An example apparatus includes a model generator to generate a learning model based on a correlation of stimulus data and response data, the learning model to predict user responses based on stimuli presented to the users in the channel or the sub-channel, the correlation indicative of stimuli contributing to user responses at a channel or a sub-channel level. The apparatus further includes an attribution engine to determine a media spend plan based on the learning model and a budget, the media spend plan including an allocation of the budget to stimuli corresponding to the channel or the sub-channel and a user interface to display the media spend plan to a user and update the media spend plan based on predictions of the learning model when the user adjusts the budget or allocations of the media spend plan in the user interface.

An example includes generating a first model based on reference demographic data corresponding to panelists, and based on second demographic data and behavioral data from a database proprietor that is separate from a first entity, the reference demographic data collected from the panelists by the first entity, and the second demographic data and the behavioral data corresponding to ones of the panelists having user accounts with the database proprietor; analyzing an output of the first model including the second demographic data partitioned into a plurality of nodes, each node representing a respective subset of the second demographic data; redistributing some of the second demographic data between some of the nodes to generate a second model; applying the second model to third demographic data of the database proprietor; and determining whether to adjust at least some of the third demographic data based on an output of the second model.

An automatic system facilitates selection of media properties on which to display an advertisement, responsive to a profile collected on a first media property, where a behavioral-targeting company calculates expected profit for an ad correlated with the profile and arranges for the visitor to be tagged with a tag readable by the selected media property. The profit can be calculated by deducting, from the revenues that are expected to be generated from an ad delivered based on the collected profile, at least the price of ad space at a media property where the BT company might like to deliver ads to the profiled visitor. When the calculated profit is positive (i.e., not a loss), the BT company arranges for the visitor to be tagged with a tag readable by the selected media property through which the BT company expects to profit.

A method for adjusting pricing for advertisements of non-guaranteed delivery (NDG) advertising auctions includes retrieving dwell time information for users that engage advertisements streamed to the users in a display content stream. The method aggregates the advertisements into different groups according to display context (such as advertisement category, viewing device, product in which the advertisement is streamed, etc.) and computes short-click ratios of advertisements in each group based on a short-click threshold assigned to the group and the user dwell time information for the group. The method further determines average dwell time by users on advertisements in respective groups. The method adjusts pricing of the advertisements of a group for NGD bidding based on the short-click ratio and average dwell time of the group, to favor groups having higher dwell times and lower short-click ratios. Prices may be dynamically adjusted across different contexts based on user engagement information.

A networked computerized advertising system used for integrating, processing and displaying location-based advertising information is provided. The system comprises at least one computing device, and a network that connects the computing device with multiple playback endpoints. The computing device includes a data storage subsystem component that stores information about the multiple end points, a data entry subsystem component that allows input of the information about the multiple end points, and a data analytics component programmed to process the information about the multiple end points and compute optimal advertising playback plans for each endpoint. Playbacks at endpoints are continuously monitored, and the playback plans are repeatedly reconstructed to provide flexible advertising campaigns to be customized in accordance with the schedule and the operations of the host business.

An internet marketing analytics system to quantify visitor website activity, the system including a database having a plurality of parameters having any portion of the following; site visits, total revenue, bounce rate, conversion rate, orders, average order value, value per visit, pages per visit, new visits, units, cart additions, cart removals, internal search, click through rate, revenue per visit, open rate, email list, impressions, visit duration, percent new visitors, percent return visitors, post volume, net promoter score, social referring traffic, total cost, search term, exit rate, page views, and product type, plus non website related visitor internet activity. The system producing a multi-variate visual spatial display of the database including at least three parameters being simultaneously displayed and modifying the display to selectively emphasize a parameter to be displayed as an X, Y, or Z axis for clarity, for modifying a component of the database to optimize website efficiency.

The systems and methods described herein attempt to capture the impact on both the promoted items and other related products. The systems and methods analyze attached sales impact only for items that are more likely to be purchased with the featured product. The systems and methods also allow for measuring a cannibalization impact by analyzing the impact on potential cannibalized products and items that are more likely to be purchased with the cannibalized products. By aggregating the promoted impact and cannibalized impact, including the items with strong co-selling relationships, a full picture of the promotion can be obtained. Further, combining the item-level results into custom groupings can allow for further business insights.

Systems and methods are provided for discovering advertisements on publisher web pages and for identifying placement pathways by which discovered advertisements have been placed on the publisher web pages. An advertisement tracking and discovery system may use multiple web crawler applications to explore multiple publisher websites. The web crawler applications may gather advertisement data that includes times associated with each request made by the web crawler application. The system may use the gathered advertisement data and the times associated with each request to determine the placement pathways by which discovered advertisements have been placed. Each placement pathway may include one or more advertising channels or combinations of advertising channels. The system may accumulate and aggregate advertising data associated with the advertisements and the placement pathways and display the aggregated advertising data to a customer.

Embodiments are disclosed for a method that may include accessing events in a field-searchable data store. The events may include raw machine data associated with a timestamp. The raw machine data may represent interactions between a mobile device and one or more network devices at a locale. The method may further include determining, based on the interactions, one or more geographic positions of the mobile device, and calculating a metric for the locale using the geographic positions.

Systems and methods for selecting, executing and optimizing marketing activities involving marketing content for defined marketing tasks contributed by a plurality of marketing content developers. The system receives multiple versions of the defined marketing task from the plurality of content developers, ranks the content developers in accordance with past performance ratings for similar marketing tasks, allocates shares of the total volume of the marketing activity in accordance with the rankings, executes the marketing activity using the multiple versions of the defined marketing task on a collection of potential customers, scores the performance of each version of the defined marketing task, calculates and stores updated past performance ratings for each content developer based on the scores, and then repeats the process for a series of consecutive execution periods. The system uses multivariate testing to score and optimize the execution of the marketing activity.