1 and assigned to offer group 200 for promotional product P2. Consumer 202 is assigned to control group 190 for promotional product P3 and assigned to offer group 200 for promotional product P4.
In another embodiment, the members of control group 190 are selected as consumers having higher probability of purchasing product P1 with the control discounted offer, while the members of offer group 200 are selected as consumers having lower probability of purchasing product P1 with the individualized discounted offer. Alternatively, the members of control group 190 are selected as consumers having lower probability of purchasing product P1 with the control discounted offer, while the members of offer group 200 are selected as consumers, having higher probability of purchasing product P1 with the individualized discounted offer. In any case, control group 190 typically has fewer members than offer group 200 because retailers 66-70 still want to get discounted offers out to a majority of the potential consumers. For example, 5-20% of the pool of target customers is assigned to control group 190 and the remaining 80-95% of the pool of target customers is assigned to offer group 200.
In another embodiment, retailers selected a product or group of products associated with a particular promotional campaign to be evaluated. The products selected for individualized discounted offers overlap the buying habits of control group 190 and offer group 200 in time, geographic region, and demographics of the consumers. The members of control group 190 and offer group 200 are randomly selected as consumers having a high probability of purchasing the promoted product(s). The consumers of control group 190 receive the control discounted offer, and the consumers of offer group 200 receive individualized discounted offers. FIG. 18 illustrates a chart 222 of consumers assigned to control group 190 and offer group 200 based on promotional time period. Consumer 192 is assigned to control group 190 for product P1 during time period T1 and assigned to offer group 200 for product P1 during promotional time period T2. Consumer 202 is assigned to control group 190 for product P1 during promotional time period T3 and assigned to offer group 200 for product P1 during promotional time period T4.
Returning to FIG. 16, consumer service provider 72 makes a control discounted offer of zero, i.e., no offer, to consumers 192-196 of control group 190. Consumer service provider 72 makes an individualized discounted offer 210 available to consumers 202-206 of offer group 200 with authorization from retailers 66-70. The individualized discounted offers 210 are less than the maximum retailer acceptable discount. In block 212, consumers 192-196 of control group 190 and consumers 202-206 of offer group 200 patronize retailers 66-70. The consumer's may or may not purchase products from retailers 66-70, but to the extent that purchases are made, the consumers of control group 190 buy the products at regular price (no offer) and the consumers of offer group 200 use individualized discounted offer 210.
For example, if the retailer has authorized a maximum discounted offer of $1.00 for product P1 and consumer 202 is offered an individualized discounted offer of $0.55, then the incremental profit is $0.45. That is, the retailer was willing to offer a maximum discount of $1.00, but consumer service provider 72 had determined that consumer 202 would likely purchase product P1 for a $0.55 discount. T-LOG data 20 shows that consumer 202 did indeed purchase product P1 with the individualized discounted offer of $0.55. The retailer realized $0.45 more profit than would have been earned if consumer 202 had received the maximum retailer acceptable discount of $1.00. The incremental profit for the transaction involving the sale of product P1 to consumer 202 is $0.45.
The evaluation metric further shows a comparison between the products purchased by consumers 192-196 of control group 190 and the products purchased by consumers 202-206 of offer group 200. If consumer 202 purchased product P1 with individualized discounted offer 210 and consumer 192, having no discounted offer, patronized the retailer but did not purchase product P1, then a statistical correlation can be determined that the individualized discounted offer 210 was a controlling factor in the purchasing decision. That is, two or more consumers having similar purchasing trends and similar weighted attributes associated with product P1, or similar probability of purchasing the product during the promotional period, would likely purchase the product with the proper motivation. The size of control group 190 and offer group 200 is sufficiently large and length of the promotional period is sufficiently long to discount the possibility that consumer 192 did not patronize the retailer during the promotional period or, if the consumer did patronize the retailer, that product P1 was not needed during the instant trip. Since consumer 202 did purchase product P1 with individualized discounted offer 180 and consumer 192 did not purchase product P1 with no discounted offer, the individualized discounted offer is deemed as the controlling factor given the other statistical similarities between the consumers.
On the other hand, if consumer 202 purchased product P1 with individualized discounted offer 210 and consumer 192, having no discounted offer, also purchased the product P1, then a statistical correlation can be determined that the individualized discounted offer 210 was not a controlling factor in the purchasing decision. The actions of control group 190 provide a statistical correlation as to the motivation of offer group 200 in purchasing product P1 with individualized discount 210. Since consumer 192 in control group 190 made the decision to purchase product P1 without a discounted offer, then motivation behind the purchase by a similarly situated consumer in offer group 200 is likely attributed to factors other than the individualized discounted offer. The evaluation of purchasing decisions made by control group 190 and offer group 200 gives a statistical weight of the correlation between the individualized discounted offer 210 and the motivation behind offer group 200 in purchasing product P1.
FIG. 19 illustrates a chart 224 of actual consumer purchases when assigned to control group 190 or offer group 200 during a promotional time period T1. Chart 224 shows consumers, assigned group, store, regular price, discounted offer, actual selling price with discount, and incremental profit. For promotional product P1 with a maximum discounted offer of $1.00, during promotional time period T1, when assigned to offer group 200, consumer 202 purchased quantity one of product P1 with individualized discounted offer 210 of $0.90 from store S1. The incremental profit for consumer 202 is $1.00−0.90=$0.10. When assigned to offer group 200, consumer 204 purchased quantity two of product P1 with individualized discounted offer 210 of $0.50 from store S1. The incremental profit for consumer 204 is 2($1.00−0.50)=$1.00. When assigned to control group 190, consumer 194 purchased quantity one of product P1 with no discounted offer from store S2. When assigned to control group 190, consumers 192 and 196 did patronize store S1 but did not purchase product P1 with no discounted offer. Note that consumer 206 assigned to offer group 200 did patronize store S2 but did not purchase product P1 with individualized discounted offer of $0.25. There is no incremental profit for consumer 206.
In the example of FIG. 19, consumer 194 did purchase product P1 with no discount during the promotional time period T1, but consumers 192 and 196 did not purchase product with no discount. Consumer service provider 72 receives a reduced share of the incremental profit because the statistical correlation between the individualized discounted offer 210 and the purchasing decisions by offer group 200 is diminished by the actions of consumer 194. On the other hand, if all consumers of control group 190 had patronized store S1 or S2 but did not purchase product P1, then consumer service provider 72 would have received a full share of the incremental profit because the strong statistical correlation of the actions taken by all consumers in control group 190. The fact that consumer 206 did not purchase product P1 can be attributed to an individualized discounted offer that was insufficient to trip the purchasing decision or lack of need for product P1 during the promotional time period T1.
π OG = ∑ x = 1 m  π ox ( 2 ) π CG = ∑ y = 1 n  π cy ( 3 ) Δπ = S OG * ( π OG ′ S OG ′ - π CG S CG ) ( 4 )
Retailers 66-70 can monitor the incremental profit in block 214, as well as the statistical correlation between the incremental profit and the individualized offers, and provide assurances to their management that the marketing budget is being well spent via individualized discounted offer 210. T-LOG data 46 shows that the consumers purchased product P1 with an individualized discounted offer 180 that is less than the maximum retailer acceptable discount. The promotional campaign achieved its goal in that the consumers actually redeemed the discounted offer. The retailer made a sale and received more profit than would have been realized with the maximum retailer acceptable discount. Retailers 66-70 benefit because they pay consumer service provider 72 only if an incremental profit is realized. If the consumer does not redeem the discounted offer, then there is no incremental profit. The retailer does not have to pay consumer service provider 72 for generating a non-redeemed discounted offer. In addition, retailers 66-70 receive the remainder of the incremental profit after distributing a share to consumer service provider 72. If the incremental profit is small, then the portion paid to consumer service provider 72 is proportionately small. If the incremental profit is shown to be statistically uncorrelated to the individualized discounted offers, then the portion paid to consumer service provider 72 is even less or zero. If the incremental profit is large and statistically correlated to the individualized discounted offers, then both retailers 66-70 and consumer service provider 72 benefit by their relative proportions of the incremental profit. The retailer can rely on effective utilization of the marketing budget as the compensation to consumer service provider 72 is based on objective, positive results with a statistical correlation between the discounted offer and the purchasing decisions of the offer group based on the purchasing decisions of the control group with the control discounted offer. The performance based pricing, promotion, and personalized offer management is effective and useful for consumers 62 and 64, retailers 66-70, and consumer service provider 72.
Δπ = ∑ x = 0 n  u x  ( d MAX - d x ) ( 5 )
Δπ = ∑ x = 0 n  u x , p  ( d MAX - d x , p ) ( 6 )
dX,P, is the individualized discounted offer or discounted offer with identifier for product P
The sharing percentage between retailers 66-70 and consumer service provider 72 can be set to a value that maximizes the revenue to the consumer service provider. The revenue or fee earned by consumer service provider 72 is the product of the incremental revenue or profit and sharing percentage. The retailer that is able to achieve the highest incremental revenue or profit and further is offering the highest sharing percentage is likely to be placed on optimized shopping list 144. Consumer service provider 72 can allow retailers 66-70 to set sharing percentage because the retailers will compete for making the best individualized discounted offer which benefits the consumer, as well as offering the highest sharing percentage which benefits consumer service provider 72. The retailer is still assured of making a profit on the allocated marketing funds because the fee paid to consumer service provider 72 is a percentage (less than 100%) of the incremental profit. The retailer gets the remainder of the incremental profit in the form of increased revenue. The retailer only pays a percentage of the measurable incremental revenue or profit and is assured of a positive net return on investment from its marketing budget.
FIG. 20 illustrates a process for controlling a commerce system by distributing the incremental revenue or profit between members of the commerce system. In step 230, a maximum discounted offer is provided for a product. In step 232, a discounted offer is generated less than the maximum discounted offer for the product. Consumers are assigned to a control group and an offer group. The control group receives a control discounted offer, such as no discounted offer. In step 234, the discounted offer is provided to the offer group to assist with a purchasing decision. The discounted offer can be an individualized discounted offer. In step 236, a sale of the product using the discounted offer is recorded. In step 238, an incremental revenue or profit is determined as a difference between the maximum discounted offer and the discounted offer. In step 240, activities within the commerce system are controlled by distributing the incremental revenue or profit between members of the commerce system by setting a sharing percentage of the incremental revenue or profit for members of the commerce system. The distribution of the incremental revenue or profit is in part based on a statistical correlation between the discounted offer and the purchasing decisions of the control group with the control discounted offer.
By evaluating the effectiveness of the marketing program and sharing the incremental profit between retailers and consumer service provider, the members of the commerce system cooperate in controlling the flow of goods with a fair distribution of compensation based on actions taken and relative value provided by each member. Retailers benefit by selling more products with a higher profit margin. Consumers receive the best value for the dollar for needed products. Consumer service provider enables an efficient and effective connection between the retailers and consumers. The consumer service provider is evaluated and compensated based on the value brought to enabling and completing transactions between members of the commerce system.
In particular, the distribution of the incremental profit between members of the commerce system, e.g., between the retailers and consumer service provider, operates to control activities within the commerce system. The distribution of the incremental profit in part controls the business interactions of retailers, consumers, and consumer service provider. Retailers offer products for sale. Consumers purchase the products. The distribution of the incremental profit influences how consumer service provider connects the retailers and consumers to control activities within the commerce system.
US20100125495 * 18. Sept. 2009 20. Mai 2010 Smith Steven M System and method of providing a mobile wallet at a mobile telephone
US20140344051 * 17. Mai 2013 20. Nov. 2014 Myworld, Inc. Commerce System and Method of Controlling the Commerce System Using One-to-One Offers and Profit Sharing
WO2014186385A1 * 13. Mai 2014 20. Nov. 2014 Ncrowd, Inc. Customer targeting, acquisition, and retention platform and method
Unternehmensklassifikation G06Q30/02, G06Q30/0211
Europäische Klassifikation G06Q30/02, G06Q30/0211
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:OUIMET, KENNETH J.;REEL/FRAME:026517/0370