Patent ID: 12254479

DETAILED DESCRIPTION

FIG.1shows a first example100of a propensity analysis system. A server102can host a website104that includes a set of web pages106, such as web pages106A,106B, . . .106N (wherein “N” represents any number of web pages greater than zero). The website104can include a variety of different web pages106, and in some examples can include different categories or groups of web pages106. The server102can be a computing device with memory that stores data associated with the web pages106.

A user device108can use a network connection, such as an Internet connection, to exchange data with the server102. As an example, the user device108can send a request for a web page to the server102, and the server102can return data associated with the requested web page to the user device108. Accordingly, the user device108can be configured to render and/or display the web pages106of the website104, based on data received from the server102. In some examples, the user device108can be a mobile phone, as shown inFIG.1. However, in other examples, the user device108can be another type of computing device, such as a personal digital assistant (PDA), a media player, a tablet computer, a gaming device, a smart watch, a personal computer (PC) such as a laptop, desktop, or workstation, or any other type of computing or communication device. The server102can interact with multiple user devices, including user device108, to serve web pages106of the website104to user devices associated with different users.

The user device108may initially load the website104from the server102via a digital channel110. A digital channel110can, for instance, be a referring channel by which the user device108arrives at the website104. As an example, the digital channel110may be associated with a link to the website104that is displayed via a search engine, on a third-party website, on a social media platform, or any other platform. As another example, the digital channel110can be a direct user entry of a URL associated with the website104into a web browser on the user device108. These and other examples of types of digital channels110are shown inFIG.2, and are discussed in more detail below with respect to that figure.

Different web pages106of the website104may have different content. For example, the website104can have a homepage that serves as a broad introduction to the website104. However, the website104can also have many other web pages106that are focused on narrower or more specific types of information. Different web pages106may include different text and/or images, be associated with different subjects, have different designs or layouts, and/or differ in other ways.

When the user device108accesses the website104via the server102, the user device108may initially load the homepage of the website104, or may instead bypass the homepage and directly load another web page106of the website104. For example, when a user performs a search on a search engine, search results displayed via the search engine may include a link to a specific web page106within the overall website104that best matches search terms entered by the user. Accordingly, the user device108may use that link to access the website104by loading the specific linked web page106, thereby bypassing the homepage of the website104.

The particular web page106that the user device108first uses to access the website104during a visit can be known as an “entry page” or “landing page” for that user device108. After requesting and loading the entry page, the user device108may navigate to other web pages106of the website104. For instance, after loading a particular entry page on the user device108, a user of the user device108may click on a link displayed on the entry page to navigate to a different web page106of the website104. An example of different types of web pages106associated with a website, any of which could be an entry page of the website for the user device108, are shown inFIG.3and are described below with respect to that figure.

As will be discussed further below, the same and/or different digital channels may cause user devices to access different entry pages of the website104. For example, if the digital channel110is associated with a search engine, the search engine may display links to either web page106A or web page106B depending on search terms entered into the search engine by the user. Accordingly, the same search engine may cause user devices to access different entry pages of the same website104.

At least some of the web pages106can include a call to action (CTA)112. A CTA112can include text, images, video, sounds, and/or other elements that can be presented via the user device108as part of a web page. The CTA112can be associated with a target user activity, such that a user can engage in the target user activity via the website104if the user selects or otherwise engages with at least a portion of the CTA112. For instance, the CTA112can include a portion of text that explains the target user activity and/or attempts to persuade users to engage in the target user activity, as well as a button or other user interface element that, if selected by a user, initiates the target user activity.

As an example, the website104may be associated with an insurance company, and the target user activity may be a “quote start” in which a user initiates a process to receive a new insurance quote via the website104. The insurance quote may be for automobile insurance, homeowner insurance, renter insurance, condominium insurance, life insurance, small business insurance, health insurance, disability insurance, pet insurance, or any other type of insurance offered via the website104. In this example, the CTA112may include an image link or text link that reads “Start a Quote,” or any other text, image, and/or other user interface element that, if selected or engaged with via user input, initiates a process by which the user can obtain an insurance quote via the website104. For instance, if a user clicks on a “Start a Quote” link associated with the CTA112, a web browser on the user device108may be redirected to a different web page106designed to accept user input about a home or automobile that the insurance company can use to generate a new insurance quote for the home or automobile.

In other examples, the target user activity may be any other type of user activity that users can engage in via the website104. For instance, the CTA112may be associated with any other type of target user activity, such as paying a bill, performing a search for an insurance agent, initiating an insurance claim, performing a search for a product or service, purchasing a product or service, and/or performing any other desired user activity.

Multiple web pages106of the website104can include CTAs112associated with the same target user activity. However, different web pages106may include different types of CTAs112associated with the same target user activity. For example, different web pages106include different CTAs112that explain the same target user activity using different text, images, and/or other user interface elements.

Different web pages106may also display CTAs112associated with the same target user activity at different positions. For example, the CTA112may be displayed relatively close to the top of web page106A, such that the CTA112is visible immediately when web page106A first loads on the user device108. However, the CTA112may be displayed at a lower position on web page106B than on web page106A. For instance, the CTA112may not be visible initially when web page106B first loads on the user device108, but may become visible on user device108once a user scrolls down web page106B.

As shown inFIG.1, the website104can be associated with a propensity analyzer114. The propensity analyzer114can receive website activity data116that includes data associated with interactions between one or more user devices and the website104. The propensity analyzer114can receive the website activity data116from the server102, from a web analytics service such as Adobe® Analytics or Google® Analytics, and/or from any other source configured to monitor interactions between user devices and the website104. In some examples, the propensity analyzer114can receive the website activity data116substantially in real time. In other examples, the propensity analyzer114can receive the website activity data116on a periodic or occasional basis, such as daily, weekly, or monthly.

The website activity data116may indicate which digital channels user devices used to access the website104, which entry pages the user devices used to access the website104, whether users associated with the user devices engaged in the target user activity, which web pages106the users used to initiate the target user activity, and/or other data about interactions between user devices and the website104. As an example, whether or not a user engages in the target user activity during a visit to the website104may be tracked as a binary value, for instance by storing a value of “1” if the user performed the target user activity or by storing a value of “0” if the user did not perform the target user activity. In other examples, any other type of data can be used to express whether or not a user engages in the target user activity during a visit to the website104.

In some examples, the website activity data116may use cookie information and/or other data to identify activity associated with different categories of users of the website104. For example, if the website104is a website for a company, traffic to the website104may include both existing customers of the company as well as users who are not currently customers of the company. The target user activity may be “quote starts” or other activity that may lead non-customers to become customers. The website activity data116may separately indicate activity of customers and non-customers, or only include data about non-customers, such that the website activity data116can indicate whether the non-customers performed the target user activity during their visits to the website104.

The propensity analyzer114can be configured to analyze the website activity data116and determine propensities of different web pages106and/or different digital channels to drive users to engage in the target user activity. A propensity can be considered a probability, chance, or likelihood that a user will perform the target user activity during a visit to the website104.

The propensity analyzer114can use a statistical analysis to determine propensities associated with different web pages106and/or different digital channels to drive users to engage in the target user activity. The statistical analysis can be based on regression, linear regression, decision tree modeling, and/or other statistical analysis techniques. For example, the propensity analyzer114can use a logistic regression model that identifies variables that lead to responses. In some examples, the logistic regression model may use a selection approach, such as forward selection, backward elimination, or stepwise selection. The propensity analyzer114can use such statistical analysis to identify and/or estimate the contributions of individual variables, such as individual digital channels and/or individual types of web pages, toward users performing, or not performing, the target user activity during visits to the website104. Because a user may perform a variety of activities during a visit to the website104, the statistical model used by the propensity analyzer114can consider website activity data116that identifies instances of the target user activity and/or other user activities associated with visits to the website.

For example, the propensity analyzer114can use a logistic regression formula to calculate probabilities that the target user activity will happen, based on contributions of different variables. In this example, a dependent variable can be the likelihood of the target user activity occurring, while independent variables may be different digital channels that can drive traffic to the website104and/or different types of web pages106that users can use as entry pages to the website104. In some examples, the calculated probabilities can be expressed on a scale of 0 to 1, with 0 indicating that the target user activity is not likely to happen and 1 indicating that the target user activity is likely to happen. In some examples, the statistical model can use a linear logistic model having a form of Log (γ)(Target User Activity)=β0x+β1x . . . , where β values are beta coefficients that represent slopes of different variables x. The model can also be expresses as

log[p1-p]=a+β⁢x,
where a is an intercept parameter. The slope values β can represent estimates associated with each variable.

In some examples, the propensity analyzer114can use one or more selection techniques to determine which variables, from a set of variables, are not contributing to the target user activity, and omit those variables from consideration within the model. For example, the logistic regression model discussed above may initially consider a set of variables, and the model may iteratively discard any variables that it finds are not sufficiently associated with the target response of whether users did or did not perform the target user activity. Accordingly, the model can keep variables determined to be the best predictors of the target user activity. In some examples, a forward selection technique can be used in which the model begins with zero variables, iteratively adds variables during the analysis, and discards variables from the model that that are determined to have low significance, such as a significance under a threshold value. In other examples, backwards elimination techniques can be used in which the model begins with a set of possible variables, and discards variables that are found to have low significance. A stepwise selection technique can also be used that incorporates both forward selection and backwards elimination. Overall, the selection technique can eliminate low-significance variables from the logistic regression model. Accordingly, statistically-determined estimates of contribution values generated by the propensity analyzer114can indicate which digital channels, and/or which types of entry pages, are most likely to drive users to perform the target user activity during their visit to the website104, and thus have the highest propensities to drive the target user activity. Non-limiting examples of statistical analyses that can be performed by the propensity analyzer114are discussed in more detail below with respect toFIG.5andFIGS.8-10.

The propensity analyzer114can accordingly generate and/or output website propensity data118that includes determined propensities associated with web pages106, digital channels, and/or combinations of web pages106and digital channels. In some examples, the website propensity data118generated by the propensity analyzer114may also include recommendations for revising one or more web pages106and/or digital channels based on relative differences between determined propensities associated with different web pages and/or different digital channels.

As a first example, due to differences in content, placement, and other attributes of CTAs on different web pages106, some web pages106may have a higher propensity than other web pages106to drive users to engage in the target user activity. The propensity analyzer114can evaluate website activity data116, as described further below, to determine which web pages106are higher-propensity web pages that have relatively high likelihoods of driving users to engage in the target user activity. The propensity analyzer114may also determine, from the website activity data116, that other web pages106are lower-propensity web pages that have relatively low likelihoods of driving users to engage in the target user activity. In some examples, the propensity analyzer114can compare the higher-propensity web pages against the lower-propensity web pages to determine differences in content and/or placement of CTAs on those web pages, and output website propensity data118that recommends modifying the lower-propensity web pages to have CTAs with the same or similar content and/or placement as the CTAs of the higher-propensity web pages.

As another example, the propensity analyzer114may generate, based on an analysis of the website activity data116, website propensity data118that indicates propensities of different types of digital channels to drive users to engage in the target user activity. In some examples, operators of the website may use the website propensity data118to identify one or more high-propensity digital channels, and prioritize resources in association with the one or more high-propensity digital channels. For instance, the operators may choose to allocate higher budgets and/or assign more developers to work on the digital channels that the website propensity data118indicates have the highest propensities to drive users to engage in the target user activity. Alternatively, or in addition, such operators may use the website propensity data118to identify lower-propensity digital channels, and allocate resources to improve the propensities associated with one or more of the identified lower-propensity digital channels.

As still another example, the website propensity data118may indicate that a certain web page106has a relatively high propensity to drive users to engage in the target user activity, but that search engines are driving more traffic to other lower-propensity web pages106. In this situation, the website propensity data118may indicate that the content and/or design of the high propensity web page106should be modified according to search engine optimization strategies, such that search engines begin directing more traffic to the high propensity web page106. In some examples, the website propensity data118may recommend modifying web pages106with high propensities, but relatively low traffic, to include content and/or design elements from other higher-traffic web pages106. For instance, if lower-propensity web pages106are receiving more traffic from search engines than higher-propensity web pages106due to certain key words, font sizes, image placements, and/or other elements, the website propensity data118can recommend modifying the higher-propensity web pages106to include the key words, font size, image placements, or other elements that may have been driving search engine traffic to the lower-propensity web pages106. Accordingly, such changes can cause search engines to direct an increased level of traffic to the identified higher-propensity web pages106.

As another example, the website propensity data118may be an alert or other notification indicating that the propensity of one type of entry page, and/or one type of digital channel, to drive the target user activity has fallen below a predetermined threshold relative to one or more other types of entry pages and/or digital channels. For instance, as web pages106are updated over time, the propensity analyzer114may determine that propensities of a particular type of web page106to drive the target user activity has fallen behind corresponding propensities of other types of web pages106to drive the target user activity by more than 10%, or any other threshold. The propensity analyzer114can generate website propensity data118that includes an alert or other notification indicating that the propensity of the particular type of web page106to drive the target user activity is lagging behind corresponding propensities of other types of web pages106. Such a notification may indicate an opportunity to revise the particular type of web page106to increase its propensity to drive the target user activity, for instance to incorporate changes previously made to the other types of web pages106that have already increased their propensities to drive the target user activity.

FIG.2shows an example of various digital channels200that can drive user visits to the website104. As discussed above, a user of a user device108can initially access the website104through a digital channel110. Types of digital channels can include direct load202, display advertisements204, email advertisements206, search engine optimization (SEO)208, search engine marketing (SEM)210, social media212, text advertisements214, print media216, and/or other digital channels.

Direct load202visits to the website104can occur when a user directly enters a URL for the website104, such as a URL for a homepage of the website104or for any other particular web page106of the website104. For example, direct load202visits can include visits when a user typed in a URL for the website104without being prompted to do so by an advertisement.

Display advertisement204visits to the website104can occur when a user clicks on a banner advertisement, or other type of advertisement, that was displayed on a different website. Links associated with such display advertisements can cause a web browser on the user's user device to be redirected to a corresponding entry page on the website104.

Email advertisement206visits to the website104can occur when a user clicks on a link in an email that redirects the user to an entry page on the website104. Links in such email advertisements can cause a web browser on the user's user device to be redirected to a corresponding entry page on the website104.

SEO208visits to the website104can occur when a user clicks on a link presented as an organic search result within a search engine. For example, when a user enters search terms into a search engine, algorithms of the search engine can cause a list of search results to be displayed. Each search result can be a link to a web page, and the search engine can sort the search results based on various factors such as relevancy of words on the web pages to the search terms, relationships between words on the web pages, how quickly the web pages load, how compatible the web pages are with mobile devices, and/or other factors. Links in such search results can cause a web browser on the user's user device to be redirected to a corresponding entry page on the website104. Depending on the entered search terms, the search engine may display one or more links to different entry pages of the website104.

SEM210visits to the website104can occur when a user clicks on a link associated with a paid advertisement presented in a search engine. For example, although a search engine can display organic search results in response to a user's search query as described above, when the search query matches criteria for a paid advertisement, the paid advertisement can also be displayed alongside the organic search results as sponsored or paid content. Such paid links can cause a web browser on the user's user device to be redirected to a corresponding entry page on the website104.

Social media212visits to the website104can occur when a user clicks on a link presented in content or advertisements on a social media website or platform. Such social media links can cause a web browser on the user's user device to be redirected to a corresponding entry page on the website104.

Text advertisement214visits to the website104can occur when a user clicks on a link presented in a text message or a multimedia message. Such text message links can cause a web browser on the user's user device to be redirected to a corresponding entry page on the website104.

Print media216visits to the website104can occur when a user visits the website104in response to viewing an advertisement or other content in a magazine, mailer, or other print media216. For example, a print advertisement may include a specialized URL associated with the website104and/or a corresponding entry page, such that visits to the website104that occur based on users entering that specialized URL can be identified as having occurred in response to the print media216.

FIG.3shows an example of various groups of web pages106of an example website300. The example website300can be associated with a company that offers products and services in areas such as insurance and finance. The groups of web pages106of the example website300can include a homepage302, fire pages304, auto pages306, life pages308, health pages310, agent pages312, bank pages314, finance pages316, payment pages318, claim pages320, search pages322, quote pages324, an insurance landing page326, a multiline landing page328, and/or groups of other types of web pages106. As discussed above, a user device108can, via a digital channel110, access a website by requesting a particular web page106of the website104from the server102. The particular web page106of the website104that the user device108accesses first via a digital channel110can be considered an “entry page” or “landing page” for the user device108. Accordingly, for the example website300, the entry page for the user device108may be a web page106any of the example groups of web pages106shown inFIG.3. The types of web pages106shown inFIG.3are not intended to be limiting, as the website300can include different types and/or groups of web pages, including fewer or more than are shown inFIG.3. Additionally, in other examples, the systems and methods described herein can be used with other types of websites that have groupings of web pages associated with other types of products and/or services.

The homepage302can be a web page106that contains an introduction and/or general overview of the website300. The homepage302may contain links to other web pages106of the website300.

Fire pages304, auto pages306, life pages308, and health pages310can include one or more web pages106that provide information associated with first insurance, automobile insurance, life insurance, and health insurance, respectively. For example, fire pages304can display information associated with homeowner's insurance, condominium insurance, and/or renter's insurance. The website300may also have other types of web pages106associated with other types of insurance.

Agent pages312can include one or more web pages106associated with insurance agents. For example, agent pages312can include web pages106for finding an insurance agent, micro websites that include one or more web pages106with content about individual insurance agents, web pages106for starting an insurance quote with the help of an insurance agent, and/or other agent-related web pages106.

Bank pages314can include one or more web pages106associated with banking products and/or services. For example, bank pages314can include web pages106about checking accounts, saving accounts, credit cards, and/or other banking information.

Finance pages316can include one or more web pages106associated with financial products or services, such as investments and banking. In some examples, finance pages316may include links to bank pages314, and/or bank pages314can link to finance pages316.

Payment pages318can include one or more web pages106associated with making payments associated with the website300. For example, payment pages318may allow users to view bills, pay bills, and/or make other types of payments.

Claim pages320can include one or more web pages106associated with filing new insurance claims, viewing claim information, and/or other claim-related information. In some examples, web pages106about claims for fire insurance, auto insurance, life insurance, health insurance, or other types of insurance may be considered to be claim pages320rather than fire pages304, auto pages306, life pages308, or health pages310. However, in other examples, some claim-related web pages106can be considered to be fire pages304, auto pages306, life pages308, or health pages310.

Search pages322can include one or more web pages106that allow users to search the website300and/or view search results. For example, one or more web pages106can include a search bar that users can use to enter search terms for a global or targeted search on the website300, and the search pages322can include web pages106that display results of such a search.

Quote pages324can include one or more web pages106associated with insurance quotes. For example, a quote page324can allow a user to start a new insurance quote, view an existing quote, allow an agent to start a quote or help start a quote, and/or other quote-related web pages106. In some examples, the target user activity for the website300can be a “quote start.” Other web pages106of the website300may include CTAs112associated with quote starts that, if selected by a user, cause the user's user device to redirect to a quote page324that the user can use to obtain a new insurance quote. Accordingly, in these examples, CTAs112of the website300may attempt to drive users to one or more quote pages324.

An insurance landing page326can be a web page106that serves as an overview of offered insurance products and services. For example, the insurance landing page326can include links to fire pages304, auto pages306, life pages308, health pages310, agent pages312, claim pages320, quote pages324, the multiline landing page328, and/or other insurance-related web pages106.

A multiline landing page328can be a web page106that serves as an overview of multiline insurance coverage. For example, the multiline landing page328can include content that describes insurance coverage that includes both home and automobile insurance, and/or links to other web pages106that more specifically describe home insurance and automobile insurance.

As discussed above with respect toFIGS.1-3, users may visit a website, such as website300, by accessing entry pages of the website through various digital channels. Some digital channels may direct users to different entry pages of the website. For example, if a user directly types in a URL for the homepage302of the website300, the entry page for that user can be the homepage302during a visit that occurs via the direct load202digital channel. However, if the user instead directly types in a URL for an insurance landing page326, the entry page for the user can instead be the insurance landing page326during a visit that occurs via the direct load202digital channel. As another example, depending on a user's search query to a search engine, different organic and/or paid search results may be displayed. Accordingly, the user may be directed to different entry pages that correspond to different search terms during visits that occur via the SEO208digital channel and/or the SEM210digital channel.

The target user activity for the example website300can be quote starts. However, some entry pages, and/or the digital channels used to direct users to those entry pages, can be more effective than others at driving users to initiate new insurance quotes. For example, users who arrive on the website300at entry pages in a first group of web pages106via a first digital channel may have a higher propensity to initiate new insurance quotes, relative to other users who arrive on the website300via entry pages in other groups of web pages and/or via other digital channels. The systems and methods described herein can be used to determine propensities of different types of entry pages, different types of digital channels, and/or different combinations of entry pages and digital channels, to drive users to initiate new insurance quotes.

FIG.4shows a flowchart of an example method400for determining propensities of digital channels to drive a target user activity on a website104. At block402, the propensity analyzer114can receive website activity data116. The propensity analyzer114can receive the website activity data116from the server102that hosts the website104, from a web analytics service, and/or from any other source. The website activity data116may indicate which digital channels user devices used to access the website104, and whether users associated with the user devices engaged in the target user activity during corresponding visits to the website104.

In some examples, the website activity data116may indicate data about activities of one or more categories of users, such as customers and/or non-customers of a company. If the target user activity is an activity intended to convert non-customers into customers, the website activity data116may, based on cookie information or other data, identify website activity associated specifically with non-customers. As an example, the process ofFIG.4can be used to identify when users who are not currently customers of an insurance company perform a target user activity of starting new insurance quotes via the example website300. In this example, the website activity data116, or the source of the website activity data116, may use cookie data or other data to identify visits to the website300from existing customers who already have insurance coverage through the company, and as such are less likely to start a new insurance quote. The website activity data116, or the source of the website activity data116, may similarly use cookie data or other data to identify visits to the website300from users who are not currently customers of the insurance company. Accordingly, in some examples, the propensity analyzer114may not receive, or may ignore, website activity data116associated the current customers, and may instead analyze the website activity data116associated with non-customers during the process ofFIG.4.

At block404, the propensity analyzer114can use the website activity data116to identify which digital channels200were used to access the website104over a period of time. As discussed above, different users may use different digital channels to access the website104. The propensity analyzer114can be configured to determine, from referring page identifiers, cookie data, and/or other information in the website activity data116, which digital channels were used to access the website104, and/or how often such digital channels were used to access the website104. For example, the propensity analyzer114may determine which of, and/or how often, various digital channels such as direct load202, display advertisements204, email advertisements206, SEO208, SEM210, social media212, text advertisements214, and/or print media216digital channels shown inFIG.2were used to access the website104. As noted above, in some examples the propensity analyzer114can identify website activity data116associated with certain categories of users, and/or ignore website activity data116associated with categories of users who may not be likely to be associated with the target user activity.

At block406, the propensity analyzer114can use the website activity data116to identify instances of the target user activity that occurred on the website104after users arrived on the website via the digital channels identified at block404. The propensity analyzer114can also associate and/or correlate instances of the target user activity with corresponding digital channels. For example, if the propensity analyzer114determines from the website activity data116that a user engaged in the target user activity while on the website104, the propensity analyzer114can determine which type of digital channel was associated with that user's visit to the website104.

At block408, the propensity analyzer114can perform a statistical analysis on the data identified during blocks404and406to generate estimates of the propensities of individual digital channels to drive the target user activity. In some examples, the data captured and tracked during blocks402through406can be stored in a database or other repository over a period of time, such as a day, a week, a month, or any other time period, and a set of data collected over the time period can later be analyzed at block408.

As discussed above, the statistical analysis can be based on regression, linear regression, decision tree modeling, and/or other statistical analysis techniques. As a non-limiting example, the statistical analysis performed during block408can be based on a logistic regression model that uses different digital channels as variables, and identifies contributions of different digital channels to a response associated with the target user activity occurring. Accordingly, the statistically determined estimates of contribution values produced during block408can indicate individual and/or relative propensities of different digital channels to drive users to perform the target user activity during visits to the website104.

In some examples, the statistical analysis performed during block408can use a logistic regression model, such as a linear logistic model, that identifies variables that lead to responses. This model can be the same or a similar model described above with respect toFIG.1, such as a logistic regression model that uses forward selection, backward elimination, or stepwise selection. The variables considered by the logistic regression model can include variables for one or more digital channels. Overall, at block408the statistical analysis can identify or estimate the contributions of individual variables, such as individual types of digital channels, toward users performing or not performing the target user activity during their visits to the website104. Accordingly, the statistically determined estimates of contribution values produced during block608can indicate which digital channels are most likely to drive users to perform the target user activity during their visit to the website104.

At block410, the propensity analyzer114can output website propensity data118associated with the digital channels. For example, the website propensity data118can identify at least some of the individual and/or relative propensities of different digital channels, determined at block408, to drive users to perform the target user activity during visits to the website104.

As an example,FIGS.5A and5Bshow a chart and graph of statistically determined estimates and other values associated with different types of digital channels that can be produced by the propensity analyzer114via the method400shown inFIG.4. The propensity analyzer114can consider, as variables, a set of digital channels502A through502H, and use a logistic model to determine relative propensities associated with digital channels502A through502H to drive a target user activity on a website104, such as quote starts on website300. Any or all of the information shown inFIGS.5A and/or5B, or similar corresponding information, can be output by the propensity analyzer114as website propensity data118at block410.

FIG.5Ashows an example chart of estimates and other statistical values associated with the digital channels502A through502H, as determined by the propensity analyzer114using a logistic model. For example, the propensity analyzer114can determine that digital channel502B is associated with an estimate value of −0.0807, while digital channel502D is associated with an estimate value of 0.7713. As shown inFIG.5, in some examples the propensity analyzer114can consider other variables, besides digital channels, during the statistical analysis, such as device types of user devices used to access the website100as indicated in the website activity data116.

FIG.5Bshows an example graph, corresponding to the example chart ofFIG.5A, of the propensity estimates of the digital channels502A through502H to drive the target user activity. The graph ofFIG.5Bvisually shows statistically determined estimated contributions of multiple variables, including digital channels502A through502H, toward a target user activity. In the graph ofFIG.5B, the data can be normalized such that the graph represents the contributions of each variable if each variable were associated with 1000 visits to the website104. The graph ofFIG.5Bcan correspond to an equation derived from the estimates shown in the chart ofFIG.5A, such as:

log[p1-p]=a+β⁢x=-2.1⁢7⁢7⁢5-0.0⁢9⁢9⁢6Ch⁢502⁢A-0.0807Ch⁢502⁢B+0.2812Ch⁢502⁢C+0.7⁢7⁢1⁢3Ch⁢502⁢D-1.1⁢2⁢0⁢5Ch⁢502⁢E+0.0884Ch⁢502⁢F+0.3⁢5⁢7⁢4Ch⁢502⁢G+0.0⁢9⁢1⁢5Ch⁢502⁢H-0.0⁢4⁢6⁢4Device.

In some examples,FIGS.5A and5Bcan represent estimates of how likely individual digital channels are to lead to non-customers starting new insurance quotes on website300. The example data shown inFIGS.5A and5Bcan show that, based on a sample of website activity data collected over a month or other period of time, non-customers visits to website300that occurred via digital channels502C,502D, and502G had relatively high likelihoods of resulting in quote starts. However, the example data shown inFIGS.5A and5Bcan also indicate that other non-customer visits to the same website300that occurred via digital channels502A and502B were relatively unlikely to result in quote starts, and that non-customer visits that occurred via digital channel502E were extremely unlikely to result in quote starts. Accordingly, website propensity data118corresponding to the example data shown ofFIGS.5A and5Bmay indicate that the website300and/or digital channels should be reviewed and/or revised such that aspects of relatively high-propensity digital channels502C,502D, and502G can be applied to lower-propensity digital channels502A,502B, and/or502E in order to increase the propensities of digital channels502A,502B, and/or502E. Alternatively, such website propensity data118may indicate that, because digital channels502C,502D, and502G have the highest propensities to drive quote starts, budgetary resources, development resources, and/or other resources should be prioritized to increase the amount of user traffic to the website300that arrives via digital channels502C,502D, and/or502G.

For instance, by normalizing the data to a common number of visits per digital channel, as shown in the example ofFIGS.5A and5B, digital channels that are effective at driving the target user activity can be identified regardless of the amount of traffic that actually arrives via those digital channels. For example, if the analysis shows that digital channel502D has a higher propensity than the other digital channels to lead to the target user activity, but that digital channel502D is not associated with as much user traffic as other digital channels, budgetary or development resources associated with digital channel502D can be increased in an attempt to increase the amount of user traffic that arrives via high-propensity digital channel502D.

Overall, the process ofFIG.4can determine relative propensities of different digital channels to drive the target user activity. For example, the determined propensity data may indicate that visits to the example website300that occurred via the SEO208and the SEM210digital channels had relatively high propensities to lead to quote starts. However, users may arrive at the website300, via SEO208and SEM210digital channels, on different entry pages. Some of those entry pages may themselves have higher or lower propensities to lead to the target user activity. However, the method ofFIG.6can be used to identify propensities of different groups of entry pages to lead to the target user activity. The method ofFIG.7can also be used to identify propensities of different combinations of digital channels and entry pages to lead to the target user activity.

FIG.6shows a flowchart of an example method600for determining propensities of entry pages to drive a target user activity on a website104. At block602, the propensity analyzer114can receive website activity data116. The propensity analyzer114can receive the website activity data116from the server102that hosts the website104, from a web analytics service, and/or from any other source. The website activity data116may indicate which specific web pages106and/or groups of web pages106were used by user devices as entry pages to the website104, and whether users associated with the user devices engaged in the target user activity via the entry pages and/or during corresponding visits to the website104. As discussed above with respect toFIG.4, in some examples, the website activity data116may indicate data about activities of one or more categories of users, such as customers and/or non-customers of a company. In these examples, the propensity analyzer114may not receive, or may ignore, website activity data116associated the current customers, and may instead analyze the website activity data116associated with non-customers during the process ofFIG.6.

At block604, the propensity analyzer114can use the website activity data114to identify which web pages106were used as entry pages to the website100over a period of time. For example, the propensity analyzer114may determine which of, and/or how often, web pages106such as the homepage302, fire pages304, auto pages306, life pages308, health pages310, agent pages312, bank pages314, finance pages316, payment pages318, claim pages320, search pages322, quote pages324, the insurance landing page326, and/or the multiline landing page328of the example website300shown inFIG.3were used as entry pages to website300. As noted above, in some examples the propensity analyzer114can identify website activity data114associated with certain categories of users, and/or ignore website activity data114associated with categories of users who may not be likely to be associated with the target user activity.

At block606, the propensity analyzer114can use the website activity data116to identify instances of the target user activity that occurred on the website104after users arrived on the website via the entry pages identified at block604. The propensity analyzer114can also associate and/or correlate instances of the target user activity with corresponding entry pages. For example, if the propensity analyzer114determines from the website activity data116that a user engaged in the target user activity while on the website104, the propensity analyzer114can determine which specific web page106, or type of web page106, that user used as an entry page during the user's visit to the website104.

At block608, the propensity analyzer114can perform a statistical analysis on the data identified during blocks604and606to generate estimates of the propensities of individual entry pages, or groups of entry pages, to drive the target user activity. In some examples, the data captured and tracked during blocks602through606can be stored in a database or other repository over a period of time, such as a day, a week, a month, or any other time period, and a set of data collected over the time period can later be analyzed at block608.

As discussed above, the statistical analysis can be based on regression, linear regression, decision tree modeling, and/or other statistical analysis techniques. As a non-limiting example, the statistical analysis performed during block608can be based on a logistic regression model that uses different types of entry pages as variables, and identifies contributions of different types of entry pages to a response associated with the target user activity occurring. Accordingly, the statistically determined estimates of contribution values produced during block608can indicate individual and/or relative propensities of different types of entry pages to drive users to perform the target user activity during visits to the website104.

In some examples, the statistical analysis performed during block608can use a logistic regression model, such as a linear logistic model, that identifies variables that lead to responses. This model can be the same or a similar model described above with respect toFIG.1, such as a logistic regression model that uses forward selection, backward elimination, or stepwise selection. The variables considered by the logistic regression model can include different groups of entry pages instead or, or in addition to, variables for one or more digital channels. Overall, at block608the statistical analysis can identify or estimate the contributions of individual variables, such as individual groups of entry pages, toward users performing or not performing the target user activity during their visits to the website104. Accordingly, the statistically determined estimates of contribution values produced during block608can indicate which groups of entry pages are most likely to drive users to perform the target user activity during their visit to the website104.

At block610, the propensity analyzer114can output website propensity data118associated with the entry pages. The website propensity data118can identify at least some of the individual and/or relative propensities of different types of entry pages, determined at block608, to drive users to perform the target user activity during visits to the website104. For example, the website propensity data118may indicate that one type of entry page has a higher propensity to drive users to perform the target user activity during visits to the website104than another type of entry page.

In some examples, the propensity analyzer114can also generate and output website propensity data118that recommends one or more changes to one or more web pages106. For example, if the propensity analyzer114determines that a first type of entry page has a higher propensity to drive users to perform the target user activity during visits to the website104than a second type of entry page, the propensity analyzer114may determine differences between CTAs112associated with the first type of entry page and the second type of entry page. If the CTAs112differ with respect to content, placement, and/or other attributes on the first type of entry page and the second type of entry, the propensity analyzer114can generate website propensity data118that includes revision recommendations for the CTAs112of the second type of entry page based on the CTAs112of the first type of entry page.

As an example, if the higher-propensity first type of entry page has CTAs112located at a different position than CTAs112on the lower-propensity second type of entry page, the website propensity data118may recommend moving the CTAs112on the second type of entry page to a position where corresponding CTAs112are located on the first type of entry page. For instance, as discussed above with respect toFIG.1, web page106A may have a CTA112positioned relatively close to the top of web page106A, while web page106B has a similar CTA112positioned lower that is not visible on the user device108until a user scrolls down web page106B. In this case, if the propensity analyzer114determines that web page106A has a higher propensity to drive the target user activity than web page106B, the propensity analyzer114can identify the differences in placement between the CTAs112on web page106A and web page106B, and generate website propensity data118that recommends moving the CTA112on web page106B to the position the CTA112is located on higher-propensity web page106B. As another example, if the higher-propensity first type of entry page has CTAs112that include key words, fonts, font sizes, colors, and/or other content or design elements that are different from CTAs on the lower-propensity second type of entry page, the website propensity data118may recommend changing the CTAs112on the second type of entry page to include content and/or design elements of corresponding CTAs112of the first type of entry page.

In some examples, the propensity analyzer114can automatically implement content and/or design changes of a web page106based on the website propensity data118. For example, the propensity analyzer114can adjust Hypertext Markup Language (HTML) code, Cascading Style Sheets (CSS) code, and/or other code associated with a web page106change the content and/or placement of a CTA112and/or other elements based on recommendations generated by the propensity analyzer114. In some examples, the adjusted code can be automatically generated as a new draft version of the web page106by the propensity analyzer114, and a human developer can determine whether or not to incorporate the automatically-generated version of the web page106into the website104. In other examples, human developers can review revision recommendations for web pages106generated by the propensity analyzer114, and manually revise web pages106accordingly.

In some examples, the propensity analyzer114can use the process ofFIG.6to, regardless of which digital channels users used to arrive at the website104, determine relative propensities of different types of entry pages to drive users to perform a target user activity on the website104. However, in other examples, the propensity analyzer114can use the process ofFIG.6to determine relative propensities of different types of entry pages to drive users to perform a target user activity on the website104, in association with a specific type of digital channel. For instance, at block604, the propensity analyzer114may be configured to identify website activity data116associated with visits to the website104that occurred via one specific digital channel, and to determine entry pages used to access the website104that that specific digital channel. Accordingly, the propensity analyzer114can use blocks606through608to determine propensities of different types of entry pages to drive the target user activity, if those entry pages are visited via the specific digital channel being analyzed.

The propensity analyzer114can also, or alternately, determine propensities to drive the target user activity that are associated with combinations of digital channels and entry pages. For example, the propensity analyzer114can determine which combination of a digital channel and an entry page type has the highest propensity to drive users to perform the target user activity.

FIG.7shows a flowchart of an example method700for determining propensities of combinations of digital channels and entry pages to drive a target user activity on a website104. At block702, the propensity analyzer114can receive website activity data116. The propensity analyzer114can receive the website activity data116from the server102that hosts the website104, from a web analytics service, and/or from any other source. The website activity data116may indicate which specific and/or groups of web pages106were used by user devices as entry pages to the website104, and whether users associated with the user devices engaged in the target user activity on the entry pages and/or during corresponding visits to the website104. As discussed above with respect toFIGS.4and6, in some examples, the website activity data116may indicate data about activities of one or more categories of users, such as customers and/or non-customers of a company. In these examples, the propensity analyzer114may not receive, or may ignore, website activity data116associated the current customers, and may instead analyze the website activity data116associated with non-customers during the process ofFIG.7.

At block704, the propensity analyzer114can determine relative propensities associated with two or more digital channels. For example, the propensity analyzer114can use the method400described above with respect toFIG.4to determine relative propensities of a set of digital channels to drive the target user activity.

At block706, the propensity analyzer114can select one of the digital channels analyzed at block704. In some examples, the propensity analyzer114can determine a subset of the digital channels analyzed at block704that were determined to have the highest propensities to drive the target user activity. For instance, the propensity analyzer114may use propensity data, such as data shown in the examples ofFIGS.5A and5B, to identify two digital channels with the two highest propensities of driving the target user activity. The propensity analyzer114can then select one of the identified highest-propensity digital channels at block706.

At block708, the propensity analyzer114can use the website activity data116to identify which web pages106were used as entry pages to the website104over a period of time, when such entry pages were accessed via the digital channel selected at block706. As noted above, different user visits to the website104may be initiated via many different entry pages, even if all of them occur via the same digital channel200. For instance, in the example ofFIG.3, a user who types “auto insurance” into a search engine may be presented with search results including a link to an auto page306, while another user who types “insurance agent” as a search query may be given search results including a link to an agent page312. Accordingly, even though both users in this example may arrive at the website300via the SEO208digital channel based on organic search results, the entry pages for the two users may be in different groups of web pages106shown inFIG.3. Accordingly, if the selected digital channel is the SEO208digital channel shown inFIG.2, the propensity analyzer114may determine which of, and/or how often, the entry pages shown in the example ofFIG.3were associated with user visits that arrived via the SEO208digital channel. As noted above, in some examples the propensity analyzer114can identify website activity data116associated with certain categories of users, and/or ignore website activity data116associated with categories of users who may not be likely to be associated with the target user activity.

At block710, the propensity analyzer114can use the website activity data116to identify instances of the target user activity that occurred on the website104after users arrived on the website via the digital channel selected at block706and via the entry pages identified at block708. The propensity analyzer114can also associate and/or correlate instances of the target user activity with corresponding entry pages. For example, if the propensity analyzer114determines from the website activity data116that a user engaged in the target user activity while on the website104, the propensity analyzer114can determine which specific web page106, or type of web page106, that user used as an entry page during the user's visit to the website104. In some examples, a user may arrive on an entry page in one group of web pages106, but navigate through the website104to web pages106in other groups. Accordingly, the propensity analyzer114can use the website activity data116to determine whether users ultimately performed the target user activity during their visits to the website104, as well as which entry page those users initially used to access the website104during their visits.

At block712, the propensity analyzer114can perform a statistical analysis on the data identified during blocks708and710to generate estimates of the propensities of individual entry pages, or groups of entry pages, to drive the target user activity, when such entry pages are visited via the digital channel selected at block706. In some examples, the data captured and tracked during blocks702-710can be stored in a database or other repository over a period of time, such as a day, a week, a month, or any other time period, and a set of data collected over the time period can later be analyzed at block712.

As discussed above, the statistical analysis can be based on regression, linear regression, decision tree modeling, and/or other statistical analysis techniques. As a non-limiting example, the statistical analysis performed during block712can be based on a logistic regression model that uses different types of entry pages as variables, and identifies contributions of different types of entry pages to a response associated with the target user activity occurring. Accordingly, the statistically-determined estimates of contribution values produced during block712can indicate individual and/or relative propensities of different types of entry pages to drive users to perform the target user activity during visits to the website104, when such visits occur via the digital channel selected at block706.

In some examples, the statistical analysis performed during block712can use a logistic regression model, such as a linear logistic model, that identifies variables that lead to responses. This model can be the same or a similar model described above with respect toFIG.1, such as a logistic regression model that uses forward selection, backward elimination, or stepwise selection. However, here the variables considered by the logistic regression model can include different groups of entry pages instead or, or in addition to, variables for one or more digital channels. Overall, at block712the statistical analysis can identify or estimate the contributions of individual variables, such as individual groups of entry pages, toward users performing or not performing the target user activity during their visits to the website104. Accordingly, the statistically determined estimates of contribution values produced during block712can indicate which groups of entry pages are most likely to drive users to perform the target user activity during their visit to the website104.

After determining propensities of entry pages, when visited via a selected digital channel, to drive the target user activity, the propensity analyzer114may select a different digital channel at block706. The propensity analyzer114may then repeat blocks708through712to determine propensities of entry pages, when visited via the different selected digital channel, to drive the target user activity. The propensity analyzer114can repeat blocks706through712for any number of digital channels. For example, if after block704the propensity analyzer114identified the two digital channels with the highest propensities of driving the target user activity, the propensity analyzer114repeat blocks706through712for each of the identified top two highest propensity digital channels.

At block714, the propensity analyzer114can output website propensity data118associated with one or more of the analyzed combinations of digital channels and entry pages. In some examples, the website propensity data118may identify a single combination of a digital channel and entry page that has the highest estimated propensity to drive the target user activity. In other examples, the website propensity data118may indicate relative propensities associated with multiple combinations of digital channels and entry pages.

As an example,FIGS.8A and8Bshow examples of website propensity data118associated with a set of entry pages802A through802N when visited via a first digital channel, whileFIGS.9A and9Bshow examples of website propensity data118associated with the same set of entry pages802A through802N when visited via a second digital channel.FIG.10displays an example bar graph1000that compares website propensity data118associated with the set of entry pages802A through802N with respect to visits that occurred via the first digital channel and the second digital channel. Any or all of the information shown inFIGS.8A,8B,9A,9B, and/or10, or similar corresponding information, can be output by the propensity analyzer114as website propensity data118at block714.

In particular,FIG.8Ashows an example chart of estimates and other statistical values associated with the set of entry pages802A through802N, as determined by the propensity analyzer114using a logistic model based on user traffic that arrived via the first digital channel.FIG.9Ashows a similar example chart of estimates and other statistical values associated with the set of entry pages802A through802N, as determined by the propensity analyzer114using the logistic model based on user traffic that arrived via the second digital channel.

FIG.8Bshows an example graph, corresponding to the example chart ofFIG.8A, of the propensity estimates of the set of entry pages802A through802N to drive the target user activity, when user traffic arrives via the first digital channel. The example graph ofFIG.8Bvisually shows statistically determined estimated contributions of multiple variables, the various type of entry pages802A through802N, toward a target user activity when user traffic arrives via the first digital channel. In the example graph ofFIG.8B, the data can be normalized such that the graph represents the contributions of each variable if each variable were associated with 1000 visits to the website104that occurred via the first digital channel. The graph ofFIG.8Bcan correspond to an equation derived from the estimates shown in the chart ofFIG.8A, such as:

log[p1-p]=a+β⁢x=-2.9⁢8⁢6⁢2-6.1⁢3⁢5⁢6Page⁢802⁢A+0.6652Page⁢802⁢B+0.5⁢0⁢9⁢0Page⁢802⁢C-0.4⁢0⁢1⁢1Page⁢802⁢D-0.1⁢6⁢0⁢6Page⁢802⁢E-0.7028Page⁢802⁢F-0.1⁢6⁢8⁢5Page⁢802⁢G-1.1⁢5⁢1⁢6Page⁢802⁢H-0.7⁢7⁢4⁢0Page⁢802⁢I-0.2115Page⁢802⁢J+0.7⁢3⁢9⁢3Page⁢802⁢K+0.8⁢7⁢3⁢3Page⁢802⁢L-1.0⁢2⁢0⁢4Page⁢802⁢M+0.2⁢7⁢4⁢1Page⁢802⁢N.

FIG.9Bshows an example graph, corresponding to the example chart ofFIG.9A, of the propensity estimates of the set of entry pages802A through802N to drive the target user activity, when user traffic arrives via the second digital channel. The example graph ofFIG.9Bvisually shows statistically determined estimated contributions of multiple variables, the various type of entry pages802A through802N, toward a target user activity when user traffic arrives via the second digital channel. In the example graph ofFIG.9B, the data can be normalized such that the graph represents the contributions of each variable if each variable were associated with 1000 visits to the website104that occurred via the second digital channel. The graph ofFIG.9Bcan correspond to an equation derived from the estimates shown in the chart ofFIG.9A, such as:

log[p1-p]=a+β⁢x=-1.9⁢4⁢0⁢3-5.4⁢2⁢7⁢2Page⁢802⁢A+0.229⁢9Page⁢802⁢B+0.0802Page⁢802⁢C-0.2⁢6⁢3⁢7Page⁢802⁢D-0.2⁢468160⁢6Page⁢802⁢E-0.5863Page⁢802⁢F-0.6⁢3⁢2⁢2Page⁢802⁢G-0.7⁢2⁢1⁢7Page⁢802⁢H-0.5⁢8⁢7⁢2Page⁢802⁢I-0.379Page⁢802⁢J+0.1⁢6⁢9⁢9Page⁢802⁢K+0.7⁢2⁢2⁢2Page⁢802⁢L-0.5⁢3⁢5⁢6Page⁢802⁢M+0.1⁢5⁢4⁢6Page⁢802⁢N.

In some examples,FIGS.8A and8Bcan represent estimates of how likely individual types of entry pages802A through802N are to lead to non-customers starting new insurance quotes on website300, if visits to those entry pages802A through802N occur via a first one of the digital channels shown inFIG.2.FIGS.9A and9Bcan represent similar estimates of how likely non-customers are to start new insurance quotes on website300, if visits to the same entry pages802A through802N instead occurred via a second one of the digital channels shown inFIG.2.FIG.10shows an example bar graph1000that compares the determined propensities associated with the set of entry pages802A through802N for the first digital channel and the second digital channel.

The example website propensity data118shown inFIGS.8A,8B,9A,9B, and/or10can indicate that entry pages802B,802C,802K,802L, and802N have the highest propensities to drive the target user activity. However, the example website propensity data118shown inFIGS.8A,8B,9A,9B, and/or10can also indicates that other types of entry pages were much less likely to result in the target user activity. For example, the example website propensity data118can indicate that users were especially unlikely to engage in the target user activity when the users arrived at the website via entry pages802A.

The example bar graph1000shown inFIG.10, and/or a comparison ofFIGS.8A and9BagainstFIGS.9A and9B, can also show that some entry pages have a higher propensity to drive the target user activity when they are visited via the first digital channel than when they are visited via the second digital channel. For instance, the example website propensity data118indicates that entry pages802B have a higher propensity to drive the target user activity when they are visited via the first digital channel than when they are visited via the second digital channel. In some situations, this may indicate that budgetary and/or development resources should be allocated to increasing the number of user visits to entry pages802B via the first digital channel, and/or that fewer resources should be allocated to the second digital channel.

FIG.11shows a flowchart of an example method1100for generating revision recommendations for one or more web pages106based on determined propensities to drive a target user activity on a website104.

At block1102, the propensity analyzer114can determine relative propensities of at least two types of entry pages to drive the target user activity. For example, the propensity analyzer114can use the methods described above, with respect toFIG.6and/orFIG.7, to determine relative propensities of types of entry pages to drive the target user activity. In some examples, the propensity analyzer114can determine the relative propensities of the least two types of entry pages to drive the target user activity based on website activity data116associated with the same digital channel. In other examples, the propensity analyzer114can determine the relative propensities of the least two types of entry pages to drive the target user activity based on website activity data116associated with different types of digital channels.

At block1104, the propensity analyzer114can select a higher-propensity entry page type and a lower-propensity entry page type. The higher-propensity entry page type can be a type of entry page that was determined, at block1102, to have a higher propensity to drive the target user activity than the lower-propensity entry page type. In some examples, the higher-propensity entry page type can be the type of entry page with the highest propensity determined at block1102. However, in other examples, the higher-propensity entry page type can be any other type of entry page with a higher propensity to drive the target user activity than the selected lower-propensity entry page type.

At block1106, the propensity analyzer114can determine differences between examples of the selected higher-propensity entry page type and the selected lower-propensity entry page type. For example, the propensity analyzer114can compare one or more examples of the higher-propensity entry page type and the selected lower-propensity entry page type to identify differences in content, format, design, placement, and/or other attributes of the web pages106that may impact the relative propensities of CTAs112on the web pages106to drive users to engage in the target user activity.

For example, the propensity analyzer114may identify differences in design, content, and/or other attributes of CTAs112on the web pages106that are driving the target user activity, such as differences in messaging content displayed on the web pages106, locations and sizes of content of CTAs112on the web pages106, navigation options present on the web pages106, load times of the web pages, the presence or lack of images on the web pages106, whether the web pages106are optimized for display on mobile devices, and/or any other attributes. In some examples, the propensity analyzer114may use website analytics and/or replay functionality to replay navigation movements of users through the website104to help understand how users progressed from arriving at an entry page to performing or not performing the target user activity.

At block1108, the propensity analyzer114can generate revision recommendations to adjust lower-propensity entry pages based on differences with the higher-propensity entry pages. Such revision recommendations can be included in website propensity data118output by the propensity analyzer114at block1110. For example, if the propensity analyzer114identifies differences between content and/or design elements of higher-propensity entry pages and lower-propensity web pages, the propensity analyzer114can generate recommendations to revise the lower-propensity web pages based on the content and/or design elements of higher-propensity entry pages. This can include a revision recommendation to adjust CTAs112of the lower-propensity web pages to be positioned at a location of CTAs112on the higher-propensity entry pages, to include key words or other content of CTAs112on the higher-propensity entry pages, to be formatted with colors, fonts, font sizes, and/or other design elements of CTAs112on the higher-propensity entry pages, and/or other suggestions.

As a non-limiting example, the propensity analyzer114may determine that life pages308have a higher propensity to drive quote starts on the example website300than fire pages304. The propensity analyzer114may also determine at block1106that the life pages308use CTAs112that include the term “free insurance quote,” while the fire pages304instead use the term “insurance quote” without indicating that such a quote would be provided for free by the website300. The propensity analyzer114may accordingly determine or predict that the additional word “free” may be helping to drive users to start quotes, and generate a recommendation to revise CTAs112on the fire pages304to include the word “free,” or otherwise emphasize that quotes are free. Digital channels200can similarly be revised based on such an analysis, such as by revising SEM advertisements with paid links to fire pages304to emphasize “free” quotes.

In some examples, the propensity analyzer114can use machine learning or other forms of artificial intelligence to automatically identify or predict which elements of higher-propensity entry pages are driving the target user activity, and/or to generate corresponding revision recommendations for lower-propensity entry pages that are predicted to increase the propensities of such entry pages to drive the target user activity. For example, the propensity analyzer114can identify a content or design element on a higher-propensity entry page as a candidate element that may be driving the target user activity. In these examples, experiments can be performed in which lower-propensity entry pages are revised to include that content or design element, such that the propensity analyzer114can determine if the revisions improve the propensities of the revised web pages to drive the target user activity. Over time, machine learning techniques can determine correlations of such design and/or content changes, to changes in the propensities of the entry pages to drive the target user activity. Accordingly, types of changes identified by such machine learning techniques as being predictive of increases in propensities to drive the target user activity can be applied to other web pages.

In some examples, such machine learning or other artificial intelligence can automatically revise lower-propensity entry pages. In other examples, machine learning or other artificial intelligence may make revision recommendations that are predicted to improve the propensity of certain groups of web pages to drive the target user activity, and human web page designers can then choose to accept or implement the revision recommendations by revising the web pages according to the revision recommendations. In still other examples, machine learning or other artificial intelligence may automatically generate revised drafts of web pages based on revision recommendations predicted to increase their propensities to drive the target user activity, and human reviewers can review and/or approve the automatically-generated revised drafts for use in the website104. In yet other examples, revisions and/or experimentation can be manually performed based on the revision recommendations included in website propensity data118output by the propensity analyzer114.

As discussed above, the propensity analyzer114can determine relative propensities associated with different types of entry pages by normalizing the data to a common number of visits per type of entry page. Accordingly, the types of entry pages that are effective at driving the target user activity can be identified regardless of the amount of traffic that actually arrives via those entry pages. For instance, the propensity analyzer114may determine that life pages308have a higher propensity to drive quote starts than fire pages304. However, the life pages308may receive relatively little user traffic relative to the fire pages304. Nevertheless, the propensity analyzer114can generate revision recommendations for revising the higher-traffic fire pages304that are predicted to increase propensities of the fire pages304to drive quote starts based on content and/or design elements of the lower-traffic, but higher-propensity, life pages308.

The systems and methods described herein can also be used to make other types of content and/or design changes to lower-propensity web pages106and/or digital channels200, based on content and/or design elements on higher-propensity web pages106and/or digital channels200. For example, for the SEO208digital channel, a search engine's algorithms may present organic search results in an order determined by many factors such as content on the web page106, relationships between words or other content on the web pages106, how quickly the web page106loads, how accessible the web page106is on mobile devices, and/or other factors. The propensity analyzer114may determine that a certain type of web page106has a relatively high propensity to drive the target user activity, in association with the SEO208digital channel, because that type of web page106arranges relevant content closer together or has fewer images such that the web pages load more quickly than other types of web pages. Accordingly, the propensity analyzer114can indicate that such content and/or design aspects of the high propensity web page106should be applied to lower-propensity web pages106, based on a prediction that such changes will increase the propensities of the SEO208digital channel to drive users to those web pages106and/or increase the propensities of those web pages106to drive the target user activity.

Additionally, data sets and statically generated propensity estimates can be tracked and compared over time as changes are made to web pages106and/or digital channels. For example, if a first type of change to the content or design or a web page106increases the propensity of that web page106to drive the target user activity during a subsequent time period relative to a previous time period, while a second type of change has less of an effect on the propensity to drive the target user activity, the propensity analyzer114can identify the first type of change as being more effective. Accordingly, in some cases, the propensity analyzer114can predict that that first type of change, if applied to other groups of web pages106, may also improve their propensities to drive the target user activity, and generate recommendations to similarly revise the other groups of web pages106. As such, over time, different types of optimizations can be compared and contrasted to determine which are most effective at improving propensities to drive the target user activity. In some examples, machine learning or other artificial intelligence can be used to, over time, identify and/or predict types of changes that can increase propensities of web pages106to drive the target user activity.

Although some examples described above focus on quote starts as the target user activity, the same or a similar analysis can be performed based on any other target user activity. For example, if the target user activity is bill payments via the website104, the systems and methods described herein can determine which digital channels200have the highest propensities for leading users to pay bills, and/or which groups of entry pages have the highest propensities for leading users to pay bills. Accordingly, based on such an analysis, content and/or design elements of pages and digital channels that have higher propensities for driving users to pay bills can be applied to other pages and digital channels that have lower propensities for driving users to pay bills.

As other examples, if the target user activity is finding or searching for insurance agents, starting an insurance claim, or performing any other desired activity, the systems and methods described herein can determine which digital channels200and/or groups of entry pages have the highest propensities for leading users to perform that target user activity. As such, based on this analysis, content and/or design elements of web pages106and digital channels that have higher propensities for driving the target user activity can be applied to other web pages106and digital channels that have lower propensities for driving the target user activity.

In some examples, the techniques described herein can be used to evaluate entry pages and/or digital channels200with respect to a set of different target user activities on a website104. For example, the systems and methods described herein may indicate that a group of entry pages has a high propensity of driving quote starts, but has a lower propensity of driving users to search for an insurance agent. As another example, the analysis may show that the homepage302of the example website300has a high likelihood of driving quote starts regardless of which digital channel200leads users to the website300. However, the analysis may also show that the homepage302is relatively likely to lead users to search for insurance agents when the users arrived via the SEO208digital channel, but is relatively unlikely to lead users to search for insurance agents when the users arrived via the direct load202digital channel. This may indicate that the SEO208digital channel is a valuable driver of agent searches and thus that investment in the SEO208digital channel should continue or increase.

Additionally, although the examples described above are discussed in terms of digital channels200that lead users to different entry pages of a website104, the same or similar processes can be used to determine propensities associated with other types of digital properties. For example, a company may have a website104and a mobile application that contains similar information and/or functionality as the website104. Accordingly, the systems and methods described herein may also be used to determine the propensities of different portions or sections of the mobile application to drive the target user activity within the mobile application.

Moreover, although the examples of the target user activity described above may be desirable activities from the perspective of the owner or operator of the website104or other digital property, the systems and methods described herein can also be used to determine propensities of digital channels and/or entry pages to lead to negative and/or non-desirable user behavior. For example, the systems and methods described herein can be used to identify which digital channels and/or entry pages have the highest propensities to be used by customers of a company who leave the company within a threshold period of time after using those digital channels and/or entry pages, relative to customers who remain customers of the company. Accordingly, by identifying digital channels and/or entry pages that have higher propensities of being associated with negative and/or non-desirable user behavior, the content, format, and/or other attributes of those digital channels and/or entry pages can be adjusted based on attributes of other digital channels and/or entry pages that have lower propensities of being associated with the negative and/or non-desirable user behavior.

The examples discussed above can be associated with determining propensities of paths, that users use to arrive at a digital system, to drive a target user activity. For instance, such arrival paths can include digital channels and/or entry pages of a website that users can use to arrive at the website, as discussed above. However, the systems and methods described herein can also be used to determine propensities of other paths, including paths that involve non-digital systems instead of or in addition to digital systems, to drive the target user activity. For example, some users may prefer to initiate a quote start, or engage in another type of target user activity, via a phone call rather than via a website. Accordingly, for some types of users, one or more non-digital systems may have higher propensities to drive the target user activity than the website or other digital systems.FIGS.12-14discussed further below, describe examples of propensity analysis with respect to user paths that may include one or more digital channels, one or more digital systems, and/or more non-digital systems.

FIG.12shows a second example1200of a propensity analysis system. The propensity analysis system can be similar to the propensity analysis system shown inFIG.1, but may be configured to determine propensities of different user paths1202, which may include digital channels110, digital systems1204, and/or non-digital systems1206, to drive the target user activity.

The digital systems1204and the non-digital systems1206can be systems that users, such as user1208shown inFIG.12, can use to engage in a target user activity. As discussed above, the target user activity may be a quote start, or any other type of target user activity. The digital systems1204can include the website104and mobile application discussed above, as well as any other type of digital system or digital property that is configured to enable users to engage in the target user activity. The non-digital systems1206can be other types of systems or interactions by which users can engage in the target user activity, such as offline interactions that may be engaged in outside of the website104, mobile application, or other digital system. For instance, non-digital systems1206can include phone calls, in-person meetings with agents or other representatives, online chats with human representatives, or other types of interactions that the user can use to engage in the target user activity.

Some non-digital systems1206may involve some types of digital technologies. For example, a video call or online text chat with a human representative can be executed via computers and/or other digital technologies, but may be considered to be at least partially non-digital due to the personal nature of the interaction between a user and the human representative.

Some user paths1202can include one or more of the digital channels110discussed above, which may direct or refer users to one or more of the digital systems1204and/or non-digital systems1206. The digital channels110can, for instance, be search engine results with links to one or more of the digital systems1204, and/or that include content directing users to one or more of the non-digital systems1206.

As an example, if the user paths1202are associated with an insurance company, and the user1208uses the user device108to enter the name of the insurance company into a search engine, the search engine may display paid and/or organic search results. The search results may include links to digital systems1204, such as a website and/or individual entry pages of the website. For instance, the search results may include links to the homepage302of the website300for the insurance company, and/or to one or more agent pages312of the website300that are associated with particular insurance agents, as discussed above with respect toFIG.3. However, the search engine may also show other paid and/or organic search results that display a phone number for the insurance company or a particular insurance agent. Accordingly, although the user1208may have used the search engine to perform a search for the name of the insurance company in this example, the search results presented via the search engine may be digital channels110that refer the user1208to digital systems1204(such as links to web pages106of the website300), and that refer the user1208to non-digital systems1206(such as the phone numbers for the insurance company and/or an insurance agent).

The user may choose to use one or more of those digital systems1204and/or non-digital systems1206, and may engage in the target user activity via one of the digital systems1204and/or non-digital systems1206. For instance, based on the example search results discussed above, the user1208could choose to click on one of the links to visit a corresponding web page of the insurance company's website300via the user device108, and may start a new insurance quote via the website300as discussed above. However, the user1208may instead choose to call a phone number displayed within the search results to speak to an agent of the insurance company instead of visiting the website300, and may start a new insurance quote after talking to the agent and requesting the insurance quote.

Although some user paths1202can include one or more digital channels110as discussed above, other user paths1202may not include digital channels110, for instance if users access non-digital systems1206directly. As an example, the user1208may call an agent, or walk into the agent's office for an in-person meeting, without having been directed to the agent via one of the digital channels110. Accordingly, in this example, the user1208may follow a user path to a non-digital system that is associated with a personal interaction with the agent, and that user path may not be associated with a digital channel.

Individual user paths1202can accordingly involve different combinations of digital channels110, digital systems1204, and/or non-digital systems1206. In some examples, a user path may include a digital system and a non-digital system. As a first example, a first user may initially use a digital system, such as a website, but may not ultimately request an insurance quote or engage in another target user activity via the digital system. However, the first user may later call an agent, or access another non-digital system, and request an insurance quote or engage in another target user activity via the non-digital system. As a second example, a second user may initially speak to an agent to learn more about insurance options, but then visit the website300to actually request and view an insurance quote instead of requesting the insurance quote offline via the agent.

Similar to different digital channels110and/or entry pages associated with an individual digital system, such as the website104, having different propensities to drive the target user activity as discussed above, different user paths1202that may be associated with different digital channels110, different digital systems1204, and/or different non-digital systems1206may also have different propensities to drive the target user activity. As shown inFIG.12, the propensity analyzer114can receive user path activity data1210that indicates how the user1208and other users have used user paths1202over time, including whether such users engaged in the target user activity via one or more of the user paths1202. The propensity analyzer114can, based on the user path activity data1210, determine relative propensities of the different user paths1202to drive the target user activity.

The user path activity data1210can include the website activity data116discussed above, which can indicate how users interacted with a website and whether the users engaged in instances of the target user activity via the website. The user path activity data1210can include similar data indicating how users interacted with other types of digital systems1204and/or non-digital systems1206, and whether the users engaged in instances of the target user activity via the digital systems1204and/or non-digital systems1206. The user path activity data1210can also indicate whether, and/or which, digital channels110lead users to the digital systems1204and/or non-digital systems1206. As an example, user path activity data1210associated with a particular agent can be based on notes or other information provided by the agent with respect to individual users, such as notes indicating identities or other attributes of the users, whether the agent helped the users get insurance quotes or engage in other target user activities, whether the users indicated that they were referred to the agent via digital channels110or digital systems1204, identifies of the digital channels110or digital systems1204that referred the users to the agent, and/or other types of information.

The propensity analyzer114can perform statistical analyses, such as analyses based on regression, linear regression, decision tree modeling, and/or other statistical analysis techniques, to determine propensities associated with different user paths1202to drive users to engage in the target user activity. For example, the propensity analyzer114can use a logistic regression model to identify variables that lead to responses, as described above. The propensity analyzer114can accordingly identify and/or estimate the contributions of individual variables, such as individual digital channels110, digital systems1204, and/or non-digital systems1206, toward users performing, or not performing, the target user activity in association with different user paths1202. Additionally, as discussed above, in some examples the propensity analyzer114can use one or more selection techniques to determine which variables, from a set of variables, are not contributing to the target user activity, and omit those variables from consideration within the model used by the propensity analyzer114.

The propensity analyzer114can accordingly generate and/or output user path propensity data1212that indicates determined propensities associated with entire user paths1202, and/or digital channels110, digital systems1204, and/or non-digital systems1206associated with user paths1202. The user path propensity data1212can be similar to the website propensity data118discussed above.

The user path propensity data1212generated by the propensity analyzer114may be used to prioritize resources associated with the user paths1202. For instance, if the user path propensity data1212indicates that one user path is more likely than another user path to lead to instances of a target user activity, an operator associated with the user paths1202may choose to allocate higher budgets to, and/or assign more developers to work on, the user path with the higher propensity to drive users to engage in the target user activity. Alternatively, or in addition, the operator may use the user path propensity data1212to identify lower-propensity user paths1202, and allocate resources to improve the propensities of the lower-propensity user paths1202to drive users to engage in the target user activity.

In some examples, the user path propensity data1212generated by the propensity analyzer114can include recommendations for revising one or more digital channels110, digital systems1204, and/or non-digital systems1206associated with one or more user paths1202, based on relative differences between determined propensities associated with different user paths1202. For instance, if the user path propensity data1212indicates that a first group of users who followed a first user path were more likely to engage in a target user activity than a second group of users who followed a second user path, attributes of the second user path can be revised based on aspects of the first user path with the higher propensity to drive the target user activity.

As a first example, if the user path propensity data1212indicates that users are more likely to engage in a target user activity via a type of non-digital system, such as a phone call with an agent, than via the website104, the user path propensity data1212may recommend revising one or more digital channels110, such as display advertisements204, email advertisements206, SEM210, and/or other digital channels110, to include content that presents phone numbers of agents instead of, or in addition to, links to the website104. In this example, the user path propensity data1212may also, or alternately, recommend revising the content of one or more web pages106of the website104to include the agent phone numbers and/or messages recommending that, or instructing, the user to call the agent phone numbers.

As a second example, the user path propensity data1212may instead indicate that users are more likely to engage in a target user activity via a type of digital system, such as the website104, than via a non-digital system such as a phone call with an agent. Accordingly, the user path propensity data1212may recommend implementing a program wherein if an agent has been speaking to a user, but the user has not engaged in a target user activity via the agent, the agent send follow-up email advertisements206to the user that include links to the website104. Accordingly, the user may be more likely to follow a website link in a follow-up email and engage in the target user activity via the website, relative to the likelihood of the user engaging in the target user activity during follow-up calls with the agent.

In some examples, such revision recommendations, or draft revisions, may be generated using the machine learning and/or artificial intelligence techniques discussed above. Additionally, similar to revisions discussed above, designers can choose to accept or implement such automatically-generated revision recommendations by revising the digital channels110, digital systems1204and/or non-digital systems1206, or by reviewing and/or approving automatically-generated revisions to the digital channels110, digital systems1204and/or non-digital systems1206.

In some examples, the user path propensity data1212may recommend certain user paths1202to be used with respect to certain categories of users. For example, the user path activity data1210may be associated with, or correlated with, cookie data, user profile data, and/or other types of data that may indicate ages, genders, locations, customer histories, web browsing histories, and/or other information associated with users. The propensity analyzer114may use such user path activity data1210to determine relative propensities of different user paths1202to drive instances of a target user activity in association with different categories of users. Accordingly, the user path propensity data1212generated by the propensity analyzer114based on such user path activity data1210can indicate which user paths1202have higher propensities to drive the target user activity with respect to users in particular user categories.

As an example, the user path propensity data1212may indicate that users who are younger than a threshold age may be most likely to engage in a target user activity via a website, while other users who are older than the threshold age may be most likely to engage in the target user activity via a call with an agent. Accordingly, if an age of a first user, such as a prospective customer can be determined or estimated from cookie data based on a previously-stored user profile, based on preliminary contact information provided by the first user, based on web browsing history, and/or based on any other information, indicates that the first user is younger than the threshold age and may thus be more likely to engage in the target user activity via the website than via an agent call, search results and/or web pages of the website can be provided or recommended to the first user that highlight options to engage in the target user activity via the website. However, if the age of a second user is similarly determined or estimated to be older than the threshold age, such that the second user may thus be more likely to engage in the target user activity via an agent call than via the website, search results and/or web pages of the website can be provided or recommended to the second user that highlight phone numbers for calling agents. Accordingly, in this example, different search results may be displayed to the two users, and/or if both users access the website104, the server102may provide first web pages106to the first user that highlight online options for engaging in the target user activity, but provide second web pages106to the second user that highlight agent phone numbers instead of or in addition to the online options for engaging in the target user activity.

FIG.13shows a flowchart of an example method1300for determining propensities of user paths1202to drive a target user activity. At block1302, the propensity analyzer114can receive user path activity data1210. The user path activity data1210may indicate which user paths1202have been used by users, including which digital channels110, digital systems1204, and/or non-digital systems1206have been used by users, and whether the users engaged in the target user activity via the corresponding user paths1202.

At block1304, the propensity analyzer114can use the user path activity data1210to identify which user paths1202were used by users over a period of time. As discussed above, different users may use different user paths1202, such as user paths1202associated with different combinations of digital channels110, digital system1204, and/or non-digital systems1206. The propensity analyzer114can be configured to determine, from referring page identifiers, cookie data, agent notes, and/or other information in the user path activity data1210, which user paths1202have been used by users, and/or how often such user paths1202have been used by users. At block1306, the propensity analyzer114can also use the user path activity data1210to identify instances of the target user activity that occurred in association with the user paths1202identified at block1304.

At block1308, the propensity analyzer114can perform a statistical analysis on the data identified during block1304and block1306to generate estimates of the propensities of individual user paths1202, and/or individual digital channels110, digital system1204, and/or non-digital systems1206associated with the individual user paths1202, to drive the target user activity. In some examples, the data captured and tracked during blocks1302through1306can be stored in a database or other repository over a period of time, such as a day, a week, a month, or any other time period, and a set of data collected over the time period can later be analyzed at block1308.

As discussed above, the statistical analysis can be based on regression, linear regression, decision tree modeling, and/or other statistical analysis techniques. As a non-limiting example, the statistical analysis performed during block1308can be based on a logistic regression model that uses different user paths1202and/or corresponding digital channels110, digital system1204, and/or non-digital systems1206as variables, and identifies contributions of different variables to a response associated with the target user activity occurring. Accordingly, the statistically determined estimates of contribution values produced during block1308can indicate individual and/or relative propensities of different user paths1202and/or corresponding digital channels110, digital system1204, and/or non-digital systems1206to drive users to perform the target user activity.

In some examples, the statistical analysis performed during block1308can use a logistic regression model, such as a linear logistic model, that identifies variables that lead to responses. This model can be the same or a similar model described above with respect toFIG.1, such as a logistic regression model that uses forward selection, backward elimination, or stepwise selection. The variables considered by the logistic regression model can include variables associated with the user paths1202, such as identifiers of entire user paths1202and/or identifiers of digital channels110, digital system1204, and/or non-digital systems1206associated with entire user paths1202. Overall, at block1308, the statistical analysis can identify or estimate the contributions of individual variables, such as individual types of digital channels110, digital system1204, and/or non-digital systems1206associated with user paths1202, toward users performing or not performing the target user activity. Accordingly, the statistically determined estimates of contribution values produced during block1308can indicate which user paths1202, or elements of such user paths1202, are most likely to drive users to perform the target user activity.

At block1310, the propensity analyzer114can output user path propensity data1212associated with the user paths1202. For example, the user path propensity data1212can identify at least some of the individual and/or relative propensities of different user paths1202and/or components of the user paths1202, determined at block1308, to drive users to perform the target user activity. In some examples, the user path propensity data1212output at block1310may include recommendations for revising one or more aspects of a first user path based on attributes of one or more other higher-propensity user paths1202, which may be predicted to increase the propensity of the first user path to drive users to engage in the target user activity.

Overall, the process ofFIG.13can determine relative propensities of different user paths1202, and/or digital channels110, digital system1204, and/or non-digital systems1206associated with the user paths1202, to drive the target user activity. In some examples, the user path activity data1210received at block1302may indicate user profile data, cookie data, and/or other information indicating activities of one or more categories of users, such as users in different age groups, different locations, and/or other types of categories. Accordingly, in some examples, the propensity analyzer114may identify user path activity data1210associated with one or more categories of users, and separately analyze the user path activity data1210associated with different categories of users during the process ofFIG.4. Accordingly, the user path propensity data1212output at block1310may indicate that different categories of users may have different propensities to engage in the target user activity via the same user path, and/or may indicate relative propensities of different user paths1202in association with different categories of users.

FIG.14shows a flowchart of an example method1400for generating user path recommendations in association with a particular user, based determined propensities of user paths1202to drive a target user activity. At block1402, the propensity analyzer114can determine relative propensities of at least two user paths1202to drive the target user activity, in association with different user categories. For example, the propensity analyzer114can use the method described above with respect toFIG.13to determine relative propensities of user paths1202to drive the target user activity, in association with different user categories such as age categories, gender categories, location categories, and/or other categories.

At block1404, the propensity analyzer114can determine a user category associated with a particular user. The particular user can, in some examples, be a prospective customer of a company associated with the user paths1202. The propensity analyzer114can use cookie data indicative of web browsing history, user profile data, information about the user provided by one or more digital systems1204and/or non-digital systems1206, and/or any other information to classify the particular user into one of the user categories, for instance by determining or estimating attributes of the user and identifying one of the user categories that corresponds to the attributes of the user.

At block1406, the propensity analyzer114can select one of the user paths1202that corresponds to user path with the highest propensity, determined at block1402, to drive the target user activity in association with the user category determined at block1404. For example, if the propensity analyzer114determined or estimated the age of the particular user, and the age of the particular use is associated with a particular user category, the propensity analyzer114can select the user path that has the highest propensity, among users of that particular user category, to lead to instances of the target user activity.

At block1408, the propensity analyzer114can recommend the user path, selected at block1406, for subsequent interactions with the particular user. For example, if the user has arrived at the website104, but the user path recommended by the propensity analyzer114includes a non-digital system, such as a phone call with an agent, the propensity analyzer114can cause the server102to serve a first web page of the website104that recommends that the user call a displayed phone number for the agent, such that the agent can assist the user with engaging in the target user activity via a phone call. However, if the user path recommended by the propensity analyzer114instead includes the website, the propensity analyzer114can cause the server102to serve a second web page of the website104that includes online options for the user to engage in the target user activity.

FIG.15depicts an example system architecture for a computing device1500in accordance with various examples. The computing device1500can include one or more servers, computers, or other computing elements. In some examples, the computing device1500can be the server102that hosts the website104. However, in other examples, the computing device1500can be one or more separate computing devices that receive the website activity data116associated with the website104. In still other examples, the computing device1500can be one or more computing devices associated with one or more elements of user paths1202, such as user paths1202associated with combinations of digital channels110, digital systems1204, and/or non-digital systems1206, and/or that can receive corresponding user path activity data1210.

As shown, the computing device1500can have memory1502storing the website activity data116, the user path activity data1210, the propensity analyzer114, and/or other modules and data1504. The propensity analyzer114can be configured to perform the statistical analysis, machine learning, and/or other computations described above to, based on the website activity data116, determine propensities of digital channels and/or web pages106to drive users to perform a target user activity during visits to the website104. The propensity analyzer114can also, or alternately, be configured to perform the statistical analysis, machine learning, and/or other computations described above to, based on the user path activity data1210, determine propensities of user paths1202to drive users to perform the target user activity. The other modules and data1504can be utilized by the computing device1500to perform or enable performing any action taken by the computing device1500. The modules and data1504can include a platform, operating system, and applications, and data utilized by the platform, operating system, and applications. The computing device1500can also have processor(s)1506, communication interfaces1508, a display1510, output devices1512, input devices1514, and/or a drive unit1516including a machine readable medium1518.

In some examples, elements of the computing device1500shown inFIG.15can be distributed among multiple computing devices1500. For example, a first computing device1500can be a web server that stores data associated with the web pages106for the website104and/or the website activity data116, while a second computing device1500is a separate server, workstation, laptop computer, or other computing device that executes the propensity analyzer114.

In various examples, the memory1502can include system memory, which may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.) or some combination of the two. The memory1502can further include non-transitory computer-readable media, such as volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. System memory, removable storage, and non-removable storage are all examples of non-transitory computer-readable media. Examples of non-transitory computer-readable media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium which can be used to store the desired information and which can be accessed by the computing device1500. Any such non-transitory computer-readable media may be part of the computing device1500.

In various examples, the processor(s)1506can be a central processing unit (CPU), a graphics processing unit (GPU), or both CPU and GPU, or any other type of processing unit. Each of the one or more processor(s)1506may have numerous arithmetic logic units (ALUs) that perform arithmetic and logical operations, as well as one or more control units (CUs) that extract instructions and stored content from processor cache memory, and then executes these instructions by calling on the ALUs, as necessary, during program execution. The processor(s)1506may also be responsible for executing all computer applications stored in the memory1502, which can be associated with common types of volatile (RAM) and/or nonvolatile (ROM) memory.

The communication interfaces1508can include transceivers, modems, interfaces, antennas, and/or other components that can transmit and/or receive data over networks or other data connections.

The display1510can be a liquid crystal display or any other type of display commonly used in computing devices1500. For example, a display1510may be a touch-sensitive display screen, and can then also act as an input device or keypad, such as for providing a soft-key keyboard, navigation buttons, or any other type of input. The output devices1512can include any sort of output devices known in the art, such as a display1510, speakers, a vibrating mechanism, and/or a tactile feedback mechanism. Output devices1512can also include ports for one or more peripheral devices, such as headphones, peripheral speakers, and/or a peripheral display. The input devices1514can include any sort of input devices known in the art. For example, input devices1514can include a microphone, a keyboard/keypad, and/or a touch-sensitive display, such as the touch-sensitive display screen described above. A keyboard/keypad can be a push button numeric dialing pad, a multi-key keyboard, or one or more other types of keys or buttons, and can also include a joystick-like controller, designated navigation buttons, or any other type of input mechanism.

The machine readable medium1518can store one or more sets of instructions, such as software or firmware, that embodies any one or more of the methodologies or functions described herein. The instructions can also reside, completely or at least partially, within the memory1502, processor(s)1506, and/or communication interface(s)1508during execution thereof by the computing device1500. The memory1502and the processor(s)1506also can constitute machine readable media1518.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example embodiments.