1. Active Users - DAU/WAU/MAU - daily/weekly/monthly

• Measures growth. Insight into how many users are actually using your product
• Determine whether DAU, WAU or MAU is a more appropriate measure for your specific product
• If you want to understand product usage or adoption, can measure on feature level

2. Stickiness - DAU/MAU, DAU/WAU, WAU/MAU, MAU/QAU, QUA/YAU - ratio

• Key indicator for continued engagement
• Measure % monthly active users who return daily
• Due to limitations of individual DAU,MAU etc. companies have moved to ratios
• Advantages is that this ratio measures growth as well as stickiness simultaneously
• Often seen as truer measure of engagement
• Choose which ratio is applicable based on whether product is a daily/weekly/monthly/quarterly/yearly usage category
• Eg. DAU/MAU = 50% means users engage with your app on an avg. 15 out of 30 days
• Eg. DAU/WAU >= 60%, then it's a daily used product. Tells ppl use product more than 4 days a week
• Eg. WAU/MAU >= 60%, then it's a weekly used product
• Eg. MAU/QAU >= 60%, then it's a monthly used product
• Eg. QAU/YAU >= 60%, then it's a quarterly used product

3. User Retention or Cohort Retention

• Looks at first time users within a specific time frame (typically 1 month or 1 week)
• and calculates the % of users that return in subsequent time periods
• User Retention looks at the individual who logs in to use a product (a usage metric), while
• Customer Retention looks at the account that pays for access to a product (a financial metric)
• Retention analytics can be used to answer questions like:
  • Do large or small accounts have higher retention rates?
  • What features are used most heavily by users who return regularly?
  • How does retention vary across different customer personas?
  • Is retention consistent among different marketing channels?
  • How well is a new feature performing?

4. NPS - Net Promoter Score

• Customer satisfaction metric
• Measure of your customer's loyalty to your product and business
• can be segmented by use case, location, size of account etc...
• You can use NPS to indicate which segment of users are unhappy and therefore more likely to churn

Insight into marketing, sales, revenue and growth

1. Conversions

• Measures effectiveness
• Eg. % who convert from free trial to paid
• Eg. % who visit signup form / who complete signup

2. MRR - Monthly Recurring Revenue

• Measure of what's being brought in by month
• can breakdown MRR into specific segments: New business MRR, expansion MRR, churned MRR, total MRR.

3. CAC - Customer Acquisition Cost

• Measures how much you are spending to acquire new customers
• Good metric during growth phase to determine whether you are operating efficiently

4. LTV - Customer Lifetime Value

• Projection of how much revenue accounts will bring in over their lifetime
• calc can be tricky, several ways, pick method most relative to your business
• Eg. If business is shifting focus towards larger enterprise accounts, then look at changes in Avg. LTV over time to measure progress towards this objective
• LTV when used alongside an efficiency metric like CAC can give better grasp on how efficient your acquisition strategy is

5. Churn Rate - monthly/quarterly/annual

• Measures people leaving
• Eg. % of accounts who cancel or choose not to renew subscription
• Churn Rate = (Customers lost in a period) / (Customers at the beginning of the period)

6. ARPU - Avg. Revenue Per User
7. TAM - Total Addressable Market

• A superset of all current and potential users

Ref: Cohort Analysis: Beginners Guide to Improving Retention

• Subset of Behavioural analytics
• Rather than looking at all users as one unit, it breaks them into related groups for analysis.
• These related groups, or cohorts, usually share common characteristics or experiences within a defined time-span.
• Tool to measure user engagement over time.
• It helps to know whether user engagement is actually getting better over time or is only appearing to improve because of growth.
• Valuable because it helps to separate growth metrics from engagement metrics as growth can easily mask engagement problems.
• In reality, the lack of activity of the old users is being hidden by the impressive growth numbers of new users, which results in concealing the lack of engagement from a small number of people.

Daily cohort of users who have launched an app first time and revisited the app in the next 10 days. Acquisition Cohorts: Finding Problem Moments in Your App Behavioral Cohorts: Customer Retention Analysis