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🛒 E-Commerce Orders 50 sales orders - dates, categories, revenue & regions 🚚 Logistics & Deliveries 50 shipments - routes, carriers, costs, delivery times & ratings
You are viewing a sample dataset:  E-Commerce Orders. Upload your own CSV above to analyze your real data.
Overview - sample1.csv
50
Rows
10
Columns
0
Missing Values
4 / 5 / 1
Numeric / Categorical / Date
0.0%
Completeness gap
Column Analysis
order_id Numeric
Count50
Missing0 (0.00%)
Min1,001.00
Max1,050.00
Mean1,025.50
Median1,025.50
Std Dev14.43
Sum51,275.00
1,001.0 1,050.0
customer_name Categorical
Count50
Missing0 (0.00%)
Unique values26
Alice Johnson 2
Bob Smith 2
Carol White 2
David Brown 2
Eva Martinez 2
Frank Lee 2
Grace Kim 2
Henry Davis 2
Isla Thompson 2
Jack Wilson 2
order_date Date
Count50
Missing0 (0.00%)
Span13.3 months
2023-01-05 → 2024-02-14 13.3 months
2023-01 2024-02
product_category Categorical
Count50
Missing0 (0.00%)
Unique values4
Electronics 16
Clothing 13
Home & Garden 11
Food & Beverage 10
quantity Numeric
Count50
Missing0 (0.00%)
Min1.00
Max20.00
Mean3.82
Median2.00
Std Dev3.83
Sum191.00
1.0 20.0
unit_price Numeric
Count50
Missing0 (0.00%)
Min6.50
Max1,499.00
Mean284.10
Median94.50
Std Dev363.30
Sum14,204.79
6.5 1,499.0
total_amount Numeric
Count50
Missing0 (0.00%)
Min68.75
Max1,499.00
Mean407.81
Median244.97
Std Dev354.64
Sum20,390.44
68.8 1,499.0
region Categorical
Count50
Missing0 (0.00%)
Unique values4
North 13
South 13
East 12
West 12
payment_method Categorical
Count50
Missing0 (0.00%)
Unique values3
Credit Card 29
PayPal 15
Bank Transfer 6
status Categorical
Count50
Missing0 (0.00%)
Unique values3
Completed 48
Refunded 1
Pending 1
Data Preview (first 10 rows)
order_idcustomer_nameorder_dateproduct_categoryquantityunit_pricetotal_amountregionpayment_methodstatus
1001Alice Johnson2023-01-05Electronics2299.99599.98NorthCredit CardCompleted
1002Bob Smith2023-01-12Clothing549.99249.95SouthPayPalCompleted
1003Carol White2023-01-18Electronics1899.00899.00EastCredit CardCompleted
1004David Brown2023-02-03Home & Garden379.50238.50WestBank TransferCompleted
1005Eva Martinez2023-02-14Clothing289.99179.98NorthCredit CardCompleted
1006Frank Lee2023-02-20Electronics11299.001299.00SouthCredit CardRefunded
1007Grace Kim2023-03-01Food & Beverage1012.99129.90EastPayPalCompleted
1008Henry Davis2023-03-08Home & Garden2149.00298.00WestCredit CardCompleted
1009Isla Thompson2023-03-15Clothing434.99139.96NorthPayPalCompleted
1010Jack Wilson2023-03-22Electronics1499.00499.00SouthCredit CardCompleted
What Can We Do With Your Data?
📈
Sales & Revenue Forecasting
73
Readiness score
Good fit
🎯
Lead Scoring & Risk Detection
68
Readiness score
Good fit
👥
Customer Profiling & Segmentation
81
Readiness score
Ready to go
📅
Trend Analysis & Demand Planning
75
Readiness score
Ready to go

✦ Positive Signals

✦ No missing values - clean data with no gaps - ready to use as-is.
✦ 4 numeric columns - rich set of measurable columns - great for forecasting and profiling.
✦ 4 low-cardinality categorical column(s) (2–20 classes) - ideal columns for scoring and risk detection.
◈ 5 categorical columns - useful for grouping customers and building audience profiles.
✦ 10 columns - enough data columns to support multi-factor analysis.
✦ 3 numeric columns show meaningful variance (CV > 15%) - good variation in the data - well suited for forecasting.
✦ Date/time column detected alongside numeric data - strong candidate for trend analysis and demand planning. (1 date column(s) confirmed)

⚠ Issues & Warnings

⚠ Small dataset (50 rows) - may limit accuracy. Consider collecting more records before proceeding.
Your data qualification report is ready
Your data is ready
for advanced analytics.
Your data is ready for customer profiling & audience segmentation and trend analysis & demand planning and sales forecasting & revenue prediction and lead scoring & automated risk detection. Get in touch - our team can turn it into real business intelligence.
Customer profiling & audience segmentation Trend analysis & demand planning Sales forecasting & revenue prediction Lead scoring & automated risk detection
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