Detects seasonal revenue and expense patterns across 12+ months of financial data using spending_summary tool. Outputs indexed monthly tables, peaks/troughs, seasonal range, and cash management recommendations.
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Identify cyclical trends in your revenue and expenses over a 12-month or multi-year window. Reveals which months are historically strong or weak, enabling better cash management, staffing, and marketing timing.
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Identify cyclical trends in your revenue and expenses over a 12-month or multi-year window. Reveals which months are historically strong or weak, enabling better cash management, staffing, and marketing timing.
spending_summary — pull monthly income and expense totals across 12+ months to build a seasonal profilespending_summary for each month in the window to get monthly revenue and expense totals.SEASONAL PATTERN ANALYSIS — [Period]
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Month Avg Revenue Index Avg Expenses Trend
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Jan $8,200 82 $6,100 Slow start
Feb $7,800 78 $5,900 ▼ Trough
Mar $9,500 95 $6,500 Recovering
Apr $11,200 112 $7,200 ▲ Above avg
May $12,500 125 $7,800 ▲ Strong
Jun $13,000 130 $8,200 ▲▲ Peak
Jul $11,800 118 $7,500 ▲ Above avg
Aug $10,200 102 $7,000 Average
Sep $10,500 105 $7,200 Average
Oct $11,000 110 $7,500 ▲ Above avg
Nov $9,500 95 $8,500 Expense spike
Dec $6,800 68 $9,000 ▼▼ Low + high cost
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Annual $122,000 100 $88,400
Peak Month: June (130) Trough Month: December (68)
Seasonal Range: 62 points
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Month column: =TEXT(Date,"YYYY-MM") and a MonthNum column: =MONTH(Date).=AVERAGE(AllMonthlyRevenues).=MonthAvgRevenue/OverallAvg*100.=FORECAST.ETS.SEASONALITY(values, timeline) to auto-detect the seasonal period length.