This disclosure describes a market segment analysis (MSA) system that facilitates a cluster analysis of market segment data and performance metrics to identify data patterns between individual market segments within a designated market region. Particularly, the MSA system may quantify current performance of existing vendor product or service offerings within a market segment relative to historical performance of the vendor product or service offering within the market segment. Further, the MSA system may quantify current performance of a vendor product or service offering relative to current peer performance of the same or substantially similar vendor product or service offerings within additional market segments. The MSA system may recommend one or more additional market segments for an offering of the vendor product or service. Alternatively, or additionally, the MSA system may recommend one or more vendor product or service offering(s) for a target market segment of a designated market region.

Techniques are described for determining locations of interest based on user visits. In some situations, the techniques include obtaining information about actual locations of users at various times, and automatically analyzing the information to determine particular locations in a geographic area that are of interest, such as for frequent destinations visited by users. After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).

A door to door sales management tool utilizes a computer system for visually dividing geographic areas among sales teams. The system can be configured to display on a digital interface a visual depiction of a map. The system can also be configured to receive one or more indications defining boundaries of a first geographic area within the map. The system can assign a first user to the first geographic area. Assigning the first user to the first geographic area can comprises receiving a physical association of a visual indicator representative of the first user with a visual indicator representative of the first geographic area through a touch interface.

In accordance with one or more aspects of selecting between client-side and server-side market detection, a determination is made at a device as to which of a client-side detected market and a server-side detected market is to have priority for a service. An application of the device is configured in accordance with a client-side market configuration setting if the client-side detected market has priority, and is configured in accordance with a server-side market configuration setting if the server-side detected market has priority.

An event occurring in a particular geographic region is identified based on disseminated information containing public commentary in the particular geographic region. Attributes that are related to the event are identified, and sentiment words relating to the identified event are extracted from the disseminated information, where the extracted sentiment words are in a local language of the particular geographic region. A sentiment trend visualization is generated that depicts a trend of sentiments of at least a particular one of the identified attributes, wherein the sentiments are based on the sentiment words for at least the particular attribute.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: displaying content elements on one or more websites to users; performing a classification of the users into segments; receiving a request from a first user of the users to display a webpage of the one or more websites; determining an amount of information in a respective posterior distribution of each of the one or more first segments of the segments in which the first user is classified, for each of the content elements based on impression response data; selecting a selected content element from among the content elements based on weightings of the content elements for the one or more first segments; and generating the webpage comprising the selected content element. Other embodiments are disclosed.

A disclosed mobile computing device is configured to establish a connection with a remote computing device, by providing login information associated with a previously-established user account on the remote computing device, and to receive a connection request from a client device. The connection request is based on connection information received from the client device or from the remote computing device, the connection information including location information of the client device. The mobile computing device then determines whether the location of the client device is within a coverage area associated with the mobile computing device and establishes a connection with the client device when the location of the client device is determined to be within the coverage area. The mobile computing device is configured to receive emergency requests from the client device and to be operated by a police officer, firefighter, paramedic, or other first responder who provides emergency response services.

The present invention relates to a system and method for generating demand groups. The system receives demand group modeling data including a product listing, point of sales data, available econometric data and product information. Attributes may then be assigned to the products based upon product identifiers, size, flavor, brand, and product descriptions utilizing natural language processing. The products may then be clustered according to the attributes and point of sales data. One or more decision trees may be generated for the product listings using the point of sales data. Demand rules may be received, which may be applied to the product clusters and the decision trees to generate demand groups. A confidence score may be generated for each product indicating how well that product fits within the demand group. These confidence scores may be compared against a threshold. Products with scores below the threshold may be flagged for user review.

Methods, systems, and computer-readable media for generating a virtual based on location. The method acquires a request for a service based on a type of service and is associated with a user, the user's location, and user preferences. The method then acquires a set of service providers based on the type of service and the user's location who are filtered from a larger set of service providers using user preferences. The method in the next step acquires a machine learning model that is based on stored information associated with other users travel patterns and with service providers providing the service and the geographical information associated with the user's location. The method executed the identified machine learning model to aggregate a subset of service providers based on output from the machine learning model. The machine learning model is inputted the set of service providers, the user's location, the user's preferences, and the geographical information.

A residual value forecasting system may utilize heterogeneous data, such as used market data, industry-specific data, and non-industry-specific data, from disparate data sources to produce residual value forecasts of an item based on a sophisticated residual value forecasting model particularly configured for agility. The system can dynamically and quickly adapt to change in data inputs and produce custom outputs. The system may determine a baseline value for an item using the used market data, a microeconomic factor using the industry-specific data, and a macroeconomic factor using the non-industry-specific data, as well as adjustments such as locality adjustments and modifications. Given the macroeconomic factor and the microeconomic factor relative to the locality-adjusted value of the item and in view of the competitive sets of similar and/or substitute items in the same industry, the system can generate an accurate forecast residual value of the item at a future time point.