Abstract: A gratuity analytics computing system, for generating gratuity analytics for a plurality of transactions of a customer at a service provider within a date range, is in communication with an electronic device of the service provider over an electronic network. The system includes a gratuity analytics computing device, a database including a memory in operable electronic communication with the gratuity analytics computing device, and a processor configured to: receive transaction data for the plurality of customer transactions occurring within the date range, match a plurality of authorization messages with a respective plurality of clearing messages, generate gratuity analytics for the plurality of customer transactions over the date range based on average tip data from the customer, and calculate a customer satisfaction score for one customer transaction of the plurality of customer transactions based on the generated gratuity analytics.

Abstract: A message is generated, with the message including a URL (uniform resource locator). The URL points to a web location. The web location is for facilitating a payment to a payment recipient. The message is transmitted to a mobile device owned by the payment recipient.

Abstract: A gratuity analytics computing device for generating gratuity analytics for one or more restaurants is provided. The gratuity analytics computing device includes a memory in communication with a processor. The processor programmed to receive a date range from a client computing device. The processor is further configured to receive transaction data for transactions occurring within the date range at a restaurant. The transaction data including a manager identifier, a time stamp, and an employee identifier associated with the transactions, the transaction data including authorization messages and clearing messages. The processor is further configured to match a plurality of authorization messages with a respective plurality of clearing messages.

Abstract: A gratuity analytics computing device for generating gratuity analytics for one or more restaurants is provided. The gratuity analytics computing device includes a memory in communication with a processor. The processor programmed to receive a date range from a client computing device. The processor is further configured to receive transaction data for transactions occurring within the date range at a restaurant. The transaction data including a manager identifier, a time stamp, and an employee identifier associated with the transactions, the transaction data including authorization messages and clearing messages. The processor is further configured to match a plurality of authorization messages with a respective plurality of clearing messages.

Abstract: A gratuity analytics computing device for generating gratuity analytics for one or more restaurants is provided. The gratuity analytics computing device includes a memory in communication with a processor. The processor programmed to receive a date range from a client computing device. The processor is further configured to receive transaction data for transactions occurring within the date range at a restaurant. The transaction data including a manager identifier, a time stamp, and an employee identifier associated with the transactions, the transaction data including authorization messages and clearing messages. The processor is further configured to match a plurality of authorization messages with a respective plurality of clearing messages.

Abstract: A gratuity analytics computing device for generating gratuity analytics for one or more restaurants is provided. The gratuity analytics computing device includes a memory in communication with a processor. The processor programmed to receive a date range from a client computing device. The processor is further configured to receive transaction data for transactions occurring within the date range at a restaurant. The transaction data including a manager identifier, a time stamp, and an employee identifier associated with the transactions, the transaction data including authorization messages and clearing messages. The processor is further configured to match a plurality of authorization messages with a respective plurality of clearing messages.

Abstract: A message is generated, with the message including a URL (uniform resource locator). The URL points to a web location. The web location is for facilitating a payment to a payment recipient. The message is transmitted to a mobile device owned by the payment recipient.

Abstract: A merchant score (MS) computing device for generating merchant lending scores for business loans is provided. The MS computing device receives a score request including a merchant identifier associated with a candidate merchant, determines a geolocation and a merchant category associated with the candidate merchant based at least in part on the score request, and retrieves transaction data associated with transactions for a plurality of merchants including the candidate merchant and a set of peer merchants. Each peer merchant is associated with the geolocation and merchant category of the candidate merchant. The MS computing device further compares the transaction data associated with the candidate merchant to the transaction data associated with the peer merchants, generates a merchant lending score associated with the candidate merchant that indicates a relative performance level based on the comparison, and transmits the merchant lending score to a requestor computing device.

Abstract: A scoring engine computing device for generating personalized lending scores is provided. The scoring engine computing device receives a request including a cardholder identifier associated with a candidate cardholder, determines demographic data associated with the candidate cardholder, and retrieves transaction data for a plurality of cardholders including the candidate cardholder and a set of peer cardholders. Each cardholder of the set of peer cardholders is associated with the determined demographic data of the candidate cardholder, and the transaction data is associated with transactions for a plurality of spending categories. The scoring engine computing device further normalizes the transaction data associated with the candidate cardholder by category, generates a personalized lending score associated with the candidate cardholder that indicates a spending trend of the candidate cardholder, and transmits the personalized lending score to a requestor computing device.

Abstract: A method for predicting parking space availability using normalized data includes: storing a number of parking spaces for each of a plurality of geographic areas; storing a number of payment transactions for each geographic area processed during a related period of time; receiving an availability request a specific period of time; normalizing parking spot availability for each geographic area based on the associated number of parking spaces and an average number of parking spaces for the geographic areas; normalizing the number of payment transactions for each geographic area based on the number of transactions processed during the specific period of time and the number of transactions processed during a corresponding period of time; calculating a composite score for each geographic area based on the associated normalized parking spot availability and transaction frequency; and transmitting the calculated composite score for each geographic area in response to the received availability request.

Abstract: A gratuity analytics computing system, for generating gratuity analytics for a plurality of transactions of a customer at a service provider within a date range, is in communication with an electronic device of the service provider over an electronic network. The system includes a gratuity analytics computing device, a database including a memory in operable electronic communication with the gratuity analytics computing device, and a processor configured to: receive transaction data for the plurality of customer transactions occurring within the date range, match a plurality of authorization messages with a respective plurality of clearing messages, generate gratuity analytics for the plurality of customer transactions over the date range based on average tip data from the customer, and calculate a customer satisfaction score for one customer transaction of the plurality of customer transactions based on the generated gratuity analytics.

Abstract: A gratuity analytics computing device for generating gratuity analytics for one or more restaurants is provided. The gratuity analytics computing device includes a memory in communication with a processor. The processor programmed to receive a date range from a client computing device. The processor is further configured to receive transaction data for transactions occurring within the date range at a restaurant. The transaction data including a manager identifier, a time stamp, and an employee identifier associated with the transactions, the transaction data including authorization messages and clearing messages. The processor is further configured to match a plurality of authorization messages with a respective plurality of clearing messages.

Abstract: A merchant analytics computing device for generating aggregated merchant analytics for a geographic sector using tip data is provided. The merchant analytics computing device is programmed to define a plurality of geographic sectors and receive transaction data occurring within a period of time. The transaction data is associated with merchants located in the sector and includes authorization and clearing transactions. The merchant analytics computing device is programmed to match a plurality of authorization and clearing transactions, calculate a tip size for each matched transaction, identify the sector for each merchant, and generate aggregated merchant analytics for each sector based on the transaction data. The aggregated merchant analytics represent a ranking of each sector relative to all other sectors of the plurality of sectors and include at least a tip size score based on the calculated tip size. The merchant analytics computing device displays on a user device the aggregated analytics.