UTILITY FOR DETERMINING COMPETITIVE RESTAURANTS

• US 20130166347A1
• Filed: 12/27/2011
• Published: 06/27/2013
• Est. Priority Date: 12/27/2011
• Status: Active Grant

• Current Assignee: GrubHub Inc.
• Original Assignee: GrubHub Inc.
• Inventors: Scotto, Stefano Alexander, Zivin, Rhiannon Sterling
• Granted Patent: US 8,595,050 B2
• Time in Patent Office: Days
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 50/12 Hotels or restaurants

Abstract

A utility for determining the competitors of a subject restaurant accesses a database that contains information about other, nearby restaurants. The utility assigns a competitor score to each restaurant within the maximum radius based on the distance from the subject restaurant, as well as the cuisine types, menus, and order histories of the subject and competitor restaurants.

Claims

1. A method of determining a set of competitors for a first restaurant within a database of restaurants, wherein each restaurant in the database has a market, a location, and one or more cuisine types, the method comprising the steps of:

i) querying the database to return a set of all restaurants within a particular market;

ii) filtering the set of all restaurants within the particular market to produce a second set of restaurants within a selected radius of the first restaurant;

iii) generating a cuisine type match score for all restaurants within the second set of restaurants based on similarity of each restaurant'"'"'s one or more cuisine types to the one or more cuisine types of the first restaurant;

iv) generating a distance score for all restaurants within the second set of restaurants based on how far each restaurant is from the first restaurant;

v) generating a competitor score for all restaurants within the second set of restaurants, the competitor score comprising a weighted combination of each restaurant'"'"'s cuisine type match score and distance score;

vi) filtering the second set of restaurants to produce a set of competitor restaurants with competitor scores greater than a predetermined level; and

vii) presenting the set of competitor restaurants.

2. The method of claim 1 wherein each restaurant in the database further comprises a field designating whether the restaurant accepts pickup orders, delivery orders, or pickup and delivery orders, and the method further comprising the step of filtering all restaurants within the market to produce a set of similar service restaurants having a selected value in the field designating whether the restaurant accepts pickup orders, delivery orders, or pickup and delivery orders.

3. The method of claim 1 wherein the distance score is generated using a Gaussian function.

4. A method of determining competitors for a first restaurant within a database of restaurants, wherein each restaurant in the database has a location, an order history, a cuisine type and a menu comprising a plurality of menu items, the method operating within a computer and comprising the steps of:

i) retrieving a plurality of restaurants from the database over a network interface wherein the network interface is coupled to a processor of the computer;

ii) filtering, by the processor, the plurality of restaurants to produce a first set of restaurants within a radius of the location of the first restaurant;

iii) filtering, by the processor, the first set of restaurants to produce a second set of restaurants with a similar cuisine as the first restaurant;

iv) comparing, by the processor, the menu items of the first restaurant to the menu items of each of the restaurants within the second set of restaurants to produce a set of menu scores for the second set of restaurants;

v) comparing, by the processor, an order history of the first restaurant with an order history of each of the restaurants within the second set of restaurants to produce a set of order history scores for the second set of restaurants, wherein each order history includes a plurality of orders wherein each order in the plurality of orders records at least a cost of the order;

vi) computing a set of weighted average scores of the set of menu scores and the set of order history scores; and

vii) based on the set of weighted average scores, filtering, by the processor, the second set of restaurants to produce a first set of competitor restaurants.

6. The method of claim 4 wherein the step of comparing the order history includes computing an average order amount for a subset of the first restaurant'"'"'s order history, and computing an average order amount for a subset of the order history of each of the restaurants within the second set of restaurants.

7. The method of claim 4 wherein the step of comparing the order history includes computing a standard deviation for a subset of the first restaurant'"'"'s order history, and computing a standard deviation for a subset of the order history of each of the restaurants within the second set of restaurants.

8. The method of claim 4 wherein the step of comparing the order history includes computing a daily order volume for a subset of the first restaurant'"'"'s order history, and computing a daily order volume for a subset of the order history of each of the restaurants within the second set of restaurants.

9. The method of claim 4 further comprising the step of computing a seasonal pattern score for the first restaurant and for each of the restaurants within the first set of competitor restaurants to produce a second set of competitor restaurants.

10. The method of claim 9 wherein the step of computing a seasonal pattern score includes computing the average monthly sales of the subject restaurant and the average monthly sales of each of the restaurants within the second set of restaurants.

11. The method of claim 4 further comprising associating each menu item of each restaurant with a true item, wherein the step of comparing the menu items comprises comparing true menu items.

12. The method of claim 4 wherein each restaurant in the database has a set of ingredients associated with each menu item within the menu associated with the restaurant, and wherein the step of comparing the menu items comprises comparing the ingredients associated with menu items.

13. The method of claim 4 wherein each restaurant in the database has an hours of operation field and further comprising the step of comparing the hours of operation of the first restaurant with the hours of operation of each of the restaurants within the first set of competitor restaurants to produce a second set of competitor restaurants.

14. The method of claim 4 wherein each restaurant in the database has one or more associated diner ratings and further comprising the step of comparing the diner ratings of the first restaurant with the diner ratings of each of the restaurants within the first set of competitor restaurants to produce a second set of competitor restaurants.

15. The method of claim 4 wherein each restaurant in the database has a delivery radius and further comprising the stop of comparing the delivery radius of the first restaurant with the delivery radius of each of the restaurants within the first set of competitor restaurants to produce a second set of competitor restaurants.

16. The method of claim 4 wherein each restaurant in the database has an associated delivery fees field and further comprising the step of comparing the delivery fees field of the first restaurant with the delivery fees of each of the restaurants within the first set of competitor restaurants to produce a second set of competitor restaurants.

17. The method of claim 4 wherein each restaurant in the database has an associated order minimums fields and further comprising the step of comparing the order minimums field of the first restaurant with the order minimums field of each of the restaurants within the first set of competitor restaurants to produce a second set of competitor restaurants.

18. The method of claim 11 further comprising assigning a similarity to each pair of true items, wherein the step of comparing true items comprises assigning the similarity between the compared true items as a match score.

19. The method of claim 4 further comprising associating each menu item of each restaurant with a set of true items to form a menu item association, wherein the step of comparing the menu items comprises computing a menu item association based match score.

5. (canceled)