Methods, apparatus, systems and articles of manufacture to monitor media presentations are disclosed. An example tangible computer readable storage medium includes instructions that, when executed, cause a machine to at least instrument media with monitoring instructions; provide the instrumented media to a media device via a hypertext transport secure protocol, the monitoring instructions to cause the media device to request a panelist identifier associated with the media device; detect a media event based on an action of the media device; determine media-identifying information based on the media event; and generate a record including the media-identifying information and the panelist identifier.

Disclosed are embodiments for the aggregation and analysis of vehicle prices via a geo-specific model. Data may be collected at various geo-specific levels such as a ZIP-Code level to provide greater data resolution. Data sets taken into account may include demarcation point data sets and data sets based on vehicle transactions. A demarcation point data set may be based on consumer market factors that influence car-buying behavior. Vehicle transactions may be classified into data sets for other vehicles having similar characteristics to the vehicle. A geo-specific statistical pricing model may then be applied to the data sets based on similar characteristics to a particular vehicle to produce a price estimation for the vehicle.

A computer system measures trust of an entity in electronic documents from electronic media sources is described. A communication network is linked to one or more of the sources. A computer server is in communication with the communication network and is configured to receive electronic documents via the communication network. The computer server having a memory and a processor accessing a database. The memory includes processor executable instructions stored in the memory and executable by the processor. The computer executable instructions comprise preliminary term vector instructions, calculating instructions for determining the preliminary term vectors in the received electronic documents, and refined term vector instructions for defining a plurality of industry-specific term vectors.

The invention relates to a computer-implemented MRP method for controlling materials in a supply chain (SC) with customer segments (CS1, CS2). The invention is advantageous in that there is calculated a safety stock curve (SSC) in a time-phased manner (m, n) to cover an uncertainty until a demand is fulfilled based on customer data (CD) and material data (MD). The time until demand is fulfilled is a demand fulfilment time (DFT), the safety stock curve (SSC) being calculated as a function of this demand fulfilment time (DFT) in order to meet specified target service level (TSL1, TSL2) and simultaneously minimize inventory levels in said plurality of distribution centres (M_DC, DC2). The invention provides advances in MRP with respect to an optimum with demand compliance to required service levels while not unnecessarily increasing safety stock levels. Simulations convincingly demonstrate the effects of the invention.

An automated advertising scheduling and distribution process reacts to the effectiveness of sales data. A hosted platform creates location-specific playlists based on key consumer variables that impact buying behavior, and dynamically performs data analytics. Utilizing a programmatic system and machine learning algorithmic methodology, the platform gathers data from the retailer's data warehouse and automatically pulls location-by-location sales data while simultaneously collecting playback data. If sales are not being affected on the particular item that is being promoted, then the platform may be configured to replace that message with a promotional message for another product with a higher likelihood of engagement and conversion. This virtual feedback loop ensures that the platform is optimizing the most effective series of promotional messages for any given location. The content management administrator accordingly delivers relevant advertising/messages to various display screens integrated into fuel pumps, through the store, and to retailer loyalty program applications.

Systems and methods for automated and explainable machine learning to generate seamlessly actionable insights by generating explainable personas directly from customer relationship management systems are disclosed. The personas are defined as a collection of segments, scored by likelihood to generate good opportunities, accompanied ranked profile attribute importance, with descriptive names and summaries, associated human and database readable queries which have been generated to optimally find cluster candidates in a broader data universe. Such a system would effectively and accurately model the composition of past clients, perform the categorization in an explainable way such that actions can be taken on the information to have predictable results. What is further required are the mean to categorize small text components, trained over dependent and independent model sets, to enable a cleaner and more explicit representation of information rich short-strings, in order to facilitate a more meaningful representation of the user profiles.

Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. Specifically, in certain embodiments, historical transaction data associated with a particular vehicle configuration may be obtained and processed to determine pricing data associated with the vehicle configuration. The historical transaction data or determined pricing data may then be presented in an intuitive manner.

Initial sales cluster is divided by the control circuit into a plurality of velocity buckets. Subsequently, each velocity bucket is divided into a plurality of micro-clusters. The micro-clusters are defined according to demographic information or store characteristic information. A importance score and a performance score for each of the micro-clusters is determined. An optimal sales cluster and a corresponding optimal planogram for each retail store in each micro-cluster are determined based upon the importance score and the performance score.

Initial sales cluster is divided by the control circuit into a plurality of velocity buckets. Subsequently, each velocity bucket is divided into a plurality of micro-clusters. The micro-clusters are defined according to demographic information or store characteristic information. A importance score and a performance score for each of the micro-clusters is determined. An optimal sales cluster and a corresponding optimal planogram for each retail store in each micro-cluster are determined based upon the importance score and the performance score.

The present disclosure provides a method of user clustering, and the method includes: acquiring a clustering condition for a predetermined user group, wherein the clustering condition includes a time selecting condition and an event selecting condition; determining at least one target time period for each user behavior data in a user behavior database based on the time selecting condition; determining association data indicating a relationship between the each user behavior data and each target time period based on the each user behavior data and the each target time period; and selecting target association data for a time period to be monitored based on the time period to be monitored and the event selecting condition, so as to determine a target user belonging to the predetermined user group according to the target association data. The present disclosure also provides an apparatus of user clustering, a computer device and a non-transitory medium.