Metrics
  • 30 Sep 2022
  • 9 Minutes to read
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Metrics

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What Metrics are included in the Tenjin Dashboard?

The Tenjin dashboard allows app developers to quickly access common analyses that marketers use to make decisions. The following video explains how we calculate some cohorted and non-cohorted metrics at Tenjin.

Below is a complete list of metrics and common cohorts available in the Tenjin dashboard.


List of metrics

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report type abbreviated name full name description
UA In App Purchase LTV In App Purchase LTV Average In App Purchase Amount ($)
UA Lifetime IAP ROI Lifetime IAP ROI Average In App Purchase ROI Percentage (%)
UA In App Purchase LTV / User In App Purchase LTV / User Average In App Purchase LTV per User Amount ($)
UA N-Day IAP LTV / User N-Day IAP LTV / User Average N-Day In App Purchase LTV per User Amount ($)
UA IAP Rev IAP Revenue Total In App Purchase Amount ($)
UA N-Day IAP LTV N-Day IAP LTV Cumulative IAP revenue N days after install
UA Ad Rev Ad Revenue Total Ad Revenue Amount estimated by Tenjin ($)
UA Ad Revenue LTV Ad Revenue LTV Total Ad Revenue LTV Amount estimated by Tenjin ($)
UA Lifetime Ad ROI Lifetime Ad ROI Average Ad ROI Percentage (%)
UA Ad Revenue LTV / User Ad Revenue LTV / User Total Ad Revenue estimated by Tenjin per User Amount ($)
UA N-Day Adrev LTV N-Day Ad Revenue LTV Cumulative ad revenue N days after install
UA N-Day Adrev LTV / User N-Day Ad Revenue LTV / User Average N-Day Ad Revenue LTV estimated by Tenjin per User Amount ($)
UA Total ARPDAU Total Revenue per Daily Active Users Average amount of Total Revenue (IAP + Ad) per DAU ($)
UA IAP ARPDAU IAP Revenue Per Daily Active Users Average amount of IAP Revenue per DAU ($)
UA Ad ARPDAU Ad Revenue Per Daily Active Users Average amount of Ad Revenue per DAU ($)
UA Total Rev Total Revenue Total Revenue Amount (IAP + Ad) ($)
UA Total LTV Total LTV Total LTV (IAP + Ad Revenue)
UA Lifetime Total ROAS Lifetime Total ROAS Average Lifetime Total ROAS Percentage (%)
UA Lifetime Total ROI Lifetime Total ROI Average Lifetime Total ROI Percentage (%)
UA Total LTV / User Total LTV / User Total Revenue (IAP + Ad) per User Amount ($)
UA N-Day Total LTV N-Day Total LTV Cumulative total revenue (IAP + ad revenue) N days after the install
UA N-Day ROAS N-Day ROAS Average N-Day ROAS Percentage (%) Amount of cumulative total revenue N days after install divided by spend
UA N-Day ROI N-Day ROI Average N-Day ROI Percentage (%) Amount of cumulative total profit (total revenue N days after install - spend) divided by spend
UA N-Day Total LTV / User N-Day Total LTV / User Cumulative total revenue (IAP + ad revenue) N days after install divided by tracked installs Total Revenu (IAP + Ad) / Total Users on Nday
UA DAU Daily Active Users Number of unique users (advertising_id) per day
UA N-Day Users N-Day Retained Users Total number of users retained after N-Day of installing the app
UA Lifetime Ret Lifetime Retention Cohorted N-day Retention Rate
UA N-Day Ret % N-Day Retention Rate Average N-Day Retention Rate Percentage (%)
UA Total Fraud Events Total Fraud Events Sum of blocked clicks and fradulent purchases
UA Blocked Clicks Blocked Clicks The number of clicks we blocked based on mean time to install(MTTI). We currently use 1 second for the MTTI threshold as default.
UA Blocked Device Events Blocked Device Events Number of test device events
UA Fradulent Purchases Fraudulent Purchases Number of purchases marked as fradulent
UA Blocked Fradulent Rev Blocked Fraudulent Revenue Total Blocked Fraudulent Revenue Amount ($)
UA Reported Clicks Reported Clicks Number of times the ad was clicked reported by Ad Networks
UA Clicks Diff Clicks Difference Total Clicks Difference. Formula : Reported Clicks - Tracked Clicks
UA Clicks Difference Percent Clicks Difference Percent Average Clicks Difference Percentage (%) Formula : 100 - 100 * Tracked Clicks / Reported Clicks
UA Lifetime Cost / User Lifetime Cost per Retained User Cohorted cost per N-day Retained User
UA CPC Cost-Per-Click (CPC) Average CPC Amount ($) spend/reported clicks
UA CPI Cost-Per-Install (CPI) Average CPI Amount ($) spend/reported installs
UA CPM Cost-Per-1000-Impressions (CPM) Average CPM Amount ($) 1000 * spend/reported impressions
UA CPR Completion Rate (CPR) Average CPR Percentage (%) 100 * reported installs / reported impressions
UA CTR Click-Through-Rate (CTR) Average CTR Percentage (%) 100* reported clicks / reported impressions
UA CVR Conversion Rate (CVR) Average CVR Percentage (%) 100 * reported installs / reported clicks
UA Impressions Reported Impressions Number of times your ad was displayed
UA Reported Installs Reported Installs Number of installs reported by Ad Networks
UA Installs Diff Installs Difference Total Installs Difference. Reported Installs - Tracked Installs
UA Installs Difference Percent Installs Difference Percent Average Installs Difference Percentage (%). 100 - 100 * Tracked Installs / Reported Installs
UA Lifetime Active Days / User Lifetime Active Days Per User Average number of active days for a user
UA Sessions Sessions Average Sessions
UA Lifetime Sess / User Lifetime Sessions Per User Average Sessions per User
UA Spend Spend Amount spent to acquire users (in USD)
UA tCPI Cost-Per-Tracked Install (tCPI) Average tCPI Amount ($) spend / tracked installs
UA Tracked Clicks Tracked Clicks Number of times your ad was clicked (Tenjin tracked)
UA Tracked CVR Tracked Conversion Rate (CVR) Average CVR Percentage (%) 100 * tracked installs / tracked clicks
UA Tracked Impressions Tracked Impressions Number of impressions tracked by Tenjin
UA Tracked Installs Tracked Installs Number of Installs tracked by Tenjin
UA CP N-Day User Cost per N-Day Retained User Cost per users that comes back N days after install (installs here are reported by the ad network). Formula - spend * (users_0d/reported installs) / users_Nd
UA N-Day Sess N-Day Sessions Number of times an app was opened after post install on the day of install (N day)
UA CP K N-Day Sessions Cost per 1000 N-Day Sessions Average Cost per 1000 N-Day Sessions Amount ($)
UA Purchasers Unique Purchasers Number of the unique purchasers
UA Transactions Purchase Transactions Number of the total purchase counts
UA N-Day Cost / Purchase N-Day Cost Per Purchase Transactions Cost divided by cumulative total purchase counts N days after the install
UA N-Day Cost / Purchaser N-Day Cost Per Returning Purchaser Cost divided by returning purchase user N days after the install
UA N-Day Cumul Revenue N-Day Cumulative Purchase Revenue Cumulative purchase revenue N days after the install
UA N-Day Revenue / User N-Day Cumulative Purchase Revenue per Acquired User Cumulative purchase revenue N days after the install, divided by the number of acquired users
UA N-Day Cumul Txns N-Day Cumulative Purchase Transactions Cumulative purchase counts N days after the install
UA N-Day Txns per User N-Day Cumulative Purchase Transactions per Acquired User Cumulative purchase counts N days after the install, divided by the number of acquired users
UA N-Day Participation N-Day Purchaser Participation Percent of unique purchasers N days after the install out of the number of acquired users
UA N-Day Purchasers N-Day Unique Purchasers Cumulative unique purchasers N days after the install
UA Average Average Purchase Value per Transaction Total purchase revenue, divided by total purchase counts
UA Revenue Purchase Revenue Total purchase revenue
AM Reported Ad Revenue Reported Ad Revenue Total Ad Revenue reported by Ad Network ($)
AM eCPC Effective Cost per Click (eCPC) Ad Revenue per Click ($)
AM eCPM Effective Cost per Mille (eCPM) Ad Revenue per 1000 Impressions ($)
AM Reported Clicks Reported Clicks Total Clicks reported by Ad Network
AM Impressions Reported Impressions Total Impressions reported by Ad Network
SKAN Conversions Conversions Number of Conversion postbacks forwarded by Ad Networks (includes redownloads and first downloads)
SKAN Conversion Value Average Conversion Value Average Average of Conversion Values forwarded by Ad Networks (includes redownloads and first downloads)
SKAN Conversion Value Total Conversion Value Total Sum of Conversion Values forwarded by Ad Networks (includes redownloads and first downloads)
SKAN Ad Network SKAN Installs Ad Network SKAN Installs Number of Conversion postbacks received for first downloads forwarded by Ad Networks
SKAN First Download CV Avg First Download Conversion Value Average Average Conversion Value for first downloads forwarded by Ad Networks
SKAN First Download CV Total First Download Conversion Value Total Sum of Conversion Values for first downloads forwarded by Ad Networks
SKAN Redownloads Redownloads Number of Conversion postbacks received for redownloads forwarded by Ad Networks
SKAN Redownload CV Avg Redownload Conversion Value Average Average Conversion Value for redownloads forwarded by Ad Networks
SKAN Redownload CV Total Redownload Conversion Value Total Sum of Conversion Values for redownloads forwarded by Ad Networks (includes redownloads and first downloads)
SKAN Assists Assists Ad network forwarded ad impressions that were shown to a user that ultimately downloaded the app, but were not awarded with an attribution. Introduced in SKAdNetwork 3.0, this corresponds to postbacks with did-win=false.
SKAN Redownload Assists Redownload Assists Number of Assist postbacks received for redownloads forwarded by Ad Networks
SKAN First Download Assists First Download Assists Number of Assist postbacks received for first downloads forwarded by Ad Networks
SKAN Apple Conversions Apple Conversions Number of Conversion postbacks forwarded by Apple (includes redownloads and first downloads)
SKAN Apple Conversion Value Average Apple Conversion Value Average Average of Conversion Values forwarded by Apple (includes redownloads and first downloads)
SKAN Apple Conversion Value Total Apple Conversion Value Total Sum of Conversion Values forwarded by Apple
SKAN Apple SKAN Installs Apple SKAN Installs Number of Conversion postbacks received for first downloads forwarded by Apple
SKAN Apple First Download CV Avg Apple First Download CV Avg Average Conversion Value for first downloads forwarded by Apple
SKAN Apple First Download CV Total Apple First Download CV Total Sum of Conversion Values for first downloads forwarded by Apple
SKAN Apple Redownloads Apple Redownloads Number of Conversion postbacks received for redownloads forwarded by Apple
SKAN Apple Redownload CV Avg Apple Redownload CV Avg Average Conversion Value for redownloads forwarded by Apple
SKAN Apple Redownload CV Total Apple Redownload Conversion Value Total Sum of Conversion Values for redownloads forwarded by Apple
SKAN Apple Assists Apple Assists Apple forwarded ad impressions that were shown to a user that ultimately downloaded the app, but were not awarded with an attribution. Introduced in SKAdNetwork 3.0, this corresponds to postbacks with did-win=false.
SKAN Apple Redownload Assists Apple Redownload Assists Number of Assist postbacks received for redownloads forwarded by Apple
SKAN Apple First Download Assists Apple First Download Assists Number of Assist postbacks received for first downloads forwarded by Apple
  • report type:
    • UA: User Acquistion
    • AM: Ad Monetization
    • SKAN: SKAdNetwork

Common cohorts in the Tenjin dashboard

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Since a set of users can be defined as a cohort, Tenjin's dashboard looks at the most common cohorts to analyze marketing data.

Here are the following common cohorts that marketers usually want to understand:

  • Acquisition date - segmenting and grouping users by acquisition date is one of the most important things app marketers do. It allows marketers to calculate metrics like x-Day retention and x-Day LTV.

  • User dimensions - segmenting and grouping users by channel, campaign, country, and creative allows marketers to see metrics on different dimensions. Analyzing users by these dimensions reveals insights about which users want your app and which ones don't.

For all custom cohort analyses and optimizations, DataVault is a powerful tool that can provide these insights. DataVault marketers build custom cohorts based on various dimensions and metrics that are only accessible with raw data. As an example, downloading advertising_id level data allows marketers to build lookalike audiences for continuous campaign optimization.



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