Methods, systems, and computer storage media are provided for tracking an advertisement using a single URL without redirection. When an ad event is received on a client-computing device, a single URL is determined to provide content for the item and to track the ad event, the URL identifying a domain server. A parameter related to the ad event is determined and encoded as a part of the URL or as a HTTP header. The URL is called, causing the domain server to extract the parameter from the URL or the HTTP header and asynchronously request tracking of the ad event. Additionally, the content for the item is received from the domain server.

An influence system for predicting advertisement impact for audience selection. An advertising probe campaign is operated by sending an advertisement to each entity in a treatment group of entities. A control group of entities which excludes the treatment group entities is selected and no campaign advertising content is sent to the treatment group entities. An influence model is created by comparing features of the treatment group converters to features of the control group converters. An individual frequency cap is selected for each entity that is a candidate for the advertising campaign based on a result of applying the influence model to the features of the candidate entity. The entity may be selected to receive an advertisement based on the individual frequency cap. Some embodiments are integrated with a real time bidding (RTB) exchange and a bid response may be configured based on the results of applying the influence model.

Techniques to allow advertising or other secondary content to be dismissed for later follow up are disclosed. In various embodiments, a user input associated with dismissing a displayed content for later follow up is received. Display of the content item discontinued and a follow up record is stored based at least in part on the indication. The follow up record is used to provide to a user with which the user input is associated a follow up content associated with the dismissed content.

A control apparatus includes a controller configured to determine whether a user who travels in a vehicle has stopped off at a place different from a destination, with reference to positional data indicating a position of the user, the user having accepted a recommendation to stop off at the place using the vehicle, the recommendation being presented together with an advertisement promoting the place.

Provided are a traffic information analysis device and a traffic information analysis method with which the effectiveness of a sign can be analyzed from the number of passages of moving bodies along a road where the sign is positioned. A traffic information analysis device 30 comprises: a visit information database update unit 311 that receives changes in location information for a plurality of vehicles 60; a storage unit 32 that stores a road on which the plurality of vehicles 60 can travel; a number of passages tallying unit 313 that tallies the number of passages of the plurality of vehicles 60 within a prescribed period for a prescribed location or a prescribed section of the road; and an output unit 314 that outputs a unit number and/or a ratio for each number of passages.

Embodiments of the present specification provide marketing methods based on a deep reinforcement learning system. One method includes the following: obtaining, from an execution environment of a deep reinforcement learning system, a plurality of execution results generated by a user in response to marketing activities, wherein the plurality of execution results correspond to a plurality of targeted effects on a marketing effect chain; determining a reward score of reinforcement learning based on the plurality of execution results; and returning the reward score to a smart agent of the deep reinforcement learning system, for the smart agent to update a marketing strategy, wherein the smart agent is configured to determine the marketing activities based on the marketing strategy and status of the execution environment.

Techniques for extracting features of entities and targets that can be applied in a set of applications, such as entity selection prediction, audience expansion, feed relevance, and job recommendation. In one technique, entity interaction data is stored that indicates, for each of multiple entities, one or more targets that are associated with items with which the entity interacted. Token association data is stored that indicates, for each of multiple tokens, one or more targets that are associated with the token. Then, using one or more machine learning techniques, entity embeddings and target embeddings are generated based on the entity interaction data and the token association data. Later, a request for content is received from a particular entity. Based on at least one entity embedding, a content item for the particular entity is identified. The content item is transferred over a computer network and presented to the particular entity.

Techniques for providing webpages based on tracking consumer device interactions are discussed herein. Some embodiments may include one or more servers configured to: receive a request for access to a promotion webpage from a consumer device, wherein the request includes a consumer device cookie and the promotion webpage includes an indication of a promotion; in response to receiving the request for access to the promotion webpage, generate the promotion webpage including widgets; generate clickstream data based on tracking widget views of the widgets within the promotion webpage; associate the clickstream data with the consumer device cookie; and generate widget analytic data based on the clickstream data. The widget analytic data may then be used to populate webpages with widgets to optimize various criteria, such as widget views or promotion purchases.

In one aspect, an example method includes (i) determining an estimated number of replacement advertisement segment viewings remaining before an end date of a first advertising campaign; (ii) determining a number of impressions remaining for the first advertising campaign in order to reach a guaranteed total; (iii) determining, using the estimated number of replacement advertisement segment viewings and the number of impressions remaining, a first value of serving a first replacement advertising segment corresponding to the first advertising campaign to a content-presentation device; (iv) determining a second value of serving a second replacement advertisement segment corresponding to a second advertising campaign to the content presentation device; (v) selecting the first replacement advertisement segment rather than the second replacement advertisement segment based on the first value being greater than the second value; and (vi) causing the first replacement advertisement segment to be transmitted to the content-presentation device.

The present solution provides a target customer identification method and a device, an electronic device and a medium, which is applicable to the field of information processing. The method includes: obtaining personal characteristics data of potential customers; calculating a customer conversion rate of a telephone sales representative during each working time period according to the total number of customers who have made a transaction and the total number of marketing target customers of the telephone sales representative in each of working time periods; inputting the customer conversion rate of the telephone sales representative in the current working time period and the personal characteristics data of the potential customers into a pre-established random forest model to output product purchase probabilities of the potential customers; and determining a potential customer whose product purchase probability is greater than a preset threshold as a target customer of the telephone sales representative in the current working time period. In the present solution, the consideration factor of the real-time marketing capability of the telephone sales representative is added, so that the telephone sales representative can accurately find out the target customers at the current time, thereby improving customer conversion rate, marketing efficiency and target customer identification accuracy.