Industry review benchmarking

• US 10,474,979 B1
• Filed: 06/01/2017
• Issued: 11/12/2019
• 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): Stamber, Eric W
• Assistant Examiner(s): Walker, III, George H
• Application Number:
  US15/611,718
• Time in Patent Office: 894 Days
• Field of Search: None
• 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:

one or more processors 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 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;

determine, from at least a portion of the received review data, a review distribution benchmark, wherein the review distribution benchmark is determined at least in part by determining, for at least the first external review website and a second external review website included in the plurality of external review websites, a respective percentage of reviews associated with at least some entities in the particular industry on the respective external review websites;

determine a distribution of reviews for a particular entity in the particular industry across the plurality of external review websites at least in part by determining, of a total number of reviews of the particular entity, a percentage of the total number of reviews that appear on each external review website in the plurality of external review websites;

based at least in part on a comparison of the distribution of reviews determined for the particular entity in the particular industry against the review distribution benchmark determined for the particular industry, determine a difference between the review distribution benchmark determined for the particular industry and the distribution of reviews for the particular entity;

in response to determining the difference in review distribution, determine that the distribution of reviews for the particular entity should be adjusted; and

provide as output an indication of at least one external review website on which placement of at least one additional review should be targeted; and

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

2. The system of claim 1 wherein determining the review distribution benchmark comprises determining a target distribution of reviews, wherein the target distribution indicates, for at least the first external review website and a second external review website, a respective first and second percentage of reviews the particular entity should have on the respective first and second external review websites.

3. The system of claim 1 wherein the one or more processors are further configured to determine, based on the comparison, that additional reviews, associated with the first external review website, should be requested from one or more potential reviewers.

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

5. The system of claim 1 wherein the one or more processors are further configured to determine a plurality of regional review distribution benchmarks for the particular industry.

6. A method, comprising:

receiving 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;

determining, from at least a portion of the received review data and using one or more processors, a review distribution benchmark, wherein the review distribution benchmark is determined at least in part by determining, for at least the first external review website and a second external review website included in the plurality of external review websites, a respective percentage of reviews associated with at least some entities in the particular industry on the respective external review websites;

determining a distribution of reviews for a particular entity in the particular industry across the plurality of external review websites at least in part by determining, of a total number of reviews of the particular entity, a percentage of the total number of reviews that appear on each external review website in the plurality of external review websites;

based at least in part on a comparison of the distribution of reviews determined for the particular entity in the particular industry against the review distribution benchmark determined for the particular industry, determining a difference between the review distribution benchmark determined for the particular industry and the distribution of reviews for the particular entity;

in response to determining the difference in review distribution, determining that the distribution of reviews for the particular entity should be adjusted; and

providing as output an indication of at least one external review website on which placement of at least one additional review should be targeted.

7. The method of claim 6 wherein determining the review distribution benchmark comprises determining a target distribution of reviews, wherein the target distribution indicates, for at least the first external review website and a second external review website, a respective first and second percentage of reviews the particular entity should have on the respective first and second external review websites.

8. The method of claim 6 further comprising determining, based on the comparison, that additional reviews, associated with the first external review website, should be requested from one or more potential reviewers.

9. The method of claim 6 further comprising determining that the particular entity does not have an account on a particular external review website.

10. The method of claim 6 further comprising determining a plurality of regional review distribution benchmarks for the particular industry.

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

receiving 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;

determining, from at least a portion of the received review data and using one or more processors, a review distribution benchmark, wherein the review distribution benchmark is determined at least in part by determining, for at least the first external review website and a second external review website included in the plurality of external review websites, a respective percentage of reviews associated with at least some entities in the particular industry on the respective external review websites;

determining a distribution of reviews for a particular entity in the particular industry across the plurality of external review websites at least in part by determining, of a total number of reviews of the particular entity, a percentage of the total number of reviews that appear on each external review website in the plurality of external review websites;

based at least in part on a comparison of the distribution of reviews determined for the particular entity in the particular industry against the review distribution benchmark determined for the particular industry, determining a difference between the review distribution benchmark determined for the particular industry and the distribution of reviews for the particular entity;

in response to determining the difference in review distribution, determining that the distribution of reviews for the particular entity should be adjusted; and

providing as output an indication of at least one external review website on which placement of at least one additional review should be targeted.