A computing system is configured to analyze historic data generated by an optimization system to provide recommended weightings for placement of creatives on publisher's pages. The weightings may be generated by providing forecasting the likelihood that a particular creative will lead to greater conversion or revenue compared to other creatives. The creatives may be grouped into one or more phases based on the amount of statistical data available for analyzing the particular creatives such that new creatives are given sufficient weighting to receive impressions despite the lack of historical data for a creative. Performance of placed creatives may be tracked by the passing of URLs with information attached to identify the particular creative and placement.

For providing a targeted advertisement to a user, a referral server sends, to a financial platform server, a request for fetching the financial information of the user. The referral server receives, from the financial platform server, a reply containing the financial information of the user. The referral server analyses the financial information of the user to determine one or more behavioural attributes of the user. The referral server uses the one or more behavioural attributes of the user to define a behavioural profile of the user. The referral server may send to a device of the user a code sequence related to a marketing campaign selected according to the behavioural profile of the user. The marketing campaign may be selected by calculating a conversion rate for each of a plurality of marketing campaigns.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: tracking touchpoints by a user over a first time period; after receiving an order, determining, using a machine-learning model, a respective contribution of each of the touchpoints, wherein the machine-learning model is trained to predict a probability of the user placing the order during a second time period based on an input feature vector representing a set of touchpoints; and allocating a respective percentage of credit for the order to the each of the touchpoints based on the respective contributions of the each of the touchpoints. Other embodiments are disclosed.

An example computer-implemented system maintains user profiles and displays external content. Method and system are provided for performing attribution of conversions with respect to the external content in a privacy safe manner by anonymizing personally identifiable information utilizing cryptographic salt.

Automated methods are provided for identifying when a first advertisement (ad) is a likely variant of a second ad. The first and second ads each include a plurality of sequential segments of a predefined time length, wherein the second ad is a reference ad, and the first ad is a sample ad. In one embodiment, a vector of segment hits is created for the second ad, wherein the second ad is used as both the sample ad and the reference ad for creating the vector of segment hits for the second ad. A vector of segment hits is also created for the first ad, wherein the first ad is used as the sample ad and the second ad is used as the reference ad for creating the vector of segment hits for the first ad. The vector of segment hits for the first ad is compared with the vector of segment hits for the second ad, and the first ad is identified as being a likely variant of the second ad when the vector of segment hits for the first ad matches at least a predetermined percentage of the segments of the second ad, and the vector of segment hits for the first ad is not the same as the vector of segment hits of the second ad. Other embodiments create different vectors of segment hits for the first and second ads which are likewise compared to one another to determine when ad variants are present.

A system and method for the automatic distribution of rewards for user content within social networks is provided. Content that is posted by users to social networks may display a qualifying product or service, as determined by the operator of the system and method. Such content is identified by software algorithms and evaluated for its marketing value. A quantity of that evaluated value is then paid out to the original user, either immediately or on a deferred basis, in the form of currency. The present invention provides for a novel method to compensate social media users for their unpaid labor they have performed by inadvertently advertising products and services, and thus represents a material benefit for a large segment of the population.

One variation of a method for selectively serving advertising content comprises: receiving identification of an advertisement slot loaded within a webpage; receiving a set of advertisement slot characteristics corresponding to the advertisement slot; accessing a model associating advertisement slot characteristics and user interactions with advertisements; for each target outcome, in a set of target outcomes, calculating an outcome score for the advertisement slot based on the set of advertisement slot characteristics and the model, the outcome score representing a probability of the user interacting with advertising content, presented within the advertisement slot, according to the target outcome; in response to a first outcome score, corresponding to a first target outcome, in the set of target outcomes, exceeding each other outcome score, assigning the first target outcome to the advertisement slot; and selecting a first advertisement, designating the first target outcome, for presentation within the advertisement slot.

The invention enables optimizing performance of a recommendation server. The invention comprises (i) receiving a first set of customer information corresponding to a first set of events recorded in a first time period in which the recommendation server operates in a first configuration state, (ii) generating a first performance evaluation score based on the first set of customer information, (iii) reconfiguring the recommendation server to operate in a second configuration state having a second performance evaluation score associated therewith, and wherein said second performance evaluation score is generated based on a second set of customer information corresponding to a second set of events recorded in a second time period in which the recommendation server operates in the second configuration state and (iv) transmitting to a terminal device, one or more electronic offers selected for transmission to the customer by the recommendation server operating in the second configuration state.

In one embodiment, a method includes obtaining, from a demand-side platform (DSP), impression data specifying service providers and consumer tokens representing consumers who have received digital impressions of a set of advertising campaigns. A set of tokenized claims data records related to a prescription of a product is then received from a database server. A result set of integrated measurement records specifying measured campaigns linking the tokenized claims data records with impression data associated with consumer tokens and/or service provider identifiers is further received from the database server. Aggregated analytics reports based on the integrated measurement records are generated and presented. A machine learning model may be trained using a training dataset comprising features selected from the impression data and tokenized claims data records, to predict bid values or other parameters for use in updating, optimizing or modifying operation of the DSP for the original campaign or for other campaigns.

Image-based features may be significantly correlated with click-through rates of images that depict a product, which may provide a more formal basis for the informal notion that good quality images will result in better click-through rates, as compared to poor quality images. Accordingly, an image assessment machine is configured to analyze image-based features to improve click-through rates for shopping search applications (e.g., a product search engine). Moreover, the image assessment machine may rank search results based on image quality factors and may notify sellers about low quality images. This may have the effect of improving the brand value for an online shopping website and accordingly have a positive long-term impact on the online shopping website.