Loading

Robin
One Booster

Product Metrics

9/23/2019 · 2 min read

Metrics to validate before launching a cleaner utility product:

Core metrics

  • Macro data (product health)
    • Day-1 active retention
    1. First use did not feel like it solved the problem
    2. First-run permissions or ads repelled users
    3. A fatal bug blocked the experience
    4. User was not in the target segment
    • Day-1 survival rate

    Ensures the product stays alive — dead products are game over Day-1 survival rate = (Day-1 uninstall retention − Day-1 active retention) / Day-1 uninstall retention

    • Stickiness rate

    Stickiness = users who used the feature twice that day / DAU Measures quality of active users and the value they bring

    • Day-3 active retention

    Validates loyalty. Days 1–3 often include onboarding and ads — a full first experience cycle.

  • Feature-level
    • Feature click-through rate

    Which features users prefer and which paths they take

    • Feature retention (7-day)

    Whether the feature solves the problem: share of feature users who return. Cleaning need recurs ~every 7 days, so 7-day window.

    • Feature usage funnel

    Where drop-off is highest in the flow

Business metrics (monetization)

  • Total daily ad impressions

Impressions per user

  • Day-1 active retention

Informs product lifecycle length

  • Daily active users

Basis for per-user calculations

  • Daily total revenue

Daily revenue per user

  • eCPM by placement

Revenue power of each slot; prioritizes optimization

  • Daily impressions by placement

Frequency per user = impressions / DAU Impression frequency per placement

—

Decompose ROI to find business metrics and levers

Analysis:

  1. Decompose ROI (per user):

    ROI = LTV / CPA

  2. Decompose LTV:

LTV (lifetime value) = LT × ARPU

1. Decompose **LT**:
> LT = sum of all retention rates (Ga theory)
> **Higher retention → higher LTV**
 
1. Decompose **ARPU**
> **ARPU** (avg revenue per user) = daily total revenue / DAU
 
3. Decompose **daily total revenue**
>  Daily total revenue = (Ad A eCPM × impressions) + (Ad B eCPM × impressions) + (Ad C eCPM × impressions) ...
> <br>
> Variables affecting daily revenue:
> - **Impression frequency per placement**
> - **Total ad slots** (not a metric to optimize directly)
> - **eCPM per placement**

Conclusions:

  • Business metrics:
    • Retention rate
    • Total ad impression frequency
    • Impression frequency per placement
    • eCPM per placement (benchmark metric, not a primary optimization target)
  • Levers to improve business metrics:
    • Number of ad slots
      • Add features that can carry ads
    • Usage frequency of ad-bearing features
      • Improve onboarding success: match scenarios, raise guided conversion
      • Increase guided touchpoints where scenarios fit
      • Shorten paths to reduce usage cost
    • Retention rate
      • Improve product experience
      • Optimize traffic sources

Related posts