# 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.

Outcomes

• 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

• What is unique identifier?
• What is revenue impact?
• What is the record count?
• What are the summary metrics?

• 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]
• Auto-computed metrics in a group by tab
• Averages, Median, Percentile, etc

Metrics over Time

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

### 📝 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

• FIRST INBETWEEN: Used for Conversion Rates

Understanding Append Activity Features

• Did XXX
• Timestamp of XX
• Days to XXX

• Using FIRST IN BETWEEN to calculate retention rate

### 📝 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?