Calculate Key Metrics

Build a dataset to calculate totals, averages, and conversion rates using your own data. Explore how these metrics have been changing over time and set up an ongoing sync to a google sheet to share with other teams.


  • Make faster decisions by pulling any KPI within minutes
  • Always have up-to-date numbers where you need them using automated Google Sheet syncs

Part 1: Creating a Basic Dataset

How to define a dataset (cohort activity) [more info]

Understanding your dataset

  • What is activity occurrence? [more info]
  • What is unique identifier?
  • What is revenue impact?
  • What is the record count?
  • What are the summary metrics?

Adding more features to your dataset

  • Add customer attributes to your dataset [more info]
  • (later) Add features from another activity to your dataset [more info]

Adding computations to your dataset

  • What are computed columns? [more info]
  • Adding a label for “First-time” vs “Recurring” based on Activity Occurrence.

Choosing occurrence

📝 Practice: Create a basic dataset

Create a dataset that includes every time a customer did a specific activity (ideally an activity that generates revenue for your company).

  • Did you choose ALL or FIRST?
  • How many times did that activity happen overall?
  • What is the timestamp of the most recent time someone did that activity?
  • Now add another column that labels when someone is a FIRST TIME vs RECURRING Visitor?

Part 2: Calculating Counts and Averages

Computing Counts and Averages

  • Understanding counts by activity feature
  • Refresher: How Group By tabs relate to the parent tab [more info]
  • How to add a group by tab [more info]
  • Using column shortcut to add a group by tab for a feature [more info]
  • Auto-computed metrics in a group by tab
  • Computing additional metrics to Group By tabs [more info]
  • Averages, Median, Percentile, etc

Metrics over Time

  • Understanding counts over time [more info]
  • Using column shortcuts to create a time-based group by tab [more info]
  • Using plots to visualize data [more info]
  • Saving plots to datasets

Bonus reading:

📝 Practice: Calculate a count and average over time

Using the dataset you created in the last practice, can you calculate how many times it has happened each week?

  • Now add a plot to visualize the volume by week
  • What if you wanted to understand the average of the revenue for that week? Can you create a plot for that too?

Part 3 Calculating Conversion + Retention Metrics

Calculating Conversion to Another Activity

  • What are relationships? [more info]
  • FIRST INBETWEEN: Used for Conversion Rates

Understanding Append Activity Features

  • Did XXX
  • Timestamp of XX
  • Days to XXX
  • Adding more append activity features [more info]

Calculating Conversion Rate [more info]

  • Understanding additional default metrics

Calculating Conversion to the SAME Activity (Retention) [more info]

  • Using FIRST IN BETWEEN to calculate retention rate

Bonus reading:

📝 Practice: Calculate retention for the activity in your dataset.

  • What is the likelihood that a customer will do that activity again?
  • Can you understand how that retention rate has been changing over time (by week)?
  • What if you wanted to understand how it changes as that customer does it more?

Part 4: Syncing your data to other systems

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