This disclosure provides methods, systems, services, and platforms for automatically prompting a user of a mobile device to follow an entity on a social media network after a triggering event. This disclosure enables Mobile Network Operators to generate more followers on social media, and, thereby, connect better with potential customers or fans. When a business, group, or other entity has an active social media campaign, a message comprising instructions to launch a browser on a mobile device and navigate to the entity's social media page is sent automatically after a triggering event occurs.

In one aspect, an example methodology implementing the disclosed techniques includes, by a survey workflow module, receiving a survey request event for which a survey is requested and generating a first feature vector for the survey request event, wherein the first feature vector comprises features extracted from the survey request event. The method also includes predicting, by a first trained prediction module, whether the survey request event will result in a survey based on the first feature vector, wherein the first trained prediction module is trained using a training dataset generated from a corpus of historical survey request event-survey data.

Methods and apparatus to compensate impression data for misattribution and non-coverage are disclosed. An example method includes receiving a first request from a first type of computing device; sending a request for demographic information corresponding to requests received at a first internet domain from the first type of computing device; generating an aggregated audience distribution including a first audience distribution of a first household aggregated with a second audience distribution of a second household; normalizing the aggregated audience distribution to generate a misattribution correction matrix, the misattribution correction matrix including a probability that an impression of the media is attributable to a first demographic group when the database proprietor determines the impression corresponds to a person in a second demographic group; and compensating misattribution error in the impressions by re-assigning the impressions from the second demographic group to the first demographic group using the misattribution correction matrix.

A computer-implemented method, computer program product, and computing system for receiving a contribution for a social platform from a client; funding the social platform; and calculating a contribution ROI experienced by the client as a result of the contribution to the social platform.

A method for providing survey consumer data to a centralized server via a consumer panel application is provided. The method includes receiving in a server, from an application installed in a mobile device of a consumer, a transaction data linking a consumer identification with a selected retailer and associating a consumer segment with the transaction data based on a demographic attribute shared between a server account for the consumer and the consumer segment. The method also includes determining a sales lift factor for the consumer segment based on an adjustment factor and the transaction data, and updating a scalable measurement protocol in the application installed in the mobile device of the consumer based on the sales lift factor. A system and a non-transitory, computer-readable medium storing instructions to perform the above method are also provided.

Methods and apparatus for multi-account adjustment in third-party privacy-protected cloud environments are disclosed herein. A disclosed example apparatus includes memory and processor circuitry to execute computer readable instructions to select a type of client device, determine a number of panelists associated with a demographic characteristic and the type of client device, the demographic characteristic defining a segment of the panelists, determine a number of user accounts associated with the number of panelists, determine a multi-account adjustment factor for the demographic characteristic and the client device based on a ratio of the number of the panelists to the number of user accounts, and adjust an initial estimate of a unique audience size based on the multi-account adjustment factor.

An example system disclosed herein includes programmable circuitry to identify donor adjustment factors and recipient adjustment factors used for correction of media impressions logged by a database proprietor, the donor adjustment factors including first donor adjustment factors associated with a first geographic region and second donor adjustment factors associated with a second geographic region, determine a first reduced donor factor set corresponding to ones of the first donor adjustment factors that satisfy a threshold, determine a second reduced donor factor set corresponding to ones of the second donor adjustment factors that satisfy the threshold, and generate imputation factors based on an aggregation of retained ones of the donor adjustment factors, the retained ones of the donor adjustment factors selected based on the first reduced donor factor set and the second reduced donor factor set, the imputation factors to reduce error in the correction.

Advertisement user feedback data may be collected and provided to an advertisement decision engine. Advertisements may be presented to users via various electronic media such as digital television, Internet, software, etc., and advertisement feedback user interfaces may be provided to users in connection with such advertisements. An advertisement feedback user interface may be provided via the same electronic medium and device as the corresponding advertisements, or via a different electronic medium and/or device. User feedback data regarding electronic advertisements, as well as additional related data, may be received and provided to one or more advertisement decision engines. An advertisement decision engine may use the advertisement user feedback data to determine a future advertisement to be presented to one or more users at an advertisement insertion opportunity identified in one or more various electronic media.

Methods, systems, apparatus, and articles of manufacture to determine causal effects are disclosed herein. An example apparatus includes a weighting engine to calculate a first set of weights corresponding to a first treatment dataset, a second set of weights corresponding to a second treatment dataset, and a third set of weights corresponding to a control dataset, the weighting engine to increase an operational efficiency of the apparatus by calculating the first set of weights, second set of weights, and third set of weights independently, a weighting response engine to calculate a first weighted response for the first treatment dataset, a second weighted response for the second treatment dataset, and determine a causal effect between the first treatment dataset and the second treatment dataset based on a difference between the first weighted response and the second weighted response, and a report generator to transmit a report to an audience measurement entity.

A real-time survey system can monitor real-time event and interaction data generated as a user interacts with an application. Using the event data in combination with static user attributes, the survey system can identify a subset of microsurveys a user is eligible to receive or participate in. Identifying eligibility can include applying one or more filters based on user attributes as well as detecting a triggering event that signals real-time relevance of the survey to the user's actions. If the user is eligible for multiple surveys, the survey system can select a single survey to send to the user. Surveys can be presented to users directly using the real-time survey system instead of via an alternate delivery method, such as email. After collecting survey data, survey system can automatically analyze survey responses, including by performing machine learning based thematic analysis on freeform text responses.