101 OOH SSP guide to setting up the ‘Data Prepping for your OOH Inventory.'
- Vineet Singh
- Jul 11
- 1 min read
Modern OOH clustering typically incorporates traffic data, mobile location intelligence, demographic overlays, point-of-interest data, and attribution measurements. Weather data, seasonality patterns, and competitive advertising presence can also inform clustering decisions.
The goal is creating actionable audience or location segments that improve campaign targeting, budget allocation, and overall OOH advertising effectiveness.
This article outlines a concise strategy for collecting and preparing data to evaluate the performance of Out-of-Home (OOH) advertising campaigns. It focuses on identifying core performance metrics, contextual variables, and the importance of data standardization for effective analysis.
1~ Data Collection and Preparation
Core Performance Metrics: Gather key performance indicators for each OOH location: impressions (traffic counts, verified audience measurement), engagement metrics (dwell time, interaction rates for digital displays), conversion attribution (foot traffic lift, sales attribution), cost efficiency (CPM, cost per visit), and reach/frequency data.
2~Contextual Variables
Contextual Variables includes location characteristics like visibility scores, proximity to competitors, demographic profiles of surrounding areas, format type (billboard, transit, digital), and environmental factors (weather impact, seasonal variations).
3~Data Standardization
Data Standardization normalizes metrics to comparable scales since performance indicators often have vastly different ranges. Use z-score normalization or min-max scaling to ensure clustering algorithms weight variables appropriately.
The key is creating clusters that align with business decisions - each cluster should have distinct media planning implications and clear performance expectations that guide buying strategies.The upcoming Tefologic post will be on the ‘Performance clustering for the OOH inventory’.