Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof for automatically embedding digital data in a message and capturing analytics for the digital data. In some embodiments, a server may retrieve information about a user. The server may execute a predictive analysis on the information about the user to identify digital data to be transmitted to the user. The digital data may include an embedded tag associated with an object. The server may automatically embed the identified digital data in a messaging prompt to be transmitted to the user. The server may identify the object associated with the embedded tag, using the embedded tag. The server may determine interaction data for the object and digital data and render the interaction data on a user interface.

Various examples are directed to systems and methods for adaptively generating leads. A marketing system may determine that a first lead score for a first lead is greater than a first lead score threshold and determine that a second lead score for a second lead is less than the first lead score threshold. The marketing system may generate a set of filtered leads including the first lead information from the first lead. The marketing system may determine a scrub rate that describes a portion of first execution cycle data having lead scores greater than the first lead score threshold and determine that the scrub rate is greater than an analysis window scrub rate by more than a scrub rate threshold. The marketing system may select a second lead score threshold that is lower than the first lead score threshold.

An information delivery method is provided, including: determining a candidate information list in response to a page request sent by a user, where the candidate information list comprises a plurality pieces of information; generating ranks of the pieces of information in the candidate information list by sorting the plurality pieces of information in the candidate information list in accordance with a predetermined rule; acquiring a current average rank of the information of a designated type in the candidate information list based on the ranks of the pieces of information in the candidate information; and selecting designated delivery information from the information of the designated type, based on the current average rank of the information of the designated type and a historical average rank corresponding to the information of the designated type.

The present disclosure relates to a parking lot management device that communicates with a provider device of a content provider that provides content for a predetermined time period and a mobile device of a content user. The parking lot management device includes one or more processors and one or more memories. One or more instructions stored in the one or more memories, when executed by the one or more processors, cause the one or more processors to perform operations including: a discount code providing operation of generating and storing a discount code and providing the generated discount code to the provider device in response to a request from the provider device of the content provider; and a discount providing operation of determining whether a discount code for the mobile device is valid in response to a discount request from the mobile device and applying a predetermined discount on a parking fee of a vehicle of the content user when the discount code is valid.

To appropriately give a reward to a provider of a content that has contributed to a sale of a product and to improve the quality of the content is a technical problem, and, as a solution to the problem, there is provided a management server that can perform a process of registering, for each product, a predetermined product introduction rule that needs to be observed in creating a product introduction video, a process of receiving the product introduction video from a provider, determining whether or not the product introduction video complies with the product introduction rule, and registering the product introduction video that complies with the product introduction rule in association with the product corresponding to the product introduction video, a process of, when a sale of the product is completed, specifying one or two or more product introduction videos that have contributed to the sale of the product among the product introduction videos that are viewed by a purchaser of the product out of a plurality of the product introduction videos that are registered in association with the product according to a predetermined specification rule determined in advance, a process of determining a reward for the provider who provides the product introduction video specified according to a predetermined reward determination rule determined in advance; and a process of storing reward information that indicates a reward determined.

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.

A digital ad-buying platform uses counterfactual-based incrementality measurement by implementing randomization and/or a correction for auction win bias to avoid the need to identify counterfactual winner types in the control group. This approach can estimate impact at the individual consumer level. Confidence levels can be determined using Gibbs sampling in the context of causal analysis in the presence of non-compliance.

This paper presents a practical method for measuring the impact of multiple marketing events on sales, including marketing events that are not traditionally trackable. The technique infers which of several competing media events are likely to have caused a given conversion. The method is tested using hold-out sets, and also a live media experiment for determining whether the method can accurately predict television-generated web conversions.

For each service scenario out of a plurality of service scenarios, matching features of a to-be-matched product corresponding to the service scenario are acquired based on user features of users accessing the service scenario. A respective user feature mapping value of the service scenario is calculated based on the matching features of the to-be-matched product corresponding to the service scenario. Out of the plurality of service scenarios, a target service scenario of the to-be-matched product is selected based on the respective user feature mapping value of the service scenario.

A filtering machine receives sponsored content and filters the sponsored content according to a quality metric generated by quality model circuitry and assigned to the instance of sponsored content. The quality model circuitry generates the quality metric in accordance with historical feedback received about other sponsored content and a collection of quality factors pertaining to the sponsored content. Based on the quality metric for the sponsored content, the filtering machine can effect service of the sponsored content to a user device for display thereon.