The Analytics Event Detail page provides visualizations for specific event totals. The graph may be moused over to display the data associated with each day within the selected timeframe.

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Analytics Interface

1. Log in to Kochava.
2. Select the desired app.
3. Select App Tools>Analytics.

 



 

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

4. Select the Analytics drop-down menu>Event Detail.

 

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Analytics Page Tools

For more information on the tools available for this Analytic Page such as the date field, exporting device IDs, sharing the page and applying Cohorts and filters, refer to our Analytics Page Tools support documentation.

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Event Detail Chart Overview

NOTE: By default no data is displayed with the Event Detail chart is initially opened. An Event must be selected from the provided drop-down menu for data to be displayed.

1. Select an Event from the Selected Event drop-down menu.

 

The Event Detail chart by default displays the Number of Users, Event Count, and any Revenue associated with the selected event. Mousing over any of the days within the chart will display the corresponding data.

NOTE: When leveraging Cross App functionality, the event data that is consistent between each app of the App Name filter will be displayed within the Event Detail. For more information about adding apps using the filter feature, refer to our Analytics Page Tools support documentation.

NOTE: The data displayed for the Number of Users, Event Volume and Revenue may also be turned on or off by clicking on the desired metrics.

 

A. Selected Event
B. Graphic display of Event Count, Number of Users and Revenue
C. Mouseover to view data

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Downloading Event Detail Data

The data displayed within the Event Detail Chart can be downloaded in several different formats.

 

Download Graphically:

Select the Download Button>Download Chart Image.

• PNG
• JPG
• SVG
• PDF

 

Download Data:

Select the Download Button>Download Data.

• CSV

 

Print Data:

Select the Download Button>Print.

 

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Event Organization

The data displayed for the selected event may be organized in many different ways in order to assist in the optimization of data visualization.

1. From the Split By drop-down menu, Select one of the following:
1. App
1. App Id
2. App Name
3. App Version

NOTE: In order to use the App split by feature, the desired 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. Campaign
1. Campaign
2. Creative
3. Segment
4. Tracker
3. Device
1. Device Carrier Name
2. Device Language
3. Device Network Conn Type
4. Device Orientation
5. Device Os
6. Device Os Version
7. Device Type
8. Device Version
4. Events

NOTE: The post-install events that are available within this section of the Split By menu are determined by the events setup for tracking. For more information about creating and configuring events, refer to our Post-Install Event Creation and Configuration support documentation.

5. Location
1. City
2. Country
3. DMA
4. Zip
6. Attribution
1. Type
2. Install Campaign
3. Install Creative
4. Install Matched By
5. Install Network Name
6. Install Site
7. Install Tracker
8. Matched To
9. Matched By
10. Network Name
11. Network Id
12. Network Key
13. Partner Ad Group ID
14. Partner Ad Group Name
15. Partner Campaign Id
16. Partner Campaign Name
17. Partner Keyword
18. Partner Platform

NOTE: For more information about how the Partner fields map to SAN metadata, please refer to our SAN Networks Campaign Data Mapping support documentation.

19. Site
7. Agency
1. Agency Name
2. Agency Id
8. Traffic Verification
1. Traffic Verification Fail Reason
2. Traffic Verified
2. From the Split Event Volume By drop-down menu, Select an additional filter to split the event volume by. (optional)

NOTE: Up to 50 results will be displayed for the initially selected filter and up to 25 results will be displayed for the secondary filter selected. For a complete list of filtered event data, a report may be generated or the Explorer tool may be used.

3. To view the data associated with the secondary metric filter, Click the Filter Expand Button.

NOTE: Mousing over any of the data displayed within the filtered charts will display the specific data associated with the day and event.

 

A. Selected Filters
B. Filter Expand Button
C. Mousover chart to display specific data for a specific day

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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.

 

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.

 

BEST PRACTICES: Within Analytics Event Detail _PredictedChurn may be selected as the primary event which may be further expanded by splitting the event by Churn Score or Churn Likelihood, as well as the associated Network Name. Filters may also be used to view the event counts for either the Churn Score or Churn Likelihood.

 

A. Filter added for Churn Likelihood or Score.
B. Event Split by Network for further clarity.

 
 

Last Modified: Mar 6, 2019 at 5:49 pm