Sophisticated matching techniques create analytically equivalent groups to measure program lift more conservatively.
Pre-Join Matching
Members and non-members are matched based on their behavior before the member joined — transactional characteristics, frequency, and order value.
Nearest Neighbor Matching
Individual members are matched to analytically similar non-members, then behavior is compared in the post-period to isolate program effects.
Control Variables
Models control for external factors like location effects and household-level data to isolate the true program lift from confounding variables.
1 Propensity Score Matching
Calculate a single score representing each customer’s probability of joining, then match members to non-members with similar scores.
- Reduces dimensionality — one score vs. many variables
- Creates balanced comparison groups on observable characteristics
- Assumes no unobserved confounders (unlike Heckman)
- Works well when you have rich pre-join behavioral data
2 Difference-in-Differences
Compare the change in spending for members vs. matched non-members from pre- to post-period.
- Controls for time-invariant differences between groups
- Accounts for external trends affecting everyone equally
- True lift = (Member post − Member pre) − (Control post − Control pre)
- Requires parallel trends assumption to hold
Difference-in-Differences Lift Calculator
Enter average spend for your members and their matched control group, before and after the join date. The calculator separates true program lift from the market trend that would have happened anyway.