Patent Issued for Determining propensities to drive website target user activity (USPTO 11449572): State Farm Mutual Automobile Insurance Company
2022 OCT 12 (NewsRx) -- By a
The patent’s assignee for patent number 11449572 is
News editors obtained the following quote 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.
“The example systems and methods described herein may be directed toward mitigating or overcoming one or more of the deficiencies described above.”
As a supplement to the background information on this patent, NewsRx correspondents 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, and/or one or more groups of entry pages on a website, to drive users to perform a target user activity on the website. The target user activity may be a specific activity that an operator of the website wants to drive users to perform while the users are browsing the website. The systems and methods described herein can use website activity data to determine relative propensities of one or more digital channels, and/or one or more groups of entry pages, 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 and/or web pages that are predicted to increase the propensities of the digital channels and/or web pages to drive the target user activity.
“According to a first aspect, a method can include receiving, by one or more processors, website activity data indicative of use of a website by one or more user devices. The method can also include identifying, by the one or more processors and based on the website activity data, instances of a target user activity occurring during visits to the website that originate via one or more digital channels. The method can further include determining, by the one or more processors and based on the website activity data, respective propensities, of combinations of the one or more digital channels and one or more entry pages of the website, to drive the target user activity during visits to the website. The method can also include identifying, by the one or more processors and based on the respective propensities, a first combination, of the one or more digital channels and the one or more entry pages, associated with a first propensity to drive the target user activity, and a second combination, of the one or more digital channels and the one or more entry pages, associated with a second propensity to drive the target user activity, wherein the second propensity is higher than the first propensity. The method can additionally include generating, by the one or more processors, a revision recommendation indicative of one or more changes to the first combination, based on one or more attributes of the second combination, that are predicted to increase the first propensity.
“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 website activity data indicative of use of a website by one or more user devices, and identifying, based on the website activity data, instances of a target user activity occurring during visits to the website that originate via one or more digital channels. The operations can further include determining, based on the website activity data, respective propensities of a set of entry pages of the website to drive the target user activity during visits to the website. The operations can also include identifying, based on the respective propensities, a first entry page type associated with a first propensity to drive the target user activity, and a second entry page type associated with a second propensity to drive the target user activity, wherein the second propensity is higher than the first propensity. The operations can additionally include generating a revision recommendation indicative of one or more changes to the first entry page type or at least one of the one or more digital channels, based on at least one of content or design elements of the second entry page type that are predicted to increase the first propensity.
“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 website activity data indicative of use of a website by one or more user devices, and identifying, based on the website activity data, instances of a target user activity occurring during visits to the website that originate via one or more digital channels. The operations can also include determining, based on the website activity data, digital channel propensities of the one or more digital channels to drive a target user activity during visits to the website, and entry page propensities of a set of entry pages of the website to drive the target user activity during the visits to the website. The operations can further include determining, based on the digital channel propensities and the entry page propensities, respective propensities of combinations of the one or more digital channels and one or more entry pages of the website to drive the target user activity. The operations can additionally include identifying, based on the respective propensities, a first combination, of the one or more digital channels and the one or more entry pages, associated with a first propensity to drive the target user activity and a second combination, of the one or more digital channels and the one or more entry pages, associated with a second propensity to drive the target user activity, wherein the second propensity is higher than the first propensity. The operations can also include generating a revision recommendation indicative of one or more changes to the first combination, based on one or more attributes of the second combination, that are predicted to increase the first propensity.”
The claims supplied by the inventors are:
“1. A method, comprising: receiving, by one or more processors, activity data indicative of use of a digital system by one or more user devices; identifying, by the one or more processors and based on the activity data, instances of a target user activity occurring, via the digital system, during uses of the digital system that originate through a plurality of digital channels; determining, by the one or more processors and based on the activity data, respective propensities of individual digital channels, of the plurality of digital channels, to drive the target user activity during the uses of the digital system; determining, by the one or more processors and based on the respective propensities, that a first propensity of a first digital channel to drive the target user activity is lower than a second propensity of a second digital channel to drive the target user activity, the first digital channel having a first attribute, and the second digital channel 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 digital channel based on the second attribute of the second digital channel.
“2. The method of claim 1, wherein the digital system comprises at least one of a website or a mobile application.
“3. The method of claim 1, wherein the digital system is associated with an insurance company, and the target user activity comprises an initiation of a new insurance quote via the digital system.
“4. 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 digital channels, of the plurality of digital channels, to drive the target user activity.
“5. The method of claim 1, further comprising: identifying, by the one or more processors, based on the activity data: customer activity data associated with customers of an entity associated with the digital system, and non-customer activity data associated with non-customers of the entity, wherein the respective propensities of the individual digital channels to drive the target user activity are determined based on the non-customer activity.
“6. The method of claim 1, wherein the plurality of digital channels comprises one or more types of digital channels, including 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.
“7. The method of claim 1, wherein the digital system comprises a plurality of entry pages, and the uses of the digital system that originate through the plurality of digital channels are associated with respective entry pages of the plurality of entry pages.
“8. The method of claim 7, wherein the first digital channel is a search engine optimization channel, and revising the first digital channel comprises revising one or more attributes of an entry page, of the plurality of entry pages, that is associated with the search engine optimization channel.
“9. 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: identifying, based on activity data indicative of use of a digital system by one or more user devices, instances of a target user activity occurring during uses of the digital system that originate via at least a first digital channel and a second digital channel; determining, based on the activity data, a first propensity of the first digital channel to lead to the target user activity; determining, based on the activity data, a second propensity of the second digital channel to lead to the target user activity, wherein the second propensity is higher than the first propensity; determining one or more differences between the first digital channel and the second digital channel; and based on the second propensity being higher than the first propensity, revising the first digital channel based on the one or more differences.
“10. The one or more computing devices of claim 9, wherein the digital system comprises at least one of a website or a mobile application.
“11. The one or more computing devices of claim 9, wherein the digital system is associated with an insurance company, and the target user activity comprises an initiation of a new insurance quote via the digital system.
“12. The one or more computing devices of claim 9, wherein the first propensity and the second propensity are determined by identifying, using a logistic regression model, relative contributions of the first digital channel and the second digital channel to the instances of the target user activity.
“13. The one or more computing devices of claim 9, wherein: the operations further comprise identifying, based on the activity data: customer activity data associated with customers of an entity associated with the digital system, and non-customer activity data associated with non-customers of the entity, and the first propensity and the second propensity are determined based on the non-customer activity.
“14. The one or more computing devices of claim 9, wherein the digital system comprises a plurality of entry pages, and the uses of the digital system are associated with respective entry pages of the plurality of entry pages.
“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 uses of a digital system by one or more user devices, wherein the activity data indicates instances of a target user activity occurring during uses of the digital system; determining, based on the activity data, arrival paths associated with the uses of the digital system, wherein an individual arrival path comprises at least one of: a digital channel used by a user device to arrive at the digital system, or an entry page, of the digital system, the user device used to initiate a use of the digital system; determining, based on the activity data, respective propensities of the arrival paths to lead to the target user activity during the uses of the digital system; determining, based on the respective propensities, that a first propensity of a first arrival path to lead to the target user activity is lower than a second propensity of a second arrival path to lead to the target user activity, wherein the first arrival path has a first attribute and the second arrival path has a second attribute different than the first arrival path; and based on determining that the first propensity is lower than the second propensity, revising at least one of a first digital channel or a first entry page associated with the first arrival path, based on the second attribute.
“16. The one or more non-transitory computer-readable media of claim 15, wherein the digital system comprises at least one of a website or a mobile application.
“17. The one or more non-transitory computer-readable media of claim 15, wherein the digital system is associated with an insurance company, and the target user activity comprises an initiation of a new insurance quote via the digital system.
“18. The one or more non-transitory computer-readable media of claim 15, wherein determining the respective propensities comprises determining, by the one or more processors using a logistic regression model, relative contributions of individual arrival paths, of the arrival paths, to lead to the target user activity.
“19. The one or more non-transitory computer-readable media of claim 15, wherein: the operations further comprise identifying, based on the activity data: customer activity data associated with customers of an entity associated with the digital system, and non-customer activity data associated with non-customers of the entity, and the respective propensities are determined based on the non-customer activity.
“20. The one or more non-transitory computer-readable media of claim 15, wherein revising the at least one of the first digital channel or the first entry page comprises revising a call to action, associated with the target user activity, on the first entry page.”
For additional information on this patent, see: Baldone, Carl. Determining propensities to drive website target user activity.
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