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  • User: your end-user/customer. Each user should have a unique identifier (User ID) to differentiate them. When the user is anonymous, some form of UUID can be used.  
  • Event: a data structure generated when a user takes a certain action (eg view a page, click a button, log in, sing a song etc) or when something happens to a user (eg send a push notification, subscription renewal, app made an API call).  Events are the most critical data for behavioral and product analytics.
  • Property: every event can have multiple properties associated with it to capture the context information when the event happens. For example: Timestamp, User ID, Country, Device, Gender, Age, App Version and Marketing Campaign etc. These properties can be used to filter events, breakdown metrics or aggregate into measures in analytics.
  • Filter: a condition applied to some properties to filter events or users. For example: Age > 18, Country = US, Date between 2/1/2020 and 2/29/2020.
  • Function: a computation method on events or its properties. For example: Count Events(Login), Unique Users(), Sum(Subscription Event’s Amount Property).  
  • Measure: a computed value from events and properties using functions and filters. A Compound Measure is composed of other measures in a math formula (eg X+Y, X/Y).    
  • Dimension: a special property usually is used for breakdown or lookup purposes. For example: Country dimension is a property called Country on all Events; Campaign dimension is a separate table which contains detailed information about every campaign while on Events there is only a campaign_id property which you can lookup (join) in the Campaign dimension table.
  • Breakdown: a way to slice a measure into groups based on dimension(s). Sometimes it is called Slice-and-Dice, or Group By. For example: breakdown by Age, Country.
  • Cohort: a group of users who match certain criteria. For example, “New Users” means those installed in the last 7 days; "Frequent Singers” means users who sang more than five songs every day.
  • Formula: a generic term for any analytics question. It includes Query, Funnel, Path, Retention, Predication and Effect.
  • Query: a question to get some measure, optionally with filters, breakdowns or cohorts.
  • Funnel: a special analysis to visualize the remaining users at every step in order to measure conversion rates.
  • Path:  a special analysis which uses a Sankey diagram to visualize users’ journey during all the steps between a starting event and ending event.
  • Retention: a special analysis which measures how users come back with a returning event over time since the occurrence of the starting event.
  • Prediction: a special analysis to find different events’ predictive value relates to how users move from a source cohort to target cohort.  
  • Effect: a special analysis showing the impact of a trigger event on an affected event by measuring the changes in event count, frequency or percentage of active users.  
  • Analysis: a detailed examination of some formula (an instance after executing a formula). Usually it is associated with a visualization view (chart).
  • Metric: an analytics view of some measure.

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