Systems and methods for generating aggregated merchant analytics for a geographic sector using tip data

Patent metadata

Field	Value
Publication number	US-2018108000-A1
Application number	US-201615296644-A
Country	US
Kind code	A1
Filing date	Oct 18, 2016
Priority date	Oct 18, 2016
Publication date	Apr 19, 2018
Grant date	—

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.

First claim

What is claimed is:

1 . A merchant analytics computing device for generating aggregated merchant analytics for a geographic sector using tip data, said merchant analytics computing device comprising a memory in communication with a processor programmed to:
define a plurality of sectors of a geographic region; receive transaction data for financial transactions occurring within a period of time, the transaction data associated with a plurality of merchants, the plurality of merchants located in the geographic region, the transaction data including authorization data messages and clearing data messages; match, for each merchant of the plurality of merchants, a plurality of authorization data messages with a respective plurality of clearing data messages; calculate a tip size for each of the plurality of matched transactions; identify, for each merchant of the plurality of merchants, one sector of the plurality of sectors in which the merchant is located; generate aggregated merchant analytics for each sector based on the transaction data associated with all merchants of the plurality of merchants located in the sector, wherein the aggregated merchant analytics represent a ranking of each sector relative to all other sectors of the plurality of sectors, the aggregated merchant analytics including at least a tip size score based on the calculated tip size; and display on a user interface of the user computing device the aggregated merchant analytics, wherein the aggregated merchant analytics are graphically represented on a map of the defined sectors.

2 . The merchant analytics computing device of claim 1 , wherein the plurality of merchants includes a plurality of eating establishments.

3 . The merchant analytics computing device of claim 1 , wherein said processor is further programmed to:
calculate an average ticket size for each sector using received transaction data for each of the plurality of merchants located in each corresponding sector, wherein the average ticket size represents an average transaction amount in each sector during the period of time, and wherein the average ticket size is calculated by dividing a total sales revenue for a sector by a number of transactions initiated in the sector during the period of time; determine a relative ranking for each sector by comparing the average ticket size of each sector of the plurality of sectors; and generate the ticket size score for each sector based on the relative ranking.

4 . The merchant analytics computing device of claim 3 , wherein said processor is further programmed to generate a sub-composite score for each sector, wherein the sub-composite score represents a relative ranking of the plurality of sectors based on an aggregation of the ticket size score and the tip size score of each sector.

5 . The merchant analytics computing device of claim 1 , wherein said processor is further programmed to:
provide an option on the user interface for filtering the plurality of merchants into eating establishments and non-eating establishments, and; only generate the tip size score for the eating establishments.

6 . The merchant analytics computing device of claim 1 , wherein said processor is further programmed to:
generate a growth score for each sector, wherein the growth score represents a first relative ranking of the plurality of sectors based on a difference in total sales revenue in each sector from a beginning of the period of time to an end of the period of time; generate a stability score for each sector, wherein the stability score represents a second relative ranking of the plurality of sectors based on a maintenance of a total sales revenue within a range of values around an average value of the total sales revenue in each sector during the period of time; generate a size score for each sector, wherein the size score represents a third relative ranking of the plurality of sectors based on the total sales revenue in each sector during the period of time; generate a traffic score each sector, wherein the traffic score represents a fourth relative ranking of the plurality of sectors based on a number of transactions initiated in each sector during the period of time; generate a ticket size score for each sector, wherein the ticket size score represents a fifth relative ranking of the plurality of sectors based on an average transaction amount in each sector during the period of time; and generate a composite score for each sector, wherein the composite score represents a sixth relative ranking of the plurality of sectors based on an aggregation of the growth score, the stability score, the size score, the traffic score, the ticket size score, and the tip size score of each sector.

7 . The merchant analytics computing device of claim 1 , wherein said processor is further programmed to calculate a tip size for each of the plurality of matched transactions by determining a difference between each of the plurality of authorization data messages and each of the corresponding clearing data messsages.

8 . A method for generating aggregated merchant analytics for a geographic sector using tip data, said method implemented by a merchant analytics computing device including at least one processor in communication with a memory, the merchant analytics computing device in communication with a user computing device, said method comprising:
defining a plurality of sectors of a geographic region; receiving, by the merchant analytics computing device, transaction data for financial transactions occurring within a period of time, the transaction data associated with a plurality of merchants, the plurality of merchants located in the geographic region, the transaction data including authorization data messages and clearing data messages; matching, for each merchant of the plurality of merchants, a plurality of authorization data messages with a respective plurality of clearing data messages; calculating a tip size for each of the plurality of matched transactions; identifying, for each merchant of the plurality of merchants, one sector of the plurality of sectors in which the merchant is located; generating aggregated merchant analytics for each sector based on the transaction data associated with all merchants of the plurality of merchants located in the sector, wherein the aggregated merchant analytics represent a ranking of each sector relative to all other sectors of the plurality of sectors, the aggregated merchant analytics including at least a tip size score based on the calculated tip size; and displaying on a user interface of the user computing device the aggregated merchant analytics, wherein the aggregated merchant analytics are graphically represented on a map of the defined sectors.

9 . The method of claim 8 , wherein the plurality of merchants includes a plurality of eating establishments.

10 . The method of claim 8 further comprising:
calculating an average ticket size for each sector using received transaction data for each of the plurality of merchants located in each corresponding sector, wherein the average ticket size represents an average transaction amount in each sector during the period of time, and wherein the average ticket size is calculated by dividing a total sales revenue for a sector by a number of transactions initiated in the sector during the period of time;
determining a relative ranking for each sector by comparing the average ticket size of each sector of the plurality of sectors; and
generating the ticket size score for each sector based on the relative ranking.

11 . The method of claim 10 further comprising generating a sub-composite score for each sector, wher

Assignees

• Mastercard International Inc

Inventors

• Bhattacharjee Manash
• Ghosh Debashis
• Tiwaree Prashanna S

Classifications

• G06Q20/12

  specially adapted for electronic shopping systems · CPC title

• G06Q20/202 Primary

  Interconnection or interaction of plural electronic cash registers [ECR] or to host computer, e.g. network details, transfer of information from host to ECR or from ECR to ECR · CPC title

• G06Q30/02 Primary

  Marketing; Price estimation or determination; Fundraising · CPC title

• G06Q30/0282

  Rating or review of business operators or products · CPC title

• G06Q30/0201

  Market modelling; Market analysis; Collecting market data · CPC title