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Net Impact (Your Audience vs Control) compares healthcare providers (HCPs) who saw your campaign against a closely matched control group. Wrango aligns the two groups on clinical background and pre-campaign prescribing behavior using 180-day, 90-day, and 30-day lookback windows before exposure, so lift reflects campaign influence rather than baseline differences.

Executive summary

Script Lift measures campaign effectiveness with an apples-to-apples comparison. Exposed HCPs are matched to controls who were similarly qualified and similarly likely to prescribe before the campaign started. Multi-stage matching, behavioral calipers, and outlier controls isolate prescribing lift driven by media exposure rather than geography, training, or volume alone. Shorter windows inside the 180-day pre-campaign period capture recent momentum and prescribing cadence, not just total volume.

Engineering a statistical twin

Wrango builds the control group through deterministic matching and caliper filtering. Outlier mitigation and backfill keep cohorts balanced so reported lift reflects marketing impact.
  • Ability to prescribe: Start from a 1:10 pool matched on credentials, state, and specialties so controls are licensed and trained like exposed HCPs.
  • Potential to prescribe: Narrow to a 1:2 pool using the 6-month pre-exposure window, matching category prescription volumes.
  • Trend shape: 90-day and 30-day category prescription windows and active-month counts help the control mirror both scale and recent trend.
  • Precision filtering: A Whale Purge removes the 99.95th percentile of outliers. Remaining candidates are ranked to lock a 1:1 identical twin based on category prescription volume similarity.

Nested pre-campaign windows

WindowRole
180 daysPrimary pre-campaign category Rx volume and active-month counts for matching
90 daysShows whether an HCP was accelerating, stable, or cooling off before exposure
30 daysCaptures immediate pre-campaign cadence
Active-month counts across 180-day and 90-day lookbacks prevent matching a sporadic prescriber to a consistently active one when totals look similar. 90-day-to-180-day and 30-day-to-90-day ratios help confirm exposed and control HCPs had a similar momentum profile before the campaign.

Phases

Before prescription behavior is evaluated, Wrango builds a pool of professional peers.
  • Exposed audience: HCPs with validated campaign impressions, using a last-touch attribution model.
  • Clinical tier matching: Controls are mapped to exposed HCPs using three descending tiers:
    • Tier 1 (High): Primary specialty, secondary specialty, state, and credential (exact match).
    • Tier 2 (Medium): Broadened slightly when exact matches are scarce.
    • Tier 3 (Low): Broadest acceptable baseline to preserve pool volume.
  • Initial ratio: Up to 10 control candidates per exposed NPI (1:10).
TierMatch criteriaPurpose
Tier 1 (High)Primary specialty + secondary specialty + state + credentialClinical gold standard for peerage
Tier 2 (Medium)Primary specialty + secondary specialty + stateClinical depth with state-level demographics
Tier 3 (Low)Primary specialty + stateRegional fail-safe for volume
Peers must show similar historical prescribing so they had the same potential to prescribe the target brand.
  • Pre-campaign lookback: Category Rx volume over 180 days, plus nested 90-day and 30-day windows.
  • Prescribing cadence: Active writing months across 180-day and 90-day lookbacks.
  • Caliper match: Control volume must fall within ±15% or ±2 scripts of the exposed NPI (whichever is larger).
Pair-quality gate:
  • 180-day active-month gap: within 2 months
  • 90-day category volume gap: within 30% of the exposed HCP’s 90-day total, with a 1-script minimum buffer
  • 30-day category volume gap: within 40% of the exposed HCP’s 30-day total, with the same 1-script minimum buffer
The qualified pool is reduced to a balanced 1:1 exposed-to-control ratio.
  • Stage 1 (best twin): Select the single best twin per exposed NPI by lowest absolute variance in pre-campaign category volume.
  • Tie-breakers: After the 180-day volume comparison, ties break on 90-day volume gap, 30-day volume gap, 180-day active-month gap, then 90-day active-month gap.
  • Stage 2 (unmatched recovery): If an exposed NPI has no match, unused controls are reconsidered when:
    • 180-day volume gap within 10% of the exposed HCP’s 180-day total
    • 90-day gap within 20% of the exposed 90-day total
    • 180-day active-month gap within 1 month
Mutual ranking then assigns the closest available replacement twin.
Extreme prescribers are capped so they do not skew results.
  • 99.95th percentile rule: Maximum TRx limit from the 99.95th percentile of total prescriptions in the exposed group.
  • Capping: Controls whose post-exposure TRx exceed that boundary are excluded from final aggregation.
  • Symmetric pair removal: If either side of a matched pair breaches the whale threshold, the full pair is removed so exposed and control counts stay balanced.
  • Pair-integrity guard: Post-match TRx gap between paired HCPs must stay within a 3-script or 1.8x band, whichever is more protective for that pair.
After matching and capping, outcomes are measured in the post-exposure window from 0 to 180 days after last exposure.
  • Total Prescriptions (TRx): Distinct claim numbers in the window.
  • New Prescriptions (NRx): Distinct claims flagged as a new fill.
  • New patient: No prior claims on record, or gap since prior claim exceeds 180 days.
  • New prescriber: No prior claims on record, or gap since last prescription exceeds 720 days.
Aggregated metrics roll up by campaign, destination, and channel to show lift and clinical impact.
  • Lift calculation: Exposed volume minus control volume (difference in differences).
  • Confidence intervals: 80%, 85%, 90%, and 95%.
  • Margin of error: Applied from sample sizes and variance for both groups.

What this enables

After matching completes, the Growth tab shows Net Impact (Your Audience vs Control) with lift percentages, confidence levels, and significance labels that reflect incremental prescribing rather than baseline differences. See the Script Lift report for how those metrics appear in the product.