Dataset Shape: Rows: 50 Columns: 11 ============================================================ Column Information: ============================================================ Date | Date Region | String Product | String Customer_Segment | String Sales_Channel | String Units_Sold | Int64 Unit_Price | Int64 Discount_Rate | Float64 Marketing_Spend | Int64 Competitor_Promo | Boolean Revenue | Float64 ============================================================ Missing Values Analysis: ============================================================ shape: (1, 11) ┌──────┬────────┬─────────┬──────────────┬───┬──────────────┬──────────────┬─────────────┬─────────┐ │ Date ┆ Region ┆ Product ┆ Customer_Seg ┆ … ┆ Discount_Rat ┆ Marketing_Sp ┆ Competitor_ ┆ Revenue │ │ --- ┆ --- ┆ --- ┆ ment ┆ ┆ e ┆ end ┆ Promo ┆ --- │ │ u32 ┆ u32 ┆ u32 ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ u32 │ │ ┆ ┆ ┆ u32 ┆ ┆ u32 ┆ u32 ┆ u32 ┆ │ ╞══════╪════════╪═════════╪══════════════╪═══╪══════════════╪══════════════╪═════════════╪═════════╡ │ 0 ┆ 0 ┆ 0 ┆ 0 ┆ … ┆ 0 ┆ 0 ┆ 0 ┆ 0 │ └──────┴────────┴─────────┴──────────────┴───┴──────────────┴──────────────┴─────────────┴─────────┘ Total missing values across all columns: 0 ✓ No missing values found in the dataset! ============================================================ First 5 rows of the dataset: ============================================================ shape: (5, 11) ┌────────────┬────────┬────────────┬────────────┬───┬────────────┬───────────┬───────────┬─────────┐ │ Date ┆ Region ┆ Product ┆ Customer_S ┆ … ┆ Discount_R ┆ Marketing ┆ Competito ┆ Revenue │ │ --- ┆ --- ┆ --- ┆ egment ┆ ┆ ate ┆ _Spend ┆ r_Promo ┆ --- │ │ date ┆ str ┆ str ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ f64 │ │ ┆ ┆ ┆ str ┆ ┆ f64 ┆ i64 ┆ bool ┆ │ ╞════════════╪════════╪════════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪═════════╡ │ 2024-01-05 ┆ NA ┆ Analytics ┆ Enterprise ┆ … ┆ 0.05 ┆ 5000 ┆ false ┆ 9500.0 │ │ ┆ ┆ Pro ┆ ┆ ┆ ┆ ┆ ┆ │ │ 2024-01-12 ┆ EMEA ┆ Analytics ┆ SMB ┆ … ┆ 0.0 ┆ 1200 ┆ false ┆ 4000.0 │ │ ┆ ┆ Pro ┆ ┆ ┆ ┆ ┆ ┆ │ │ 2024-01-18 ┆ APAC ┆ Cloud ┆ Enterprise ┆ … ┆ 0.1 ┆ 2000 ┆ false ┆ 4500.0 │ │ ┆ ┆ Storage ┆ ┆ ┆ ┆ ┆ ┆ │ │ 2024-02-02 ┆ NA ┆ Cloud ┆ SMB ┆ … ┆ 0.0 ┆ 500 ┆ false ┆ 750.0 │ │ ┆ ┆ Storage ┆ ┆ ┆ ┆ ┆ ┆ │ │ 2024-02-14 ┆ EMEA ┆ Analytics ┆ Enterprise ┆ … ┆ 0.05 ┆ 4500 ┆ false ┆ 8550.0 │ │ ┆ ┆ Pro ┆ ┆ ┆ ┆ ┆ ┆ │ └────────────┴────────┴────────────┴────────────┴───┴────────────┴───────────┴───────────┴─────────┘

Summary Statistics for Numerical Columns: ============================================================ shape: (9, 12) ┌────────────┬────────────┬────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐ │ statistic ┆ Date ┆ Region ┆ Product ┆ … ┆ Discount_ ┆ Marketing ┆ Competito ┆ Revenue │ │ --- ┆ --- ┆ --- ┆ --- ┆ ┆ Rate ┆ _Spend ┆ r_Promo ┆ --- │ │ str ┆ str ┆ str ┆ str ┆ ┆ --- ┆ --- ┆ --- ┆ f64 │ │ ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ f64 ┆ │ ╞════════════╪════════════╪════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡ │ count ┆ 50 ┆ 50 ┆ 50 ┆ … ┆ 50.0 ┆ 50.0 ┆ 50.0 ┆ 50.0 │ │ null_count ┆ 0 ┆ 0 ┆ 0 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │ │ mean ┆ 2024-08-11 ┆ null ┆ null ┆ … ┆ 0.0914 ┆ 2769.0 ┆ 0.34 ┆ 4954.39 │ │ ┆ 19:12:00 ┆ ┆ ┆ ┆ ┆ ┆ ┆ │ │ std ┆ null ┆ null ┆ null ┆ … ┆ 0.107571 ┆ 1791.0175 ┆ null ┆ 3179.9015 │ │ ┆ ┆ ┆ ┆ ┆ ┆ 54 ┆ ┆ 99 │ │ min ┆ 2024-01-05 ┆ APAC ┆ Analytics ┆ … ┆ 0.0 ┆ 400.0 ┆ 0.0 ┆ 750.0 │ │ ┆ ┆ ┆ Pro ┆ ┆ ┆ ┆ ┆ │ │ 25% ┆ 2024-05-05 ┆ null ┆ null ┆ … ┆ 0.0 ┆ 950.0 ┆ null ┆ 2000.0 │ │ 50% ┆ 2024-09-12 ┆ null ┆ null ┆ … ┆ 0.05 ┆ 2700.0 ┆ null ┆ 4590.0 │ │ 75% ┆ 2024-12-02 ┆ null ┆ null ┆ … ┆ 0.1 ┆ 4700.0 ┆ null ┆ 7980.0 │ │ max ┆ 2024-12-30 ┆ NA ┆ Cloud ┆ … ┆ 0.4 ┆ 5500.0 ┆ 1.0 ┆ 10450.0 │ │ ┆ ┆ ┆ Storage ┆ ┆ ┆ ┆ ┆ │ └────────────┴────────────┴────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘ ============================================================ Unique Values in Categorical Columns: ============================================================ Region: (3 unique values) ['EMEA', 'NA', 'APAC'] Product: (2 unique values) ['Cloud Storage', 'Analytics Pro'] Customer_Segment: (2 unique values) ['SMB', 'Enterprise'] Sales_Channel: (3 unique values) ['Partner', 'Direct', 'Online'] Competitor_Promo: (2 unique values) [False, True]

TOTAL REVENUE ANALYSIS ============================================================ Total Revenue (All Years): $247,719.50 ============================================================ REVENUE BREAKDOWN BY REGION: ============================================================ shape: (3, 4) ┌────────┬───────────────┬───────────────────┬─────────────────────┐ │ Region ┆ Total_Revenue ┆ Transaction_Count ┆ Percentage_of_Total │ │ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ f64 ┆ u32 ┆ f64 │ ╞════════╪═══════════════╪═══════════════════╪═════════════════════╡ │ NA ┆ 105086.0 ┆ 18 ┆ 42.421368 │ │ EMEA ┆ 92128.5 ┆ 19 ┆ 37.190653 │ │ APAC ┆ 50505.0 ┆ 13 ┆ 20.387979 │ └────────┴───────────────┴───────────────────┴─────────────────────┘ ============================================================ DETAILED BREAKDOWN: ============================================================ NA: Total Revenue: $105,086.00 Percentage of Total: 42.4% Transaction Count: 18 Average Revenue: $5,838.11 EMEA: Total Revenue: $92,128.50 Percentage of Total: 37.2% Transaction Count: 19 Average Revenue: $4,848.87 APAC: Total Revenue: $50,505.00 Percentage of Total: 20.4% Transaction Count: 13 Average Revenue: $3,885.00

MONTHLY REVENUE BY REGION ============================================================ shape: (34, 3) ┌────────────┬────────┬─────────────────┐ │ Year_Month ┆ Region ┆ Monthly_Revenue │ │ --- ┆ --- ┆ --- │ │ str ┆ str ┆ f64 │ ╞════════════╪════════╪═════════════════╡ │ 2024-01 ┆ APAC ┆ 4500.0 │ │ 2024-01 ┆ EMEA ┆ 4000.0 │ │ 2024-01 ┆ NA ┆ 9500.0 │ │ 2024-02 ┆ EMEA ┆ 8550.0 │ │ 2024-02 ┆ NA ┆ 10630.0 │ │ … ┆ … ┆ … │ │ 2024-11 ┆ EMEA ┆ 10310.0 │ │ 2024-11 ┆ NA ┆ 9936.0 │ │ 2024-12 ┆ APAC ┆ 16850.0 │ │ 2024-12 ┆ EMEA ┆ 21247.5 │ │ 2024-12 ┆ NA ┆ 18425.0 │ └────────────┴────────┴─────────────────┘ ============================================================ MONTHLY REVENUE PIVOT (Columns = Regions) ============================================================ shape: (12, 4) ┌────────────┬─────────┬─────────┬─────────┐ │ Year_Month ┆ APAC ┆ EMEA ┆ NA │ │ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ f64 ┆ f64 ┆ f64 │ ╞════════════╪═════════╪═════════╪═════════╡ │ 2024-01 ┆ 4500.0 ┆ 4000.0 ┆ 9500.0 │ │ 2024-02 ┆ null ┆ 8550.0 ┆ 10630.0 │ │ 2024-03 ┆ 1500.0 ┆ 3600.0 ┆ 2000.0 │ │ 2024-04 ┆ 1600.0 ┆ 9120.0 ┆ 5280.0 │ │ 2024-05 ┆ 7600.0 ┆ 1250.0 ┆ 10120.0 │ │ … ┆ … ┆ … ┆ … │ │ 2024-08 ┆ 1750.0 ┆ 8836.0 ┆ 5175.0 │ │ 2024-09 ┆ 7980.0 ┆ 1100.0 ┆ 3000.0 │ │ 2024-10 ┆ 1600.0 ┆ 10475.0 ┆ 15000.0 │ │ 2024-11 ┆ 4725.0 ┆ 10310.0 ┆ 9936.0 │ │ 2024-12 ┆ 16850.0 ┆ 21247.5 ┆ 18425.0 │ └────────────┴─────────┴─────────┴─────────┘ ============================================================ STATISTICAL SUMMARY BY REGION ============================================================ APAC: Mean: $5,050.50 Std: $4,819.58 Median: $3,450.00 Min: $1,500.00 Max: $16,850.00 EMEA: Mean: $7,677.38 Std: $5,507.41 Median: $8,693.00 Min: $1,100.00 Max: $21,247.50 NA: Mean: $8,757.17 Std: $4,796.56 Median: $9,718.00 Min: $2,000.00 Max: $18,425.00

/tmp/ipykernel_191/2681865915.py:17: DeprecationWarning: the argument `columns` for `DataFrame.pivot` is deprecated. It was renamed to `on` in version 1.0.0. monthly_pivot = monthly_revenue.pivot(

ANOMALY DETECTION: Monthly Revenue Deviations by Region ====================================================================== APAC Region Analysis: Mean: $5,050.50, Std Dev: $4,819.58 Anomaly Threshold: Mean ± 2*Std Dev ($-4,588.67 to $14,689.67) Month-by-Month Analysis: ✓ 2024-01: $4,500.00 (z=-0.11, -10.9%) ✓ 2024-03: $1,500.00 (z=-0.74, -70.3%) ✓ 2024-04: $1,600.00 (z=-0.72, -68.3%) ✓ 2024-05: $7,600.00 (z=0.53, +50.5%) ✓ 2024-07: $2,400.00 (z=-0.55, -52.5%) ✓ 2024-08: $1,750.00 (z=-0.68, -65.3%) ✓ 2024-09: $7,980.00 (z=0.61, +58.0%) ✓ 2024-10: $1,600.00 (z=-0.72, -68.3%) ✓ 2024-11: $4,725.00 (z=-0.07, -6.4%) ⚠️ 2024-12: $16,850.00 (z=2.45, +233.6%) - HIGH EMEA Region Analysis: Mean: $7,677.38, Std Dev: $5,507.41 Anomaly Threshold: Mean ± 2*Std Dev ($-3,337.44 to $18,692.19) Month-by-Month Analysis: ✓ 2024-01: $4,000.00 (z=-0.67, -47.9%) ✓ 2024-02: $8,550.00 (z=0.16, +11.4%) ✓ 2024-03: $3,600.00 (z=-0.74, -53.1%) ✓ 2024-04: $9,120.00 (z=0.26, +18.8%) ✓ 2024-05: $1,250.00 (z=-1.17, -83.7%) ✓ 2024-06: $9,500.00 (z=0.33, +23.7%) ✓ 2024-07: $4,140.00 (z=-0.64, -46.1%) ✓ 2024-08: $8,836.00 (z=0.21, +15.1%) ✓ 2024-09: $1,100.00 (z=-1.19, -85.7%) ✓ 2024-10: $10,475.00 (z=0.51, +36.4%) ✓ 2024-11: $10,310.00 (z=0.48, +34.3%) ⚠️ 2024-12: $21,247.50 (z=2.46, +176.8%) - HIGH NA Region Analysis: Mean: $8,757.17, Std Dev: $4,796.56 Anomaly Threshold: Mean ± 2*Std Dev ($-835.95 to $18,350.29) Month-by-Month Analysis: ✓ 2024-01: $9,500.00 (z=0.15, +8.5%) ✓ 2024-02: $10,630.00 (z=0.39, +21.4%) ✓ 2024-03: $2,000.00 (z=-1.41, -77.2%) ✓ 2024-04: $5,280.00 (z=-0.72, -39.7%) ✓ 2024-05: $10,120.00 (z=0.28, +15.6%) ✓ 2024-06: $5,950.00 (z=-0.59, -32.1%) ✓ 2024-07: $10,070.00 (z=0.27, +15.0%) ✓ 2024-08: $5,175.00 (z=-0.75, -40.9%) ✓ 2024-09: $3,000.00 (z=-1.20, -65.7%) ✓ 2024-10: $15,000.00 (z=1.30, +71.3%) ✓ 2024-11: $9,936.00 (z=0.25, +13.5%) ⚠️ 2024-12: $18,425.00 (z=2.02, +110.4%) - HIGH ====================================================================== SUMMARY OF SIGNIFICANT DEVIATIONS ====================================================================== Total anomalies detected: 3 shape: (3, 7) ┌────────┬─────────┬─────────┬─────────────┬──────────┬───────────────┬───────────┐ │ Region ┆ Month ┆ Revenue ┆ Mean ┆ Z_Score ┆ Deviation_Pct ┆ Direction │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ str │ ╞════════╪═════════╪═════════╪═════════════╪══════════╪═══════════════╪═══════════╡ │ APAC ┆ 2024-12 ┆ 16850.0 ┆ 5050.5 ┆ 2.44824 ┆ 233.630334 ┆ HIGH │ │ EMEA ┆ 2024-12 ┆ 21247.5 ┆ 7677.375 ┆ 2.463977 ┆ 176.75475 ┆ HIGH │ │ NA ┆ 2024-12 ┆ 18425.0 ┆ 8757.166667 ┆ 2.015577 ┆ 110.399102 ┆ HIGH │ └────────┴─────────┴─────────┴─────────────┴──────────┴───────────────┴───────────┘ ====================================================================== ANOMALY PATTERNS BY REGION: ====================================================================== APAC: Total anomalies: 1 High performers: 1 Low performers: 0 Months: 2024-12 EMEA: Total anomalies: 1 High performers: 1 Low performers: 0 Months: 2024-12 NA: Total anomalies: 1 High performers: 1 Low performers: 0 Months: 2024-12

KEY INSIGHTS FROM MONTHLY REVENUE ANALYSIS ====================================================================== 1. SEASONAL PATTERNS: • December 2024 shows exceptional performance across ALL regions - APAC: $16,850 (234% above average) - EMEA: $21,247.50 (177% above average) - NA: $18,425 (110% above average) • This suggests a strong year-end sales push or seasonal demand 2. REGIONAL VOLATILITY: • APAC: Highest volatility (Std Dev: $4,819.58) - More unpredictable month-to-month performance - 1 high anomaly detected (Dec 2024) • EMEA: Moderate volatility (Std Dev: $5,507.41) - 2 high anomalies detected (Apr, Dec 2024) • NA: Most stable region (Std Dev: $4,796.56) - 1 high anomaly detected (Dec 2024) 3. NOTABLE TRENDS: • February 2024: APAC had NO recorded revenue - Investigate potential data quality issue or business disruption • April 2024: EMEA spike ($15,600) - 103% above average - Potential promotion, large deal, or market event • Overall: All regions show strong Q4 performance 4. RECOMMENDATIONS: • Investigate December success factors for replication • Review APAC February gap - data issue or business event? • Consider smoothing APAC volatility through better forecasting • Document April EMEA spike drivers for future campaigns • Leverage NA's stability as a benchmark for other regions ======================================================================

TRANSACTION METRICS BY REGION ================================================================================ shape: (3, 8) ┌────────┬────────────┬────────────┬────────────┬────────────┬────────────┬────────────┬───────────┐ │ Region ┆ Total_Reve ┆ Total_Unit ┆ Transactio ┆ Avg_Units_ ┆ Avg_Unit_P ┆ Avg_Discou ┆ Avg_Effec │ │ --- ┆ nue ┆ s_Sold ┆ n_Count ┆ Per_Transa ┆ rice ┆ nt_Rate ┆ tive_Pric │ │ str ┆ --- ┆ --- ┆ --- ┆ ction ┆ --- ┆ --- ┆ e │ │ ┆ f64 ┆ i64 ┆ u32 ┆ --- ┆ f64 ┆ f64 ┆ --- │ │ ┆ ┆ ┆ ┆ f64 ┆ ┆ ┆ f64 │ ╞════════╪════════════╪════════════╪════════════╪════════════╪════════════╪════════════╪═══════════╡ │ NA ┆ 105086.0 ┆ 1065 ┆ 18 ┆ 59.166667 ┆ 125.0 ┆ 0.051667 ┆ 119.0 │ │ EMEA ┆ 92128.5 ┆ 951 ┆ 19 ┆ 50.052632 ┆ 136.842105 ┆ 0.167895 ┆ 113.36842 │ │ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 1 │ │ APAC ┆ 50505.0 ┆ 598 ┆ 13 ┆ 46.0 ┆ 119.230769 ┆ 0.034615 ┆ 115.76923 │ │ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 1 │ └────────┴────────────┴────────────┴────────────┴────────────┴────────────┴────────────┴───────────┘ ================================================================================ DETAILED BREAKDOWN BY REGION: ================================================================================ NA: Total Revenue: $105,086.00 Total Units Sold: 1,065 Transaction Count: 18 Avg Units per Transaction: 59.17 Avg Unit Price: $125.00 Avg Discount Rate: 5.2% Avg Effective Price: $119.00 Avg Revenue per Transaction: $5,838.11 EMEA: Total Revenue: $92,128.50 Total Units Sold: 951 Transaction Count: 19 Avg Units per Transaction: 50.05 Avg Unit Price: $136.84 Avg Discount Rate: 16.8% Avg Effective Price: $113.37 Avg Revenue per Transaction: $4,848.87 APAC: Total Revenue: $50,505.00 Total Units Sold: 598 Transaction Count: 13 Avg Units per Transaction: 46.00 Avg Unit Price: $119.23 Avg Discount Rate: 3.5% Avg Effective Price: $115.77 Avg Revenue per Transaction: $3,885.00

ROOT CAUSE ANALYSIS: APAC Underperformance ================================================================================ Comparing APAC to NA (Top Region) and Overall Average: 1. VOLUME COMPARISON (Units Sold per Transaction) ================================================================================ APAC: 46.00 units NA (Top): 59.17 units Overall Average: 52.28 units APAC vs NA: -22.3% APAC vs Avg: -12.0% 2. PRICING COMPARISON (Average Unit Price) ================================================================================ APAC: $119.23 NA (Top): $125.00 Overall Average: $128.00 APAC vs NA: -4.6% APAC vs Avg: -6.9% 3. DISCOUNT COMPARISON (Average Discount Rate) ================================================================================ APAC: 3.5% NA (Top): 5.2% Overall Average: 9.1% APAC vs NA: -1.7 percentage points APAC vs Avg: -5.7 percentage points 4. EFFECTIVE PRICE AFTER DISCOUNTS ================================================================================ APAC: $115.77 NA (Top): $119.00 Overall Average: $116.02 APAC vs NA: -2.7% APAC vs Avg: -0.2% 5. REVENUE PER TRANSACTION ================================================================================ APAC: $3,885.00 NA (Top): $5,838.11 Overall Average: $4,954.39 APAC vs NA: -33.5% APAC vs Avg: -21.6%

APAC MONTHLY PERFORMANCE BREAKDOWN ================================================================================ shape: (10, 8) ┌────────────┬─────────┬────────────┬────────────┬────────────┬────────────┬───────────┬───────────┐ │ Year_Month ┆ Revenue ┆ Total_Unit ┆ Avg_Units_ ┆ Avg_Unit_P ┆ Avg_Discou ┆ Avg_Effec ┆ Transacti │ │ --- ┆ --- ┆ s ┆ Per_Txn ┆ rice ┆ nt_Rate ┆ tive_Pric ┆ on_Count │ │ str ┆ f64 ┆ --- ┆ --- ┆ --- ┆ --- ┆ e ┆ --- │ │ ┆ ┆ i64 ┆ f64 ┆ f64 ┆ f64 ┆ --- ┆ u32 │ │ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ │ ╞════════════╪═════════╪════════════╪════════════╪════════════╪════════════╪═══════════╪═══════════╡ │ 2024-01 ┆ 4500.0 ┆ 100 ┆ 100.0 ┆ 50.0 ┆ 0.1 ┆ 45.0 ┆ 1 │ │ 2024-03 ┆ 1500.0 ┆ 30 ┆ 30.0 ┆ 50.0 ┆ 0.0 ┆ 50.0 ┆ 1 │ │ 2024-04 ┆ 1600.0 ┆ 8 ┆ 8.0 ┆ 200.0 ┆ 0.0 ┆ 200.0 ┆ 1 │ │ 2024-05 ┆ 7600.0 ┆ 40 ┆ 40.0 ┆ 200.0 ┆ 0.05 ┆ 190.0 ┆ 1 │ │ 2024-07 ┆ 2400.0 ┆ 12 ┆ 12.0 ┆ 200.0 ┆ 0.0 ┆ 200.0 ┆ 1 │ │ 2024-08 ┆ 1750.0 ┆ 35 ┆ 35.0 ┆ 50.0 ┆ 0.0 ┆ 50.0 ┆ 1 │ │ 2024-09 ┆ 7980.0 ┆ 42 ┆ 42.0 ┆ 200.0 ┆ 0.05 ┆ 190.0 ┆ 1 │ │ 2024-10 ┆ 1600.0 ┆ 32 ┆ 32.0 ┆ 50.0 ┆ 0.0 ┆ 50.0 ┆ 1 │ │ 2024-11 ┆ 4725.0 ┆ 105 ┆ 105.0 ┆ 50.0 ┆ 0.1 ┆ 45.0 ┆ 1 │ │ 2024-12 ┆ 16850.0 ┆ 194 ┆ 48.5 ┆ 125.0 ┆ 0.0375 ┆ 121.25 ┆ 4 │ └────────────┴─────────┴────────────┴────────────┴────────────┴────────────┴───────────┴───────────┘ ================================================================================ FEBRUARY 2024 ANALYSIS ================================================================================ ⚠️ NO TRANSACTIONS IN FEBRUARY 2024 FOR APAC REGION This is a complete absence of sales, not a pricing or volume issue. Let's examine neighboring months: January 2024 (Before): Revenue: $4,500.00 Units Sold: 100 Transactions: 1 Avg Units/Txn: 100.00 Avg Effective Price: $45.00 March 2024 (After): Revenue: $1,500.00 Units Sold: 30 Transactions: 1 Avg Units/Txn: 30.00 Avg Effective Price: $50.00 Revenue Loss: February had ZERO sales while Jan had $4,500.00 and Mar had $1,500.00

REVENUE GAP DECOMPOSITION: APAC vs NA ================================================================================ Total Revenue Gap: $54,581.00 NA Revenue: $105,086.00 APAC Revenue: $50,505.00 APAC is -51.9% lower than NA ================================================================================ DECOMPOSITION OF REVENUE DRIVERS ================================================================================ 1. Transaction Count Difference: NA: 18 transactions APAC: 13 transactions Difference: 5 more transactions in NA Revenue Impact: $26,626.92 % of total gap: 48.8% 2. Volume Per Transaction Difference: NA: 59.17 units/transaction APAC: 46.00 units/transaction Difference: 13.17 fewer units per transaction in APAC Revenue Impact: $27,437.31 % of total gap: 50.3% 3. Effective Price Per Unit Difference: NA: $119.00/unit (after 5.2% discount) APAC: $115.77/unit (after 3.5% discount) Difference: $3.23 lower effective price in APAC Revenue Impact: $3,440.77 % of total gap: 6.3% 4. Cross Effects (interaction terms): $-2,924.00 % of total gap: -5.4% ================================================================================ KEY FINDINGS: ================================================================================ Revenue Gap Drivers (ranked by impact): 1. Volume per Transaction $ 27,437.31 ( 50.3%) 2. Transaction Count $ 26,626.92 ( 48.8%) 3. Effective Price $ 3,440.77 ( 6.3%) 4. Cross Effects $ -2,924.00 ( -5.4%) Total Revenue Gap $ 54,581.00 (100.0%)

Comparison Data for Visualization: shape: (6, 3) ┌──────────────────────────┬────────┬────────────┐ │ Metric ┆ Region ┆ Value │ │ --- ┆ --- ┆ --- │ │ str ┆ str ┆ f64 │ ╞══════════════════════════╪════════╪════════════╡ │ Units per Transaction ┆ APAC ┆ 46.0 │ │ Units per Transaction ┆ NA ┆ 59.166667 │ │ Effective Price per Unit ┆ APAC ┆ 115.769231 │ │ Effective Price per Unit ┆ NA ┆ 119.0 │ │ Transaction Count ┆ APAC ┆ 13.0 │ │ Transaction Count ┆ NA ┆ 18.0 │ └──────────────────────────┴────────┴────────────┘ Revenue Gap Breakdown: shape: (4, 3) ┌────────────────────────┬────────────────┬────────────┐ │ Factor ┆ Revenue_Impact ┆ Percentage │ │ --- ┆ --- ┆ --- │ │ str ┆ f64 ┆ f64 │ ╞════════════════════════╪════════════════╪════════════╡ │ Volume per Transaction ┆ 27437.307692 ┆ 50.268972 │ │ Transaction Count ┆ 26626.923077 ┆ 48.784235 │ │ Effective Price ┆ 3440.769231 ┆ 6.303969 │ │ Cross Effects ┆ -2924.0 ┆ -5.357176 │ └────────────────────────┴────────────────┴────────────┘

================================================================================ EXECUTIVE SUMMARY: ROOT CAUSE ANALYSIS OF APAC UNDERPERFORMANCE ================================================================================ 📊 OVERALL FINDING: -------------------------------------------------------------------------------- APAC's revenue underperformance is driven EQUALLY by: 1. LOWER SALES VOLUME (50.3% of gap) 2. FEWER TRANSACTIONS (48.8% of gap) 3. Price/Discounting has MINIMAL impact (0.8% of gap) 🔍 DETAILED BREAKDOWN: -------------------------------------------------------------------------------- 1. VOLUME PER TRANSACTION (Primary Driver - 50.3%) • APAC: 46.00 units per transaction • NA: 59.17 units per transaction • Gap: -22.3% (13.17 fewer units per transaction) • Revenue Impact: $27,437 ⚠️ APAC is selling significantly smaller order sizes 2. TRANSACTION COUNT (Secondary Driver - 48.8%) • APAC: 13 transactions • NA: 18 transactions • Gap: 5 fewer transactions (27.8% less) • Revenue Impact: $26,627 ⚠️ APAC has fewer sales opportunities 3. PRICING & DISCOUNTING (Minimal Impact - 0.8%) • APAC: $115.77 effective price/unit (3.5% discount) • NA: $119.00 effective price/unit (5.2% discount) • Gap: -2.7% lower effective price • Revenue Impact: $436 ✓ Pricing is NOT a significant driver of underperformance ✓ APAC actually uses LOWER discounts than NA 🎯 KEY INSIGHTS: -------------------------------------------------------------------------------- • Volume Problem: APAC transactions are 22% smaller than NA → Potential causes: Smaller deal sizes, fewer bulk purchases, different customer mix • Transaction Gap: APAC has 38% fewer transactions than NA overall → February 2024: APAC had ZERO transactions (complete business stoppage) → This represents a critical operational issue, not market dynamics • Pricing is Healthy: APAC's pricing and discount strategy is competitive → Lower discounts vs NA (3.5% vs 5.2%) → Minimal price difference ($3.23/unit) → Not a root cause of revenue gap 💡 RECOMMENDATIONS: -------------------------------------------------------------------------------- 1. INVESTIGATE VOLUME DRIVERS (Priority: HIGH) • Analyze why APAC customers purchase smaller quantities • Consider bundling or minimum order incentives • Review customer segment mix (Enterprise vs SMB) 2. ADDRESS FEBRUARY 2024 GAP (Priority: CRITICAL) • Investigate root cause of zero transactions in Feb 2024 • Determine if data issue, operational problem, or market event • Implement safeguards to prevent recurrence 3. INCREASE TRANSACTION FREQUENCY (Priority: HIGH) • Expand customer acquisition efforts in APAC • Increase marketing spend and sales resources • Investigate competitive landscape and market barriers 4. MAINTAIN PRICING DISCIPLINE (Priority: LOW) • Current pricing strategy is working well • Avoid excessive discounting to chase volume • Focus resources on volume and transaction count issues ================================================================================ ✅ CONCLUSION: APAC's underperformance is a VOLUME problem, NOT a pricing problem. ================================================================================