Patent Issued for Determining propensities to drive website target user activity (USPTO 11836749): State Farm Mutual Automobile Insurance Company

2023 DEC 25 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Baldone, Carl (Dunwoody, GA, US), Cuttell, Matthew (Cumming, GA, US), Zheng, Shuhui (Atlanta, GA, US), filed on August 12, 2022, was published online on December 5, 2023, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11836749 is assigned to State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Users can arrive at a website through a variety of digital channels. For example, a user may load the website by directly typing a Uniform Resource Locator (URL) for the website into a web browser. In other examples, users may load the website by clicking on paid advertisements or organic search results in search engines, clicking on banner advertisements displayed on other websites, or loading the website via other types of digital channels. Such digital channels can cause users to arrive at various entry pages of the website, such as a homepage of the website or other web pages that are focused on certain products or services.

“An operator of a website may want users to perform a particular target user activity during visits to the website. As an example, an operator of an insurance website may want users to perform a target user activity of initiating a new insurance quote via the website. As another example, an operator of a website for a streaming video service may want users to perform a target user activity of signing up for a streaming video account via the website.

“Some digital channels may be more successful than other digital channels at driving users to perform a target user activity during visits to a website. Similarly, some types of entry pages may be more successful than other types of entry pages at driving users to perform a target user activity on a website. However, it can be difficult to determine the propensities of different digital channels, and/or different types of entry pages, to drive users to perform the target user activity. Accordingly, it can also be difficult to identify opportunities to revise web pages and/or digital channels based on their respective propensities to drive the target user activity, or to determine how to revise such web pages and/or digital channels to increase their propensities to drive the target user activity. It can similarly be difficult to determine how to prioritize resources associated with web pages or digital channels, based on their respective propensities to drive the target user activity.

“Some users may also be more likely to engage in the target user activity via non-digital systems, such as phone calls or in-person meetings, relative to the likelihood of such users engaging in the target user activity via the website or other digital systems. However, it can be difficult to identity which users may be more likely to engage in the target user activity via non-digital systems relative to digital systems, and thus which type of system to recommend for use with respect to individual users.

“The example systems and methods described herein may be directed toward mitigating or overcoming one or more of the deficiencies described above.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “Described herein are systems and methods that can determine propensities of one or more digital channels, one or more digital systems, such as a website, one or more groups of entry pages of the website, and/or one or more non-digital systems, to drive users to perform a target user activity. The target user activity may be a specific activity that an operator of the website, other digital systems, and/or the non-digital systems wants to drive users to perform. The systems and methods described herein can use activity data to determine relative propensities of digital channels, entry pages, digital systems, and/or non-digital systems to drive the target user activity. In some examples, the systems and methods described herein can also, based on the determined propensities, recommend changes to digital channels, entry pages, digital systems, and/or non-digital systems that are predicted to increase respective propensities to drive the target user activity.

“According to a first aspect, a method can include receiving, by one or more processors, activity data indicative of use of a set of user paths by users. Individual user paths, of the set of user paths, can include one or more digital channels, one or more digital systems, and/or one or more non-digital systems. The method can also include identifying, by the one or more processors and based on the activity data, instances of a target user activity occurring in association with the individual user paths. The method can further include determining, by the one or more processors and based on the activity data, respective propensities of the individual user paths to drive the target user activity. The method can also include determining, by the one or more processors and based on the respective propensities, that a first propensity of a first user path to drive the target user activity is lower than a second propensity of a second user path to drive the target user activity. The first user path can have a first attribute, and the second user path can have a second attribute different than the first attribute. The method can additionally include, based on determining that the first propensity is lower than the second propensity, revising, by the one or more processors, the first user path based on the second attribute of the second user path.

“According to a second aspect, one or more computing device can include one or more processors and memory storing computer-executable instructions that, when executed by the one or more processors, cause the one or more computing devices to perform operations. The operations can include receiving activity data indicative of use of a set of user paths by users. Individual user paths, of the set of user paths, can include one or more digital channels, one or more digital systems, and/or one or more non-digital systems. The operations can also include identifying, based on the activity data, instances of a target user activity occurring in association with the individual user paths. The operations can further include determining, based on the activity data, respective propensities of the individual user paths to drive the target user activity. The operations can also include determining, based on the respective propensities, that a first propensity of a first user path to drive the target user activity is lower than a second propensity of a second user path to drive the target user activity. The first user path can have a first attribute, and the second user path can have a second attribute different from the first attribute. The operations can additionally include, based on based on determining that the first propensity is lower than the second propensity, revising the first user path based on the second attribute of the second user path.

“According to a third aspect, one or more non-transitory computer-readable media can store computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations. The operations can include receiving activity data indicative of use of a set of user paths by users. The activity data can indicate user classifications of the users. Individual user paths, of the set of user paths, can include one or more digital channels, one or more digital systems, and/or one or more non-digital systems. The operations can also include identifying, based on the activity data, instances of a target user activity occurring in association with the individual user paths and individual user classifications of the user classifications. The operations can additionally include determining, based on the activity data, propensities of the individual user paths to drive the target user activity, in association with the individual user classifications. The operations can further include determining a particular user classification associated with a particular user. The operations can additionally include selecting a particular user path, of the set of user paths, associated with a highest propensity, of the propensities, to drive the target user activity in association with the particular user classification. The operations can also include recommending usage of the particular user path in association with the particular user.”

The claims supplied by the inventors are:

“1. A method, comprising: receiving, by one or more processors, activity data indicative of use of a set of user paths by users, wherein individual user paths, of the set of user paths, comprise at least one of: one or more digital channels, one or more digital systems, or one or more non-digital systems; identifying, by the one or more processors and based on the activity data, instances of a target user activity occurring in association with the individual user paths; determining, by the one or more processors and based on the activity data, respective propensities of the individual user paths to drive the target user activity; determining, by the one or more processors and based on the respective propensities, that a first propensity of a first user path to drive the target user activity is lower than a second propensity of a second user path to drive the target user activity, the first user path having a first attribute, and the second user path having a second attribute different from the first attribute; and based on determining that the first propensity is lower than the second propensity, revising, by the one or more processors, the first user path based on the second attribute of the second user path.

“2. The method of claim 1, wherein the one or more digital systems comprise at least one of a website or a mobile application.

“3. The method of claim 1, wherein the one or more non-digital systems comprise at least one of phone calls or in-person meetings.

“4. The method of claim 1, wherein the set of user paths is associated with an insurance company, and the target user activity comprises an initiation of a new insurance quote via the one or more digital systems or the one or more non-digital systems.

“5. The method of claim 1, wherein determining the respective propensities comprises determining, by the one or more processors using a logistic regression model, relative contributions of the individual user paths to drive the target user activity.

“6. The method of claim 5, wherein determining the respective propensities further comprises determining, by the one or more processors using the logistic regression model, the relative contributions of instances of the one or more digital channels, the one or more digital systems, or the one or more non-digital systems, associated with the individual user paths, to drive the target user activity.

“7. The method of claim 1, wherein the one or more digital channels comprise at least one of a direct load channel type, a display advertisement channel type, an email advertisement channel type, a search engine optimization channel type, a search engine marketing channel type, a social media channel type, a text advertisement channel type, or a print media channel type.

“8. The method of claim 1, wherein revising the first user path comprises adjusting the one or more digital channels associated with the first user path to direct future users from the one or more digital channels associated with the first user path to the one or more digital systems or the one or more non-digital systems associated with the second user path.

“9. The method of claim 1, wherein: the activity data indicates user classifications of the users, the one or more processors identify the instances of the target user activity occurring in association with the individual user paths and individual user classifications, of the user classifications, and the one or more processors determine the respective propensities of the individual user paths to drive the target user activity in association with the individual user classifications.

“10. One or more computing devices, comprising: one or more processors; memory storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving activity data indicative of use of a set of user paths by users, wherein individual user paths, of the set of user paths, comprise at least one of: one or more digital channels, one or more digital systems, or one or more non-digital systems; identifying, based on the activity data, instances of a target user activity occurring in association with the individual user paths; determining, based on the activity data, respective propensities of the individual user paths to drive the target user activity; determining, based on the respective propensities, that a first propensity of a first user path to drive the target user activity is lower than a second propensity of a second user path to drive the target user activity, the first user path having a first attribute, and the second user path having a second attribute different from the first attribute; and based on determining that the first propensity is lower than the second propensity, revising the first user path based on the second attribute of the second user path.

“11. The one or more computing devices of claim 10, wherein the set of user paths is associated with an insurance company, and the target user activity comprises an initiation of a new insurance quote via the one or more digital systems or the one or more non-digital systems.

“12. The one or more computing devices of claim 10, wherein the first propensity and the second propensity are determined by identifying, using a logistic regression model, relative contributions of the first user path and the second user path to the instances of the target user activity.

“13. The one or more computing devices of claim 10, wherein revising the first user path comprises adjusting the one or more digital channels associated with the first user path to direct future users from the one or more digital channels associated with the first user path to one or more digital systems or one or more non-digital systems associated with the second user path.

“14. The one or more computing devices of claim 10, wherein: the activity data indicates user classifications of the users, the instances of the target user activity occurring are identified in association with the individual user paths and individual user classifications, of the user classifications, and the respective propensities of the individual user paths to drive the target user activity are determined in association with the individual user classifications.

“15. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations, comprising: receiving activity data indicative of use of a set of user paths by users, wherein the activity data indicates user classifications of the users, and individual user paths, of the set of user paths, comprise at least one of: one or more digital channels, one or more digital systems, or one or more non-digital systems; identifying, based on the activity data, instances of a target user activity occurring in association with the individual user paths and individual user classifications of the user classifications; determining, based on the activity data, propensities of the individual user paths to drive the target user activity, in association with the individual user classifications; determining a particular user classification associated with a particular user; selecting a particular user path, of the set of user paths, associated with a highest propensity, of the propensities, to drive the target user activity in association with the particular user classification; and recommending usage of the particular user path in association with the particular user.

“16. The one or more non-transitory computer-readable media of claim 15, wherein determining the propensities comprises determining, using a logistic regression model, contributions of the individual user paths to lead to the target user activity.

“17. The one or more non-transitory computer-readable media of claim 16, wherein determining the propensities further comprises determining, using the logistic regression model, the relative contributions of instances of the one or more digital channels, the one or more digital systems, or the one or more non-digital systems, associated with the individual user paths, to drive the target user activity.

“18. The one or more non-transitory computer-readable media of claim 15, wherein the user classifications are based on ages of the users.

“19. The one or more non-transitory computer-readable media of claim 15, wherein: the one or more digital systems comprise a website hosted by a server, and recommending usage of the particular user path in association with the particular user causes the server to serve, to a user device associated with the particular user, a webpage that presents content that directs the particular user to a digital system or to a non-digital system associated with the particular user path.

“20. The one or more non-transitory computer-readable media of claim 15, wherein recommending usage of the particular user path in association with the particular user causes one or more of the digital channels to direct the particular user to a digital system or to a non-digital system associated with the particular user path.”

URL and more information on this patent, see: Baldone, Carl. Determining propensities to drive website target user activity. U.S. Patent Number 11836749, filed August 12, 2022, and published online on December 5, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11836749)&db=USPAT&type=ids

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