A computer-implemented method for dynamic link redirection includes determining current retailer product links in online content, for each current retailer product link among the plurality of current retailer product links, generating a current retailer monetization assessment for the current retailer product link based on current retailer monetization parameters, obtaining a plurality of alternative retailer product links based on the current retailer product link, generating a plurality of alternative retailer monetization assessments for each alternative retailer product link, determining, or receiving from a user, a selected retailer product link among the plurality of alternative retailer product links based on the current retailer monetization assessment and the plurality of alternative retailer monetization assessments, and replacing the current retailer product link in the online content with the selected retailer product link.

Disclosed are methods and systems for generating ideal and opt-in business leads utilizing targeted advertising campaigns. The method comprises first retrieving and statistically analyzing qualified leads from a CRM system to identify an ideal customer profile (ICP), and retrieving candidates leads that either match the ICP or are lookalikes from one or more lead data sources. Next, generating test advertising campaigns, receiving feedback on the test advertising campaigns from candidate leads in test target groups, scoring each test advertising campaign based on received feedback, and selecting ideal business leads that match a profile of a test target group that responded to a test advertising campaign with a high score. Furthermore, generating main advertising campaigns, receiving responses, and generating ideal and opt-in business leads by selecting ideal business leads that responded affirmatively. The present invention provides a closed-loop architecture to produce qualified, ideal, and opt-in leads for B2B companies.

Methods and systems for quantitative collaborative cognition in open market systems are described herein. Aspects relating to indexing, discovery, attribution, optimization, and forecasting in open market systems are disclosed. The present invention allows for network learning, identification, and discovery of heterogeneous data held remotely by a multitude of participants in a way that protects the integrity of the data. From this data, behavior patterns of people and groups of people spanning data sets and organizational boundaries can be predicted. The data can be monetized by a variety of interested parties without disclosing the identities of parties associated with the data. The time value of data is extended under the methods and systems of the present invention.

Systems and methods for evaluating online videos. One method includes receiving a URL; determining a URL type; detecting whether the URL includes one or more videos; determining at least one of a size of the video, a position of the video on a web page of the web page URL, whether the video is set to autoplay, and whether the video is set to mute; computing a score based on one or more of the size of the video, the position of the video on the web page of the web page URL, whether the video is set to autoplay, and whether the video is set to mute; obtaining at least two frames of at least part of the video, wherein each frame is obtained at one or more predetermined intervals during playback of the video; and classifying each detected video based on the at least two frames.

An online system predicts, using a first targeting model, a first group of users as candidates to be in a targeting cluster, and predicts, using a second targeting model, a second group of users as candidates to be in the targeting cluster. The online system determines a first set of users that are not part of the first group of users, and a second set of users that are not part of the second group of users, and provides surveys to the first and second set of users. The online system determines a first subgroup of the first group of users and a second subgroup of the second group of users, and provides an ad preferences tool to the first subgroup and the second subgroup. The online system scores the first and second targeting models based in part on responses to the surveys and/or the ad preferences tools.

Campaign effectiveness determination techniques and systems are described that are usable to determine campaign effectiveness with improved accuracy and computing performance by reduction of confounding bias through dimension reduction. In one example, campaign data that pertains to first and second campaign groups is characterized using a plurality of features that describe subjects included in the first and second campaign groups. The characterized campaign data is projected, automatically and without user intervention, for the first and second campaign groups into a reduced dimension space, e.g., using linear or non-linear techniques. Subjects in the first and second campaign groups are associated, one to another using the projected campaign data, such that a number of subjects in the first campaign group is matched against a number of subjects in the second campaign group. Generation of a campaign effectiveness result is then controlled using the associated subjects in the first and second campaign groups.

Methods and systems are provided herein for the analysis of information about online actions of a plurality of users. The analysis methods and systems allow for the creation of new online and offline business methods based on online consumer behavior. The methods and systems may obtain an input data set comprising information about online actions of a plurality of users, convert the input data set into data files having a common file format with each data file corresponding to a user of the plurality of users and comprising an identifier for the user and a plurality of Uniform Resource Locations (URLs) associated with online actions of the user, access online information relating to search terms and webpages, and determine one or more metrics of user behavior, including a verticals metric and a search terms metric.

Multiple copies of the same coupon are delivered to a consumer simultaneously via different delivery channels. After one copy of the coupon has been presented in connection with a transaction and used, the coupon is marked as used with respect to all delivery channels for that consumer. Different copies of the coupon can have different coupon identifiers, for example when delivered through different channels. Consumers also can access such multichannel coupons without having to login each time they wish to access their coupons from multiple channels. In one implementation, each consumer has a user profile. A promotion is associated with that consumer's user profile. Each user profile also is associated with different channel identifiers representing communication channels to deliver instances of the promotion to the consumer. Each delivered instance of the promotion includes a coupon identifier associated with the promotion.

A method that incorporates teachings of the present disclosure may include, for example, the steps of transmitting media content to a group of set top boxes for presentation with an overlay superimposed onto the media content, receiving a first comment from a first set top box of the group of set top boxes where the first comment is presentable with the overlay and the media content by the group of set top boxes, determining a first advertisement based on the first comment, and transmitting the first advertisement to the first set top box for presentation with the overlay and the media content. Other embodiments are disclosed.

A/B experiment validation implementations are presented that generally validate an A/B experiment prior to its release. One implementation involves employing multiple test execution engines to test a A/B experiment, and then aggregating the results. More particularly, a request to validate an A/B experiment is received from a requesting entity along with data pertaining to the A/B experiment. A category of the A/B experiment is then determined, and test execution engines applicable to the A/B experiment category are identified. For each test execution engine identified, the A/B experiment data is passed to the test execution engine, the test execution engine is requested to execute a test for the A/B experiment, and test results from the test of the A/B experiment are received. Once test results are received from the identified test execution engines, the test results are aggregated to produce a validation indicator.