How to View the Percentage of Users Meeting Activation Metrics in Time?
how do I look at the % of users who have hit an activation metric, i.e. doing x actions in y days?
how do I look at the % of users who have hit an activation metric, i.e. doing x actions in y days?
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You can do this in a few different ways depending on whether you care about: - “Did X at least N times in M days and then converted?” (correlation to a goal), or - “How many users/accounts have at least N ‘active’ users?” (B2B activation), or - “How many users did an event on at least N days in a period?” (frequency-style activation).
1. Correlate “X actions in Y days” with a goal (Signal)
Use the Signal report when you want “doing X actions in Y days” as an activation metric tied to a goal event (e.g., retention or purchase): 1. Open Signal. 2. Choose your goal event (e.g., “Subscribed”, “Week 2 Retention”). 3. Choose the candidate event that represents activation (e.g., “Play Song”, “Send Message”). 4. Signal will evaluate combinations of: - Frequency: number of times the event was done (up to 10), and - Intervals: number of days in which it was done (up to 15). [Signal reference] 5. In the detailed view heatmap, look at the cell that matches “at least X times within Y days”; the Precision value there is effectively “% of users who converted among those who did X in Y days.” [Signal interpreting]This gives you the percentage of users who hit that activation pattern and then achieve the goal.
2. B2B / account-level activation (“number of users who did…”)
If your activation metric is “accounts with ≥ N active users (each doing X in Y days)”, and you have Group / Company Analytics: 1. Use a Group Analytics–enabled report (e.g., Insights or Funnels at company level). 2. Use the computed property “Number of users who did…” as a filter or breakdown. 3. Define the qualifying event and time window (e.g., “logged in ≥1 time in last 7 days”). 4. Filter to “Number of users who did… ≥ N” to get the set of activated accounts and see their share of all accounts. [Group activation metrics]This answers questions like “% of trial accounts with ≥2 active users.”
3. Frequency-style activation (“did on N days in a week/month”)
If your activation is “used the product on at least N days in a week/month”: 1. Open Retention and switch to Frequency mode. [Frequency view] 2. Choose the event that represents value (e.g., “Complete Purchase”, “Watch Video”). 3. Choose the interval (week or month). 4. Use Cumulative Frequency to see the % of users who did the event in at least X unique days in that interval. [Frequency view]That gives you “% of users who were active on ≥ N days in the period,” which is a common activation definition.---If you clarify whether your activation is user-level vs account-level and whether it must be tied to a specific goal event, I can point you to the single best report among these.
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Let me check the knowledge base for you again. One moment! 📚
You’ll model “3 actions in 9 days” as a behavioral/computed property and then use it to segment new signups.Because the docs don’t show this exact pattern end‑to‑end, I’ll outline the closest supported approaches and where they rely on general Mixpanel behavior.---
Option 1 – Use a behavioral property in Retention (closest to “X in Y days”)
Behavioral properties let you compute “frequency per user” of an event over a time range and then use that as a property in filters/breakdowns. [Computed props]1. Go to Retention.
2. Define your birth event as your signup event (e.g. Sign Up Completed).
3. Define your returning event as the same “core action” you used for your activation definition (e.g. Core Action).
4. Set the retention window to at least 9 days.
5. Add a behavioral property:
- Choose Frequency per User.
- Choose your “core action” event.
- Time range: After Step 1 (after signup) up to the expiration window. [Computed props]
6. Use that behavioral property:
- Either as a filter: Frequency per User (Core Action) ≥ 3 to isolate users who hit the threshold.
- Or as a breakdown to see buckets (0, 1–2, ≥3 actions).Then: - Set Measurement to Unique Users to see the count of new signups who hit the threshold. [Retention quick start]
- Divide by total new signups in the same period (or add a second metric for total signups) to get the percentage.> Limitation: The docs don’t show a built‑in “% of signups who did event ≥3 times in 9 days” metric, so you’re effectively using behavioral properties + counts to derive that percentage.---
Option 2 – Use Signal only to validate the 3‑in‑9 rule (not to get the %)
Signal is what you likely used to discover that “3 actions in 9 days” correlates with conversion; it shows the precision for “did event ≥X times within Y days” vs a goal event. [Signal overview;Signal reference]- In Signal, set: - Goal event: your paid conversion. - Correlation event: your core action. - In the heatmap, the cell for “3 times in 9 days” shows: - Precision = % of users who converted among those who did 3 in 9 days. [Signal interpreting]Signal does not directly show “% of all new signups who did 3 in 9 days,” so you still need Option 1 (or an Insights‑style query) to get that base rate.---
Option 3 – Cohort of “activated” users, then compare to all new signups
You can also define a cohort of users who meet the “3 actions in 9 days” condition and then compare its size to all new signups. Cohorts can be defined by “users who did <event> more than <threshold> in a time range.” [Cohorts]Workflow: 1. In a report (e.g. Insights or Retention), use the behavioral property Frequency per User on your core action over the first 9 days after signup (as above).
2. Filter to Frequency per User ≥ 3.
3. Use View Users → Save as Cohort to save this as “Activated: 3 actions in 9 days.” [Cohorts via report]
4. In Users → Cohorts, you can see the cohort size and compare it to:
- A cohort of “New Users” (e.g. “signed up in last 30 days”). [Cohorts examples]
5. The ratio Activated cohort size / New Users cohort size is your % of new signups who hit the activation threshold.---Because the docs don’t show a single canned report that outputs “% of new signups who did event ≥3 times in 9 days,” the most reliable pattern is: - Use behavioral properties to compute “# of core actions per user in first 9 days after signup,” then
- Use filters/breakdowns + cohorts to get the count and percentage of signups who reach ≥3.
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