A travel company is creating a promotional campaign for frequent flyers who have not booked a trip in the past six months. Which segmentation strategy is appropriate?

Question

Options

• AUse a dynamic segment based on recent booking behavior.
• BCreate a static segment using historical data.
• CLeverage edge-based segmentation for real-time updates.
• DApply probabilistic segmentation to predict future bookings.

• A
  9% (3)
• B
  74% (26)
• C
  3% (1)
• D
  14% (5)

Explanation

Option B is correct because the company needs to identify a fixed group of customers based on a specific historical criterion - not booking in the past six months - which is a snapshot of past behavior that doesn't need to change in real time. A static segment is built once from historical data and suits this "find-and-target" promotional use case perfectly.

Why the distractors are wrong:

• A (Dynamic segment): Dynamic segments update automatically as conditions change, which is overkill here - the campaign targets a defined past window, not an evolving behavior.
• C (Edge-based segmentation): Edge segmentation processes data at the network edge for real-time, low-latency decisions (e.g., in-session personalization), not historical batch targeting.
• D (Probabilistic segmentation): This predicts future behavior using statistical models - useful for churn prediction, but the question asks about identifying customers based on known past inactivity, not predicted behavior.

Memory tip: Think "static = stamp in time." If the segmentation criterion is a fixed historical fact (e.g., "no booking in the last 6 months"), use a static segment. If it needs to react to live behavior, go dynamic.

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