Patent Application
20220335448
In recent years, data-analytics systems have been developed to track and report on user experiences as users progress along a path of interactions with a company or other entity. Along such an interaction path, users of a system often interact with companies, product providers, or other entities over a period leading up to, including, or after an experience and/or purchase. For example, users can interact with companies and/or product providers by calling over the phone, visiting a website, visiting a location in-person, or a combination of any such interactions. In addition to user-driven experiences, companies and providers can use computing systems to reach out to users and potential users via phone calls, emails, and other forms of communication.
To visualize a user-interaction path, some conventional data-analytics systems can generate a Sankey diagram or other graph that conventionally represents one or more patterns of user interactions along a user-interaction path and provide supplementary information about such user interactions on separate interfaces. Although conventional data-analytics systems can track user interactions and generate graphs for user-interaction paths, conventional systems often demonstrate a number of technical problems that hinder user navigation among graphical user interfaces, limit visualizations of a user-interaction path to a static model, and limit graphs of user-interaction paths to inflexible options and dimensions. For example, conventional systems often generate Sankey diagrams or other graphs of user-interaction paths (along with separate and supplementary information) about user interactions that foment inefficient user navigation among graphical user interfaces. To illustrate, some conventional systems require excessive interactions to switch between a graph and underlying data on separate graphical user interfaces. For instance, some conventional systems generate a static graph of user interaction paths separately from a graphical user interface with data about user interactions. Further, large datasets compound the tedious back-and-forth user navigation between graphical user interfaces because graphs may include a growing number of static nodes to represent such large datasets and have a corresponding (albeit separate and disconnected) data sheet. In such conventional data-analytics interfaces, users must switch back and forth between data sheets reporting on large datasets and graphs depicting many user interactions.
In addition to inefficient user navigation, many conventional data-analytics systems utilize a static model to generate Sankey diagrams or other graphs depicting a path of user interactions. To illustrate, many conventional systems utilize a static graph only showing user interactions—without visualization of time or variations among users along a user-interaction path. More specifically, conventional systems generate graphs without representing how particular users correspond to interactions represented by nodes (or other icons) within the graph. Accordingly, such conventional systems require additional user navigation away from the graph to determine data regarding users of the conventional systems.
Beyond a static model, conventional data-analytics systems often lack flexibility in presenting a user-interaction path. For instance, many conventional systems utilize inflexible templates that lack options for customization, such as pre-set nodes that cannot be adjusted or modified. Further, some conventional systems rigidly limit the type of visualizations of data to a single dimension, such as a static graph showing possible user interactions along a user-interaction path. To illustrate, conventional systems often include limited options for visualization types of interactions and data regarding the interactions. Additionally, as mentioned, many conventional systems do not provide any options for visualizations of users corresponding to the interactions.
This disclosure describes embodiments of systems, non-transitory computer-readable media, and methods that solve one or more of the foregoing problems or provide other benefits in the art. For example, the disclosed systems generate a dynamic journey graph that represents various activities experienced by users along a user-segment journey by displaying nodes with visual indicators that report portions of such users who experienced the various activities. To illustrate, in one or more embodiments, the disclosed systems receive datasets representing activities experienced by users from a user segment during a user-segment journey. The disclosed systems further determine different portions of users from the user segment that experienced particular activities, such as events or actions experienced by particular portions of the user segment. Based on determining different user portions that experienced different activities, the disclosed systems generate a journey graph including nodes with visual indicators reporting the different portions of users corresponding to particular activities.
Additional features and advantages of one or more embodiments of the present disclosure are outlined in the description which follows, and in part will be obvious from the description, or may be learned by the practice of such example embodiments.
The detailed description describes one or more embodiments of the disclosed systems, non-transitory computer-readable media, and methods with additional detail through the use of the accompanying drawings, as briefly described below.
This disclosure describes one or more embodiments of a journey graphing system that generates and dynamically updates a journey graph including various activity nodes that report portions of users that experienced various activities during a user-segment journey. To illustrate, in some embodiments, the journey graphing system receives activity datasets representing activities experienced by users from a user segment during a user-segment journey. Having received such activity datasets, the journey graphing system determines portions of users that experienced particular activities, such as users who experienced events and/or actions performed for users during the user-segment journey. The journey graphing system further maps particular portions of users to activity nodes. In some such cases, the journey graphing system determines percentages or other portions of a total user pool that experience an activity represented by a node. Based on those mappings, the journey graphing system generates a journey graph including activity nodes with visual indicators reporting portions of users that experienced various activities represented by the activity nodes.
As mentioned, in one or more embodiments, the journey graphing system receives activity datasets from a variety of sources. To illustrate, in some embodiments, the journey graphing system receives activity datasets from user devices, administrator devices, and/or third-party devices. In one or more embodiments, the activity datasets include event datasets corresponding to events carried out or otherwise experienced by users. For example, in some embodiments, activity datasets include action datasets representing actions performed for users from a user segment. Further, in one or more embodiments, activity datasets include event datasets representing events experienced by users (e.g., events carried out or executed by users) from the user segment. Additionally, in one or more embodiments, the activity datasets include condition datasets corresponding to users' satisfaction or failure to satisfy journey-entry conditions.
After receiving or accessing such datasets, in some embodiments, the journey graphing system determines portions of users that experienced certain activities—and have proceeded from one activity to another activity—during a user-segment journey. In one or more embodiments, for instance, the journey graphing system determines portions of users from a segment of users that experienced an activity as of a particular time period. To illustrate, in some embodiments, the journey graphing system continuously monitors and determines portions and/or subsets of users based on various activity datasets. In one or more embodiments, the journey graphing system tracks the actions, events, conditions, etc. performed and/or experienced by users to determine which users from the user segment have experienced particular activities as of a particular time and to determine the flow of users between activities.
After determining portions of users that experienced particular activities, in some embodiments, the journey graphing system maps the determined portions of users to nodes of a journey graph. To illustrate, in one or more embodiments, the journey graphing system determines nodes based on administrator creation of a journey graph. To illustrate, the journey graphing system maps a first portion of users that experienced an event to an event node. Further, the journey graphing system maps a second portion of users that experienced an action to an action node.
Having mapped particular portions of users from a user segment to activity nodes, the journey graphing system generates the journey graph. In some cases, the journey graph includes customized nodes selected and/or generated by an administrator device. As noted above, the journey graphing system generates each of the activity nodes including a visual indicator reporting the different portions of users that experienced different activities represented by the various activity nodes. More specifically, in one or more embodiments, the journey graphing system generates the activity nodes including visual indicators reporting a percentage of users for which an activity was successfully executed and/or identified. In some embodiments, the journey graphing system utilizes activity datasets to render the visual indicators within the journey graph. As explained further below and depicted in various figures, the journey graphing system can render the journey graph in real time (or near-real time) with dynamic reporting features based on selection of a reporting option.
As noted above, in one or more embodiments, the journey graphing system generates action nodes and event nodes as different types of activity nodes within a journey graph. In some embodiments, action nodes correspond to actions taken by a system or other entity for a user. For example, an action node can represent an email, an instant message, or a notification sent by a system or various other actions performed by a system. By contrast, in one or more embodiments, event nodes correspond to events completed and/or undertaken by a user of a user segment (e.g., for a system). For example, event nodes represent users checking in at a venue, presenting a ticket for entry, moving within a geofence, or other events undertaken by a user. In some embodiments, the journey graphing system detects actions and/or events from a user device and/or from administrator or third-party devices.
In addition to action nodes or event nodes, in some embodiments, the journey graphing system also generates or utilizes condition nodes in a journey graph. A condition node can represent a journey-entry condition that users from a user segment have satisfied (or not satisfied) for entering a user-segment journey. For instance, the journey graphing system can receive condition datasets corresponding to a variety of types of journey-entry conditions for users to begin or be included within a user-segment journey. Accordingly, the journey graphing system can receive and utilize data corresponding to condition nodes that correspond to a variety of condition types. For example, the journey graphing system can utilize condition nodes representing journey-entry conditions such as a membership status, progress toward a goal, pass or fail status, etc.
When generating or updating a journey graph, in one or more embodiments, the journey graphing system also generates edges between activity nodes representing users flowing between the activity nodes. As mentioned above, in one or more embodiments, the journey graphing system determines portions of users that performed different activities or moved from one activity to another represented by activity nodes. In some embodiments, for instance, the journey graphing system determines the portion of users that performed or experienced a particular activity represented by an activity node out of the total number of users in a user segment. Alternatively, the journey graphing system determines the portion of users that both successfully experienced a preceding activity represented by a preceding node and successfully experienced a subsequent activity represented by the subsequent node. In one or more embodiments, the journey graphing system generates edges between nodes with an edge thickness representing the determined portion of users or the users from the user segment targeted (or that have moved closer) to an activity represented by a subsequent node.
As suggested above, in some embodiments, the journey graphing system dynamically updates a journey graph. In one or more embodiments, the journey graphing system initializes a journey graph after creation in a pre-render state. Upon receiving user selection of a reporting option at an administrator device, the administrator device renders the journey graph by rendering activity nodes with visual indicators reporting in real (or near-real) time different portions of users that experienced different activities corresponding to the activity nodes. Additionally, in one or more embodiments, the journey graphing system provides continuous updates to various datasets to the administrator device and determines value changes corresponding to the various nodes. Accordingly, in some embodiments, the administrator device renders the value changes for nodes in a journey graph in real time or near-real time.
The journey graphing system provides many advantages and benefits over conventional systems and methods. For example, by consolidating real time reporting and journey data into a single graphical user interface, the journey graphing system improves navigational efficiency relative to conventional data-analytics systems. Specifically, the journey graphing system generates and provides a unified graphical user interface including a dynamic journey graph that comprises nodes including both journey information and user information corresponding to various activities along a user-segment journey. More specifically, in one or more embodiments, the journey graphing system generates nodes with visual indicators that dynamically report different portions of users that experienced different activities (e.g., actions, events) represented by activity nodes (e.g., action nodes, event nodes). As noted above, conventional data-analytics systems often display separate graphical user interfaces for static conventional graphs showing a user-interaction path and for statistics reporting user data for user interactions relevant to the user-interaction path. By contrast, the journey graphing system generates a journey map that represents the flow of users from a user segment (e.g., different portions of users) between activities represented by the activity nodes. Further, the journey graphing system reduces or eliminates excessive interactions required by conventional data-analytics systems to locate user data by providing reporting options and segmentation options within the unified graphical user interface.
Beyond improved user navigation, in some embodiments, the journey graphing system improves computing efficiency over conventional data-analytics systems by utilizing an integrated node-and-edge model. Instead of utilizing multiple models for generating a separate and static journey graph, a separate Sankey diagram, and separate graphics showing statistics for particular users performing user interactions along a user-interaction path, the journey graphing system utilizes an integrated node-and-edge model to generate the disclosed journey graph comprising nodes with visual indicators that integrate reporting features. Accordingly, the journey graphing system conserves computing resources over conventional systems by reducing at least three separate computing models into a unified computing model. As explained further below with respect to
In addition to improved navigation efficiency or improved algorithm efficiency, the journey graphing system improves efficiency relative to conventional systems by providing dynamic reporting of user data within journey graphs in real time or near-real time. More specifically, the journey graphing system updates the visual indicators of activity in real-time to indicate updated datasets. For example, the journey graphing system updates the visual indicator of an event node based on receiving an updated events dataset and determining an updated portion of users. Further, the journey graphing system updates the visual indicator of an action node based on receiving an updated action dataset and determining an updated portion of users. In one or more embodiments, the journey graphing system continuously updates the visual indicators based on a selection of a reporting option.
The journey graphing system also improves flexibility relative to conventional data-analytics systems by including various interaction options within the unified graphical user interface. To illustrate, the journey graphing system provides interactive options to view graphical representations of sub-segments from within portions of a user segment corresponding to activity nodes. More specifically, the journey graphing system provides options within the journey graph itself to view visual indicators corresponding to various types of user characteristics. For example, the journey graphing system can provide options for presenting visual indicators corresponding to user sub-segments for demographic information, user interactions, user device type, etc. Additionally, in some embodiments, the journey graphing system also adjusts and changes the journey graph by adding or deleting specific nodes as selected by an administrator device. Thus, the journey graphing system reduces or eliminates excessive interactions required by conventional systems to page back and forth to view such user information.
As illustrated by the foregoing discussion, the present disclosure utilizes a variety of terms to describe features and advantages of the journey graphing system. Additional detail is now provided regarding the meaning of such terms. For example, as used herein, the term “user-segment journey” refers to an organized set of activities experienced by some or all users of a user segment during interactions with an entity. To illustrate, a user-segment journey can include an ordered set of conditions, events, and/or actions encountered, performed, and/or experienced by a user from a user segment during a user experience with the products or services of a company, an organization, or another entity. Such conditions, events, and/or actions may be recorded or tracked by computing devices of an entity or the user.
Additionally, as used herein, the term “journey graph” refers to a graphical representation of user interactions during a user-segment journey. In particular, a journey graph can include a graphical representation of a user-segment journey including a node corresponding to each of a variety of activities in the user-segment journey. To illustrate, a journey graph can include activity nodes (e.g., event nodes, action nodes), condition nodes, wait nodes, end nodes, edges, percentage indicators, sub-segmentation options, etc.
Also, as used herein, the term “activity” refers to an interaction between a user of a user segment and one or more systems corresponding to an entity. In particular, the term activity can include a variety of actions or events during a user-segment journey as a user interacts with products or services through various computing devices. To illustrate, an activity can include an event, an action, a condition and/or other points of interaction for a user during a user-segment journey.
Additionally, as used herein, the term “event” refers to a user interaction experienced by a user during a user-segment journey. In some cases, an event includes an activity performed directly or indirectly by a user or the user's computing device during a user-segment journey. For example, an event can include a user interaction within a graphical user interface, entry into an amusement park or ride of an amusement park detected by a sensor or by a computing device, a location traveled to by a computing device as detected or indicated by a Global Positioning System (GPS) unit, Bluetooth interactions from a user's computing device, a location or movement indicted by Near Field Communication (NFC) antenna or coil or Radio-Frequency Identification (RFID) tag, etc.
Further, as used herein, the term “action” refers to an action performed for a user during a user-segment journey. In some cases, an action includes an activity performed directly or indirectly by an entity, an entity's system, or an entity's computing device for a user during a user-segment journey. For example, an action can include a computing system of an entity sending an email or other message to a user; modifying user data or status for a user; generating, modifying, or cancelling a user reservation for a user, etc.
Additionally, as used herein, the term “activity dataset” refers to various data and metadata corresponding to an activity or set of activities experienced by users from a user segment. For example, in some cases, an activity dataset includes an events dataset representing a set of events experienced by users from a user segment during a user-segment journey or an action dataset representing a set of actions performed for the users from the user segment during the user-segment journey. In one or more embodiments, an activity dataset includes data and metadata for a portion of users corresponding to an activity, including user identifiers, user characteristic data, data corresponding to user instances of the activity, etc.
Also, as used herein, the term “user segment” refers to a set of users corresponding to a common or shared attribute or characteristic. For example, a user segment can include a set of users corresponding to a common or shared journey or set of user interactions with respect to an entity (e.g., company, application, website, store). In one or more embodiments, a user segment can further include sub-segments of users based on a variety of criteria, including particular user characteristics.
Further, as used herein, the term “journey-entry condition” refers to an activity, a rule, and/or a characteristic that define entrance or inclusion within a user-segment journey. For example, a journey-entry condition can include a common user interaction or activity defining a user segment that begins or enters a journey with respect to an entity (e.g., company, application, web site, store).
Also, as used herein, the term “activity node” refers to a graphical-user-interface element for a connecting point at which an edge of a graph ends or edges of the graph intersect. To illustrate, an activity node can include a circular graphical-user-interface element at which edges of a journey graph end or intersect. In particular, the term node can include a portion of a journey graph corresponding to a user interaction with a system. To illustrate, a node can include a graphical representation corresponding to an activity (e.g., an event, action, condition) in a journey graph.
Additionally, as used herein, the term “visual indicator” refers to a graphical-user-interface element that graphically indicates a portion of users from a user segment that experienced an activity. In particular, a visual indicator can graphically indicate within or around an activity node (within a journey graph) a portion (e.g., percentage) of users from a user segment that experienced an activity represented by the activity node. To illustrate, a visual indicator can include a circumference indicator, pie chart, multiple line bars, collections of shapes, etc.
Further, as used herein, the term “edge” refers to a connection or vertices between nodes of a nodal graph. To illustrate, an edge can include a graphical line segment that connects nodes in a journey graph.
Additionally, as used herein, the term “reporting option” refers to a graphical-user-interface element that (upon selection or other user interaction by a computing device) causes a rendering or update of a journey graph with dynamic reporting features. In particular, a reporting option can include graphical-user-interface elements, such as a button, a toggle, a switch, etc., for initiating reporting features of visual indicators corresponding to activity nodes. To illustrate, in one or more embodiments, the journey graphing system renders and continuously updates a journey graph comprising activity nodes with visual indicators reporting in real (or near-real) time different portions of users that experienced different activities corresponding to the activity nodes—based on receiving user selection of a reporting option.
Additional detail regarding the journey graphing system will now be provided with reference to the figures. For example,
As shown in
As further shown in
As further shown in
As mentioned, the system 100 also includes the user devices 112a-112n. The user devices 112a-112n can include a variety of computing devices, including a smartphone, a tablet, a smart television, a desktop computer, a laptop computer, a virtual reality device, an augmented reality device, or another computing device as described in relation to
The system 100 also includes the administrator device 114. In one or more embodiments, the administrator device 114 includes the analytics application 116 and the journey graphing application 118. The administrator device can include a variety of computing devices, including a smartphone, a tablet, a smart television, a desktop computer, a laptop computer, a virtual reality device, an augmented reality device, or another computing device as described in relation to
In one or more embodiments, the administrator device 114 renders journey graphs utilizing the analytics application 116 and/or the journey graphing application 118. In some embodiments, the administrator device 114 utilizes the analytics application 116 to collect, manage, and analyze various datasets. Additionally, in one or more embodiments, the administrator device 114 utilizes the journey graphing application 118 to generate, render, and dynamically update journey graphs. The analytics application 116 and/or the journey graphing application 118 can include a native application and/or a web application on the administrator device 114. Although
As mentioned above, in one or more embodiments, the journey graphing system 106 generates a journey graph including various activity nodes.
As also shown in
Further, as shown in
As illustrated in
Additionally, as shown in
As further shown in
Based on the foregoing mappings, the journey graphing system 106 can also perform an act 216 of generating a journey graph for the user-segment journey including the event node with a first visual indicator and the action node with a second visual indicator. More specifically, the journey graphing system 106 generates a journey graph consistent with administrator input organizing various activity nodes into the journey graph representing a user-segment journey. In some embodiments, the journey graphing system 106 generates the journey graph based on the user-segment journey and receiving activity datasets (e.g., action datasets and event datasets) corresponding to activities within the user-segment journey.
As shown in
In
As further shown in
As further shown in
As also shown in
As shown in
As further shown in
As shown in
Turning to the lower branch, the computing device 300 presents the action node 312 as part of the lower branch of the journey graph 301. The action node 312 is labelled “Welcome Email.” Additionally, the action node 312 includes the percentage text “99.5%” and a corresponding visual indicator (here, a circumference indicator) reflecting 99.5%—where both the percentage text and circumference indicator indicate that a portion of users who satisfy the journey-entry condition for the condition node 306 (and are selected or moved along activities for the lower branch) have successfully experienced an activity represented by an action node 312. In particular, both the percentage text and the circumference indicator indicate that, for users from the user segment that are targeted for the welcome email corresponding to the action node 312, an entity or an entity's system successfully performed the action for such users 99.5% of the time.
In one or more embodiments, the analytics system 104 or a third-party system provides an email or another message to users of the user segment as part of an event. A message sent as part of an event, including the welcome email of the action node 312, can include a variety of text and/or various multimedia selected by an administrator and/or the third-party system. For example, the welcome email can include information about various available digital achievements at a venue based on the date and/or time that the event is triggered.
As further shown in
As just indicated, the end node 318 is labelled “End.” Further, the end node 318 includes the number “610,” which indicates that 610 of the 3000 users that entered into the user-segment journey have proceeded to the end node 318 at the time of rendering the journey graph 301. Similarly, the computing device 300 presents the end node 342 labelled “End” and including the number “825,” indicating that 825 of the 3000 users that entered into the user-segment journey have proceeded to the end node 342 at the time of rendering.
In one or more embodiments, the computing device 300 determines users that have proceeded to an end node based on an end condition, such as leaving the venue. To illustrate, the computing device 300 can receive an end dataset corresponding to users relating to an end condition. Accordingly, the computing device 300 determines users who proceed to the end node based on the received end dataset. Additionally, or in the alternative, the computing device 300 automatically determines that a user proceeds to an end node after execution of a preceding node.
Turning to the middle branch, as also illustrated in
As mentioned above, an action can include sending a variety of customized emails and other messages. Additionally, in one or more embodiments, an action message can include user-specific data from action datasets. For example, the progress email associated with the action node 320 can include a user-specific proportion of progress made toward the digital achievement.
As further shown in
As indicated above, the analytics system 104 can receive event datasets including data relating to a variety of progression events. For example, an event dataset can include user interactions with a graphical user interface on a user device. Additionally, or in the alternative, an event dataset can include various other user interactions, such as GPS, RFID, NFC, and/or Bluetooth interactions on a user device.
As also shown in
Turning to the upper branch, as further shown in
Additionally, as shown in
As also shown in
As mentioned above, in one or more embodiments, the journey graphing system 106 renders and updates a journey graph in real time or near-real time.
As shown by the static-journey-graph rendering 401 in
As also shown by the static-journey-graph rendering 401 in
As indicated above, in some embodiments, the journey graphing system 104 adds (or removes) activity nodes to (or from) a journey graph based on user interactions with an administrator device. As shown by the static-journey-graph rendering 401 in
In one or more embodiments, the journey graphing system 106 provides options to add various nodes to the journey graph. For example, the journey graphing system 106 provides options for node types, such as options to select an event node, an action node, a condition node, or an end node. Further, the journey graphing system 106 provides options for associating the node with a particular activity. For example, in one or more embodiments, the journey graphing system 106 provides a collapsible menu including a variety of categories of activity for selection of a particular activity by an administrator computing device.
As further shown by the static-journey-graph rendering 401 in
As shown by the dynamic-journey-graph rendering 403a in
In some embodiments, the computing device 400 renders circumference indicators within the condition node 406, the action nodes 408a-408b, and the new activity node 409 increasing in size until each circumference indicator indicate the corresponding percentage shown within each of the condition node 406, the action nodes 408a-408b, and the new activity node 409. For example, the computing device 400 increases the length of the circumference indicators at predetermined increments and/or at a predetermined speed. That is, the computing device renders the nodes within the graphical user interface with the circumference indicators growing until they proportionally represent the determined percentage value shown in the dynamic-journey-graph rendering 403a.
As further shown by the dynamic-journey-graph rendering 403a in
In addition to such visual indicators, the computing device 400 renders edges within the journey graph 402 in dynamic form to indicate a percentage of users in a user segment targeted for an activity represented by a following activity node or to indicate a percentage of users in the user segment represented by the following activity node. In particular, the computing device 400 renders the edge thicknesses of the edges between the condition node 406 and the action nodes 408a-408b to reflect either (i) percentages of users from a user segment targeted for actions represented by the actions nodes 408a and 408b, respectively, or (ii) a percentages of users from the user segment that satisfy the journey-entry condition represented by the condition node 406 and that have experienced the actions represented by the actions nodes 408a and 408b, respectively. Similarly, the computing device 400 renders the edge thickness of the edge between the action node 408a and the new activity node 409 to reflect either (i) a percentage of users from a user segment targeted for an activity represented by the new activity node 409, respectively, or (ii) a percentage of users from the user segment that have experienced the action represented by the actions node 408a and experienced the activity represented by the new activity node 409.
As shown by the dynamic-journey-graph rendering 403b in
Thus, as shown by the transition between the dynamic-journey-graph rendering 403a and the dynamic-journey-graph rendering 403b, the computing device 400 determines a value change for each of the updated percentages. To illustrate, the computing device 400 determines that the condition node 406 corresponds to a value change of 0% (with zero change between 99% and 99%), the action node 408a corresponds to a value change of 19% (with a change from 90% to 71%), the new activity node 409 corresponds to a value change of 19% (with a change from 80% to 99%), and the action node 408b corresponds to a value change of −19% (with a change from 10% to 29%). The computing device 400 renders the value change to the percentages for the condition node 406, the action nodes 408a-408b, and the new activity node 409 as shown by the dynamic-journey-graph rendering 403b. For example, the computing device 400 modifies the length of the circumference indicators at predetermined increments and/or at a predetermined speed in each activity node in which corresponding percentage values have changed.
As further shown by the transition between the dynamic-journey-graph rendering 403a and the dynamic-journey-graph rendering 403b, in some embodiments, the computing device 400 further modifies the edge thicknesses of each corresponding edge. To illustrate, as shown in
Turning to
As shown in
As further shown in
If the journey graph is published in static form, the journey graphing system 106 can proceed to an act 508 for determining whether a reporting option is selected. If the reporting option is not selected, the journey graphing system 106 iteratively proceeds back to the act 508. Thus, the journey graphing system 106 continuously monitors selection of the reporting option.
However, if the journey graphing system 106 determines that the reporting option is selected, the journey graphing system 106 proceeds to an act 510 of retrieving datasets corresponding to the journey graph. To illustrate, the journey graphing system 106 retrieves the most up-to-date datasets corresponding to the journey graph. In one or more embodiments, retrieving the datasets includes retrieving particular datasets corresponding to particular activity nodes within the journey graph, such as by retrieving one or both of action datasets and event datasets. Further, in some embodiments, retrieving the datasets includes retrieving sub-segmentation data based on user characteristics and other metadata.
Upon retrieving the datasets, the journey graphing system 106 can proceed to an act 512 of updating an node-and-edge model. To illustrate, in some embodiments, the journey graphing system 106 organizes the datasets and then maps users to nodes and edges utilizing a node-and-edge model. In one or more embodiments, the node-and-edge model organizes nodes into classes and determines inputs and outputs for each node. Additionally, the node-and-edge model can associate each node with functionality for generating output (e.g., determining users that exit the node).
In some embodiments, the node-and-edge model further controls visual display of the journey graph, including nodes and edges. In particular, the node-and-edge model includes instructions for a computing device to render a journey graph. Further, in some embodiments, the node-and-edge model includes instructions for a computing device to update rendering of a journey graph. Thus, the journey graphing system 106 provides increased computing efficiency over conventional data-analysis systems that use separate models to generate a variety of separate graphs and charts. To illustrate, rather than utilizing separate and conventional computing models for a separate and static journey graph, a separate Sankey diagram, and separate graphics showing statistics for particular users performing user interactions along a user-interaction path, the journey graphing system 106 utilizes the node-and-edge model for generating a consolidated journey graph.
Upon updating the node-and-edge model, the journey graphing system 106 can proceed to an act 514 of determining whether the nodes are being rendered for the first time. If the nodes are being rendered for the first time, the journey graphing system 106 performs an act 516 of rendering the nodes based on a difference between a zero value and a current value. In particular, the journey graphing system 106 causes the rendering computing device to render any visual indicators associated with the journey graph (e.g., color circumference indicators) based on a difference between a zero value and a current value for an activity node to indicate a corresponding portion of users (e.g., percentage of users) within the activity node.
However, if the journey graphing system 106 determines that the nodes are not being rendered for the first time, the journey graphing system 106 can proceed to an act 518 of rendering a value change of the nodes. More specifically, the journey graphing system 106 determines a value change between the prior value for a node and an updated value for a node. Further, in one or more embodiments, the journey graphing system 106 causes the rendering computing device to render the value change for the node.
For example, at a first time, an action node corresponds to a portion of 500 users. At an updated time, the action node corresponds to an updated portion of 600 users. Thus, the journey graphing system 106 determines that a value change of 100 (600-500). Accordingly, the journey graphing system 106 can provide the value change of 100 to a rendering computing device to update the visual indicators within the journey graph.
Regardless of whether the journey graphing system 106 performs the act 516 or the act 518, as shown in
As further shown in
As discussed above, in one or more embodiments, the journey graphing system 106 dynamically provides sub-segmentation data within a journey graph.
As just indicated,
In one or more embodiments, the computing device 600 presents the sub-segment indicators 610a-610d in response to user selection of the activity node 608. In some embodiments, the sub-segment indicators 610a-610d indicate users corresponding to the activity node 608 included in various sub-segments of a portion of users. As suggested
Further, as shown in
As just indicated by the sub-segment indicators 610a-610d for the activity node 608, in one or more embodiments, the journey graphing system 106 can receive datasets including data corresponding to time elapsed between activities. As shown in
As further shown in
As mentioned, the journey graphing system 106 can also provide data for sub-segmentation of users in an overlay.
Additionally, in one or more embodiments, the computing device 600 presents an additional sub-segmentation option 626. In some embodiments, in response to detecting user input at the additional sub-segmentation option 626, the computing device 600 provides options for rendering additional (or alternate) circumference indicators reflecting user characteristics of additional or different sub-segments of users corresponding to the activity node 608.
Further, as shown in
As also mentioned above, the journey graphing system 106 can determine and provide data for sub-segmentation of users based on user interaction corresponding to a particular node.
In one or more embodiments, the journey graphing system 106 determines sub-segments of users from among a portion of users corresponding to a particular node. More specifically, the journey graphing system 106 can generate a journey graph by generating graphical indicators (e.g., circumference indicators, pie charts, line bars, etc.) representing sub-segments of users having various interaction types corresponding to a node. To illustrate, the graphical indicators can represent sub-segments of users performing different user actions in response to a system action. Additionally, or in the alternative, the graphical indicators can represent segments of users performing variations of an event corresponding to an event node.
As shown in
Additionally, the journey graphing system 106 can determine and provide segmentation data for a variety of interaction types. For example, the journey graphing system 106 can determine sub-segments for types of user interaction during an event, such as a type of entry pass presented by a user's computing device at entry to a venue, a type of device interaction detected by the system at a checkpoint, a message type sent by a user's computing device corresponding to a user inquiry, etc. Further, the journey graphing system 106 can determine a variety of types of user interaction with or response to a system-generated action. For example, the journey graphing system 106 can determine a payment type completed in response to a prompt, a membership type generated based on a sent message, etc.
Looking now to
As just mentioned, the journey graphing system 106 includes the data manager 802. In particular, the data manager 802 manages, maintains, generates, identifies, accesses, organizes, parses, or otherwise utilizes various data (e.g., activity datasets). For example, the data manager 802 manages various datasets for various types of user interactions included in a user-segment journey. In one or more embodiments, the data manager 802 receives datasets from a variety of sources, including administrator devices, user devices, and/or third-party devices.
The journey graphing system 106 also includes the user segmentation manager 804. In one or more embodiments, the user segmentation manager 804 determines and manages user segments, portions of users, and/or user sub-segments. In one or more embodiments, the user segmentation manager 804 receives user segments, portions of users, and/or user sub-segments from various sources, including administrator devices, user devices, and/or third-party devices. In addition, or in the alternative, in some embodiments, the user segmentation manager determines user segments, portions of users, and/or user sub-segments based on received activity datasets.
Further, the journey graphing system 106 includes the graph rendering manager 806. In one or more embodiments, the graph rendering manager 806 generates journey graphs representing user-segment journeys. Further, in some embodiments the graph rendering manager 806 renders the journey graphs in real-time based on receiving an indication of a user interaction (e.g., from an administrator device) at a reporting option. Additionally, the graph rendering manager 806 can update the journey graph in real-time based on continually receiving updated data (e.g., activity datasets).
Additionally, the journey graphing system 106 includes the storage manager 808, including an analytics database 810. In one or more embodiments, the storage manager 808 operates in conjunction with the analytics database 810 that store various data such as the activity datasets and/or models described herein. The storage manager 808 (e.g., via a non-transitory computer memory/one or more memory devices) stores and maintain data associated with user interactions and generating journey graphs (e.g., within the analytic database 810).
In one or more embodiments, each of the components of the journey graphing system 106 are in communication with one another using any suitable communication technologies. Additionally, the components of the journey graphing system 106 is in communication with one or more other devices including one or more client devices described above. It will be recognized that although the components of the journey graphing system 106 are shown to be separate in
The components of the journey graphing system 106 can include software, hardware, or both. For example, the components of the journey graphing system 106 can include one or more instructions stored on a computer-readable storage medium and executable by processors of one or more computing devices (e.g., the computing device 800). When executed by the one or more processors, the computer-executable instructions of the journey graphing system 106 can cause the computing device 800 to perform the methods described herein. Alternatively, the components of the journey graphing system 106 can comprise hardware, such as a special purpose processing device to perform a certain function or group of functions. Additionally, or alternatively, the components of the journey graphing system 106 can include a combination of computer-executable instructions and hardware.
Furthermore, the components of the journey graphing system performing the functions described herein may, for example, be implemented as part of a stand-alone application, as a module of an application, as a plug-in for applications including content management applications, as a library function or functions that may be called by other applications, and/or as a cloud-computing model. Thus, the components of the causality-visualization system 102 may be implemented as part of a stand-alone application on a personal computing device or a mobile device. Alternatively, or additionally, the components of the causality-visualization system 106 may be implemented in any application that allows creation and delivery of marketing content to users, including, but not limited to, applications in ADOBE MARKETING CLOUD, such as ADOBE CAMPAIGN, ADOBE EXPERIENCE CLOUD, and ADOBE ANALYTICS. “ADOBE,” “EXPERIENCE CLOUD,” “MARKETING CLOUD,” “CAMPAIGN,” and “ANALYTICS” are trademarks of Adobe Inc. in the United States and/or other countries.
As mentioned,
As shown in
As shown in
Additionally, in one or more embodiments, the act 904 includes receiving an updated events dataset representing the set of events or an updated actions dataset representing the set of actions, determining an updated first portion of users that experienced the event or an updated second portion of users for whom the action was performed, and based on determining the updated first portion of users or the updated second portion of users, updating the first visual indicator for the event node to report that the updated first portion of users experienced the event; or updating the second visual indicator for the action node to report that the action was performed for the updated second portion of users. Further, in one or more embodiments, the act 904 includes determining a first value change reflecting a difference between the first portion of users and the updated first portion of users or a second value change reflecting a difference between the second portion of users and the updated second portion of users, and updating the first visual indicator for the event node to reflect the first value change, or updating the second visual indicator for the action node to reflect the second value change.
As shown in
As shown in
As shown in
Further, in one or more embodiments, the act 910 includes determining that the first portion of users experienced the event during a first part of the user-segment journey, and determining that the action was performed for the second portion of users during a second part of the user-segment journey. Additionally, the act 910 can include receiving, from the client device, an indication of a user selection of a reporting option to graphically depict the user-segment journey with dynamic reporting features and based on the user selection of the reporting option, provide the journey graph for display on the client device.
Also, in one or more embodiments, the act 910 can include generating the journey graph by generating the first edge with a first edge thickness corresponding to the first portion of users, and generating the second edge with a second edge thickness corresponding to the second portion of users. In some embodiments, the act 910 further includes determining the first portion of users that experienced the event by determining a percentage of users from the user segment that experienced the event after experiencing a preceding event or after having a preceding action performed for the percentage of users, and generating the journey graph by generating the first visual indicator to represent the percentage of users that experienced the event from among users represented by a preceding node for the preceding event or for the preceding action. Additionally, the act 910 can include determining the second portion of users from whom the action was performed by determining a percentage of users from the user segment for whom the action was performed after experiencing a preceding event or after having a preceding action performed for the percentage of users, and generating the journey graph by generating the second visual indicator to represent the percentage of users for whom the action was performed from among users represented by a preceding node for the preceding event or for the preceding action.
Further, in one or more embodiments, the act 910 includes determining a first time elapsed between a preceding event or a preceding action and the event experienced by a first sub-segment from the first port of users, determining a second time elapsed between the preceding event or the preceding action and the event experienced by a second sub-segment from the first port of users, and generating a first graphical indicator representing the first sub-segment corresponding to the first time elapsed and a second graphical indicator representing the second sub-segment corresponding to the second time elapsed. Additionally, in one or more embodiments, the act 910 includes wherein the event node comprises a graphical indication of an event type for the event and the action node comprises a graphical indication of an action type for the action.
In some embodiments, the act 910 also includes determining sub-segments of users from the first portion of users that performed different actions in response to the event experienced by the first portion of users, and generating the journey graph by generating, for the event node, graphical indicators representing the sub-segments of users that performed the different users actions. Further, the act 910 can include determining sub-segments of users from the second portion of users that performed different actions in response to the action performed for the second portion of users, and generating the journey graph by generating, for the action node, graphical indicators representing the sub-segments of users that performed the different users actions.
The act 910 can also include receiving an updated events dataset representing the set of events, determine an updated first portion of users that experienced the event, and based on determining the updated first portion of users that experienced the event: updating the first visual indicator for the event node to report that the updated first portion of users experienced the event, and updating an edge thickness of the at least first edge to indicate the updated first portion of users experienced the event. Additionally, in one or more embodiments, the acts 910 includes receiving an updated actions dataset representing the set of actions, determining an updated second portion of users for whom the action was performed, and based on determining the updated second portion of users from whom the action was performed: updating the second visual indicator for the action node to report that the action was performed for the updated second portion of users, and updating an edge thickness of the at least second edge to indicate the updated second portion of users experienced the event. Further, the act 910 can include determining sub-segments of users having different user characteristics among the second portion of the users and generating the journey graph by generating the event node comprising graphical indicators representing the sub-segments of users having the different user characteristics among the second portion of users.
In some embodiments, the series of acts 900 can also include receiving a condition dataset representing users tracked with respect to a journey-entry condition, determining a portion of users from the user segment satisfying the journey-entry condition to enter the user-segment journey, mapping the portion of users to a condition node representing the journey-entry condition, and generating the journey graph further comprising the condition node with a third visual indicator reporting that the portion of users satisfy the journey-entry condition. Additionally, the series of acts 900 includes receiving an updated dataset representing the set of events experienced by an updated subset of users from the user segment during the user-segment journey, determining an updated subset of users from the user segment that experienced a subset of events, and based on determining the updated subset of users that experiences the subset of events, modify a subset of nodes within the journey graph to indicate the updated subset of users that experienced the subset of events.
In addition to the foregoing, the series of acts 900 can also include performing a step for constructing a journey graph representing the user-segment journey comprising nodes indicating portions of users from the user segment that experienced particular events or particular actions. For instance, the algorithms and acts described in relation to
Embodiments of the present disclosure may comprise or utilize a special purpose or general-purpose computer including computer hardware, such as, for example, one or more processors and system memory, as discussed in greater detail below. Embodiments within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. In particular, one or more of the processes described herein may be implemented at least in part as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices (e.g., any of the media content access devices described herein). In general, a processor (e.g., a microprocessor) receives instructions, from a non-transitory computer-readable medium, (e.g., a memory, etc.), and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein.
Computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer system. Computer-readable media that store computer-executable instructions are non-transitory computer-readable storage media (devices). Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, embodiments of the disclosure can comprise at least two distinctly different kinds of computer-readable media: non-transitory computer-readable storage media (devices) and transmission media.
Non-transitory computer-readable storage media (devices) includes RAM, ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM), Flash memory, phase-change memory (“PCM”), other types of memory, other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer.
A “network” is defined as one or more data links that enable the transport of electronic data between computer systems and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a transmission medium. Transmissions media can include a network and/or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. Combinations of the above should also be included within the scope of computer-readable media.
Further, upon reaching various computer system components, program code means in the form of computer-executable instructions or data structures can be transferred automatically from transmission media to non-transitory computer-readable storage media (devices) (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computer system RAM and/or to less volatile computer storage media (devices) at a computer system. Thus, it should be understood that non-transitory computer-readable storage media (devices) can be included in computer system components that also (or even primarily) utilize transmission media.
Computer-executable instructions comprise, for example, instructions and data which, when executed at a processor, cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. In some embodiments, computer-executable instructions are executed on a general-purpose computer to turn the general-purpose computer into a special purpose computer implementing elements of the disclosure. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code. 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 defined in the appended claims is not necessarily limited to the described features or acts described above. Rather, the described features and acts are disclosed as example forms of implementing the claims.
Those skilled in the art will appreciate that the disclosure may be practiced in network computing environments with many types of computer system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, and the like. The disclosure may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices.
Embodiments of the present disclosure can also be implemented in cloud computing environments. In this description, “cloud computing” is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources. For example, cloud computing can be employed in the marketplace to offer ubiquitous and convenient on-demand access to the shared pool of configurable computing resources. The shared pool of configurable computing resources can be rapidly provisioned via virtualization and released with low management effort or service provider interaction, and then scaled accordingly.
A cloud-computing model can be composed of various characteristics such as, for example, on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, and so forth. A cloud-computing model can also expose various service models, such as, for example, Software as a Service (“SaaS”), Platform as a Service (“PaaS”), and Infrastructure as a Service (“IaaS”). A cloud-computing model can also be deployed using different deployment models such as private cloud, community cloud, public cloud, hybrid cloud, and so forth. In this description and in the claims, a “cloud-computing environment” is an environment in which cloud computing is employed.
In particular embodiments, processor(s) 1002 includes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions, processor(s) 1002 may retrieve (or fetch) the instructions from an internal register, an internal cache, memory 1004, or a storage device 1006 and decode and execute them.
The computing device 1000 includes memory 1004, which is coupled to the processor(s) 1002. The memory 1004 may be used for storing data, metadata, and programs for execution by the processor(s). The memory 1004 may include one or more of volatile and non-volatile memories, such as Random-Access Memory (“RAM”), Read Only Memory (“ROM”), a solid-state disk (“SSD”), Flash, Phase Change Memory (“PCM”), or other types of data storage. The memory 1004 may be internal or distributed memory.
The computing device 1000 includes a storage device 1006 includes storage for storing data or instructions. As an example, and not by way of limitation, storage device 1006 can comprise a non-transitory storage medium described above. The storage device 1006 may include a hard disk drive (HDD), flash memory, a Universal Serial Bus (USB) drive or a combination of these or other storage devices.
The computing device 1000 also includes one or more input or output (“I/O”) devices/interfaces 1008, which are provided to allow a user to provide input to (such as user strokes), receive output from, and otherwise transfer data to and from the computing device 1000. These I/O devices/interfaces 1008 may include a mouse, keypad or a keyboard, a touch screen, camera, optical scanner, network interface, modem, other known I/O devices or a combination of such I/O devices/interfaces 1008. The touch screen may be activated with a writing device or a finger.
The I/O devices/interfaces 1008 may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers. In certain embodiments, devices/interfaces 1008 is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces and/or any other graphical content as may serve a particular implementation.
The computing device 1000 can further include a communication interface 1010. The communication interface 1010 can include hardware, software, or both. The communication interface 1010 can provide one or more interfaces for communication (such as, for example, packet-based communication) between the computing device and one or more other computing devices 1000 or one or more networks. As an example, and not by way of limitation, communication interface 1010 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI. The computing device 1000 can further include a bus 1012. The bus 1012 can comprise hardware, software, or both that couples components of computing device 1000 to each other.
In the foregoing specification, the invention has been described with reference to specific example embodiments thereof. Various embodiments and aspects of the invention(s) are described with reference to details discussed herein, and the accompanying drawings illustrate the various embodiments. The description above and drawings are illustrative of the invention and are not to be construed as limiting the invention. Numerous specific details are described to provide a thorough understanding of various embodiments of the present invention.
The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. For example, the methods described herein may be performed with less or more steps/acts or the steps/acts may be performed in differing orders. Additionally, the steps/acts described herein may be repeated or performed in parallel with one another or in parallel with different instances of the same or similar steps/acts. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.
