WO2001004777A2 - Method, system, and article of manufacture for conducting and tracking marketing campaigns - Google Patents

Abstract

Methods and systems consistent with the present invention provide a marketing campaign system that conducts and manages marketing campaigns. The marketing campaign system recommendation system to match items or offers with users using user preferences, a distribution system to notify users, and campaign software to create promotions and track user activity. As users browse and respond to various offers, the marketing campaign system monitors, records, and analyzes their actions to create feedback used as input to be used by the recommendation system. The marketing campaign system also provides an interface to create a campaign, and to select users and items to include in the campaign. The marketing campaign system uses the received feedback to improve the personalization and effectiveness of future offers.

Description

Title of the Invention Method, System, and Article of Manufacture for Conducting And Tracking Marketing Campaigns

Related Applications

Provisional U.S. Patent Application No.60/143, 165, entitled "An Integrated Tool for Conducting and Tracing Personalized Marketing Campaigns," filed July 12, 1999, is relied upon and is incorporated by reference in this application. Background Art

A. Technical Field

This invention relates generally to data processing systems and, more particularly, to recommendation systems and marketing campaigns.

B. Description of the Related Art

A basic goal of marketing is to make the right offer to the right customer at the right time. Marketers attempt to maximize both their response rate (the percentage of contacted customers who take up the offer) and their return on investment (the ratio of revenue generated by a marketing campaign to its cost). Customers, too, have an interest in receiving the right offer and avoiding "junkmail. " Indeed, at its extreme, well-matched offers are perceived as opportunities, not as disturbances. The success of high-end retailers in art, real estate, and even businesses is knowing exactly which customers to approach for a given product.

Traditional real-world marketing campaigns are designed to target groups of individuals with non-personal offers. For example, a print advertisement for golf clubs in a sporting magazine is designed to target individuals interested in sports and, quite possibly, interested in new golf clubs. While group targeting may be economical for bulk-preprinted matter, it does not provide any flexibility. For example, these type of advertisements do not distinguish between someone who has a high degree of interest in golf and someone who has no interest in golf. Instead, the advertisements attempt to reach as many customers as possible to make an eventual sale.

The Internet has brought about new medium for marketing. For example, e-mail provides an "e-marketer" direct contact with a user's mailbox. . However, traditional e-mail suffers from the same limitations as conventional marketing campaigns. That is, traditional e-mail campaigns, such as "SPAM," use a brute force approach, and send e-mail to as many users as possible with the hope for someone to respond. Like print advertisements, these e-mail campaigns may persuade some users, however e-mail campaigns are extremely inefficient and generally a large waste of resources. It is not uncommon to receive a response rate from SPAM of less than two percent.

It is therefore desirable to alleviate the need to use brute force in e-marketing as well as improve the accuracy of e-marketing campaigns.

Disclosures of the Invention

Methods and systems consistent with the present invention solve the inherent problems with existing marketing systems by providing a marketing campaign system that conducts and manages marketing campaigns. Specifically, the marketing campaign system uses a recommendation server to match items or offers with users using user preferences. As users browse and respond to various offers, the marketing campaign system monitors, records, and analyzes their actions to create feedback used as input to be used by the recommendation system. The marketing campaign system also provides an interface to create a campaign, and to select users and items to include in the campaign. The marketing campaign system uses the received feedback to improve the personalization and effectiveness of future users.

In one implementation consistent with the present invention, a method for conducting and tracking marketing campaigns is provided. The method receives criteria in a server that define a marketing campaign, selects eligible users and eligible items from a database to include in the campaign based on the criteria, generates recommendations for the eligible users based on characteristics associated with the eligible users and the eligible items, and notifies the eligible users of the recommendations.

In another implementation consistent with the present invention, a method for personalizing marketing campaigns is provided. The method receives a set of criteria from a client, locates users and items in a database with the set of criteria, generates a recommendation for each of the located users based on characteristics associated with each user and the located items, and distributes e-mail messages to the located users, wherein each e-mail message includes information and a recommendation corresponding to the located user. Brief Description of the Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an implementation of the invention and, together with the description, serve to explain the advantages and principles of the invention.

Figure 1 depicts a data processing system suitable for practicing methods and systems consistent with the present invention.

Figure 2 depicts a more detailed diagram of the client computer depicted in Fig. 1.

Figure 3A depicts a more detailed diagram of the marketing campaign server depicted in Fig. 1.

Figure 3B depicts a more detailed diagram of the recommendation server depicted in Fig. 1.

Figure 3C depicts a more detailed diagram of the database server depicted in Fig. 1.

Figure 3D depicts a more detailed diagram of the application server depicted in Fig. 1 .

Figure 3E depicts a more detailed diagram of the e-mail server depicted in Fig. 1.

Figure 4 depicts a flow chart of the steps performed by the data processing system of Fig. 1 when providing marketing campaigns in accordance with methods and systems consistent with the present invention.

Figure 5 A depicts a more detailed flow chart of the create marketing campaign process depicted in Fig. 4.

Figure 5B depicts a more detailed flow chart of the run campaign process depicted in Fig. 4.

Figure 6 depicts a flow chart of receiving user feedback process in accordance with methods and systems consistent with the present invention.

Figure 7A depicts an example campaign report in accordance with methods and systems consistent with the present invention,

Figure 7B depicts an exemplary user interface in accordance with methods and systems consistent with the present invention.

Figure 7C depicts an example promotion in accordance with methods and systems consistent with the present invention. Detailed Description

The following detailed description of the invention refers to the accompanying drawings. Although the description includes exemplary implication, other implementations are possible, and changes may be made to the implementations ions described without departing from the spirit and scope of the invention. The following detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims. Wherever possible, the same reference numbers will be used throughout the drawings and the following description to refer to the same or like parts. Overview

Methods and systems consistent with the present invention solve the inherent problems with existing marketing systems by providing a marketing campaign system that conducts and manages personal marketing campaigns. Specifically, the marketing campaign system uses a recommendation system to match items or offers with users using user preferences. As users browse and respond to various offers, the marketing campaign system monitors, records, and analyzes their actions to create additional feedback used by the recommendation system to conduct personal marketing campaigns. The marketing campaign system also provides an interface to create a campaign, and to select users and items to include in the campaign.

A marketing campaign is one or more promotions groups together to accomplish a business goal, such as selling golf clubs. A promotion is a sublevel of a marketing campaign sent to a user, and is configurable by an administrator (e-marketer). In each promotion, the administrator builds attribute filtering criteria that define the number of users and number of items considered for each promotion. By limiting the number of users and items in each promotion, a recommendation engine can effectively match users and items in the least amount of time.

The marketing campaign system provides a number of benefits over traditional mass marketing systems. First, the marketing campaign system creates campaigns that target users with offers that include items so that the campaigns are of interest to the user. The marketing system selects eligible users and eligible items from a universe of users and items. A user is considered eligible when the user passes the attribute filtering criteria for the given promotion. The marketing campaign system creates personalized notifications that serve as a directed one-to-one relationship with each eligible user. For example, the marketing campaign system may transmit all e-mail message that includes a coupon to purchase golf to a set of eligible users that have shown an interest in golfing.

Second, the marketing campaign system continuously obtains feedback from users, in the form of community preference data and user attributes or user ratings, to improve offers to users. Feedback is defined as information received from an outside source, such as; click-through information or purchase information. Community preference data includes feedback from users with attributes similar to a particular user, such as all people who live in Minnesota; all males under the age of 21 ; all people who have HTML email reader types; or all people who have expressed an interest in travel.

User ratings may be, for example, unary, binary, or Likert. Unary ratings are ratings where positive information is available for some items, but other items have no information available. For example, purchase records are often converted into unary ratings. Items that arc bought arc rated positively; for all other items, no information is available. Binary rating are used in cases where items can be classified into "good" and "bad" (and optionally unknown), but not to different degrees of good and bad. For example, if a user has expressed interest in comedies, any movie that is a comedy would be rated 1 (good) and any movie that is not would be rated 0 (bad). Likert ratings (scales such as 1 to 5 or 1 to 7) allow a wider range of ratings. For example, if movies are to be rated based on box office success, then the top blockbusters could be rated 5, successful movies rated 4, average movies rated 3, etc. Different algorithms exist in the recommendation system to perform neighbor selection and prediction in different rating.

To obtain the feedback from users, the marketing campaign system places feedback monitors in all personalized notifications sent to users. For example, the marketing campaign system may include a specially coded Uniform Resource Locator (URL) in each e-mail message. The URL enables the marketing campaign system to obtain click-stream and purchase information from a web site each time a link is followed. A recommendation system may then use the feedback to match users and items in a future campaign.

Recommendation systems provide recommendations to users based on various attributes. For example, collaborative filtering (CF) systems are a specific type of recommendation system that recommends items to a user based on the opinions of other users. In their purest form, CF systems do not consider the content of an item at all, relying exclusively on the judgement of humans of the item's value. In this way, CF systems attempt to recapture the cross-topic recommendations that are common in communities of people. One example of a CF system is the GroupLens Research system that provides CF systems for Usenet news and movies. More information on CF systems may be found at "http://www.grouplens.org."

Third, the marketing campaign system provides an integrated marketing environment that enables the administrator to control all aspects of a marketing campaign, including filtering users and items, defining notification templates used to notify eligible users with offers, and updating databases to create future campaigns that are more targeted than the previous campaign. System Components

Fig. 1 depicts a data processing system 100 suitable for practicing methods and systems consistent with the present invention. Data processing system 100 comprises client computers 102 connected to a marketing system 110 via a network 130, such as the Internet. A user uses a client computer 102 to request and submit information to application server 118. An administrator uses a marketing campaign server 112 to create and manage marketing campaigns at marketing system 110. Recommendation server 114 is used to provide recommendations for users, database server 116 contains a listing of all available items and users, application server 118 provides an e-commerce store to users, and distribution server 120 distributes promotions to users.

Figure 2 depicts a more detailed diagram of client computer 102, which contains a memory 220, a secondary storage device 230, a central processing unit (CPU) 240, an input device 250, and a video display 260. Memory 220 includes browser 222 that allows users to interact with application server 118 by transmitting and receiving files, such as web pages. A web page may include images or textual information to provide an interface to receive ratings and requests for evaluations from a user using hypertext markup language (HTML), Java or other techniques. An example of a browser suitable for use with methods and systems consistent with the present invention is the Netscape Navigator browser, from Netscape.

As shown in Figure 3A, marketing campaign server 112 includes a memory 302, a secondary storage device 308, a CPU 316, an input device 318, and a video display 320. Memory 302 includes campaign software 304 and interface 306. Campaign software 304 creates marketing campaigns and mail messages for users. Interface 306 provides access to various servers 114, 116, 118, and 120. Interface 306 may be a web page, Application Program Interfaces (API), or other input interface. An API is a set of routines, protocols, or tools for communicating with software applications. API's provide efficient access to the campaign software without the need for additional software to interface with the campaign software. Secondary storage device 308 contains a database 310 that includes campaign table 312, which holds information relating to various campaigns, such as parameter definitions (described below), lists of users and items, and campaign configurations (e.g., rules, filtering criteria, promotion configuration, etc.). Database 308 also contains history table 314, which holds purchase information relating to users in various campaigns, such as response history, and click-throughs.

As shown in Figure 313, recommendation server 114 includes a memory 330, a secondary storage device 334, a CPU 340, an input device 342, and a video display 344. Memory 330 includes recommendation engine 332, which determines if an item should be recommended to the user. Recommendation engine 332 may use many different techniques to generate recommendations based on user preferences and user attributes. One technique that may be used to generate recommendations is automated collaborative filtering as described in Resnick, Iacovo, Susha, Bergstrom, and Riedl. "GroupLens: An Open Architecture For Collaborative Filtering Of Netnews," Proceedings of the 1994 Computer Supported Collaborative Work Conference (1994). Other recommendation techniques are described in U.S. application serial no.08/729,787, filed October 8, 1996, U.S. application serial no.08/733,806, filed October 18, 1996, U.S. application serial no. 60/155,467, filed September 23, 1999, U.S. application serial no. 09/404,597, filed September 24, 1999, U.S. application no. 09/4-38,664, filed November 12, 1999, and U.S. application serial no. 09/438,846, filed November 12, 1999, all incorporated by reference. Recommendation systems may also be based on well-known CF systems, logical rules derived from data, or on statistical or machine learning technology. For example, a recommendation system may use well-known rule-induction learning, such as Cohen's Ripper, to learn a set of rules from a collection of data as described in Good, N., Schafer, J.B., Konstan, J., Borchers, A., Sarwar, B., Herlocker, J., and Riedl, J., "Combining Collaborative Filtering with Personal Agents for Better Recommendations," Proceedings of the 1999 Conference of the American Association of Artificial Intelligence (AAAI-99). Recommendation systems may also be based on well-known data mining techniques that include a variety of supervised and unsupervised learning strategies and produce "surprising" results expressed as associations or rules embedded in a data set. Recommendation systems may also contain rating functions (models) programmed by a system administrator. The rating functions are either a formula or a table of ratings that determines business goals (e.g., the formula may specify a low rating for low-stock and out-of-stock items). These mentioned systems also require user data as input to produce personalized recommendations for users.

Recommendation engine 332 receives requests for recommendations from marketing campaign server 112. To receive the requests, recommendation engine 332 may receive the requests in a query form from interface 306. For example, marketing campaign server 1 12 may request a list of recommended items each for a given list of users. To provide the recommendations, recommendation engine 332 recommends from a list of items (or a limited list of items) for each user on the list of users. Conversely, marketing campaign server 112 may request a list of recommended users each for a given list of items. Secondary storage device 334 includes a database 336 that stores ratings in a rating table 338.

As shown in Figure 3C, database server 116 includes a memory 350, a secondary storage device 354, a CPU 358, an input device 360, and a video display 362. Memory 350 includes database software 352 that provides access to database 356 in secondary storage device 354. An example of such a program suitable for use with methods and systems consistent with the present invention is the Sybase Adaptive Server Enterprise from Sybase, of Emeryville, California. Database 356 includes information about all items as well as all users available to marketing campaign server 112. For example, database 356 may contain item characteristics, such as size, color, type, options, categories, and user characteristics, such as demographics, psychometrics, purchases, and interaction information.

As shown in Figure 3D, application server 118 includes a memory 364, a secondary storage device 368, a CPU 374, an input device 376, and a video display 378. Memory 364 includes an e-commerce application 366 that provides a web application. For example, e-commerce application 366 may be an online bookstore or an online grocer. E-commere application 366 also contains well known web server software (not shown) to transmit and receive files to the user. Secondary storage device 368 includes a database 370 that stores details of any placed orders by users in an order table 372. For example, order table 372 may store user identification, items purchased, quantity purchased, and price paid. One skilled in the art will appreciate that order table 372 may contain other details.

As shown in Figure 3E, distribution server 120 includes a memory 380, a secondary storage device 384, a CPU 386, an input device 388, and a video display 390. Memory 382 includes distribution software 382 that distributes promotions with recommendations to users. For example, distribution software 382 may distribute prepared e-mail messages created from templates. An example of such a program suitable for use with methods and systems consistent with the present invention is the Sendmail software available from Sendmail, Inc., of Emeryville, California.

Although aspects of the present invention are described as being stored in memory, one skilled in the art will appreciate that these aspects may be stored on or read from other computer readable media, such as secondary storage devices, like hard disks, floppy disks, and CD-ROM, a carrier wave received from a network like the Internet; or other forms of ROM or RAM. Additionally, although specific components and programs of client computers 102, and various servers have been described, one skilled in the art will appreciate that these may contain additional or different components or programs. Marketing Campaign Process

Figure 4 depicts a flow chart of the steps performed by marketing campaign server 112. A create marketing campaign process 402 (further described below) creates a marketing campaign consisting of a bundle of promotions for users. Each promotion contains a targeted marketing offer for a set of users, and an e-mail template the contains an offer message and tags where a user's name and recommended items are inserted before delivery. After a campaign is created, an administrator may initiate a run campaign process 404 (further described below). This entails recommending an item for users and delivering personalized e-mail messages to the users.

Once the campaign has run, marketing campaign software 304 receives feedback from various locations regarding the pending or previous Campaigns and promotions (step 406). In one instance of feedback, marketing campaign software 304 receives a notification each time the user clicks on a traceable URL. The traceable URL includes a parameter referring to the campaign promotion and directs browser 222 to interface 306 at marketing campaign server 112. When the user clicks on the traceable URL, marketing campaign software 112 records a tracking event in history table 3 14 and rating table 338, locates the promotion in campaign table 312 to identify the "real" product URL, and subsequently redirects the user's HTTP stream to the correct product page. Thus, the user is unaware of the redirection. One skilled in the art will appreciate that e-commerce application 306 may notify interface 306 of other information, such as time spent of the web page, or links traversed.

In another instance of feedback, marketing campaign software 304 receives information on the number of promotional items viewed and/or bought from application server 118. At a predesignated time, application server 118 may locate promotional items bought in order table 372. Regardless of the type of feedback received from the users, the feedback identifies the user and items involved, and is submitted to history table 314, and rating table 338 in recommendation server 118. Once the databases are updated with the feedback, future and more accurate campaigns and promotions may be created using the feedback.

After feedback is received, an admimstrator may run various reports to evaluate the progress I of the campaign (step 408). For example, the administrator may generate a report that depicts the number of promotional items purchased by the users or click- through rate for the users. Figure 7A depicts an exemplary report 700 for use with methods and systems of the present invention. Further details and operations of create marketing campaign process 402 and run campaign process 404 will now be explained with reference to the flowcharts of Figures 5A and 5B.

Create Marketing Campaign Process

As shown in Fig. 5A, create marketing campaign process 402 is initiated, for example, by defining a campaign universe (Step 502). The campaign universe includes a list of user names available for the campaign. For example, the universe may include all male users with valid e-mail addresses. Once the universe is defined, promotions are created within the campaign universe (step 504). Each promotion is directed to a portion of the campaign universe, and includes eligible users to send a promotion to and eligible items to include in the promotion. Eligible users and items are selected from database 356. Database 356 may be linked to e-commerce server database 370 to provide live item characteristics and prices. Alternatively, database 356 may receive periodic updates for items and users. One skilled in the art will appreciate that database 356 may be populated in other ways, such as by importing item catalogs.

To create the promotion, marketing campaign software 304 locates eligible users and items by filtering users and items from a set of all users and items located in the campaign universe in database 356 (step 506). Marketing software 304 uses attribute filtering criteria, such as (LAST NAME, AGE, DATE_OF_BIRTH,LOCATION, CATEGORY, etc.), operators (e.g., = <, >=, >, >=, etc.), values (e.g., 100, SMITH, 1/1/1995), and conjunctions (e.g., AND, OR) to filter the users and items. For example, a promotion may contain a geographic limitation that includes only east coast users and a category limitation that includes only sporting goods. Marketing campaign software 304 converts the inputted information to a Structured Query Language (SQL) statement used to filter users and items. Users that are located containing characteristics that match the criteria are labeled eligible users, and items that are located containing characteristics that match the criteria are labeled eligible items. One skilled in the art will appreciate that other customizable characteristics may be used to locate eligible users and items, such as age, gender, dislikes, or cost of items. Alternatively, eligible users and items may be randomly selected for promotions. Once eligible users arid items are located, an accompanying e-mail template is created for the promotion (step 508). The administrator may create an e-mail template using interface 306. Figure 7B depicts an exemplary e-mail template interface 702 used with methods and systems consistent with the present invention to create an e-mail template 704. Once the filtering and template are completed, eligible users and items and the associated e-mail template 704 are stored in campaign table 312 as a single promotion, (step 510).

Once the template and eligible users and items arc stored in campaign table 312, the administrator may create additional promotions for the campaign (step 512). For example, one promotion may target cast coast users, while a second promotion may target west coast users. Multiple promotions may be included in a single marketing campaign to ensure that sets of users within the campaign universe receive a promotion, or to limit the number of promotions directed to each user. Run Campaign Process

Figure 5B depicts a flow chart of the steps performed when marketing software 304 initiates a campaign. A campaign may be initiated, for example, by receiving an indication to initiate the campaign (step 514). An indication may be from a scheduled event. A scheduled event may be a "one-time" event (e.g., in administrator creates the campaign and launches it immediately), or a scheduled event may be a "recurring" event (e.g., an administrator creates the campaign and creates a schedule for the campaign, such as run once a week). Alternatively, a triggered event may initiate the run campaign process. In triggered campaigns, the administrator first creates campaign rules and, associates the rules with a triggering event (e.g., transmittal of an order confirmation to a user from application server 118). Each time an event occurs at application server 118, application server 118 notifies marketing campaign software 304 through interface 306 (e.g., an API) to include the user that triggered the triggering event (e.g., placing an order) as an eligible user in the campaign. Therefore, each time a user triggers the event, that user will receive an e-mail using the method described below.

Regardless of the method used to initiate the run campaign process, marketing campaign software 304 then queries recommendation engine 332 to return recommendations for eligible users and items (step 516). Marketing campaign software 304 provides recommendation engine 332 with a list of user IDs, item IDs, or both from campaign table 312. Recommendation engine 332 uses the rating information in rating table 336 to match users and items. A recommendation may be based on click-throughs, historical purchases for the user, or cross sell lists. Once the recommendation request is received at recommendation engine 332, the engine may use the different techniques described above to generate a list of recommendations, and return the list to marketing campaign software 304.

Marketing campaign software 304 then merges the recommendation and data identifying each user with an e-mail template to create an e-mail message for that user (step 518). By merging the information, a personalized e-mail message is distributed to each user listed in the promotion. Also, tracking information is created and inserted into each e-mail message (step 520). For example, tracking information may be a traceable URL. Each time a user clicks the URL to access web pages located at application server 118, after marketing campaign software 304 records the user click-through and forwards the request to application server 118, e-commerce application 366 continues to follow the user's activity, and notifies marketing campaign software 304 of the user's activity, such as purchasing a promotional item, or viewing additional web pages. An exemplary personalized e-mail message 720 for use with methods and systems of the present invention is depicted in Figure 7C. E-mail message 720 includes recommended items 722, and traceable URLs 724.

Finally, the e-mail message is sent to distribution server 120 (step 522) whereupon each personalized e-mail message 720 is delivered using well-known delivery techniques (step 524). User Feedback Process

Figure 6 depicts a flow chart of the steps performed when receiving feedback from a user. The first step performed is for the user to visit application server 118 using the tracking information placed in e-mail 720 (step 602). For example, the user may view a web page at application server 118 using the traceable URL. When visited, e-commerce application 366 notifies marketing campaign software 304 using interface 306 of user activity. For example, each time a user views a web page at application server 118, information is sent to both campaign server 112 and recommendation server 114 using well-known transmission techniques, such as HTTP or TCP/IP. Additionally, if a user purchases a promotional item (step 606), e-commerce application 366 updates order table 372 (step 608) and then notifies marketing campaign software 304 as well as recommendation engine 332 (step 610) of the purchase. Conclusion

As explained, systems consistent with the present invention overcome the shortcomings of existing marketing systems by providing a marketing system for conducting marketing campaigns.

The foregoing description of an implementation of the invention has been presented for purposes of illustration and description. It i not exhaustive and does not limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practicing of the invention. For example, the described implementation includes software but the present invention may be implemented as a combination of hardware and software or in hardware alone. The invention may be implemented with both object-oriented and non-object- oriented programming systems.

Claims

Claims

1. A marketing system for conducting and tracking marketing campaigns, comprising: a database containing items and users, an interface for receiving criteria that define a marketing campaign; a selecting module for selecting eligible users and eligible items from the database to include in the campaign based on the criteria; and a recommendation engine for generating recommendations for the eligible users based on characteristics associated with the eligible users and the eligible items.

2. The system of claim 1 , further comprising a notification subsystem for notifying the eligible users.

3. The system of claim 1 , wherein the interface is a web interface.

4. The system of claim 2, wherein the notification subsystem further creates e-mail messages by inserting the recommendations and characteristics associated with each eligible user into an e-mail template that is used to create the e-mail message.

5. The system of claim 2, wherein the notification subsystem further creates an e-mail message for each eligible user, includes common text and a recommendation associated with the eligible user in the e-mail message, and transmits the e-mail message to the associated eligible user.

6. The system of claim 2, wherein the notification subsystem further notifies each eligible user with an e-mail message that includes a traceable URL.

7. The system of claim 1 , wherein the recommendation engine matches a user with at least one item based on characteristics associated with the eligible users and the eligible items.

8. The system of claim 1, wherein the marketing system further comprises a feedback subsystem that receives click-stream information or purchase-stream information from an eligible user based on a traceable URL, and that updates the database and the recommendation engine based on the received information.

9. The system of claim 8, wherein the feedback subsystem further updates the recommendation engine by providing the information as user preference data.

10. The system of claim 9. wherein the marketing system further comprises a reporting subsystem that creates reports based on the feedback.

11. The system of claim 1 , wherein the marketing system further comprises a feedback subsystem that receives information from an eligible user based on the eligible user's purchases or web page views, and that updates the database and the recommendation engine based on the received information.

12. The system of claim 1, wherein a user is eligible with characteristics corresponding to the user match the criteria, and wherein an item is eligible when characteristics corresponding to the item match the criteria.

13. The system of claim 12, wherein the selection module further selects a random number of users in the database.

14. The system of claim 1 , wherein criteria include logical operations, demographic characteristics, or category characteristics.

15. The system of claim 1, further comprising a reporting subsystem that provides reports that indicate the status of the marketing campaign.

16. The system of claim 1, wherein the recommendation engine generates recommendations for the eligible users based on a triggered event.

17. A method for conducting and tracking marketing campaigns, comprising the steps, executed in a data processing system, of: receiving criteria in a server that define a marketing campaign; selecting eligible users and eligible items from a database to include in the campaign based on the criteria; and generating recommendations for the eligible users based on characteristics associated with the eligible users and the eligible items.

18. The method of claim 17, wherein receiving criteria includes the step of receiving the criteria from an interface.

19. The method of claim 17, further including the step of notifying the eligible users of the recommendations.

20. The method of claim 19, wherein notifying the eligible users further includes the steps of creating e-mail messages; and inserting the recommendations and characteristics associated with each eligible user into e-mail template that is used to create the e-mail message.

21. The method of claim 19, wherein notifying the eligible users includes the steps of creating an e-mail message for each eligible user; including common text and a recommendation associated with the eligible user in the e-mail message, and transmitting the e-mail message to the associated eligible user.

22. The method of claim 19, wherein notifying the eligible user includes the step of notifying the eligible user with an e-mail message that includes a traceable URL.

23. The method of claim 22, further including the steps of: receiving click-stream information and purchase-stream information from an eligible user based on the traceable URL; and updating the database and a recommendation engine based on the received information.

24. The method of claim 23, wherein updating the database and recommendation engine further includes the step of updating the recommendation engine by providing the information as user, preference data.

2 5. The method of claim 26, further including the step of creating a report based on the click-stream and purchase information.

26. The method of claim 17, further including the steps of: receiving rating information from an eligible user based on the user's purchases or web page views; and updating the database and a recommendation engine based on the received information

27. The method of claim 17, wherein a user is eligible when characteristics corresponding to the user match the criteria, and wherein an item is eligible when characteristics corresponding to the item match the criteria.

28. The method of claim 27, wherein selecting eligible users and items includes the step of locating, in the database, a random number of users.

29. The method of claim 17, wherein criteria include logical operations, demographic characteristics, or category characteristics.

30. The method of claim 17, further including the step of providing reports that indicate the status of the marketing campaign.

31. The method of claim 17, wherein recommendations are generated for the eligible users based on a triggered event.

32. A method for personalizing marketing campaigns, comprising the steps, executed in a data processing system, of: receiving a set of criteria; locating users and items in a database with the set of criteria; generating a recommendation for each of the located users based on characteristics associated with each user and the located items; and distributing e-mail messages to the located users, wherein each e-mail message includes information and a recommendation corresponding to the located user.

33. The method of claim 32 , wherein generating a recommendation includes the step of generating a recommendation for each of the located items based on characteristics associated with the located users and each item.

34. The method of claim 33, wherein distributing includes the steps of: creating an e-mail template that contains text and a placeholder for a recommendation and user information; and merging the user information and recommendations with the e-mail template to create an individualized e-mail message for each of the located users.

35. The method of claim 32, wherein distributing includes the step of transmitting the e-mail message with a traceable link.

36. The method of claim 34, further including the steps of: tracking user activity with the e-mail messages using a traceable URL in the e-mail message; and updating the database to indicate any user activity.

37. The method of claim 36, wherein tracking user activity includes the step of receiving click-through and purchase data from a web server that receives the traceable URL.

38. The method of claim 36, wherein updating the database includes the step of receiving from a located user a response that includes a rating based on the distributed e-mail message.

39. A computer for personalizing marketing campaigns, comprising: a transmission module that receives a set of criteria from a client; a memory containing: a database; and a program that locates users and items in the database with the set of criteria, and that generates a recommendation for each of the located users based on characteristics associated with each user and the located items; a distribution module that distributes e-mail messages to the located users, wherein each e-mail message includes information and a recommendation corresponding to the located user; and a processor configured to run the program.

40. The computer of claim 39, wherein the program further generates a recommendation for each of the located items based on characteristics associated with the located users and each item.

41. The computer of claim 39, wherein the distribution module further creates an e-mail template that contains text and a placeholder for a recommendation and user information, and merges the user information and recommendations with the e-mail template to create an individualized e-mail message for each of the located users.

42. The computer of claim 39, wherein the distributing module further transmits the e-mail message using a traceable link.

43. The computer of claim 39, further comprising: a feedback module that tracks user activity with the e-mail messages using a traceable URL in the e-mail message; and wherein the program further updates the database to indicate any user activity.

44. The computer of claim 43, wherein the transmission module further receives click-through and purchase data from a web server that receives the traceable URL.

45. The computer of claim 43, wherein the transmission module further receives from a located user a response that includes a rating based on the distributed e-mail message.

46. A computer readable medium for controlling a data processing system to perform a method for conducting and tracking marketing campaigns, executed in a data processing system, the computer readable medium comprising: a receiving module for receiving criteria in a server that define a marketing campaign; a selecting module for selecting eligible users and eligible items from a database to include in the campaign based on the criteria; and a generating module for generating recommendations for the eligible users based on characteristics associated with the eligible users and the eligible items.

47. The computer readable medium of 46, wherein the receiving module further receives criteria includes the step of receiving the criteria from an interface.

48. The computer readable medium of claim 46, further including a notifying module for notifying the eligible users of the recommendations.

49. The computer readable medium of claim 48, wherein the notifying module further: creates e-mail messages; and inserts the recommendations and characteristics associated with each eligible user into an e-mail template that is used to create the e-mail message.

50. The computer readable medium of claim 48, wherein the notifying module further: creates an e-mail message for each eligible user; includes common text and a recommendation associated with the eligible user in the e-mail message; and transmits the e-mail message to the associated eligible user.

51. The computer readable medium of claim 48, wherein the notifying module further notifies the eligible user with in e-mail message that includes a traceable URL.

52. The computer readable medium of claim 51 , further includes: a receiving module for receiving click-stream information and purchase-stream information from an eligible user based on the traceable URL, and an updating module for updating the database and a recommendation engine based on the received information.

53. The computer readable medium of claim 52, wherein the updating module further updates the recommendation engine by providing the information as user preference data.

54. The computer readable medium of claim 53 , further including a report module for creating reports based on the click-stream and purchase information.

55. The computer readable medium of claim 53 , further including a reporting module for providing reports that indicate the status of the marketing campaign.

56. The computer readable medium of claim 46, further includes: a receiving module for receiving information from an eligible user based on the eligible user's purchases and web page views; and an updating module for updating the database and a recommendation engine based on the received information.

57. The computer readable medium of claim 46, wherein a user is eligible when characteristics corresponding to the user match the criteria, and wherein an item is eligible when characteristics corresponding to the item match the criteria.

58. The computer readable medium of claim 57, further including a locating module for locating, in the database, a random number of users.

59. The computer readable medium of claim 57, wherein criteria include logical operations, demographic characteristics, or category characteristics.

60. The computer readable medium of claim 46, wherein recommendations are generated for the eligible users based on a triggered event.