Dashboard Analytics

The Tenjin dashboard allows app developers to quickly access common analyses that marketers use to make decisions.

Vocabulary

It's very common to know what you want in an analysis, but not be able to describe it. Tenjin uses the below vocabulary to communicate generic concepts for communicating analytics.

  • Metrics - A metric is a calculated value. Examples include DAU, MAU, today's Ad Revenue, today's IAP revenue, 90-day Total LTV, 4-day Total ROI, etc. Virtually any number in the dashboard is a metric.
  • Filters - A filter specifies the data to analyze based on a criteria. For example, if you're looking at total DAU (metric) and filter for only Organic traffic, you will only see Organic DAU. Another example filter is a date range. I.e. only looking at the DAU over the last month. You can also apply multiple filters at the same time (look at only Organic DAU for the last month).
  • Dimensions - A dimension is a property that spans a data set. A few examples include dimensions like: country locale, platform, marketing channel. These are properties that the entire metric/dataset contains and can be broken down by.
  • Groups - Grouping allows you to see metrics by a specific dimension. If you group DAU by country, all your DAU will be split by a country.

Metrics

These are common metrics and definitions on Tenjin.

  • Spend: Amount spent to acquire users(in USD)
  • Impressions: Number of times your ad was displayed
  • Clicks: Number of times your ad was clicked
  • Reported Installs: Number of installs Ad-networks tracked
  • Tracked Installs: Number of installs Tenjin tracked
  • CPM: Cost per thousand impressions
  • CPC: Cost per click
  • CPI: Cost per reported installs
  • tCPI: Cost per tracked installs
  • Daily Active Users (DAU): number of unique users (advertising_id) per day
  • Sessions: Number of times an app was opened
  • x-day Retention: Percentage of users that came back X days after install
  • x-day IAP Revenue: cumulative IAP revenue X days after install
  • x-day Ad Revenue: Cumulative ad revenue X days after install
  • x-day Total Revenue: Cumulative total revenue (IAP + ad revenue) X days after install
  • x-day Total Revenue per user: Cumulative total revenue (IAP + ad revenue) X days after install divided by tracked installs
  • Cost per x-day User: Cost per user that comes back X days after install
  • x-day ROI: Amount of cumulative total profit (total revenue - spend) divided by spend to acquire that cumulative total revenue X days after the install
  • x-day ROAS: Amount of cumulative total revenue divided by spend to acquire that cumulative total revenue X days after the install

Cohorts / User Segments

A cohort is an specific group of users put together by a certain metric, dimension, or property. Filters, dimensions, groups, and metrics can all define a cohort/segment of users.

Let's look at the illustration below.

Everything on the above illustration is a cohort. Every type of grouping, filtering, and metric defining set of users is considered a segment of the user base and is therefore also known as a cohort.

Common cohorts in the Tenjin dashboard

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

Here are the following common cohorts that marketers care about:

  • 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 what users want your app and which ones don't.

For all custom cohort analyses and optimizations, DataVault is a powerful tool that provides 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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