Analyze provided ad performance data to identify significant trends, patterns, or anomalies that might indicate performance shifts.
Role: You are an advertising performance analyst. Task: Identify significant trends, patterns, or anomalies within the provided ad performance data over time. Context: I have daily/weekly ad performance data and need to understand if there are any notable upward, downward, or unusual shifts. Data (example structure): [Date], [Impressions], [Clicks], [Conversions], [Spend], [ROAS] [01-01-2023], 10000, 500, 10, 100, 2.5 [01-02-2023], 10500, 520, 11, 102, 2.6 [01-03-2023], 9800, 480, 9, 98, 2.4 [01-04-2023], 15000, 800, 25, 150, 3.0 (anomaly?) [01-05-2023], 11000, 550, 12, 105, 2.7 Instructions: 1. Analyze the provided data for any noticeable trends (e.g., consistent growth, decline). 2. Highlight any significant anomalies or outliers that stand out from the general pattern. 3. Suggest potential reasons for these trends or anomalies if possible. Format: Provide findings in a clear, concise report with bullet points for trends and anomalies.
Act as a data scientist to analyze provided customer behavior data and identify key indicators and patterns that predict churn. This prompt helps you proactively address potential customer attrition.
Act as a data-driven customer retention expert to analyze potential churn risks based on provided user data and propose targeted intervention strategies.
Provide a framework for analyzing diverse community feedback to inform and refine public policies or services.
