Analytics Explorer

The Analytics Explorer page provides a detailed breakdown of any dimension and metric within the selected app. The Explorer page is fully customizable and provides the ability to include, exclude and reorganize data to maximize efficiency.

NOTE: From any of the other Analytics pages, you can navigate to the Explorer tool and retain the current filter settings. This provides in depth views into any aspect of an app’s data.


Analytics Interface

  1. Log in to Kochava.
  2. Select the desired Account and App.
  3. Select Analytics > Explorer.
  4.  


Analytics Page Tools

For more information about the tools that can be used on the Analytics page such as date range, filters, sharing the page and exporting device ID, refer to our Analytics Page Tools support documentation.

Filters

Filters can be used to further refine, organize and visualize the displayed data in a method that is most beneficial. Multiple filters can be added and saved for later use.

  1. Click Add Filter.
  2. Added filters may be inclusive or exclusive. Select from the following filters categories:

    1. App — Based on the available apps within account.
    2. Campaign — Based on campaign naming conventions.
    3. Device — Based on user agent.
    4. Events — Based on Standard or Custom events.
    5. Location — Based on IP address.
    6. Attribution — Based on attributed installs and campaign attribution settings.
    7. Agency — Based on the available apps within the agency account.
    8. Traffic Verification — Based on Traffic success or failure.

     

    Once the filter category has been selected, one or more values may be added per filter. Filters can be saved and reapplied within any of the Analytics pages.

  3. Select the Filter drop-down menu, Select desired Filter.
  4. Add desired values.
  5. Click “X” to remove Filter.

 


A. Filters
B. Click “+” to add metric
C. Click “X” to remove metric
D. Click “X” to remove Filter and associated Metric(s)


Save App Filters —

  1. Click the “X” to remove a value.
  2. Click “+” to add a value.
  3. Click Save View.

 


Selecting a Saved Filter —

  1. Select the Saved States drop-down box.
  2. Select the desired Filter Set.

 


A. Select the desired Filter Set


Deleting Filter Sets:

When saved Filter Sets are no longer needed, they can be deleted.

WARNING: Analytics filters are also used to generate dynamic Audience Export reports, if a filter that is used to create an Audience Export report is deleted an error will be generated when the report is pulled. Before deleting a filter, verify that the filter is not used to pull a report.

  1. Select the Saved States drop-down box.
  2. Select the desired Filter Set.
  3. Click “X” to remove.
  4. Click Proceed.

 


A. Click “X” to remove



Explorer Chart Overview

The Explorer chart is divided into 2 main sections, the Dimensions and Metrics selection areas, and the interactive chart.

Dimensions and Metrics can be added, removed and reordered as needed.

Mousing over Dimensions displays the expand button. By clicking on the Dimension expand button, specific level data can be displayed.

NOTE: When leveraging Cross App functionality, Explorer data for all apps within the App Name filter will be displayed. Explorer data may be displayed for each app by utilizing the dimensions feature. For more information on viewing Explorer data by app, refer to the Explorer Organization section.

NOTE: Only the first 250 rows will be displayed for the highest level dimension defined within the view. Adding a highest level dimension with over 250 unique combinations can produce varying results depending on dimension order.

 

Chart Overview
A. Dimensions and Metrics can be updated and organized as needed
B. Chart displaying selected Dimensions and Metrics
C. Dimension expand button.
D. Summary values are a sum of each row in the Metrics column. The only exception(s) to this are the RPU and RPI Metrics in which Kochava takes an average of the column.


Explorer Organization

Explorer data can be organized in many different ways in order to assist in the optimization of data visualization.

NOTE: The dimensions available is dependent on the data that is being sent to Kochava.

  1. Within the Dimensions section, Click the “+” icon and Select one of the following:
    1. Agency Name
    2. App ID
    3. App Name
    4. App Version
    5. Campaign
    6. Creative
    7. Segment
    8. Tracker
    9. Device Carrier Name
    10. Device Language
    11. Device Network Conn Type
    12. Device Orientation
    13. Device Os
    14. Device Os Version
    15. Device Type
    16. Device Version
    17. Ad Size
    18. Ad Type
    19. Churn Liklihood
    20. Churn Score
    21. Currency
    22. Friends Invited
    23. Items in Cart
    24. Mtr Rejected
    25. Data
    26. Device Type
    27. Placement
    28. Product Brand
    29. Product Name
    30. Product Sku
    31. Product Style
    32. Push Campaign
    33. Push Segment
    34. Receipt Status
    35. Revenue
    36. Session Duration
    37. Subscription Action
    38. Sum
    39. Total Sessions
    40. User Id
    41. User Total Revenue To Date
    42. Event Name
    43. City
    44. Country
    45. DMA
    46. Region
    47. Zip
    48. Type
    49. Install Campaign
    50. Install Creative
    51. Install Matched By
    52. Install Network Name
    53. Install Site
    54. Install Tracker
    55. Matched To
    56. Matched By
    57. Network Name
    58. Network Id
    59. Network Key
    60. Partner Ad Group Id
    61. Partner Ad Group Name
    62. Partner Campaign Id
    63. Partner Campaign Name
    64. Partner Keyword
    65. Partner Platform
    66. NOTE: For more information about how the Partner fields map to SAN metadata, please refer to our SAN Networks Campaign Data Mapping support documentation.

    67. QR Code
    68. Site
    69. Traffic Verification Fail Reason
    70. Traffic Verified
    71. By Hour
    72. By Day
    73. By Week
    74. By Month

    NOTE: By default, Campaign, Segment, Tracker, Event Name and By Day Dimensions are displayed.

    NOTE: In order to use the App dimension feature (App ID, App Name, App Version), the desired corresponding apps must be added utilizing the filter feature. For more information about adding apps using the filter feature, refer to our Analytics Page Tools support documentation.

  2. Within the Metrics section, Click the “+” icon and Select one of the following:
  3. NOTE: The metrics available is dependent on the data that is being sent to Kochava.

    1. Events
    2. Clicks
    3. New Users
    4. ROI
    5. RPU
    6. Revenue
    7. Cohort Installs
    8. LTV
    9. RPI
    10. New Users Attr
    11. Users
    12. CVR
    13. Impressions
    14. Sessions
    15. Average Session Count
    16. Average Session Time
    17. Events per User
    18. Total
    19. Search
    20. Events per User
    21. Total Users
    22. Ad View
    23. Purchase
    24. Push Opened
    25. Rating
    26. Logout
    27. Invite Friend
    28. Login
    29. Uninstall
    30. Subscribe
    31. Register
    32. Custom events being sent to the app

    NOTE: By default, Users, Events, Revenue, RPU, and Average Session Time Metrics are displayed.

    NOTE: (Distinct) Metrics limit the associated event to display only the initial occurrence of a device ID for the given date range.

    Specific Dimensions and Metrics can be removed by Clicking on the “X”.

    Dimensions and/or Metrics can also be reordered by dragging and dropping the Dimensions and/or Metrics into any desired order. Once Dimensions and/or Metrics have been reordered, the interactive chart will be updated to reflect the change.

 

Explorer Organization
A. Original Configuration
B. Drag and Drop to reorder
C. New Configuration


Predicted Churn

Churn is the rate at which customers install an app and shortly afterwards abandoned the usage of the app. Churn rate is often used as an indicator of the health of an app’s user base. Kochava has created an algorithm that will score the likelihood to churn within only seven days providing marketers the opportunity to mitigate churn or reengage with their retained users. Our machine learning models observe over 30 data points which can include standard post-install events, custom post-install events tracked by the advertiser, and derived/engineered features.

NOTE: By default, churn modeling is not enabled for an app. If churn modeling needs to be enabled, contact your Client Success Management team.

 

Churn Score:

The Churn Score is a numeric representation of the probability that the device will churn. The Churn Score is a number that is between 0 and 1, where the closer the score is to 1 the more likely the device is to churn.

 

Churn Likelihood:

Churn Likelihoods are categories of devices based on the likelihood to churn. The groups represent four ranges of churn scores and are based on a dynamic mid-point that has been optimized for each app/model.

  • Low — A group of devices that has a very low risk of churning. On average, these devices will have a churn score between 0 and 0.25.
  • Medium Low — A group of devices that have a moderately low risk of churning. On average, these devices typically have a churn score between 0.25 and 0.50.
  • Medium High — A group of devices that have a moderately high risk of churning. On average, these devices typically have a churn score between 0.50 and 0.75.
  • High — A group of devices that have the highest likelihood of churn. On average, these devices typically have a churn score of 0.75 or higher.

 

Analytics Explorer provides the most in-depth look into churn, completing the following steps provides the deepest look into churn:

  1. Click Add a Filter.
  2. Select Churn Likelihood.
  3. Add the desired Likelihood Levels:
    • Low — A group of devices that has a very low risk of churning. On average, these devices will have a churn score between 0 and 0.25.
    • Medium Low — A group of devices that have a moderately low risk of churning. On average, these devices typically have a churn score between 0.25 and 0.50.
    • Medium High — A group of devices that have a moderately high risk of churning. On average, these devices typically have a churn score between 0.50 and 0.75.
    • High — A group of devices that have the highest likelihood of churn. On average, these devices typically have a churn score of 0.75 or higher.
    •  

      BEST PRACTICES: Kochava recommends that the focus should be on the higher levels of churn. Focus mainly on the High, Medium High and possibly the Medium Low levels of churn.

  4. Remove Dimensions, except for Network Name.
  5. Add Dimensions > Churn Likelihood.
  6.  

    BEST PRACTICES: It is recommended that Network Name be the primary Dimension with Churn Likelihood being the secondary Dimension.

     

  7. Expand the desired Network to view the associated churn data.

 

Predicted Churn
A. Add a Churn Likelihood filter.
B. Add the Churn Likelihood Dimension and remove other Dimensions except for Network Name.
C. Churn Likelihood Data.

 

NOTE: The device IDs associated with the higher levels of churn may be exported or saved as an audience for reengagment or push campaigns. For more information about exporting device IDs, refer to our Analytics Page Tools support documentation.


Exporting the Explorer Data

The data displayed within the Explorer chart can be exported in CSV format.

NOTE: Only the highest dimension selected will be displayed as part of the export.

 

Exporting Data

 
 

Last Modified: Jan 17, 2024 at 2:46 pm