The present invention relates to methods for evaluating the effectiveness of all types of communication carried on offline communication media so as to generate data equivalent to the data obtained in online communication media. The present invention uses tangible tools such as fixed-line telephones, cellphones, computers, tablets and any other wearable mobile device to generate that data that will be used in media plans that are much more precise and efficient than the plans currently known. Finally, the present invention relates to said media plan obtained using one of said methods as well as the purchase of optimized media.

This paper presents a practical method for measuring the impact of multiple marketing events on sales, including marketing events that are not traditionally trackable. The technique infers which of several competing media events are likely to have caused a given conversion. The method is tested using hold-out sets, and also a live media experiment for determining whether the method can accurately predict television-generated web conversions.

The present teaching relates to a method and a system for advertising. The method obtains information about a conversion associated with an advertisement and generates with respect to the conversion, an operational smart attribution evaluation package (SAEP). The SAEP includes a conversion parameter and a reward. The method transmits the SAEP to a platform to be posted, and thereafter receives from the SAEP, an indication of an entity which is estimated to be associated with the conversion and to which the reward is to be allocated. The entity is determined by the SAEP based on the conversion parameter and information from a plurality of entities that displayed the advertisement.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for segmenting attribution of user actions and adjusting physical location usage of one or more geographic locations. Trip data specifying a geographic path traversed by a given set of users is obtained. Semantic data specifying content to which the given set of users was exposed while traversing the geographic path is also obtained. An exposure time indicating an aggregate amount of time that the given set of users was exposed to specific content while traversing the geographic path is determined. A contribution score is generated for the content to which the given set of users was exposed while traversing the geographic path. Based on the contribution score, attribution of user actions is segmented and physical location usage is adjusted based on a portion of the segmented attribution that is assigned to the content.

An information processing apparatus according to an embodiment of the present technology includes a plan creation section. The plan creation section creates a plan of movement of a mobile object to a destination of the mobile object in association with advertisement effectiveness on the basis of the destination and a desired time of arrival at the destination, the advertisement effectiveness being provided by advertisement content played back outward from the mobile object. This makes it possible to create the movement plan enabling the advertisement vehicle to arrive at the destination by the desired arrival time, and achieving a highest degree of the advertisement effectiveness. This results in being able to achieve a high degree of advertisement effectiveness in advertisement using the mobile object.

Methods and systems for determining reach information. Watching habits of viewers in a designated market can be analyzed using machine learning algorithms to obtain campaign spot plans. The campaign spot plans can be applied to single viewer data to calculate campaign spot plan Television Average Ratings Points (TARP) pattern information. The TARP pattern information can be translated into reach information determining how many people were uniquely exposed to each campaign spot.

A system including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, perform: receiving user session activity information and campaign impression information; determining a sample of the user session activity information and the campaign impression information based on a sampling criterion; analyzing the sample using (i) a first logistic regression model and (ii) a second linear regression model; determining a weighting value for the campaign impression information based on a first output of the first logistic regression model and a second output of the second linear regression model; and determining a sub cut lift measurement for the campaign impression information based on a first lift measurement for the campaign impression information and the weighting value for the campaign impression information. Other embodiments are described.

A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include receiving, via a computer network, an ad request. The method also can include retrieving ad candidates from an ad database. The method further can include determining a respective ad ranking score for each of the ad candidates, based at least in part on the ad request and respective historical retrieval scores for each of the ad candidates. In some embodiments, each of the respective historical retrieval scores for each of the ad candidates is determined at least in part by a multi-channel search engine and a respective historical search query, by: (a) determining, by a semantic search model of the multi-channel search engine, one or more semantic search results from the ad candidates and a respective semantic ranking score for each of the one or more semantic search results, based on a respective query vector embedding of the respective historical search query and a respective ad vector embedding of each of the one or more semantic search results; (b) determining, by a syntactic search model of the multi-channel search engine, one or more syntactic search results from the ad candidates and a respective syntactic ranking score for each of the one or more syntactic search results, based on the respective historical search query; (c) unifying the respective semantic ranking score for each of the one or more semantic search results; (d) unifying the respective syntactic ranking score for each of the one or more syntactic search results; and (e) merging the one or more semantic search results and the one or more syntactic search results into one or more historical ad candidates based on the respective semantic ranking score, as unified, or the respective syntactic ranking score, as unified. The one or more historical ad candidates, as merged, can comprise the ad candidates, and each of the respective historical retrieval scores for each of the ad candidates can be the respective semantic ranking score, as unified, or the respective syntactic ranking score, as unified. The method additionally can include determining one or more ad finalists based at least in part on the respective ad ranking score for each of the ad candidates. Moreover, the method can include transmitting, via the computer network, the one or more ad finalists to be displayed on a user interface. Other embodiments are described.

Methods and systems for determining and using disliked content are described. The method includes obtaining, by a service provider system, channel viewing data from a user device of a user, pre-processing, by a content analysis unit, the channel viewing data to mitigate inaccuracies and inconsistencies in the channel viewing data, scaling, by the content analysis unit, the pre-processed channel viewing data to highlight temporal dislike aspects of the pre-processed channel viewing data, applying, by the content analysis unit, machine learning algorithms to the scaled channel viewing data to generate a disliked content ratings matrix, and outputting, by the content analysis unit to user interface systems in the service provider system, to provide enhanced user viewing. The user interface systems including a recommender system, an alternate content generation system, or both.

A computer implemented method of adapting an application according to user interaction comprising using one or more processors for executing a code for collecting autonomously a plurality of action events describing a plurality of actions taken by a plurality of users to navigate through a plurality of pages presented by an application to accomplish one or more goals of the application, the plurality of pages are presented on a GUI at a plurality of user devices used by the plurality of users, analyzing the action events to identify one or more behavioral patterns of at least some of the users for accomplishing the goal(s) and generating automatically one or more recommended adaptations for the application according to the behavioral pattern(s) to adapt a layout of the application in order to increase a probability for one or more users to successfully accomplish the goal(s).