Onboarding Activation Impact Model
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Determine how reducing onboarding steps impacts user activation rates by modeling drop-off per step.
NOTEAbout This Model
## Determine how reducing onboarding steps impacts user activation rates by modeling drop-off per step. This model estimates **Projected Activation Rate** based on uncertain inputs: Reduced Onboarding Steps, Current Activation Rate, Drop Off Rate Per Step. Run the simulation to see the distribution of possible outcomes.
VARIABLECurrent Activation Rate
beta(6, 14)
Typical baseline activation for SaaS onboarding flows (20-40%).
current_activation_rate
VARIABLEDrop Off Rate Per Step
beta(2, 18)
The probability a user drops off at any single step in the funnel (5-15%).
drop_off_rate_per_step
VARIABLEReduced Onboarding Steps
poisson(5)
A conservative to aggressive reduction from the current 7 steps.
reduced_onboarding_steps
FORMULAProjected Activation Rate
max(0, min(1, current_activation_rate * pow(1 - drop_off_rate_per_step, reduced_onboarding_steps - 7)))
Calculation of estimated activation rate after reducing steps, assuming drop-off occurs independently at each step.
projected_activation_rate
OUTPUTProjected Activation Rate
projected_activation_rate
Projected user activation rate after onboarding improvements. Higher activation correlates with better retention and LTV. The spread shows how sensitive activation is to your assumptions.
What is Carlo?
Carlo is a visual tool for Monte Carlo simulation. Model uncertainty by dragging probability distributions, connecting them visually, and running thousands of scenarios instantly.