Customer Segmentation Lift Model
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Determine the total customer conversions resulting from a segmentation-driven marketing campaign considering lift and cluster quality.
NOTEAbout This Model
## Determine the total customer conversions resulting from a segmentation-driven marketing campaign considering lift and cluster quality. This model estimates **Total Conversions** based on uncertain inputs: Cluster Quality, Campaign Response Rate Base, Lift Factor. Run the simulation to see the distribution of possible outcomes.
VARIABLECampaign Response Rate Base
beta(2, 18.9)
The historical baseline response rate for unsegmented 'blast' campaigns.
campaign_response_rate_base
VARIABLELift Factor
lognormal(0.3918, 0.1655)
The theoretical multiplier on response rate achievable through perfect segmentation.
lift_factor
VARIABLECluster Quality
beta(14.5, 5.5)
Represents the accuracy and relevance of the clusters; lower quality dilutes the expected lift.
cluster_quality
FORMULAAdjusted Response Rate
campaign_response_rate_base * (1 + (lift_factor - 1) * cluster_quality)
Response rate for segmented customers adjusted for the quality of the clustering logic.
adjusted_response_rate
FORMULATotal Conversions
customer_base * adjusted_response_rate
Total number of responding customers based on the improved segmentation strategy.
total_conversions
OUTPUTTotal Conversions
total_conversions
Expected number of conversions from the targeted segment. Higher values validate the segmentation strategy; compare against the untargeted baseline to measure true lift.
CONSTANTCustomer Base
10000
Total number of customers targeted in the campaign.
customer_base
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.