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On the Growth tab, Net Impact (Your Audience vs Control) applies this methodology by comparing exposed prescribers (healthcare providers who saw your campaign) to a carefully constructed control group of similar, unexposed peers. Rather than relying on raw prescription totals alone, Wrango uses a test-and-control comparison to isolate campaign-related change from underlying prescribing behavior. The goal is to estimate net impact in a way that reflects true incremental lift rather than baseline differences across audiences.

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Nested pre-campaign windows

To evaluate comparability before exposure, Wrango reviews historical prescribing behavior across multiple pre-campaign lookback windows. Using more than one lookback period helps distinguish long-term prescribing patterns from more recent changes in activity. This allows the methodology to assess both baseline prescribing level and recent momentum before a campaign begins.

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Phases

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Phase 1: Clinical qualification and base pool definition

Wrango begins by building a candidate control pool for each exposed prescriber using a staged peer-matching approach. This process identifies unexposed prescribers who are clinically and professionally similar to the exposed audience, creating a strong starting population for further comparison. The purpose of this phase is to establish a relevant peer set before deeper behavioral screening begins.

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Phase 2: Behavioral alignment and caliper filtering

After the initial control pool is created, Wrango evaluates whether candidate controls and exposed prescribers show comparable historical prescribing behavior. This phase applies behavioral similarity checks across pre-campaign activity patterns to ensure both groups had a similar baseline opportunity to prescribe the brand or category. Candidates that do not meet quality standards are removed before final matching.

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Phase 3: 1:1 twin optimization and recovery

From the qualified candidate pool, Wrango selects the closest available control for each exposed prescriber. This stage is designed to create a balanced one-to-one matched comparison using a multi-factor similarity framework. When an initial match is not available, additional matching steps may be used to preserve both sample quality and methodological consistency.

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Phase 4: Outlier mitigation (Whale Cap)

Wrango applies outlier controls to reduce the influence of unusually high-volume prescribing behavior on final results. These safeguards help prevent a small number of extreme observations from disproportionately affecting aggregate lift calculations. Outlier handling is applied in a way that maintains balance between exposed and control groups and protects the integrity of matched comparisons.

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Phase 5: Clinical measurement

Once matching is complete, Wrango measures prescribing outcomes during the post-exposure observation period. These outcomes include clinically relevant prescription metrics that reflect both overall prescribing activity and new prescribing behavior. Comparing exposed prescribers with their matched controls provides the basis for estimating incremental campaign impact.

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Phase 6: Statistical significance and confidence reporting

After outcomes are measured, Wrango aggregates exposed-versus-control differences into net impact metrics and supporting statistical outputs. Results are reported with confidence ranges and significance indicators to help you assess both the magnitude of observed lift and the reliability of the finding. This provides a clearer view of campaign performance by pairing impact estimates with statistical context.

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What this enables

This methodology is designed to help you evaluate campaign performance through the lens of incrementality rather than simple correlation. By combining matched controls, behavioral alignment, outcome measurement, and confidence reporting, Script Lift provides a more reliable view of whether campaign exposure contributed to meaningful prescribing change. After matching completes, the Growth tab shows Net Impact (Your Audience vs Control) with lift percentages, confidence levels, and significance labels. See the Script Lift report for how those metrics appear in the product.