MATCHING SOCIAL MEDIA USER TO MARKETING CAMPAIGN

Abstract

Disclosed are systems and methods that gather information in levels of various categories about the “likes” of a user of a social site and, by assigning a weight factor to each level of category of “likes”, a “match” score for the user is calculated. The calculation of “match” scores can be performed for a plurality of users to obtain a set of users who are potential customers of a marketing campaign. The set of users whose “likes” are evaluated can be compared to the “target” of an advertising campaign to generate potential customers for that campaign; this serves to improve the conversion rate for the advertising campaign. Alternatively, the “like” profiles can be used to analyze the advertising campaign portfolio of an entity and provide recommendations as to those advertising campaigns likely to be of interest to a particular set of users, based on calculated “like” profiles.

Description

CROSS-REFERENCED APPLICATIONS

This application is related, and claims priority, to U.S. Provisional Application Ser. No. 61/791,042, filed on Mar. 15, 2013 that is incorporated herein in its entirety by reference. This application is also related, and claims priority to, U.S. Provisional Application Ser. No. 61/788,969, filed on Mar. 15, 2013 that is also incorporated herein in its entirety by reference.

BACKGROUND OF THE DISCLOSURE

1. Field of the Disclosure

The present disclosure relates generally to systems and methods for determining and identifying individuals who may be receptive to a marketing campaign. More particularly, the present disclosure relates to systems and methods for determining and identifying a social media user to a marketing campaign.

2. Background of the Disclosure

Web or network-based applications having a social aspect are increasing in both number and popularity. For example, websites such as Facebook®, Twitter®, and LinkedIn®, to name just a few, are fast becoming some of the most visited and used websites on the Internet. These sites provide channels of communication, comment, viewpoint, etc., for all the users, as well as for the people with who the user is in contact, or who are in contact with the user, whether directly or indirectly. Although each of these social sites is quite different, they share in common some key concepts. For instance, each of these social sites allows a user to define his or her relationship with other users, and in some instances, other objects or entities. These relationships can be defined as, e.g., “followers” on Twitter®, “friends” on Facebook® or “connections” on LinkedIn®. In addition, each of these social sites allows users to provide comments concerning the information displayed, or shown, by other users with who the user is in contact, such as “likes” and/or “comments” relating to postings, messages, viewpoints, reviews, pictures and the like.

Entities, such as advertisers, websites and other marketers, are constantly revising and updating their marketing strategies in an effort to have their advertising/marketing campaigns be more effective. One measure of the effectiveness of an advertising/marketing campaign is to measure the “conversion” rate of the campaign. In general, conversion rate refers to the number of customers obtained versus the number of potential customers contacted/sent/exposed to an advertising/marketing campaign. Stated otherwise, conversion rate is the percentage of potential customers who take a desired action.

In the online world, the desired action can take many forms, varying from site to site. Examples include sales of products, membership registrations, newsletter subscriptions, software downloads, or just about any activity beyond simple page browsing. A high conversion rate depends on several factors, all of which must be satisfactory to yield the desired results—the interest level of the visitor, the attractiveness of the offer, and the ease of the process. The interest level of the visitor is maximized by matching the right visitor, the right place, and the right time.

Traditional data collected and stored in entity information systems in user profiles generally includes demographics, ARPU, ordering habits, usage and so on. This data can be considered “high” level data in that it touches upon potential customers based on general characteristics, but it does not take into consideration or analyze the specifics of a potential customer's likes, dislikes and/or preferences.

Thus, there exists a need for systems and methods that can identify potential customers with more likelihood that the potential customer will have a level of interest matching a particular advertising/marketing campaign. Conversely, a need exists for identifying a particular advertising/marketing campaign that will likely be more appealing and attractive to a set or subset of potential customers.

These and other needs are met according to the present disclosure, as will be more fully described in the paragraphs that follow.

SUMMARY OF THE DISCLOSURE

The present disclosure provides improved conversion rates on campaigns so that offers sent to prospects should be unique and compelling. Targeting the most likely potential customers based on relevant data that provides a more precise picture of the potential customer's likes can identify those customers who are most likely to be positively influenced by a particular advertising/marketing campaign. The more specific, detailed and comprehensive the picture that is able to be gained about the potential customer(s), the more effective a personalized campaign will be. Enriching the profile of potential customers with social information that includes likes, interests, preferences, social activity and what friends are doing can open up a new level of personalization.

The present disclosure provides a component to the profile of potential customer(s) that is aimed at extending the personalization capabilities of the advertising/marketing campaigns of an entity such as an advertiser or website by leveraging data collected from interacting with the user in the multiple social media networks. Therefore, according to the present disclosure, it is now possible to identify with more confidence potential customers who are likely be attracted to a particular advertisement.

The present disclosure also provides that an entity, such as an advertiser or website or the like, will be able to select among its portfolio of marketing campaigns to identify those that are more likely to have impact upon a set of potential customers.

One embodiment of the present disclosure provides systems and methods that identify a set of users that meet the characteristics defined by the campaign (target audience). The result of these methods and systems is a list of users ranked by their level of appropriateness for the campaign. This information will enable an entity, such as an advertiser or website or the like, to distribute campaigns to the ideal target audience without having to manually segment and analyze customer profiles. The campaign owner will only have to define the attributes of the campaign and their weights. The entity, such as an advertiser or website or the like, will have the option to configure a threshold score so that the system returns only users whose scores are above the threshold.

Another embodiment of the present disclosure provides systems and methods that identify for a specific user, a set of the most appropriate campaigns in an entity's list of campaigns ranked by the level of suitability to the user. This information will enable an entity, such as an advertiser or website, to propose the most beneficial offers to a customer without having to browse through the full campaign catalog and estimate which campaign will be optimal for the user's needs.

According to the present disclosure, each potential customer-campaign relationship is represented by a score calculated by the systems and methods of the present disclosure. The score is derived by the quality of the match.

Also according to the present disclosure, each campaign attribute is given a weight that represents the level of its importance for the matching. Then, according to the present disclosure, the attribute is matched with the user profile characteristics (based on his/her interests, behavior, preferences, demographics, and the like), and the result of the match is a final score for the campaign that is the sum of all campaign-attribute/user-characteristic scores.

Also according to the present disclosure, to make these mechanisms applicable, the systems and methods also include the option to distribute the campaigns through social channels, as well as to track the success of the campaigns (by monitoring the conversion rate). By using the systems and methods of the present disclosure, the entity, such as an advertiser or website, can gain several advantages, such as faster time to market of new campaigns, greater effectiveness in targeting customers, simplified management of campaign catalogue(s), and detailed tracking of campaign success through social channels.

The present disclosure further provides a system and a method that includes: gathering information in levels of various categories about the “likes” of a user of a social site; assigning a weight factor to each level of category of “likes”; multiplying the weight factor for each level of category of “likes” of information to obtain a product for each level of category of information; and adding the products obtained to obtain a “match” score for the user.

The present methods and systems further provide for repeating the gathering, assigning, multiplying and adding for a plurality of users. In this way, the systems and methods obtains a set of users who are potential customers of the entity such as an advertiser or website. The set of users can be ranked in order of “match”. However, unlike the high level analysis that is presently undertaken such as by looking at demographics (as mentioned above), the systems and methods of the present disclosure employ multi-level analysis that looks at different levels of specificity of the “likes” (or equivalent, depending on the particular social site) of a potential customer to ultimately provide matches of interest(s) that are close or identical to the “target” of the advertising campaign(s) of the entity.

The method of the above embodiment can also include updating the information of a user based upon the user's activity while logged onto a social site that includes: recognizing that a user of a social site is logging onto a social site; accessing the social site concurrently with the user logging on; monitoring activity of the user while the user is on the social site; gathering information concerning the activity of the user while the user is logged onto the social site; and updating the “likes” (or equivalent, depending on the particular social site) of the user using the information gathered concerning the user activity while the user is logged onto the social site.

According to the present disclosure, there is also provided an apparatus/system that performs the method including: a processor; and a memory that contains instructions that are readable by said processor and cause said processor to: gather information in levels of various categories about the “likes” of a user of a social site; assign a weight factor to each level of category of “likes”; multiply the weight factor for each level of category of “likes” of information to obtain a product for each level of category of information; and add the products obtained to obtain a “match” score for the user. The apparatus/system further provide for repeating the instructions to gather, assign, multiply and add for a plurality of users. In this way, the processor apparatus/system of the present disclosure allow for obtaining a set of users who are potential customers of the entity such as an advertiser or website. The set of users can be ranked further in order of “match”. The apparatus/system can also include instructions that cause the processor to: recognize that the user of a social site is logging onto a social site; access the social site concurrently with the user logging on; monitor activity of the user while the user is on the social site; gather information concerning the activity of the user while the user is logged onto the social site; and update the absolute social score of the user using the information gathered concerning the user activity while the user is logged onto the social site.

According to the present disclosure, there is also provided a storage device comprising instructions that are readable by a processor and cause the processor to: gather information in levels of various categories about the “likes” of the user of a social site; assign a weight factor to each level of category of “likes”; multiply the weight factor for each level of category of “likes” of information to obtain a product for each level of category of information; and add the products obtained to obtain a “match” score for the user. The apparatus/system further provide a storage device comprising instructions that are readable by the processor and cause the processor to gather, assign, multiply and add for a plurality of users. In this way, the processor of the present disclosure can obtain a set of users who are potential customers of the entity such as an advertiser or website. The set of users can also be ranked in order of “match” strength or closeness to the particular advertising campaign. The apparatus/system can include instructions that further cause the processor to: recognize that the user of a social site is logging onto a social site; access the social site concurrently with the user logging on; monitor activity of the user while the user is on the social site; gather information concerning the activity of the user while the user is logged onto the social site; and update the absolute social score of the user using the information gathered concerning the user activity while the user is logged onto the social site.

According to the present disclosure, the “likes” (or equivalent, depending upon the social site) of the user of a social site are compiled and can be “matched” to an advertising campaign of an entity in any one of a number of ways. For instance, an entity can have a particular advertising campaign that it wishes to promote and requests the identification of users of a social site most likely to be positively influenced by the campaign. In this instance, the methods and system of the present disclosure are capable of comparing the attributes of the particular advertising campaign to the compiled “likes” of a plurality of social site users. Thereafter, a list of users of the social site best “matching” the attributes of the particular advertising campaign can be selected and targeted. Alternatively, an advertising campaign portfolio of an entity can be analyzed using the methods and systems of the present disclosure and the best “matching” campaigns of the entity can be identified and selected for presentation to the list of social site users best fitting the particular advertising campaign.

The apparatus/system and methods described herein are applicable to any social site for the gathering of information concerning the activity and evaluation of one or more user's “match” score and for determining the relative “match” score for a plurality of users. The evaluation can be tailored to the needs or interests of any entity having a desire to know which user(s) may be interested or influenced with respect to any one or more of a set of advertising campaigns offered by that entity.

All such embodiments as mentioned above are included within the teachings of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system that employs the techniques described herein;

FIG. 2 is a general hierarchy chart of a system and method of the present disclosure;

FIG. 3 is an exemplary specific hierarchy chart of a system and method of the present disclosure;

FIG. 4 is an exemplary comparative table between a user's interest profile and an exemplary campaign profile;

FIG. 5 is an exemplary score calculation and match calculation for the comparative tables of FIG. 4; and

FIG. 6 is a flow chart of a process that employs the techniques described herein.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to the drawings and, in particular, FIG. 1, a system generally represented by reference numeral 100 is shown that employs the methods described herein. System 100 includes a server 105, a user device 135, a social network server 180, and a client server 190, each of which is communicatively coupled to a network 170, e.g., the Internet. User device 135 is utilized by a user 101.

Server 105 includes a processor 110 and a memory 115. Although server 105 is represented herein as a standalone device, it is not limited to such, but instead can be coupled to other devices (not shown) in a distributed processing system. Server 105 is also communicatively coupled to a database 125. Server 105 can also operate to support performance of relevant operations of system 100 in a “cloud computing” environment or within the context of “software as a service” (SaaS). At least some operations of server 105 can be performed by a group of computers (as examples of machines including processors), these operations being accessible via network 170 via one or more appropriate interfaces, e.g., application program interfaces (APIs).

Processor 110 is an electronic device configured of logic circuitry that responds to and executes instructions. Memory 115 is a tangible computer-readable storage device encoded with one or more computer programs. In this regard, memory 115 stores data and instructions readable and executable by processor 110 for controlling the operation of processor 110. Memory 115 can be implemented in a random access memory (RAM), a hard drive, a read only memory (ROM), or a combination thereof. One component of memory 115 is a program module 120.

User device 135 includes a user interface 140, a processor 150 and a memory 160. User 101 utilizes user device 135 to access social network server 180, e.g., Facebook®, Twitter®, and/or LinkedIn®, via network 170. User device 135 can be implemented, for example, as a cell phone, a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), or any device capable of executing instructions, sequential or otherwise, that specify actions to be taken by that device.

User interface 140 includes a display 141 and a keyboard 142. Display 141 is a device by which system 100 presents information in visual form to user 101. By keyboard 142, user 101 inputs information to user device 135, and to social network server 180 via network 170. User interface 140 also includes a cursor control mechanism (not shown), such as a mouse, track-ball, joy stick, or a touch-screen, that is compatible with display 141 that allows user 101 to manipulate a cursor (not shown) on display 141 for communicating additional information and command selections to user device 135 and social network server 180.

Processor 150 is an electronic device configured of logic circuitry that responds to and executes instructions.

Memory 160 is a tangible computer-readable storage device encoded with a computer program. In this regard, memory 160 stores data and instructions readable and executable by processor 150 for controlling the operation of processor 150. Memory 160 can be implemented in a RAM, a hard drive, a ROM, or a combination thereof. One component of memory 160 is a program module 161.

The term “module” is used herein to denote a functional operation that can be embodied either as a stand-alone component or as an integrated configuration of a plurality of subordinate components. Thus, each of program modules 120 and 161 can be implemented as a single module or as a plurality of modules that operate in cooperation with one another. Moreover, although program modules 120 and 161 are described herein as being installed in memory 115 and memory 160, respectively, and therefore being implemented in software, they could be implemented in any of hardware, e.g., electronic circuitry, firmware, software, or a combination thereof.

While program modules 120 and 161 are indicated as already being loaded into memories 115 and 160, respectively, they can be configured on a storage device 175 for subsequent loading into memories 115 and 161. Storage device 175 is a tangible computer-readable storage medium that stores program modules 120 and 161 thereon. Examples of storage device 175 include a compact disk, a magnetic tape, a read only memory, an optical storage media, a hard drive or a memory unit having multiple parallel hard drives, and a universal serial bus (USB) flash drive. Alternatively, storage device 175 can be a random access memory, or other type of electronic storage device, located on a remote storage system (not shown) and coupled to server 105 and user device 135 via network 170.

Client server 190 accesses server 105 via a password or other security mechanism to, in turn, access data from server 105 for reasons and methods described in more detail herein below.

In practice, system 100 will include participation by many users (not shown) each of which employ a respective user device (not shown) similar to that of user device 135 to utilize and interact with social network server 180.

FIG. 2 shows a general hierarchy 200 of a system and method of the present disclosure. In FIG. 2 there is an advertising campaign chart 210 and a social user “likes” chart 260. Advertising campaign 210 has various levels of “categories”, designated as level “1” 220, level “2” 230, level “n” 240 and level “n+1” 250. Likewise, social user “likes” 260 has various levels of “categories”, designated as level “1” 270, level “2” 280, level “n” 290 and level “n+1” 295. The number of levels set forth in FIG. 2 has been kept to four (4) for simplicity sake, but many more levels are possible depending upon the needs of any particular desired degree of “match” between social user “likes” 260 and advertising campaign 210. Similarly, each level 1, 2, n and n+1 is divided into various subcategories. For example, advertising campaign 210 level 1 220 is divided into four (4) subcategories 221, 222, 223 and 224, while social user “likes” 260 level 1 270 is divided into four (4) subcategories 271 [Phil-not in FIG. 2—incorrectly states 221], 272, 273 and 274. Once again, for simplicity sake, the number of subcategories in each level in FIG. 2 has been limited. Of course, as will be apparent, the number of categories, i.e., level 1, level 2, level n and level n+1, and the number of subcategories can be as many as desired in order to obtain the degree of specificity in the “match” between advertising campaign 210 and social user “likes” chart 260. Also in FIG. 2, each level, e.g., 220, 270 is designated with a “weight” W¹, W², Wⁿ and Wⁿ⁺¹ that will be explained in more detail with respect to the Figures that follow. In general, each of weights W¹, W², Wⁿ and Wⁿ⁺¹ increases in value as the respective level of category with which the weight is associated increases in number. Stated otherwise, the weight for category level n+1 greater that the weight for category level n, which is greater than the weight for category level 2, and so forth.

FIGS. 3-5 are a specific example 300 of a how a “match” between social user “likes” 260 and advertising campaign 210 is determined according to the present disclosure. In FIG. 3, level 1 220 of advertising campaign 210 is divided into subcategories, including, “sports” 222 and “music” 223 [Phil-not in FIG. 3—incorrectly states 233]. Likewise, level 1 270 of social user “likes” 260 is divided into subcategories, including “movies” 271, “sports” 272, and “music” 273. For purposes of FIG. 3, further subcategories of “music” 223, 273 will be discussed. First, focusing on advertising campaign 210, subcategory “music” 223 is further broken down into subcategories on level 2 230, including “pop” 2321, which is then further broken down in level 3 240 into subcategories including “musician” 241. Providing more specificity, musician subcategory of level “n” of advertising campaign 210 includes “tickets to Lady Gaga” concert at level “n+1”.

Turning to social user “likes” 260, in level 270 “music” 273 matches with the “music” 223 of advertising campaign 210. The “match” between social user “likes” 260 and advertising campaign 210 in level 1 220, 270, is accorded a weight 1, W¹. Moving to level 2 280, among social user's “likes” chart 260 within “music” 273 in level 1 is “pop” 282 in level 2. In advertising campaign 210, the level 2 target is likewise “pop” 232. Thus, again, the level 2 interests of social user “likes” c260 matches advertising campaign 210. The “match” between social user 260 and advertising campaign 210 in level 2 230, 280, is accorded a weight 2, W², that is greater than W¹. The relative weights between levels can be assigned in any particular circumstance depending up the needs/desires of advertising campaign 210. Continuing, FIG. 3 shows that in level 3, social user “likes” 260 and advertising campaign 210 again provide a “match” with “musician” 241, 291, which is accorded a weight 3, W³. Continuing further, as shown in FIG. 3, in level 4 250, 295 there is no “match” between social user “likes” 260 (“Madonna”) and advertising campaign 210 (“Tickets to Lady Gaga”).

FIG. 4 shows a social user “likes” profile 400 in table form. Social user “likes” profile 400 in FIG. 4 is more complete than exemplified in FIG. 3. Advertising campaign profile 410 is set forth well, with the ultimate target of the campaign to locate and interest users in “Lady Gaga Concert Tickets”. As can be seen from FIG. 4, social user “likes” profile 400 matches advertising campaign profile 410 in category levels 1, 2 and 3, but do not match in category level 4. In more detail, FIG. 4 shows that social user “likes” profile 400, in category level 1 270 (“music”), matches three (3) times (guitar, U2 and Madonna) with advertising campaign profile 410. Continuing, social user “likes” profile 400, in category level 2 280 (“pop” (genre)) matches one (1) time with advertising campaign profile 410, and in category level 3 290 (“musician/band”) matches one (1) time with advertising campaign profile 410 (under the category level 2 match). Lastly, in category level 4 295 social user “likes” profile 400 does not match with advertising campaign profile 410.

FIG. 5 shows a “weighted” calculation for the matches between social user “likes” profile 400 and advertising campaign profile 410 of FIG. 4. In FIG. 5, the “weights” per category level “match” are set forth in the table 510 [Phil-not shown in FIG. 5]. In table 510, the weight for a category level 220 match 501 is 1; the weight for a category level 230 match 502 is 2; the weight for a category level 240 match 503 is 5 and the weight for a category level 2504 match 504 is 10. The individual calculated scores for each category level match between advertising campaign profile 410 and social user “likes” profile 400 is set forth in score consolidation table 520. The total score of matches for social user “likes” profile 400 forth in FIG. 4 is “12”. The matching and score consolidation for any number of a plurality of users according to the present disclosure can be calculated in accordance with the description respect set forth in FIGS. 3-5.

User(s) 101 usually have information on two levels. The first level is public information; information which is generally available to everyone, such as a profile on Facebook® or LinkedIn®, and this information is available and can be gathered whether or not the user(s) 101 are logged onto the site. In some cases, the publicly available information is obtained by querying a social site, for example, via an application programming interface (API) request. For instance, social network services provide various public interfaces (e.g., API's) through which information can be obtained. Also, all user(s) 101 have an electronic ID which, if available to server 105, allows server 105 to identify user(s) 101 moving from one social site to another, and to determine if user(s) 101 can be considered a social leader on more than one social site.

The second level is private information; information that can only be seen by, e.g., “friends” or perhaps “friends of friends”. This information can only be gathered when user(s) 101 are actively logged onto the social site. As user(s) 101 log onto a social site, social network server 180 notifies server 105 of user(s) 101 activity. As with public information, server 105 can query the social site via an API to obtain the private information of user(s) 101 when any user 101 logs onto a social site and begin a session. Server 105 monitors the activity of user(s) 101 while user(s) 101 are logged onto social site. As user(s) 101 input “like” information on the social site, server 105 gathers that “like” information and updates user(s)' 101 “like” profile.

Thus, gathering public “like” information about user(s) 101 can be performed at any time by server 105 in a batch-type collection manner. On the other hand, private “like” information is gathered when user(s) 101 are logged onto a social site and perform activities on the social site in a dynamic-type collection manner. Once again, user(s) 101 “like” profile is updated as user(s) 101 perform activity when logged onto a social site.

The “like” profile for user(s) 101 is described in more detail below.

Using the public and private information gathered by server 105, the present disclosure describes methods and systems for quantitatively calculating the “like” profile of different user(s) 101 of social sites based on an analysis of various sources of social information. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various aspects of different embodiments of the present disclosure. It will be evident, however, to one skilled in the art, that the present disclosure can be practiced without all of the mentioned specific details.

The operation of apparatus/system and the method of the present disclosure will be described in more detail below, in accordance with FIG. 6.

FIG. 6 shows a method 600 for gathering information concerning user's 101 utilization of, and interaction with, social network server (SNS) 180. While FIG. 6 shows the steps of method 600 being performed sequentially, it will be appreciated that certain of the steps may be performed concurrently or continuously. Method 600 commences with step 605.

In step 605, user 101 logs onto social network server 180 via network 170 using, e.g., user device 135 shown in FIG. 1. From step 605, method 600 progresses to step 610.

In step 610, social network server 180, in a communication via network 170, notifies server 105 that user 101 has logged onto to social network server 180. From step 610, method 600 progresses to step 615.

In step 615, as user 101 performs activities on social network server 180, server 105 gathers information about user's “likes” activity, including user's 101 “likes” activities made with respect to “posts”, “comments”, others' “likes”, as well as to specific genres, entities, personalities, people and categories of various types. In step 615, server 105 can continually update user's 101 social score or, alternatively, server 105 can update user's 101 social score at regular timed intervals or, still alternatively, can update user's 101 social score only when user 101 logs off from social network server 180. From step 615, method 600 progresses to step 620.

In step 620, user 101 logs off the social site and server 105 no longer monitors and gathers information concerning user's 101 activities while logged onto the social site. From step 620, method 600 progresses to step 625.

In step 625, server 105 updates user's 101 “likes” profile and these updated social scores are stored in memory 115 and/or in data base 125. From step 625, method 600 progresses to step 630.

In step 630, server 105 receives a query from client server 190 for a “like” profile analysis for comparison to a particular advertising campaign of client server 190 based upon parameters designated by the query sent from client server 190 to server 105. The parameters can be parameters set by client server 190 from previous queries or specific parameters can be included in the new query. As mentioned above, the query from client server 190 can be predicated upon any analysis of users' 101 “like” profile desired by client server 190. From step 630, method 600 progresses to step 635.

In step 635, server 105 calculates users' 101 “like” profile “match” score based upon parameters set forth in the query made by client server 190. From step 635, method 600 progresses to step 640.

In step 640, based upon the query by client server 190 and the “like” profile analysis performed by server 105, server 105 provides client server 190 with the identity of users 101 resulting from the analysis of the “like” profile data of users 101 by client server 105 in accordance with the query and parameters set by client server 190.

Method 600 then ends.

As mentioned above, method 600 is applicable to the situation where, based upon a compilation of users' 101 “like” profiles, server 105 is able to evaluate the portfolio of advertising campaigns of client server 190 and select the advertising campaign most likely to yield positive results for the advertiser. This methodology saves the advertiser from having to parse through a library of its advertising campaign portfolios to select the advertising campaign well-suited for a particular population of users 101 having a specific “like” profile. Stated another way, the methods and systems of the present disclosure gather and analyze the “likes” of users 101 to a level of detail and specificity not previously available. For example, as set forth above with respect to FIGS. 3-5, an advertiser can have a promotion for Lady Gaga Concert Tickets and would like to direct the promotion at the most likely purchasers. The methods and systems of the present disclosure are capable of providing an audience of potential customers to that advertiser based upon the detailed analysis of a population of users 101 “like” profiles. However, rather than merely being interested in users 101 who may have the specific interest in Lady Gaga, an advertiser may wish to broaden the audience of potential customers to whom its advertisements are directed. In such an instance, the advertiser may wish to direct its advertising not only to individual users 101 who “like” Lady Gaga, but to those who “like” other specific female artists or, even slightly more broadly, to individual users who “like” female pop singers. The methods and systems according to the present disclosure allow for all such variations, as can be seen from the foregoing examples.

Thus, the present systems and methods for determining and identifying individuals who may be receptive to a marketing campaign derives information that can be of particular interest to and for use by entities, e.g., advertisers and websites, who can then direct more precisely targeted advertising/marketing campaigns or other information to the identified individuals. The identification of potentially receptive individuals, whether individually or in groups, is based on surveying the individual's “likes” (e.g., on Facebook) or similar indicators that the individual has an appreciation of certain subject matter, and should allow a higher “conversion” rate with respect to the advertising/marketing campaign, as well as allowing entities to select the most appropriate advertising/marketing campaign(s) from the entities' portfolio.

The terms “comprises” or “comprising” are to be interpreted as specifying the presence of the stated features, integers, steps or components, but not precluding the presence of one or more other features, integers, steps or components or groups thereof.

It should be understood that various alternatives, combinations and modifications could be devised by those skilled in the art. For example, steps associated with the processes described herein can be performed in any order, unless otherwise specified or dictated by the steps themselves. The present disclosure is intended to embrace all such alternatives, modifications and variances that fall within the scope of the appended claims.

Claims

1. A method comprising:

compiling data about “likes” of a user of a social site in different defined levels of specificity of a category of information;

assigning a weight factor to each level of specificity of the category of information, wherein the weight factor increases when the specificity of the level increases;

multiplying the weight factor for each level of specificity of the category of information to obtain a product for each level of specificity of the category of information; and

adding the products obtained to derive a “like” profile for the user.

2. The method of claim 1, further comprising updating the “like” profile of the user based upon the user's activity while logged onto the social site.

3. The method of claim 1, wherein the updating the “like” profile comprises:

recognizing that the user of the social site is logging onto the social site;

accessing the social site concurrently with the user logging on;

monitoring “like” activity of the user while the user is on the social site;

gathering information concerning the “like” activity of the user while the user is logged onto the social site; and

updating the “like” profile of the user using the information gathered concerning the user activity while the user is logged onto the social site.

4. The method of claim 1, further comprising:

compiling “like” profiles for a plurality of users;

evaluating an advertising campaign to determine the “like” profile of a user who may be a target of the campaign;

comparing the “like” profile of the plurality of users to the advertising campaign

5. The method of claim 4, further comprising identifying a group of members of the plurality of users who match the target of the campaign.

6. An apparatus comprising:

a processor; and

a memory that contains instructions that are readable by the processor and cause the processor to: gather data about “likes” of a user of a social site in different levels of specificity of a category of information; assign a weight factor to each level of specificity of the category of information, wherein the weight factor increases when the specificity of the level increases; multiply the weight factor for each level of specificity of the category of information to obtain a product for each level of specificity of the category of information; and add the products obtained to obtain a “like” profile for the user for the category of information.

7. The apparatus of claim 6, wherein said instructions further cause the processor to:

recognize that a user of the social site is logging onto the social site;

access the social site concurrently with the user logging on;

monitor “like” activity of the user while the user is on the social site;

gather information concerning the “like” activity of the user while the user is logged onto the social site; and

update the “like” profile of the user using the information gathered concerning the user activity while the user is logged onto the social site.

8. A storage device comprising instructions that are readable by a processor and cause a processor to:

recognize that a user of a social site is logging onto the social site;

access the social site concurrently with the user logging on;

monitor “like” activity of the user while the user is on the social site;

gather information concerning the “like” activity of the user while the user is logged onto the social site; and

update the “like” profile of the user using the information gathered concerning the user activity while the user is logged onto the social site.

9. The storage device of claim 8, wherein the instructions further cause the processor to:

recognize that the user of the social site is logging onto the social site;

access the social site concurrently with the user logging on;

monitor “like” activity of the user while the user is on the social site;

gather information concerning the “like” activity of the user while the user is logged onto the social site; and

update the “like” profile of the user using the information gathered concerning the user activity while the user is logged onto the social site.

10. The storage device of claim 9, wherein the “like” profile can be used to analyze an advertising campaign portfolio of an entity and provide recommendations to the advertising campaign.