An example includes generating panelist-user data based on reference demographic information and self-reported demographic information, the reference demographic information and the self-reported demographic information corresponding to audience members of an audience member entity panel that are also registered users of a database proprietor, the reference demographic information from a panelist database of an audience measurement entity, and the self-reported demographic information from a user database of the database proprietor; generating a first training model and a second training model, the first training model based on a first portion of the panelist-user data, the second training model based on a second portion of the panelist-user data; selecting the first training model based on outputs of the first and second training models; and generating a third model by making an adjustment to a demographic category of the first training model, the third model to adjust third demographic information.

Systems, apparatus, methods, and computer program products are provided for optimized and effective leading messages, which may be an email subject that may provoke a consumer to access the body portion of an email message. A system may include circuitry configured to programmatically determine a predicted access rate for a leading message when the leading message is provided as a portion of a promotional message. Circuitry may be configured to track historical data indicating the access rates of leading messages and/or leading message terms. The circuitry may be configured to leverage the historical data to determine predicted access rates for leading messages, such as based at least in part on historical access rates associated with one or more leading message terms of the leading message.

Techniques and systems are described to enable users to optimize a digital marketing content system by analyzing an effect of components of digital marketing content on audience segments, environments of consumption, and channels of consumption. A computing device of an analytics system receives user interaction data describing an effect of user interaction with multiple items of digital marketing content on achieving an action for multiple audience segments. The analytics system identifies which of a plurality of components are included in respective items of digital marketing content. The analytics system generates data identifying different aspects that likely had an effect on the achieving an action on the items of digital marketing content, such as components of the items of digital marketing content, environments of consumption, channels of consumption. The analytics system outputs a result based on the data in a user interface.

The present teaching relates to generating combination distributions for ads. A prediction model is obtained via machine learning with respect to a criterion. Training data are associated with multiple ads each having multiple attributes, and include combinations with recorded performance for each ad. Each combination has multiple assets representing respective attributes of an ad. Using the prediction model, performance of each combination of each ad can be predicted and used for generating combination distributions for the ads. Such generated combination distributions are then sent to an explore/exploit layer (EEL) at a frontend ad serving engine so that it can draw a combination associated with an auction winning ad for rendering on a webpage viewed by a user on a user device.

The present teaching relates to displaying ads. An explore/exploit layer (EEL) is provided at frontend ad serving engine for storing combination distributions with respect to multiple ads. Each ad has multiple attributes. Each attribute can be instantiated using one of multiple assets. The frontend ad serving engine requests a recommended ad for bidding an ad display opportunity in a slot of a webpage viewed by a user on a user device. The recommended ad is one of the multiple ads. When the auction is successful, a combination of assets for the ad is drawn from the combination distributions in EEL and each of the assets instantiates a corresponding attribute of the ad. The combination is transmitted to the user device to render the ad.

A system for advertisement space recommendation and a checkout process is provided. The system includes an advertisement space recommendation module determines an expected network traffic for each product. The advertisement space recommendation module determines expected profits corresponding to merchants based on expected network traffic and generates a recommendation corresponding to a set of advertisement space to the merchants for selling the products. The system includes an auction module enables the merchants to bid for the set of advertisement space to exhibit the corresponding products. The system includes a checkout URL generator module creates a distinctive URL for the set of advertisement space and enables one-click checkout process for the products displayed on the set of advertisement space on an advertisement platform based on URL for each product across merchants.

Methods and apparatus related to determining and/or utilizing one or more attributes for an Internet Protocol (IP) address. In some of those implementations, the attributes may include a physical address associated with the IP address. Some implementations are directed to determining physical addresses for inclusion in a postal campaign based on computing devices having IP addresses associated with those physical addresses having retrieved content of one or more electronic resources (e.g., webpages) assigned to the campaign.

An advertising campaign may be carried out using moving outdoor advertising. Advertisers typically like to manage and track the success of their advertising campaigns. Aspects of the present application may be seen to assist with such management and tracking by providing impression analytics, location analytics, time analytics and frequency reports. Furthermore, aspects of the present application may be seen to provide an ability to augment a moving outdoor advertising campaign with a mobile device retargeting advertising campaign. Then, such metrics as return on investment, profit and gross profit may be tracked. In some aspects, Wi-Fi signals and Bluetooth signals may be collected from mobile devices that are in the range of a Moving Impression Analytics Tracking Device.

Some implementations disclosed herein measure an effect of content exposure (e.g., lift attributable to ad campaign content) by comparing exposed and unexposed conversion (e.g., visit) rates. The measurement processing is distributed between the computing systems used/controlled by different entities: a processing entity and a conversion data collection entity (e.g., a selling entity) so that identifier-level conversion data (e.g., user-level conversion data, device-level conversion data, household-level conversion data, etc.) does not need to leave the conversion data collection entity system(s).

Methods, systems, and media for providing audience recommendations are provided. In some embodiments, the method for providing content recommendations includes: receiving an indication that a user account has been authenticated; determining whether a campaign associated with the user account meets one or more criterion to receive a recommendation interface for creating a custom audience for one or more promotional content items in the campaign, wherein the custom audience is applied to a campaign to assist a content provider reach audiences searching for a product or a service associated with the content provider on a plurality of platforms; determining that a performance metric in response to applying the custom audience to the campaign is greater than a threshold performance metric; in response to determining that the campaign associated with the user account meets the one or more criterion, generating the recommendation interface that prompts the content provider to create the custom audience for application to the campaign, wherein the recommendation interface includes the performance metric and a plurality of suggested keywords for reaching the custom audience; and causing the recommendation interface to be presented.