Estimating Highway Billboard Reach and Impressions
An out-of-home advertising team wants to estimate reach and impressions for a single highway billboard over a specified time window, such as one week.
Design a defensible estimation approach suitable for a data-science interview.
Constraints & Assumptions
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Define the billboard face, direction of travel, time window, and whether the board is static or digital.
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Distinguish passings, viewable impressions, reach, and frequency.
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Make assumptions explicit for traffic volume, vehicle occupancy, mobile-device representativeness, and visibility.
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Discuss uncertainty rather than presenting the estimate as exact.
Clarifying Questions to Ask
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What is the campaign time window and share of voice for the billboard?
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Which lanes and travel directions can see the billboard?
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Is the objective total impressions, unique reach, or frequency distribution?
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What data is available: DOT counts, mobile location samples, camera counts, ad-server logs, or survey calibration?
Part 1 - Define Metrics and Scope
Define impressions, reach, frequency, and the measurement scope.
What This Part Should Cover
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Impressions as expected ad views, not merely vehicles passing the location.
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Reach as unique people with at least one exposure during the window.
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Frequency as impressions divided by reached people or as a distribution.
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Scope choices for direction, daylight, weather, digital rotation, and audience segment.
Part 2 - Estimate Passings and Exposure Probability
Describe how to convert traffic into expected impressions.
What This Part Should Cover
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Segment traffic by time, direction, lane, vehicle type, speed, and weekday or weekend.
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Combine traffic counts with vehicle occupancy and a visibility or attention probability.
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Account for digital share of voice, view angle, time in view, obstructions, lighting, and weather.
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Produce a formula such as sum of passings times occupancy times viewability times share of voice.
Part 3 - Estimate Unique Reach
Describe how to estimate the number of unique people exposed at least once.
What This Part Should Cover
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Use mobile-panel IDs, origin-destination data, commute recurrence, or modeled duplication rates.
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Correct for panel bias and device-to-person scaling.
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Avoid double-counting repeat commuters across days.
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Report uncertainty intervals and sensitivity to duplication assumptions.
Part 4 - Validate and Communicate the Estimate
Explain how you would validate the model and present results.
What This Part Should Cover
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Compare against camera counts, independent traffic sources, mobile exposure vendors, or brand-lift surveys.
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Run sensitivity analysis for occupancy, viewability, and repeat-rate assumptions.
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Communicate point estimates with confidence ranges and caveats.
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Separate operational measurement limitations from decision recommendations.
Follow-up Questions
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How would the method change for a digital billboard with rotating creatives?
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How would you estimate incremental reach across multiple billboard locations?
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What bias would you expect from mobile-location data, and how would you correct it?