Coupons are offered by many businesses as a way of attracting consumers to various products and services. Many consumers use coupons to receive discounts when purchasing such products and services. Sometimes consumers may even receive money, free items, or other credits towards future purchases through the use of certain types of coupons.
The following detailed description references the drawings, wherein:
Coupons are ubiquitous. Businesses utilize them as a way of attracting consumers to various products and services. Consumers receive coupons from many different sources including mailings, the Internet, faxes, flyers, handouts, and inserts. Saving money is of keen interest to many consumers. However, a difficulty with this process is the trouble of finding and then sorting through the hundreds or even thousands of potential coupons to find those that are relevant. Consumers may get frustrated with this process, given the time and effort required. Additionally, businesses may waste time and money by not attracting a sufficient number of consumers through this process.
A system that could sift through a large collection of coupons, using customer specific information and preferences, to find relevant coupons would be beneficial to both consumers and businesses. Such a system could allow customer to find, select, discard, print and utilize coupons by only having to sort through a very small list that is customized for that particular user in a way that is quick and effective. Preferably, this system could also remember what a customer had previously selected, discarded, and printed, and then utilize that history to learn which coupons will likely be relevant to the customer in the future.
As used herein, the terms “coupon”, “coupons”, “coupon offer” and “coupon offers” refer to an offer and/or discount for a good or service. For example, the coupon may be a discount for the grocery store, a department store, or a restaurant, such as a dollar amount off items or a price reduction by a specific percentage. The coupon may also be an offer for a free item, such as a buy one get one free offer. The coupon may additionally be an offer to receive money or other credit based on a purchase or other action.
As used herein, the terms “non-transitory storage medium” and non-transitory computer-readable storage medium” refer to any media that can contain, store, or maintain programs, information, and data. Non-transitory storage medium and non-transitory computer-readable storage medium may include any one of many physical media such as, for example, electronic, magnetic, optical, electromagnetic, or semiconductor media. More specific examples of suitable non-transitory storage medium and non-transitory computer-readable storage medium include, but are not limited to, a magnetic computer diskette such as floppy diskettes or hard drives, a magnetic tape, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory, a flash drive, a compact disc (CD), or a digital video disk (DVD).
As used herein, the term “processor” refers to an instruction execution system such as a computer/processor based system, an Application Specific Integrated Circuit (ASIC), or a hardware and/or software system that can fetch or obtain the logic from a non-transitory storage medium or a non-transitory computer-readable storage medium and execute the instructions contained therein.
An example of a system 10 for presenting coupons in a personally relevant and useful order which is directed at achieving these objectives is shown in
System 10 also includes a non-transitory storage medium 14 that includes a first database of information relating to the user collected via user interface 12 and stored therein, as indicated by arrow 13. Non-transitory storage medium 14 also includes a second database of coupons. Although the first and second databases in this example of system 10 are located on single non-transitory storage medium 14, it is to be understood that in other examples, the first and second databases may be on separate non-transitory storage media or each database may be on multiple non-transitory storage media depending on a variety of factors such as the amount of information and the need for back-up redundancy.
As can be seen in
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An expanded example of system 10 is shown in
System 10 then generates an initial set of coupons in a personally relevant and useful order for user1, as indicated by block 42, as well as all other users from user1 through userN, as indicated by block 44. Each user may then interact with his or her individual initial personalized set of coupons, as indicated by block 46, as well as all other users from user1 through userN, as indicated by block 48. Such interaction may take any number of a variety of forms such as printing one or more coupons, skipping one or more coupons, selecting or “clipping” one or more coupons, or deleting one or more coupons. These individual user interactions with their initial personalized set of coupons are collected and recorded in first database 50 for user1 through database 52 for userN, as information relating to these users. Other information relating to users of system 10 may also be stored in databases 50 through 52, such as the user profiles and preferences previously entered, as indicated by blocks 38 and 40. Although databases 50 through 52 are illustrated as residing on separate non-transitory storage media in
As discussed above, coupon engine 16 aggregates, collects, and/or receives coupons from a variety of sources 18, as indicated by arrow 20, such as internet sites or e-mails. Coupon sources 18 may also include mailings, faxes, flyers, handouts, and inserts received by the operators of system 10 that have been digitally converted into electronic data, for example by scanning. These various methods of initial coupon intake are generally represented by block 54. Coupon engine then normalizes or homogenizes these coupons into a system coupon data format for use by system 10, as indicated by block 56. This system coupon data format can be any type of data format that is sufficiently uniform to allow processing and use by system 10, as described herein. Coupon engine 16 next edits the coupon data to correct things such as typos and duplicate offers (e.g., “ABCs Pizza and ABC's Pizza), as indicated by block 58. Coupon engine 16 finally stores the coupons in database of coupons 60 of a non-transitory storage medium, as indicated by arrow 62.
The above-described process engaged in by coupon engine 16 to acquire and process coupons from sources 18 can occur on a periodic bases (e.g., daily, weekly, etc.) for each individual source. Alternatively, it can occur at different periods for different sources (daily for internet-based sources and weekly for paper-based sources). Additionally, it can be initiated on an off-period basis for one or more different sources by an administrator of system 10.
As also discussed above, offer engine 24 analyzes the coupons in database of coupons 60, as indicated by arrow 64, based in part on the collected information stored in databases 50 through 52 relating to the users1 through N of system 10, as indicated by arrows 66 and 68. Subsequent to this analysis, offer engine 24 creates a new or revised set of coupons in a personally relevant and useful order for each of the users of system 10 to interact with via user interfaces 30 through 32, as indicated by arrows 70 and 72. This analysis for user1 through userN can occur at different times or simultaneously. It can also occur multiple times over users' interaction with system 10. That is, offer engine 24 can reanalyze one or more of databases 50 through 52 and database 60 any number of times to provide updated, new or revised sets of personalized coupons for any or all of the users of system 10. This reanalysis can occur on a periodic basis, each time a user logs-in to system 10, and/or it can be initiated by an administrator of system 10.
As can be seen in
Scorer 76 of offer engine 24 includes a collection of scoring modules, each of which is tuned to a specific task. Each of these modules may return a normalized score which are then individually weighted to produce a cumulative score that indicates coupon offer relevance for a particular user of system 10 (e.g., a relevancy score). For example, one scoring module may utilize user category preferences. Another scoring module may utilize specific keywords in coupons previously printed or selected by the particular user. Another scoring module may utilize categories the user had previously printed or selected. Another scoring module may utilize attribute groups and make inferences about relevancy based on previously printed or selected coupons and/or whether the particular user fits into an attribute class (e.g. pet owner, parent with young children, customer who likes to eat out, etc). The number of scoring modules of scorer 76 is dynamic and each scoring module can be change or added to based on a user-by-user basis or on a system-wide basis, depending on the particular objectives of system 10.
Once all coupon offers have passed through filer 74 and scorer 76, inapplicable coupon offers are removed and the remaining coupon offers are sorted based on their relevancy score. The one or more rules 78 are responsible for additional organizing, grouping, and placing of the remaining coupon offers within a set of coupons provided to a user based on specific rules and the relevancy score list. An example of a rule would be ensuring at least two (2) “big deal” coupon offers are presented to each user, even though such coupon offers might not have been near the top of the sorted relevancy scored list. Another rule might be that a children's theme park coupon offer shouldn't be next to an alcoholic beverage coupon offer. These rules typically enforce layout requirements, but can also affect specific needs (e.g. a new business may pay the operator of system 10 to ensure that 75% of all users see a specific offer in a given time period). It is these rules that enable system 10 to provide advertiser based targeting of offers to specific users or groups of users.
Offer engine 24 may include an additional element or component indicated by collaborate block 80 in
Each of the resulting sets of coupons generated by offer engine 24 are ordered based on relevancy to a particular individual user. Additionally, offer engine 24 helps ensure the resulting sets of generated coupons meet specific business objectives using specific rules. Each set of coupon offers can be presented to a user in a variety of ways. These include, for example, a printed index sheet that allows a user to select coupon offers by filling in bubbles and then transmit this index sheet to system 10 (e.g., by scanning or faxing), a web page that displays coupon offers to the user in a variety of dynamic and/or list based styles, as well as on mobile platforms like smart phones and tablet devices.
An example of a non-transitory computer-readable storage medium 82 is shown in
As can be seen in
An example of a method for presenting personalized coupon offers 94 is shown in
Method 94 may additionally include one or more of the following additional elements. Method 94 may include the additional element of correcting the coupons to remove duplicate offers, as indicated by block 112 in
Although several examples have been described and illustrated in detail, it is to be clearly understood that the same are intended by way of illustration and example only. These examples are not intended to be exhaustive or to limit the invention to the precise form or to the exemplary embodiments disclosed. Modifications and variations may well be apparent to those of ordinary skill in the art. For example, the offer engine may additionally analyze the coupons stored in a database based in part on additional collected information relating to a different user to create a set of coupons for the user. As another example, data may be recorded relating to interaction of a user to a present set of coupons. This recorded data may then be utilized create a new coupon that can be stored on the system and presented to the user. As an additional example, the offer engine may utilize both positive and negative feedback from a user's actions in the analysis of which coupons to present to the user (e.g., printing/clipping offers repeatedly from a given category or a given brand would cause positive reinforcement behavior for that category, brand and/or offer, while repeatedly skipping/avoiding offers would cause negative reinforcement behavior for that category, brand, and/or offer). As a further example, the offer engine may include a filter or rule relating to coupon expiration behavior (e.g., if a user has clipped an offer that is sitting in queue to be printed, and that offer expires before it can be printed, the offer engine will find an equivalent offer, if one exists, to replace that expired offer, possibly notifying the user and/or asking if this “replaced” offer should be added to his or her queue. The spirit and scope of the present invention are to be limited only by the terms of the following claims.
Additionally, reference to an element in the singular is not intended to mean one and only one, unless explicitly so stated, but rather means one or more. Moreover, no element or component is intended to be dedicated to the public regardless of whether the element or component is explicitly recited in the following claims.