Incremental Conversions#
Extra conversions caused by a treatment beyond the baseline.
Important
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What it is#
Incremental conversions measure the additional number of conversions caused by a treatment — a campaign, promotion or intervention — compared with what would have happened without it (the control). They are the extra conversions directly attributable to the treatment, not the raw total.
The formula#
The basic form is treatment conversions minus control conversions. To scale the effect to a full population, use the rate-based version,
where \(y^T/n^T\) and \(y^C/n^C\) are the treatment and control conversion rates and \(N\) is the total population you would target. This adjusts for the baseline rate and scales the per-user uplift up to everyone.
A worked example#
Run a campaign on 10,000 users: the treatment group (5,000) converts at 12% (600), the control group (5,000) at 8% (400). The rate difference is 4%, so across the full 10,000 the incremental conversions are \(0.04 \times 10{,}000 = 400\) — the extra conversions the campaign genuinely caused.
Why it matters#
Total conversions mislead, because some users would have converted anyway. Incremental conversions isolate the causal effect of the treatment, which is why they are the foundation of uplift modelling and incrementality testing across marketing, ads and A/B testing.
Theme: Causal Inference & Uplift · All terminology
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Mind map — connected ideas
Conversion Rate Uplift · Treatment Effect · Causal Effect · Incremental Revenue · Uplift · Causal Inference
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More in Causal Inference & Uplift
AUUC (Area Under the Uplift Curve) · Causal Effect · Causal Impact · Causal Inference · Causal ML (Causal Machine Learning) · Causal Trees · Cumulative Incremental Gain (CIG) · Cumulative Uplift · Incremental Gain · Incremental Recovery Rate (IRR) · Incremental Revenue · Incremental Sales · Qini Coefficient · Qini Curve
See also
Source article Adapted (context, re-expressed) in our own words from: Incremental Conversions (insightful-data-lab.com).