Zip Code Level Precision, Market Level Impact

The solution started with a structured geospatial workflow: (1) aggregate sales by zip code, (2) combine first-party performance/sales data with Census data to isolate roughly the top ~10 variables where LaserAway customers over- or under-index, and (3) use those variables to score every zip code to identify overperforming, underperforming, and “as-expected” areas. From there, zips were tiered to quantify the opportunity set and guide where to test zip-level activation.

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Next, the team selected a test market through a blend of data-driven recommendations, geospatial visualization, and real-world business context, identifying Virginia as a viable test state given budget, opportunity size/quality, and practical considerations. A 30-day Reach & Frequency campaign was executed, and impact was evaluated using adjacent markets and a statistical match approach to define control markets. Finally, lift was validated multiple ways (zip-level reads, DMA-level reads, an AA-test check for seasonality directionality, and visual analysis) to ensure observed performance was not an artifact of timing or market noise.