Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Metrics we should know - or not! but we are gonna learn it!

Dinorah Tovar
November 11, 2021

Metrics we should know - or not! but we are gonna learn it!

This is a story about maths!
In this talk, we are gonna focus completely on metrics, from AB testing to analytics using Firebase and the importance that we need to address metrics when we start working on a project!
We are gonna talk about Bayesian and predictions models to track our AB testings and also we are gonna talk about Conversion vs. Retention vs. Count perusers

Bring your calculators! because this is a story about Firebase and Charts!

Dinorah Tovar

November 11, 2021
Tweet

More Decks by Dinorah Tovar

Other Decks in Science

Transcript

  1. Metrics we should know -
    or not! but we are gonna
    learn it!
    Dinorah Tovar

    Google Developer Expert - Android
    @ddinorahtovar
    @ddinorahtovar

    View Slide

  2. Disclaimer

    View Slide

  3. A story about
    data

    View Slide

  4. @ddinorahtovar

    View Slide

  5. Data gives the answer to the correct
    questions
    @ddinorahtovar
    All hit on the same place
    Planes on the research
    Result
    Research

    View Slide

  6. Analytics

    View Slide

  7. Data vs Metrics
    @ddinorahtovar
    •They are not the same
    •Data is
    generated by
    metrics

    •You don’t pick
    your data, you
    pick your
    metrics
    Data
    Metrics
    •Metrics are what
    you measure

    •Consistent

    •Cheap

    •Quick to collect

    View Slide

  8. Analytics
    @ddinorahtovar
    •Analytics is the systematic computational analysis of data or
    statistics
    •Provides insight on app usage and user engagement.
    •Helps you understand how your users behave

    View Slide

  9. Firebase has what you ned and more
    @ddinorahtovar

    View Slide

  10. How does an analytic looks in Firebase
    @ddinorahtovar
    firebaseAnalytics.logEvent("SomeNameOfTheEvent") {


    // Extra parameters if you need more info


    param(FirebaseAnalytics.Param.ITEM_ID, id)


    param(FirebaseAnalytics.Param.ITEM_NAME, name)


    param(FirebaseAnalytics.Param.CONTENT_TYPE, "image")


    }


    •On android

    View Slide

  11. How does an analytic looks in Firebase
    @ddinorahtovar
    Analytics.logEvent(AnalyticsEventSelectContent, parameters: [


    AnalyticsParameterItemID: "id-\(title!)",


    AnalyticsParameterItemName: title!,


    AnalyticsParameterContentType: "cont",


    ])
    •On iOS

    View Slide

  12. Conversion

    View Slide

  13. First, an example
    @ddinorahtovar
    app-ca app-email app-account
    🏆

    View Slide

  14. Conversion
    @ddinorahtovar
    •Conversion analysis is the process of analyzing data related to
    conversions
    •A conversion is defined as a specific, desirable action that’s
    taken by a user
    •Depends on the analytics selected

    View Slide

  15. Conversion
    @ddinorahtovar
    •Conversion rate can be calculated over a math func
    coR = totalConversions/totalInteractions
    coR = 50/1000
    coR = 5%

    View Slide

  16. Conversion
    @ddinorahtovar
    Conversion
    Event

    app-account
    Second Event

    app-email

    First Event


    app-ca

    View Slide

  17. Conversion
    @ddinorahtovar

    View Slide

  18. Conversion
    @ddinorahtovar
    •Conversions are
    related to multiple
    events
    •We decide which is
    the event we are
    interested

    View Slide

  19. Conversion
    @ddinorahtovar
    •Conversions are directly
    related to the num of users
    and the number of clicks on
    the conversion
    •But it also can be unique

    View Slide

  20. Conversion
    @ddinorahtovar
    •Conversions are directly
    related to time dimensions

    View Slide

  21. Retention

    View Slide

  22. Retention
    @ddinorahtovar
    •Retention analysis (or survival analysis) is the process of analyzing
    user metrics to understand how and why customers churn
    •Retention analysis is key to gain insights on how to maintain a
    profitable customer base by improving retention and new user
    acquisition rates

    View Slide

  23. Retention
    @ddinorahtovar
    Time - Survival probability
    0
    0.25
    0.5
    0.75
    1
    Time (months)
    0 2 3 5 7 9 10 12
    60% of probability
    of surviving beyond
    5 months
    - 25% of probability
    of surviving beyond
    11 months

    View Slide

  24. Retention
    @ddinorahtovar
    •Firebase - has this
    chart - you don’t have
    to do anything just
    plug the dependencies
    in your app and you
    are ready to go

    View Slide

  25. AB Testing

    View Slide

  26. But first - what is an prediction model
    @ddinorahtovar
    •Assume the
    probabilities
    for both data
    and
    hypotheses(para
    meters
    specifying the
    distribution of
    the data)
    Bayesian
    Frequentist
    •Assume the
    observed data is
    sampled from
    some
    distribution
    •Frequentist vs Bayesian

    View Slide

  27. Bayesian Models
    @ddinorahtovar
    •How does it looks
    More overlap = Less con
    fi
    dence
    Less overlap = More
    con
    fi
    dence

    View Slide

  28. Bayesian Models
    @ddinorahtovar
    •How does it looks

    View Slide

  29. Metrics we should know -
    or not! but we are gonna
    learn it!
    Dinorah Tovar

    Google Developer Expert - Android
    @ddinorahtovar
    @ddinorahtovar

    View Slide