I need to identify the main categorical columns and find their most common values with frequencies. From the evidence, I can see the categorical dimensions in b2b_sales_funnel_pipeline are: sales_agent, product, account, and deal_stage. Let me query the frequency distributions for each of these categorical columns in a single efficient query: ```json { "action": "tool_call", "reasoning": "I'll write one query to get the top values and their frequencies for all main categorical columns (sales_agent, product, account, deal_stage). This is more efficient than querying each column separately.", "tool": "query_sql", "args": { "sql": "WITH sales_agent_freq AS (SELECT 'sales_agent' as column_name, sales_agent as value, COUNT(*) as frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) as percentage FROM raw.b2b_sales_funnel_pipeline GROUP BY sales_agent ORDER BY frequency DESC LIMIT 10), product_freq AS (SELECT 'product' as column_name, product as value, COUNT(*) as frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) as percentage FROM raw.b2b_sales_funnel_pipeline GROUP BY product ORDER BY frequency DESC), account_freq AS (SELECT 'account' as column_name, account as value, COUNT(*) as frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) as percentage FROM raw.b2b_sales_funnel_pipeline GROUP BY account ORDER BY frequency DESC LIMIT 10), deal_stage_freq AS (SELECT 'deal_stage' as column_name, deal_stage as value, COUNT(*) as frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) as percentage FROM raw.b2b_sales_funnel_pipeline GROUP BY deal_stage ORDER BY frequency DESC) SELECT * FROM sales_agent_freq UNION ALL SELECT * FROM product_freq UNION ALL SELECT * FROM account_freq UNION ALL SELECT * FROM deal_stage_freq ORDER BY column_name, frequency DESC", "purpose": "Get the most common values and their frequencies for the main categorical columns: sales_agent, product, account, and deal_stage", "source": "raw" } } ```