A method of delivering advertising in an online environment includes determining a context of a user operating a client computer to interact with an e-commerce website, where the determined context representing an intent of the user to locate a product for purchase, defining a relation between one or more of a plurality of advertisements and the product based on at least one of a plurality of relevance types, and displaying, to the user, at least one of the advertisements having the relation to the product.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing application program interface calls.

Portable devices include environmental sensors that generate metrics about the environment (e.g., accelerometers detecting impulses and vibration, and GPS receivers detecting position and velocity). Such devices often use environmental metrics to extract user input directed at the device by the user, and status information about the device and the environment. Presented herein are techniques for using environmental metrics to infer physical activities performed by the user while attached to the device. For example, jogging may be inferred from regular, strong impulses and typical jogging speed; walking may be inferred from regular, weak impulses and typical walking speed; and riding in a vehicle may be inferred from low-level vibrations and high speed (optionally identifying the type of vehicle ridden by the user). Based on these inferences, the device may automatically present applications and/or or adjust user interfaces suitable for the user's physical activity, rather than responsive to user input.

The present invention provides a fractional advertising platform comprising an online interactive interface which is provided by a service provider and accessible by at least one business and at least one client/respondent via a communication means of an apparatus, and is in communication with the online interactive interface via a global communications network, an advertising campaign having at least one advert divided into a plurality of sellable units and comprising a Call to Action and wherein the at least one client/respondent are directed to one of the businesses based on a random weighted probability algorithm.

An online system evaluates the quality of a content publisher displaying sponsored content items. To determine a likelihood of conversion actions associated with the sponsored content items, the online system uses information about users and their interactions with sponsored content items featured within the content publisher against interactions with sponsored content items featured within a benchmark system (e.g., online system). By determining a ratio of these interactions, the online system can determine a likelihood of conversion actions for the content publisher. The online system uses this likelihood of conversions to determine a publisher quality score that it uses to normalize third party value contributions toward placing sponsored content items on the content publisher. Thus, third party systems no longer need to be concerned about the intrinsic value of a given content publisher as third party value contributions are normalized based on the content publisher's conversion rates.

Dynamic selection of an advertisement to present to a user including detecting, by an advertisement selection module, a proximity between the user and a device including retrieving sensor data from at least one sensor of the device; generating, by the advertisement selection module, a current environmental profile comprising the detected proximity between the user and a device; matching, by the advertisement selection module, the current environmental profile to a first personal skip profile of a plurality of personal skip profiles for the user, wherein the first personal skip profile comprises data indicating a likelihood that the user will skip the advertisement for the detected proximity; selecting the advertisement based on the first personal skip profile including the data indicating the likelihood that the user will skip the advertisement for the detected proximity; and sending, to the device, the selected advertisement for presentation to the user.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reducing latency in network communications and data presentation. In one aspect, a user session is initiated in which data related to an account is presented to the user. A user group to which the given user has been assigned is identified. A first dataset related to the account is selected based on the user group. A second dataset related to the account is selected based on types of data previously requested by various other users in the user group. A user interface for the account is updated to present at least a portion of the first dataset. Latency in updating the user interface is reduced when presenting additional portions of the first dataset or the second dataset by providing, to the client device, the second dataset prior to receiving a request for the second dataset.

Systems and methods provide a customer engagement platform experimentation framework. Experiments are created to determine which variation of a particular promotion (i.e., an experiment) is most effective. Initially, users are split into buckets that define whether a particular user is in a control group or a treatment group for a particular experiment. Users are assigned into buckets differently for unrelated experiments and similarly for related experiments. This ensures the users are in either the control group or the treatment group for all related experiments. Key performance indicators (KPIs) of users that experienced the experiment are compared with KPIs of users that did not experience the experiment. An assessment of the experiment can then be provided based on the compared KPIs.

An example method involves a computing device displaying a first action-outcome node corresponding to a first marketing-campaign action, displaying a plurality of outcomes of the first marketing-campaign action, positioned around the first action-outcome node, wherein each outcome corresponds to a potential result of the first marketing-campaign action, displaying a second action-outcome node corresponding to a second marketing-campaign action, displaying a plurality of outcomes of the second marketing-campaign action, positioned around the second action-outcome node, wherein each outcome corresponds to a potential result of the second marketing-campaign action, and displaying a graphical link connecting an outcome of the first action-outcome node to the second action-outcome node.

An online system predicts, using a first targeting model, a first group of users as candidates to be in a targeting cluster, and predicts, using a second targeting model, a second group of users as candidates to be in the targeting cluster. The online system determines a first set of users that are not part of the first group of users, and a second set of users that are not part of the second group of users, and provides surveys to the first and second set of users. The online system determines a first subgroup of the first group of users and a second subgroup of the second group of users, and provides an ad preferences tool to the first subgroup and the second subgroup. The online system scores the first and second targeting models based in part on responses to the surveys and/or the ad preferences tools.