Determining propensities to drive website target user activity

One or more computing devices, comprising:

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

One or more non-transitory computer-readable media storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations, comprising:

• receiving activity data indicative of use of a set of user paths by users, wherein the activity data indicates user classifications of the users, and individual user paths, of the set of user paths, comprise at least one of:
• one or more digital channels,
• one or more digital systems, or
• one or more non-digital systems;
• identifying, based on the activity data, instances of a target user activity occurring in association with the individual user paths and individual user classifications of the user classifications;
• determining, based on the activity data, propensities of the individual user paths to drive the target user activity, in association with the individual user classifications;
• determining a particular user classification associated with a particular user;
• selecting a particular user path, of the set of user paths, associated with a highest propensity, of the propensities, to drive the target user activity in association with the particular user classification;
• and
• recommending usage of the particular user path in association with the particular user.