```json { "action": "tool_call", "reasoning": "I need to identify correlations between columns. From the evidence, I can see the dataset has a mix of summary rows (weekly totals) and individual call records. The columns that appear to be numeric and suitable for correlation analysis are: Total Calls, AI Handled, Human Handled, Voicemail (all VARCHAR but containing numbers), and _unnamed_8 (which appears to be sentiment scores). I should first clean the data to exclude summary rows and convert the VARCHAR columns to numeric types, then compute correlations between all numeric pairs.", "tool": "query_sql", "args": { "sql": "WITH cleaned_data AS (SELECT CAST(REPLACE(REPLACE(\"Total Calls\", ',', ''), ' ', '') AS INTEGER) as total_calls, CAST(REPLACE(REPLACE(\"AI Handled\", ',', ''), ' ', '') AS INTEGER) as ai_handled, CAST(REPLACE(REPLACE(\"Human Handled\", ',', ''), ' ', '') AS INTEGER) as human_handled, CAST(REPLACE(REPLACE(Voicemail, ',', ''), ' ', '') AS INTEGER) as voicemail, TRY_CAST(_unnamed_8 AS DOUBLE) as sentiment_score FROM raw.callyeah_analytics_2026_07_23 WHERE Day NOT IN ('Thu', 'Fri', 'Sat', 'Sun', 'Mon', 'Tue', 'Today', 'TOTAL', '', 'Date') AND \"Total Calls\" != 'Total Calls' AND TRY_CAST(REPLACE(REPLACE(\"Total Calls\", ',', ''), ' ', '') AS INTEGER) IS NOT NULL) SELECT 'Total Calls vs AI Handled' as pair, CORR(total_calls, ai_handled) as correlation FROM cleaned_data UNION ALL SELECT 'Total Calls vs Human Handled', CORR(total_calls, human_handled) FROM cleaned_data UNION ALL SELECT 'Total Calls vs Voicemail', CORR(total_calls, voicemail) FROM cleaned_data UNION ALL SELECT 'AI Handled vs Human Handled', CORR(ai_handled, human_handled) FROM cleaned_data UNION ALL SELECT 'AI Handled vs Voicemail', CORR(ai_handled, voicemail) FROM cleaned_data UNION ALL SELECT 'Human Handled vs Voicemail', CORR(human_handled, voicemail) FROM cleaned_data UNION ALL SELECT 'Total Calls vs Sentiment', CORR(total_calls, sentiment_score) FROM cleaned_data UNION ALL SELECT 'AI Handled vs Sentiment', CORR(ai_handled, sentiment_score) FROM cleaned_data UNION ALL SELECT 'Human Handled vs Sentiment', CORR(human_handled, sentiment_score) FROM cleaned_data UNION ALL SELECT 'Voicemail vs Sentiment', CORR(voicemail, sentiment_score) FROM cleaned_data ORDER BY ABS(correlation) DESC", "purpose": "Calculate pairwise correlations between all numeric columns (call volumes and sentiment scores) to identify the strongest relationships" } } ```