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 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 app.
  3. Select App Tools>Analytics.
  4.  

     

    NOTE: By default, the Analytics Dashboard Interface is loaded.

  5. From the Analytics drop down menu, Select Explorer.
  6.  


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.


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.

 

analyticsexploreroverviewhd

A. Dimensions and Metrics can be updated and organized as needed
B. Chart displaying selected Dimensions and Metrics
C. Dimension expand button


Explorer Organization

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

  1. Within the Dimensions section, Click the “+” icon and Select one of the following:
    1. App ID
    2. App Name
    3. App Version
    4. Campaign
    5. Creative
    6. Install Campaign
    7. Install Creative
    8. Install Tracker
    9. Segment
    10. Tracker
    11. Device Carrier Name
    12. Device Network Conn Type
    13. Device Orientation
    14. Device Os
    15. Device Os Version
    16. Device Type
    17. Device Version
    18. Ad Size
    19. Ad Type
    20. Churn Liklihood
    21. Churn Score
    22. Countrycode
    23. Currency
    24. Currencycode
    25. Data
    26. Device Type
    27. Game
    28. Pack
    29. Placement
    30. Product
    31. Push Campaign
    32. Push Message Variant
    33. Push Segment
    34. Receipt Status
    35. Revenue
    36. Sum
    37. TV Brand Name
    38. TV Channel
    39. TV Channel Name
    40. TV DMA
    41. TV Episode Title
    42. TV Genre
    43. TV Product Name
    44. TV Program Name
    45. NOTE: Kochava now provides the tracking of events associated with the serving of advertisements through television. Once the TV advertisement data has been collected, the event data can be visualized within the Analytics dashboard. For instructions on visualizing TV tagging data within Analytics, refer to our TV Tagging support documentation.

    46. Event Name
    47. City
    48. Country
    49. DMA
    50. Zip
    51. Type
    52. Install Matched By
    53. Install Network Name
    54. Install Site
    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. Site
    68. Agency Name
    69. Agency Id
    70. Traffic Verification Fail Reason
    71. Traffic Verified
    72. By Hour
    73. By Day
    74. By Week
    75. 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:
    1. Cohort Installs
    2. LTV
    3. RPI
    4. ROI
    5. Cost
    6. New Users
    7. New Users Attr
    8. Clicks
    9. CVR
    10. Impressions
    11. Sessions
    12. Average Session Count
    13. Average Session Time
    14. Events per User
    15. Total
    16. Events per User
    17. Total
    18. 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.

 

analyticsexplorderreorderdimensionmetricshd

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.

 

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.

 

AnalyticsExplorerExportDataHD

 
 

Last Modified: Sep 26, 2018 at 10:13 am