Onboarding Activation Impact Model
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Quantify the expected lift in user activation by reducing onboarding friction.
NOTEAbout This Model
## Quantify the expected lift in user activation by reducing onboarding friction. This model explores how **Activation Lift** could play out, depending on Drop Off Rate Per Step, Activation Rate Baseline. Hit **Run** to see the range of possibilities.
VARIABLEDrop Off Rate Per Step
beta(2.5, 17.5)
Typical SaaS onboarding steps see 5-20% drop-off depending on complexity.
drop_off_rate_per_step
VARIABLEActivation Rate Baseline
beta(6, 14)
Baseline intent levels vary; this represents the probability a user activates if they finish all steps.
activation_rate_baseline
FORMULABaseline Activation Rate
pow(1 - drop_off_rate_per_step, 7) * activation_rate_baseline
Probability of a user completing all steps in the current onboarding flow.
baseline_activation_rate
FORMULANew Activation Rate
pow(1 - drop_off_rate_per_step, 4) * activation_rate_baseline
Probability of a user completing all steps in the streamlined onboarding flow.
new_activation_rate
FORMULAActivation Lift
new_activation_rate - baseline_activation_rate
The percentage point increase in activation rate from reducing steps.
activation_lift
OUTPUTActivation Lift
activation_lift
Track the activation lift output
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.