Systems and methods for generating gratuity analytics for one or more restaurants

Patent metadata

Field	Value
Publication number	US-10430900-B2
Application number	US-201615375476-A
Country	US
Kind code	B2
Filing date	Dec 12, 2016
Priority date	Dec 12, 2016
Publication date	Oct 1, 2019
Grant date	Oct 1, 2019

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. The processor is further configured to calculate tip data for the restaurant based on the plurality of matched messages, generate gratuity analytics for the restaurant over the date range based on the tip data, and display on a user interface of the client computing device the gratuity analytics.

First claim

What is claimed is:

1. A gratuity analytics computing device for generating gratuity analytics for one or more restaurants, said gratuity analytics computing device being associated with a payment processor configured to process transactions, said gratuity analytics computing device comprising a memory in communication with a processor, said processor programmed to:
receive a date range from a client computing device, wherein the data range is input by a user to the client computing device;
receive historical transaction data from at least one of the payment processor and an associated database configured to store the transaction data for transactions occurring within the received date range at the one or more restaurants, the transaction data including an authorization message and a clearing message for each transaction, each authorization message including (i) a transaction identifier, (ii) a manager identifier, (iii) a time stamp, (iv) an employee identifier, and (v) an initial transaction total, and each clearing message including (i) a transaction identifier, and (ii) a final transaction total;
match a plurality of the authorization messages with a respective plurality of the clearing messages based on the respective transaction identifiers included in the authorization messages and the clearing messages;
calculate tip data for the one or more restaurants by determining a difference between the initial transaction total and the final transaction total for each pair of matched messages of the plurality of matched messages;
generate gratuity analytics for the one or more restaurants over the received date range based on the calculated tip data, wherein the generated gratuity analytics include a ranking of the one or more restaurants based on the calculated tip data; and
display on a user interface of the client computing device the generated gratuity analytics.

2. The gratuity analytics computing device of claim 1 , wherein the generated gratuity analytics further include a comparison of tip data between different time intervals within the date range.

3. The gratuity analytics computing device of claim 1 , wherein said processor is further programmed to:
determine a plurality of subsets of the plurality of matched messages, each subset associated with a respective manager identifier;
for each subset, calculate an average tip size; and
compare the average tip size between the subsets to generate manager gratuity analytics.

4. The gratuity analytics computing device of claim 1 , wherein said processor is further programmed to:
determine a plurality of subsets of the plurality of matched messages, each subset associated with a respective employee identifier;
for each subset, calculate an average tip size; and
compare the average tip size between subsets to generate employee gratuity analytics.

5. The gratuity analytics computing device of claim 1 , wherein the restaurant is a first restaurant and the tip data is first tip data, and wherein said processor is further programmed to:
calculate second tip data for a second restaurant; and
compare the first tip data to the second tip data to generate inter-location gratuity analytics.

6. The gratuity analytics computing device of claim 1 , wherein said processor is further programmed to:
identify a competitor restaurant of the restaurant;
retrieve transaction data for the competitor restaurant;
calculate, using the transaction data for the competitor restaurant, competitor tip data for the competitor restaurant; and
compare the tip data for the restaurant to the competitor tip data to generate competitor gratuity analytics.

7. The gratuity analytics computing device of claim 6 , wherein to identify the competitor restaurant, said processor is further programmed to:
calculate, using the transaction data for the restaurant, an average ticket size and sales volume for the restaurant;
retrieve transaction data for a plurality of potential competitor restaurants;
calculate, using the transaction data for the plurality of potential competitor restaurants, a respective average ticket size and sales volume for each of the plurality of potential competitor restaurants; and
identify the competitor restaurant as a potential competitor restaurant of the plurality of potential competitor restaurants having an average ticket size and sales volume most similar to the restaurant.

8. The gratuity analytics computing device of claim 1 , wherein the transaction data further includes a card type of a payment card used to initiate each respective transaction, and wherein said processor is further programmed to:
determine at least one commonly used card type;
identify an issuer associated with each at least one commonly used card type; and
transmit an issuer identifier for each identified issuer to the restaurant.

9. A method for generating gratuity analytics for one or more restaurants, said method implemented by a gratuity analytics computing device including at least one processor in communication with a memory, the gratuity analytics computing device in communication with a client computing device and associated with a payment processor configured to process transactions, said method comprising:
receiving a date range from a client computing device, wherein the data range is input by a user to the client computing device;
receiving historical transaction data from at least one of the payment processor and an associated database configured to store the transaction data for transactions occurring within the received date range at the one or more restaurants, wherein the transaction data includes an authorization message and a clearing message for each transaction, wherein each authorization message includes (i) a transaction identifier, (ii) a manager identifier, (iii) a time stamp, (iv) an employee identifier, and (v) an initial transaction total, and wherein each clearing message includes (i) a transaction identifier, and (ii) a final transaction total;
matching a plurality of the authorization messages with a respective plurality of the clearing messages based on the respective transaction identifiers included in the authorization messages and the clearing messages;
calculating tip data for the one or more restaurants by determining a difference between the initial transaction total and the final transaction total for each pair of matched messages of the plurality of matched messages;
generating gratuity analytics for the one or more restaurants over the received date range based on the calculated tip data, wherein the generated gratuity analytics include a ranking of the one or more restaurants based on the calculated tip data; and
displaying on a user interface of the client computing device the generated gratuity analytics.

10. The method of claim 9 further comprising comparing the tip data between different time intervals within the date range.

11. The method of claim 9 further comprising:
determining a plurality of subsets of the plurality of matched messages, each subset associated with a respective manager identifier;
for each subset, calculating an average tip size; and
comparing the average tip size between subsets to generate manager gratuity analytics.

12. The method of claim 9 further comprising:
determining a plurality of subsets of the plurality of matched messages associated with a respective employee identifier;
for each subset, calculating an average tip size; and
comparing the average tip size between the subsets to generate employee gratuity analytics.

13. The method of claim 9 , wherein the restaurant is a first restaurant and the tip data is first tip dat

Assignees

• Mastercard International Inc

Inventors

• Bhattacharjee Manash
• Tiwaree Prashanna S

Classifications

• G06Q50/12 Primary

  Hotels or restaurants · CPC title

• G06Q10/0639

  Performance analysis of employees; Performance analysis of enterprise or organisation operations · CPC title

• G06Q30/0202

  Market predictions or forecasting for commercial activities · CPC title