Industry review benchmarking

• US 9,697,490 B1
• Filed: 12/30/2012
• Issued: 07/04/2017
• Est. Priority Date: 03/05/2012
• Status: Active Grant

• Current Assignee: Reputation.com, Inc.
• Original Assignee: Reputation.com, Inc.
• Inventors: Piper, Jeffrey James, Franson, Brent Michael, Dignan, Thomas Gerardo
• Primary Examiner(s): Padot, Timothy
• Assistant Examiner(s): Walker, George H
• Application Number:
  US13/731,060
• Time in Patent Office: 1,647 Days
• Field of Search: 705 739, 705347
• CPC Class Codes: G06F 16/24 Querying
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G06Q 10/107 Computer-aided management of electronic mailing [e-mailing]
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0281 Customer communication at a business location, e.g. providing product or service information, consulting
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

Review data associated with a plurality of entities is received. Each of the entities is associated with a particular industry and the review data comprises review data obtained from a plurality of review sites. From at least a portion of the received review data, a review benchmark for the industry is determined. The benchmark is provided as output.

Claims

1. A system, comprising:

a set of one or more interfaces configured to;

receive review data associated with a plurality of entities, wherein each of the entities is associated with a particular industry, and wherein the review data comprises review data collected, over a network, from a plurality of disparate, external review websites;

wherein the review data is collected by a plurality of instances of different types of helpers that are executed to obtain, over the network, information from the plurality of disparate, external review websites, wherein each type of helper is configured with instructions to fetch review data from a particular type of source, wherein, for a first external review website for which review data is available via an Application Programming Interface (API), an instance of a first helper configured with instructions to obtain review data from the first external review website using the API is executed, and wherein, for a second external review website for which review data is not available via an API, an instance of a second helper configured with instructions to scrape review data from the second external review website is executed; and

wherein at least some of the received review data is received from a data store configured to store heterogeneous data records, and wherein the data store includes review data from different external review websites that is stored in heterogeneous record formats;

a set of one or more processors configured to;

generate, from at least a portion of the received review data collected, over the network, from the plurality of disparate, external review websites, at least one online review benchmark for the particular industry, including by determining at least one of an online review volume benchmark and an online review distribution benchmark, wherein;

the online review volume benchmark is determined at least in part by counting a first number of reviews associated with at least some entities in the particular industry on the plurality of disparate, external review websites; and

the online review distribution benchmark is determined at least in part by determining, for at least the first and second external review websites included in the plurality of disparate, external review websites, a respective proportion of reviews associated with at least some entities in the particular industry on the respective external review websites; and

store the generated at least one online review benchmark for the particular industry;

compare, at a time subsequent to generating the at least one online review benchmark for the particular industry, review data associated with a first entity in the particular industry to the stored industry benchmark, including by comparing at least one of an online review volume of the first entity to the online review volume benchmark for the particular industry and an online review distribution of the first entity to the online review distribution benchmark for the particular industry;

determine, based at least in part on the comparison of the review data associated with the first entity in the particular industry to the stored industry benchmark, that an adjustment to the online review distribution of the first entity should be performed, wherein the adjustment includes increasing a number of reviews associated with the first entity on one or more external review websites;

model an impact that additional reviews on the one or more external review websites would have for the first entity, wherein modeling the impact includes;

running a first simulation in which a first volume of additional positive reviews are obtained on the first external review website;

determining a first modeled online reputation score based at least in part on the first simulation;

running a second simulation in which a second volume of additional positive reviews are obtained on the second external review website, wherein the first volume and the second volume are different; and

determining a second modeled online reputation score based at least in part on the second simulation;

determine, based at least in part on the first and second modeled online reputation scores, that additional reviews associated with the first external review website should be requested from one or more potential reviewers;

in response to determining that additional reviews associated with the first external review website should be requested;

identify, in a list of potential reviewers, individuals in the list of potential reviewers that have accounts with the first external review website for which it has been determined that additional reviews should be requested, wherein the identified individuals are identified based at least in part on an evaluation of corresponding email addresses associated with the identified individuals; and

facilitate transmission of review requests to the identified individuals, wherein facilitating transmission of the review requests includes facilitating transmission, over the network, of an electronic message to an individual included in the identified individuals, wherein the electronic message includes a link to the first external review website; and

at a time subsequent to transmission of the electronic message, perform a follow-up action based at least in part on a determination of whether the individual has performed at least one of opening the electronic message and clicking on the link included in the electronic message; and

a memory coupled to the set of one or more processors and configured to provide the set of one or more processors with instructions.

2. The system of claim 1 wherein the impact that additional reviews on the one or more external review websites would have for the first entity is modeled at least in part by using target distributions, wherein the target distributions indicate, for at least the first external review website and the second external review website, a respective first and second number of reviews the first entity should have on the respective first and second external review websites.

3. The system of claim 2 wherein the first and second number of reviews are expressed as percentages of a total volume of reviews.

4. The system of claim 1 wherein the set of one or more processors is further configured to determine that the first entity does not have an account on a particular external review web site.

5. The system of claim 1 wherein the set of one or more processors is further configured to determine a plurality of regional benchmarks for the particular industry including by determining at least one of an online review volume benchmark and an online review distribution benchmark for entities in each of one or more regions.

6. The system of claim 1 wherein generating the at least one online review benchmark for the particular industry further includes determining a review age.

7. The system of claim 6 wherein the set of one or more processors is further configured to compare a review age determined for the first entity to the stored industry benchmark.

8. The system of claim 1 wherein the set of one or more processors is further configured to determine a plurality of benchmarks for sub-industries associated with the particular industry.

9. The system of claim 1 wherein the identified individuals are identified at least in part by evaluating a domain portion of the corresponding email addresses to determine that the identified individuals have accounts on the first external review website for which it has been determined that additional reviews should be requested.

10. The system of claim 1 wherein the identified individuals are identified at least in part by querying, using the corresponding email addresses, a second API associated with the first external review website and obtaining one or more responses indicating that the corresponding email addresses are associated with accounts on the first external review web site for which it has been determined that additional reviews should be requested.

11. The system of claim 1 wherein the identified individuals are identified at last in part by using a mapping between email addresses and accounts on the first external review website for which it has been determined that additional reviews should be requested.

12. A method, comprising:

receiving, via a set of one or more interfaces, review data associated with a plurality of entities, wherein each of the entities is associated with a particular industry, and wherein the review data comprises review data collected, over a network from a plurality of disparate, external review websites;

wherein the review data is collected by a plurality of instances of different types of helpers that are executed to obtain, over the network, information from the plurality of disparate, external review websites, wherein each type of helper is configured with instructions to fetch review data from a particular type of source, wherein, for a first external review website for which review data is available via an Application Programming Interface (API), an instance of a first helper configured with instructions to obtain review data from the first external review website using the API is executed, and wherein, for a second external review website for which review data is not available via an API, an instance of a second helper configured with instructions to scrape review data from the second external review website is executed; and

wherein at least some of the received review data is received from a data store configured to store heterogeneous data records, and wherein the data store includes review data from different external review websites that is stored in heterogeneous record formats;

generating, using a set of one or more processors, and from at least a portion of the received review data collected, over the network, from the plurality of disparate, external review websites, at least one online review benchmark for the particular industry, including by determining at least one of an online review volume benchmark and an online review distribution benchmark, wherein;

the online review volume benchmark is determined at least in part by counting a first number of reviews associated with at least some entities in the particular industry on the plurality of disparate, external review websites; and

the online review distribution benchmark is determined at least in part by determining, for at least the first and second external reviews website included in the plurality of disparate, external review websites, a respective proportion of reviews associated with at least some entities in the particular industry on the respective external review websites;

storing the generated at least one online review benchmark for the particular industry;

comparing, at a time subsequent to generating the at least one online review benchmark for the particular industry, review data associated with a first entity in the particular industry to the stored industry benchmark, including by comparing at least one of an online review volume of the first entity to the online review volume benchmark for the particular industry and an online review distribution of the first entity to the online review distribution benchmark of the particular industry;

determining, based at least in part on the comparison of the review data associated with the first entity in the particular industry to the stored industry benchmark, that an adjustment to the online review distribution of the first entity should be performed, wherein the adjustment includes increasing a number of reviews associated with the first entity on one or more external review websites;

modeling an impact that additional reviews on the one or more external review websites would have for the first entity, wherein modeling the impact includes;

running a first simulation in which a first volume of additional positive reviews are obtained on the first external review website;

determining a first modeled online reputation score based at least in part on the first simulation;

running a second simulation in which a second volume of additional positive reviews are obtained on the second external review website, wherein the first volume and the second volume are different; and

determining a second modeled online reputation score based at least in part on the second simulation;

determining, based at least in part on the first and second modeled online reputation scores, that additional reviews associated with the first external review website should be requested from one or more potential reviewers;

in response to determining that additional reviews associated with the first external review website should be requested;

identifying, in a list of potential reviewers, individuals in the list of potential reviewers that have accounts with the first external review website for which it has been determined that additional reviews should be requested, wherein the identified individuals are identified based at least in part on an evaluation of corresponding email addresses associated with the identified individuals; and

facilitating transmission of review requests to the identified individuals, wherein facilitating transmission of the review requests includes facilitating transmission, over the network, of an electronic message to an individual included in the identified individuals, wherein the electronic message includes a link to the first external review website; and

at a time subsequent to transmission of the electronic message, performing a follow-up action based at least in part on a determination of whether the individual has performed at least one of opening the electronic message and clicking on the link included in the electronic message.

13. The method of claim 12 wherein the impact that additional reviews on the one or more external review websites would have for the first entity is modeled at least in part by using target distributions, wherein the target distributions indicate, for at least the first external review website and the second external review website, a respective first and second number of reviews the first entity should have on the respective first and second external review websites.

14. The method of claim 13 wherein the first and second number of reviews are expressed as percentages of a total volume of reviews.

15. The method of claim 12 further comprising determining that the first entity does not have an account on a particular external review website.

16. The method of claim 12 further comprising determining a plurality of regional benchmarks for the particular industry including by determining at least one of an online review volume benchmark and an online review distribution benchmark for entities in each of one or more regions.

17. The method of claim 12 wherein generating the at least one online review benchmark for the particular industry further includes determining a review age.

18. The method of claim 17 further comprising comparing a review age determined for the first entity to the stored industry benchmark.

19. The method of claim 12 further comprising determining a plurality of benchmarks for sub-industries associated with the particular industry.

20. A computer program product embodied in a non-transitory computer readable storage medium, and comprising computer instructions for:

receiving, via a set of one or more interfaces, review data associated with a plurality of entities, wherein each of the entities is associated with a particular industry, and wherein the review data comprises review data collected, over a network, from a plurality of disparate, external review websites;

wherein the review data is collected by a plurality of instances of different types of helpers that are executed to obtain, over the network, information from the plurality of disparate, external review websites, wherein each type of helper is configured with instructions to fetch review data from a particular type of source, wherein, for a first external review website for which review data is available via an Application Programming Interface (API), an instance of a first helper configured with instructions to obtain review data from the first external review website using the API is executed, and wherein, for a second external review website for which review data is not available via an API, an instance of a second helper configured with instructions to scrape review data from the second external review website is executed; and

wherein at least some of the received review data is received from a data store configured to store heterogeneous data records, and wherein the data store includes review data from different external review websites that is stored in heterogeneous record formats;

generating, using a set of one or more processors, and from at least a portion of the received review data collected, over the network, from the plurality of disparate, external review websites, at least one online review benchmark for the particular industry, including by determining at least one of an online review volume benchmark and an online review distribution benchmark, wherein;

the online review volume benchmark is determined at least in part by counting a first number of reviews associated with at least some entities in the particular industry on the plurality of disparate, external review websites; and

the online review distribution benchmark is determined at least in part by determining, for at least the first and second external review websites included in the plurality of disparate, external review websites, a respective proportion of reviews associated with at least some entities in the particular industry on the respective external review websites;

storing the generated at least one online review benchmark for the particular industry;

comparing, at a time subsequent to generating the at least one online review benchmark for the particular industry, review data associated with a first entity in the particular industry to the stored industry benchmark, including by comparing at least one of an online review volume of the first entity to the online review volume benchmark for the particular industry and an online review distribution of the first entity to the online review distribution benchmark for the particular industry; and

determining, based at least in part on the comparison of the review data associated with the first entity in the particular industry to the stored industry benchmark, that an adjustment to the online review distribution of the first entity should be performed, wherein the adjustment includes increasing a number of reviews associated with the first entity on one or more external review websites;

modeling an impact that additional reviews on the one or more external review websites would have for the first entity, wherein modeling the impact includes;

running a first simulation in which a first volume of additional positive reviews are obtained on the first external review web site;

determining a first modeled online reputation score based at least in part on the first simulation;

running a second simulation in which a second volume of additional positive reviews are obtained on the second external review website, wherein the first volume and the second volume are different; and

determining a second modeled online reputation score based at least in part on the second simulation;

determining, based at least in part on the first and second modeled online reputation scores, that additional reviews associated with the first external review website should be requested from one or more potential reviewers;

in response to determining that additional reviews associated with the first external review website should be requested;

identifying, in a list of potential reviewers, individuals in the list of potential reviewers that have accounts with the first external review web site for which it has been determined that additional reviews should be requested, wherein the identified individuals are identified based at least in part on an evaluation of corresponding email addresses associated with the identified individuals; and

facilitating transmission of review requests to the identified individuals, wherein facilitating transmission of the review requests includes facilitating transmission, over the network, of an electronic message to an individual included in the identified individuals, wherein the electronic message includes a link to the first external review website; and

at a time subsequent to transmission of the electronic message, performing a follow-up action based at least in part on a determination of whether the individual has performed at least one of opening the electronic message and clicking on the link included in the electronic message.