Get Insights from tracked data
Introduction
Use Insights to get an aggregate overview of business operations.
- Use App Users Insights (formerly Leaderboards) to get insights by people
- Use Orders Insights to get insights for orders serviced
- Use Geofences Insights to get insights by places
- Use Geotags Insights to get insights by work logged through the app
All insights are available as embeddable views and CSV exports.
Region and Profile selector
Insights may be viewed by app users, orders, geofences, or geotags. For each entity, you may select region or profile set through metadata.
Group by Tags
When data loads for your insights query, you will see your metadata keys as a list of tags.
By selecting a tag, the data shown in the table below will be organized into folders (shown as gray rows with ‘show more’), and numerical fields will become totals or averages. If an additional tag is selected, sub-groups will show once the top level row is expanded, yielding full visibility of the data in relation to your business logic. At any time, disable these groupings by selecting the first tag of the tags that you selected.
You may search against the values of any column to produce an even more finely filtered view as needed.
App Users Insights
App Users insights give a snapshot of your users’ performance over a selected time range. In addition to the columns listed below, you will also see your app users’ profile metadata listed alongside, so that you can sort and make groups using it. Grouping by app user metadata allows you to slice and dice HyperTrack data by the attributes that you use to segment users. Data will be included for all orders, geofences, and geotags that your users may have been assigned, or triggered.
| Column | Description |
|---|---|
| Rating | Overall tracking score of this app user. This is computed using tracking percentage, and behavior for orders, geotags and geofences. A 5-star rating means the user performed perfectly as expected. Use this to measure productivity and reward top performers |
| App User | App user name |
| Outages | Outages that happened during selected period and impacted the tracking rate. For a full list of outages check out the Track App Users guide. |
| Tracking % | Percentage of the time when device was tracked, relative to the time you intended to track the device that day. Disabling permissions for example would reduce the percentage. Use this to review devices that did not track as intended, understand the reasons why, and take corrective action |
| Orders | Number of orders. The number of orders typically corresponds to your orders, routes or work days, depending on your use case and implementation. Use this to review how much work was done by each user. Shown when using trips |
| Arrived % | Percentage of orders that arrived at the destination. A high arrival percentage indicates that the app user made it to the destination more often. Use this to measure address accuracy and user behavior. Shown when using trips |
| On time % | Percentage of orders that arrived on time. A high on-time percentage indicates that the app user arrived at the destination on or before the scheduled time. Use this to measure punctuality. Shown when using trips |
| Geofences | Number of geofences the app user visited. Geofences typically corresponds to relevant locations in your business, and a higher number corresponds to more such visits |
| Time Spent (avg) | Arithmetic mean of the time spent at the geofences. A higher time spent at geofences corresponds to more time spent at your relevant locations |
| Route (avg) | Arithmetic mean of the distance traveled to get to the geofence |
| Idle Time | Total time the user was stopped on the way to geofences. A higher number corresponds to less efficient travel to get to the geofence |
| Geotags | Number of app events resulting in geotags. The number of markers typically correspond to your orders, and depends on your use case and implementation. Use this to review how much work was done by each user. Shown when using geotags |
| Stops | Total stop duration by the app user on this day. Use this to review time spent at work or customer locations |
| Steps | Total steps walks by the app user on this day. Use this to review the amount of movement at work or customer locations |
Orders Insights
Orders insights provide aggregates of your app users’ orders serviced within the selected time range. In addition to the columns listed below, you will also see your orders’ metadata listed alongside, so that you can sort and make groups using these values. Grouping by order metadata to slice your data by the attributes that segment the journeys of your day-to-day operations. An additional group by tag of ‘App User’ is provided to slice data by user.
| Column | Description |
|---|---|
| Date | Date and time of order delivery start |
| App User | The app user's name |
| Order Duration | The order's elapsed duration. Use this to flag abnormally short order durations for work orders you expected a full day's worth of work |
| Service time | The amount of time the app user spent at the destination. Use this to flag shorter or longer than expected time at a destination |
| Arrived | Boolean of whether the app user arrived at the destination |
| On Time | Boolean of whether the app user was on time upon arriving at the destination |
| Completed at destination | Boolean of whether the order was completed at the destination |
| Drive Distance | Total distance driven by the app user in the order's range from start to completion. Use this to review expenses, payouts and reimbursements related to distance driven |
| Step Count | Total steps walked by the app user in the order's range from start to completion. Use this to review the amount of movement at work or customer locations |
Geofences Insights
Geofences insights provide aggregates of the geofences that have been hit by app users in the selected time range. In addition to the columns listed below, you will also see your geofences’ metadata so that you can sort and make groups using these valuyes. Group by geofence metadata to slice your data by the attributes that segment the key locations of your business. An additional group-by tag of ‘App User’ is provided to slice data by user.
| Column | Description |
|---|---|
| Geofence Markers | The number of times this geofence was visited |
| App Users | The app users that visited this geofence |
| Avg Time Spent | The average time app users spent at the geofence. Use this to flag abnormally long stays and to optimize micro-operations on-site |
| Avg Route Duration | The average time app users spent getting to the geofence. Use this to flag unexpectedly delayed routes and to improve route efficiency. |
| Avg Route Idle Duration | The average time app users spent stopped while en-route to the geofence. Use this to flag longer than expected delays. |
| Avg Route Distance | The average distance app users travelled getting to the geofence. Use this to flag longer than expected route and to improve route efficiency. |
Geotags Insights
Geotags insights provide aggregates of the geotags that have been logged by your app users in the selected time range. In addition to the columns listed below, you will also see your geotags’ metadata so that you can sort and make groups using these values. Group by geotag metadata to slice your data by the attributes that segment the key moments of your day to day operations. An additional group-by tag of ‘App User’ is provided to slice your data by user.
| Column | Description |
|---|---|
| Date | Date and time of the geotag |
| App User | The app user's name |
| Avg Time Spent | The average time app users spent at the geofence. Use this to flag abnormally long stays and to optimize micro-operations on-site |
| Route Duration | The time that the app user spent between the previous geotag to this geotag. Use this to flag unexpectedly delayed routes and to improve route efficiency. |
| Route Distance | The distance that the app user travelled between the previous geotag to this geotag. Use this to flag longer than expected route and to improve route efficiency. |
Export Insights
You can export Insights into your favorite BI tool.
The “Export” button at the top right corner of the Insights page produces a CSV file, created in response to the selection of App Users, Trips, Geofences, Geotags, region, or device metadata. Learn more about working with device and profile metadata.
The Insights view allows operations teams to download a CSV file and share across your organization. While this allows the ops team to use location data without engineering help, some use cases need more detailed data.
HyperTrack’s data export API gives you all the insights data plus the data the insights view is based on—locations and marker data for geotags, trips, geofences, and device status (activity and outages)—to be consumed by data and engineering teams.
To get the data, call our history API:
The API call returns a pre-signed URL that you can easily download. Here is a full shell example using jq:
Note the link is pre-signed and will expire after 60min. Once you downloaded the file, use your favorite gzip tool (e.g., gunzip) or library (e.g., python gzip) to decompress to get the full json file.
The returned json file contains an array of devices, which each entry given you details about the device. Here is a sample in pseudo json (with comments added for clarity):
The json file contains a json array, with each item representing one user in the insights view. Each user contains the following fields:
| Field | Description |
|---|---|
| rating | Overall tracking score of this app user. This is computed using tracking percentage, and behavior for trips, geotags and geofences. A 5-star rating means the user performed perfectly as expected. Use this to measure productivity and reward top performers. |
| device_name | App user name |
| device_id | Device Identifier that uniquely identifies a device within HyperTrack |
| tracking_percentage | Percentage of the time when device was tracked, relative to the time you intended to track the device that day. Disabling permissions for example would reduce the percentage. Use this to review devices that did not track as intended, understand the reasons why, and take corrective action. |
| custom custom_marker_count | Number of app events resulting in geotags. The number of markers typically correspond to your orders, and depends on your use case and implementation. Use this to review how much work was done by each user. |
| custom_marker_mean_distance | Arithmetic mean of distance between geotags. A higher number means there was a larger average distance covered from marker to marker. This indicates lower efficiency where more work needs to happen per unit distance, and higher efficiency where more distance implies more work done. Use this to measure route efficiency. |
| trip_count | Number of trips. The number of trips typically corresponds to your orders, routes or work days, depending on your use case and implementation. Use this to review how much work was done by each user. |
| arrival_percentage | Percentage of trips that arrived at the destination. A high arrival percentage indicates that the app user made it to the destination more often. Use this to measure address accuracy and user behavior. |
| on_time_percentage | Percentage of trips that arrived on time. A high on-time percentage indicates that the app user arrived at the destination on or before the scheduled time. Use this to measure punctuality. |
| drive_distance | Total distance driven by the app user on this day. Use this to review expenses, payouts and reimbursements related to distance driven. |
| stop_duration | Total stop duration by the app user on this day. Use this to review time spent at work or customer locations. |
| steps | Total steps walks by the app user on this day. Use this to review the amount of movement at work or customer locations. |
| locations | Object representing the location time series of the trips. Follows GeoJSON geometry of type LineString |
| markers | Array of markers. The markers show you visited geofences, geotags as well as details about the device status. |
| trips | Array of trips per device |
Device status markers
The device status markers explains what the device was doing at a given time. It gives details about
the tracking status (active when the device is tracking properly, inactive when the device can't be tracked or
disconnected when HyperTrack lost device-server communications). For active devices, we furthermore split the
markers into activities like stop, drive or walk:
Geofence markers
For every visit to one of your geofences, we add a marker to the markers array. The geofence marker gives you detailed data about the geofence arrival, exit, time spent inside and how the user got to this geofence. Here is an example:
Geotag markers
Geotags let you geotag import app events. The geotag markers provide detailed data such as location and metadata provided:
Questions?
For urgent needs to help you get your data, or for questions or comments on this topic, please do not hesitate to contact us.