Project Health Equality
Data-Backed Playbook for Offline Screening Lift
Overview
We built the measurement infrastructure before we could prove the outcome—linking media timing to offline mammography utilization across 3 geographies, with a 90-day lag built to reflect the real screening journey, not standard attribution windows
Goal
Drive real-world mammography utilization among Black women despite disparities in access and conversion—and build a repeatable measurement framework so future BCAM* cycles have evidence to plan from, not assumptions.
Team
Product, analytics & optimization, data science, omnichannel
Role
Owned the activation strategy across experience design, go-to-market, and measurement, aligning campaign execution with a repeatable framework for evaluating real-world screening behavior.
Problem
Completed mammograms happen offline, while access, trust, and capacity constraints can erase digital intent before it becomes screening behavior
The challenge was not awareness alone—it was proving whether BCAM* media drove completed mammograms across 3 geographies despite a 90-day conversion lag, unstable local baselines, and offline care pathways. That mattered because Black women face 38% higher breast cancer mortality despite 5% lower incidence1.
The true outcome was offline
Standard web metrics could not prove screening lift
- Completed mammograms happened in clinics, not on websites, so the outcome had to be measured through offline utilization data rather than digital conversion events.1, 2
- CDC notes that mortality differences reflect inequities across the full continuum, including screening, follow-up, and treatment.2
- Standard attribution couldn't answer the question. We had to build the measurement method—which is why the framework is the durable product, not the campaign.
The behavior was delayed
Screening often converted weeks later, not immediately
- Black women were 40% less likely than White women to receive diagnostic imaging follow-up within 15 days after an abnormal screening mammogram.3
- When biopsy was recommended, Black women waited 1.75x as long to obtain a tissue diagnosis.3
Baselines were noisy
Lift detection was unstable because local conditions also changed outcomes
- In a cohort of 26,476 women, disparities appeared across the screening continuum, not just at the initial screen.4
- Offline screening outcomes were also affected by follow-up access, treatment coordination, and facility resources, which made local baselines harder to interpret.2, 5
The equity stakes were high
The outcome mattered because the disparity was large
- The measurement stakes were high because the health stakes were high. Black women face 38% higher breast cancer mortality despite 5% lower incidence1 — which meant proving lift wasn't just analytics rigor. It was the evidence base for where to invest next.
- The disparity is longstanding; CDC previously reported Black women were 40% more likely to die of breast cancer than White women.6
*Breast Cancer Awareness Month (BCAM); 1. American Cancer Society, Breast Cancer Facts & Figures 2024–2025; 2. CDC MMWR, Racial Disparities in Breast Cancer Severity and Survival; 3. Manik et al., 2024 review, Racial Disparities and Strategies for Improving Equity in Breast Imaging; 4. Ganguly et al., 2023, Racial disparities in the screening mammography continuum; 5. Karliner et al., 2018, Mammography facilities serving vulnerable women; 6. CDC Vital Signs, Breast Cancer
Solution
We built a 3-geo offline-lift framework with a 90-day lag to estimate whether BCAM media drove completed mammograms
The solution was a practical measurement playbook, not a perfect attribution model. We aligned pre-, during-, and post-flight periods to campaign timing, added a 90-day lag to reflect the real screening journey, and used Unifi exam volumes to estimate directional lift by market.
Multi-geo lift detection
Hologic's Unifi network provided anonymized mammography exam counts across Houston, Atlanta, and Miami—allowing us to compare screening volume trends during and after campaign flight periods against pre-campaign baselines.
90-day lag window
Instead of standard 7- or 30-day attribution, we built a 90-day post-campaign window to capture the full scheduling-to-screening behavior cycle, acknowledging that real-world screening journeys don't happen instantly.
Baseline comparison approach
Pre-flight screening volumes established local baselines for each geography, so we could detect directional lift—even if local conditions (facility capacity, competing health events) introduced noise into the signal.
Confounder tracking
We documented external factors (local health campaigns, facility closures, pandemic disruptions) alongside exam volume data to help separate media-driven lift from environmental noise—critical for building a repeatable playbook.
Measurement Framework
Campaign Inputs
Geo selection
Flight timing
Message penetration
Unifi exam volumes
Lift Model
Pre / during / post periods
90-day lag window
Baseline comparison
Significance check
Decision Outputs
Lift by market
Confounder readout
Geo prioritization
Next-cycle playbook
This framework made the next cycle plannable—not just reportable. Geo selection, flighting, and investment decisions now have an evidence base instead of assumptions.
Scale
Houston showed a +4.7% statistically significant lag effect, while Miami was positive at +2.8% and Atlanta appeared confounded at -5.5%
The results mattered less as a campaign score and more as a market readout. Houston's lag-only lift pointed to scheduling behavior; Miami's sustained lift suggested awareness plus follow-through; Atlanta's -5.5% correlated with COVID-driven utilization spikes—not media failure. Knowing the difference is what makes future geo selection defensible.
| Geo | Est. message penetration | Window evaluated | Δ Exams (est.) | Lift vs baseline | Significance / readout |
|---|---|---|---|---|---|
| Houston | 27-32% | Lag only | +4,563 | +4.7% | Post-flight lift consistent with scheduling delay |
| Atlanta | 33-38% | During + Lag | -10k | -5.5% | Likely confounded |
| Miami | 23-27% | During + Lag | +3,600 | +2.8% | Sustained lift suggests awareness + follow-through |
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Signals captured
The strongest signal appeared where baseline patterns and operational constraints allowed conversion—which made local context as important as media performance.
Intelligence
The analysis became a repeatable BCAM measurement + optimization playbook built around capacity, baseline stability, and care-pathway readiness
The most durable product outcome was not the Houston result—it was the playbook. We translated the lift analysis into a geo selection framework, flighting strategy, and reporting template—identifying the conditions under which awareness converts: capacity, appointment access, provider alignment, and a stable geo baseline.
Impact Factors
Capacity constraint · baseline volatility · competing campaigns · journey friction
Conversion Conditions
Capacity available · appointment access · reminder / retargeting · provider alignment · geo baseline stable
Playbook Outputs
Geo selection framework · flighting strategy · reporting template · stronger quasi-experimental measurement roadmap
This reframed the work from campaign reporting into a decision product—future plans optimized for outcome conditions, not just impressions.