Diagnose Low CTR in an Advertising Campaign Funnel

# Diagnose Low CTR in an Advertising Campaign Funnel An analysis has flagged several ad campaigns because their click-through rate (CTR) is below the average campaign-level CTR. For each campaign, you currently have only: - campaign dates and daily budget, - total impressions, - total clicks, - total conversions. Using only these fields, explain plausible reasons for weak performance. Separate what the aggregates let you observe from what they do not let you conclude, and state which explanations remain hypotheses. ```hint Start with the funnel Decompose the path from **impression → click → conversion** before proposing causes. A low CTR and a low post-click conversion rate point to different parts of the funnel. ``` ```hint Treat aggregates cautiously Ask whether the comparison is stable across time, audience, placement, device, geography, and creative. An overall average can hide small samples and composition effects. ``` ### Constraints & Assumptions - The available metrics are campaign-level aggregates, not user- or event-level records. - `daily_budget` is a budget cap; it is not confirmed actual spend. - Tracking definitions, attribution windows, and traffic mix have not yet been validated. - The goal is a diagnosis plan, not a claim that one cause has already been proven. ### Clarifying Questions to Ask - How are an impression, click, and conversion defined, deduplicated, and attributed? - Is the benchmark a simple average across campaigns or an impression-weighted aggregate CTR? - How much traffic does each flagged campaign have, and over what dates? - Did targeting, creative, placement, bidding, or tracking change during the campaign? - What decision will this analysis support: pausing spend, changing creative, changing targeting, or launching an experiment? ### What a Strong Answer Covers ```premium-lock What a Strong Answer Covers ``` ### Follow-up Questions 1. How would you compare a campaign with 10 impressions and 0 clicks to one with 100,000 impressions and a slightly below-average CTR? 2. What could cause overall CTR to fall even when CTR improves within every device segment? 3. What additional data would you request before choosing between a creative and targeting explanation? 4. Which guardrail metrics would you monitor if a new creative raises CTR? 5. Design an experiment to distinguish a creative problem from a targeting problem.