Last-Mile Delivery Performance
This dataset provides granular last-mile delivery records, including shipment details, delivery timing, accuracy, courier performance, and customer satisfaction metrics. It enables logistics teams to analyze delivery speed, identify operational bottlenecks, and optimize customer experience, making it ideal for performance benchmarking and process improvement.
Sample rows
preview · 8 of 200 rows · all 21 columns| shipment_idstring | delivery_statusstring | customer_ratingfloat | delivery_accuracyboolean | delivery_issue_codestring | order_idstring | customer_idstring | delivery_address_streetstring | delivery_address_citystring | delivery_address_statestring | delivery_address_postal_codestring | delivery_address_countrystring | scheduled_delivery_datedate | actual_delivery_datetimedatetime | delivery_speed_hoursfloat | courier_idstring | courier_namestring | customer_feedbackstring | shipment_weight_kgfloat | shipment_valuefloat | delivery_attemptsinteger |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SHIP0001 | delivered | 4.7 | true | blank | ORD100001 | CUST0001 | 123 Main St | New York | NY | 10001 | USA | 2024-05-12 | 2024-05-12T16:35:00 | 3.5 | CR001 | Tommy Lee | Fast delivery, package arrived in perfect condition. | 2.1 | 120.5 | 1 |
| SHIP0002 | delayed | 3.9 | true | traffic_delay | ORD100002 | CUST0002 | 456 Queen St | Sydney | NSW | 2000 | Australia | 2024-05-10 | 2024-05-11T14:10:00 | 32.7 | CR002 | Priya Singh | Delivery was late but the package was intact. | 5 | 250 | 1 |
| SHIP0003 | delivered | 5 | true | blank | ORD100003 | CUST0003 | 22 Orchard Road | Singapore | SG | 238842 | Singapore | 2024-05-09 | 2024-05-09T10:45:00 | 2.1 | CR003 | Wei Tan | Excellent and prompt service. | 0.5 | 49.99 | 1 |
| SHIP0004 | delayed | 3.2 | true | recipient_unavailable | ORD100004 | CUST0004 | 7 Rue de Rivoli | Paris | IDF | 75001 | France | 2024-05-10 | 2024-05-12T09:18:00 | 48.3 | CR004 | Jean Dupont | Package arrived after two days, not ideal. | 8.25 | 350 | 2 |
| SHIP0005 | delivered | 4.8 | true | blank | ORD100005 | CUST0005 | 8 Flinders Ln | Melbourne | VIC | 3000 | Australia | 2024-05-11 | 2024-05-11T12:55:00 | 1.9 | CR005 | Sophie Miller | Package arrived quickly. Satisfied. | 3.4 | 75 | 1 |
| SHIP0006 | delayed | 4.2 | true | traffic_delay | ORD100006 | CUST0001 | 123 Main St | New York | NY | 10001 | USA | 2024-05-13 | 2024-05-14T09:30:00 | 25.5 | CR002 | Priya Singh | Slight delay but service was good. | 1.2 | 40 | 1 |
| SHIP0007 | delivered | 4.9 | true | blank | ORD100007 | CUST0006 | 15 Oxford St | London | LDN | W1D1BS | UK | 2024-05-08 | 2024-05-08T15:20:00 | 4.5 | CR006 | Amelia Clark | Always reliable and on time. | 0.85 | 60 | 1 |
| SHIP0008 | delayed | 2.8 | true | weather_disruption | ORD100008 | CUST0007 | 80 Church St | Auckland | AUK | 1010 | New Zealand | 2024-05-11 | 2024-05-13T17:32:00 | 54.2 | CR007 | Harry Turner | Much longer than expected, not satisfied. | 12.6 | 399.9 | 2 |
| SHIP0009 | delivered | 4.6 | true | blank | ORD100009 | CUST0008 | 110 MG Road | Bangalore | KA | 560001 | India | 2024-05-10 | 2024-05-10T13:12:00 | 2.7 | CR008 | Rahul Sharma | Fast and friendly delivery. | 2.3 | 99.99 | 1 |
| SHIP0010 | delivered | 4.7 | true | blank | ORD100010 | CUST0009 | 12 Market St | San Francisco | CA | 94105 | USA | 2024-05-09 | 2024-05-09T11:05:00 | 2 | CR009 | Maria Gonzales | Quick delivery, no issues at all. | 1.9 | 79.99 | 1 |
| SHIP0011 | delayed | 3.5 | true | recipient_unavailable | ORD100011 | CUST0010 | 210 King St | Toronto | ON | M5H1K5 | Canada | 2024-05-13 | 2024-05-14T15:42:00 | 26 | CR010 | Emily Chen | Delivery was a day late but package ok. | 6.7 | 180 | 1 |
| SHIP0012 | in_transit | blank | blank | blank | ORD100012 | CUST0002 | 456 Queen St | Sydney | NSW | 2000 | Australia | 2024-05-08 | blank | 0 | CR005 | Sophie Miller | blank | 2.9 | 60 | 1 |
| SHIP0013 | delayed | 2.5 | true | traffic_delay | ORD100013 | CUST0011 | 99 Collins St | Melbourne | VIC | 3000 | Australia | 2024-05-13 | 2024-05-15T19:15:00 | 50.5 | CR011 | Jack Brown | Late delivery, not satisfied. | 10.8 | 220 | 2 |
| SHIP0014 | failed | blank | false | address_not_found | ORD100014 | CUST0012 | 14 Park Ave | London | LDN | EC1A1BB | UK | 2024-05-10 | blank | 0 | CR012 | Meghan Evans | blank | 2 | 0 | 2 |
| SHIP0015 | returned | blank | false | recipient_unavailable | ORD100015 | CUST0013 | 18 George St | Sydney | NSW | 2000 | Australia | 2024-05-12 | blank | 0 | CR013 | Oliver Jones | blank | 1.6 | 0 | 2 |
| SHIP0016 | delivered | 4.5 | true | blank | ORD100016 | CUST0014 | 5 Champs Elysees | Paris | IDF | 75008 | France | 2024-05-13 | 2024-05-13T16:00:00 | 4.2 | CR014 | Camille Laurent | Efficient and polite courier. | 3.7 | 150 | 1 |
| SHIP0017 | delivered | 4.9 | true | blank | ORD100017 | CUST0015 | 200 Broadway | New York | NY | 10007 | USA | 2024-05-11 | 2024-05-11T12:05:00 | 2.6 | CR001 | Tommy Lee | Excellent service and quick delivery. | 1.25 | 89.99 | 1 |
| SHIP0018 | delayed | 3.1 | true | traffic_delay | ORD100018 | CUST0016 | 66 Nathan Road | Hong Kong | HK | 999077 | Hong Kong | 2024-05-10 | 2024-05-12T18:22:00 | 48.4 | CR015 | Chun Wong | Late delivery, courier was apologetic. | 7.2 | 340 | 2 |
| SHIP0019 | delivered | 4.6 | true | blank | ORD100019 | CUST0017 | 16 College St | Toronto | ON | M5G1L7 | Canada | 2024-05-11 | 2024-05-11T18:45:00 | 7 | CR010 | Emily Chen | Good delivery experience. | 2.9 | 110 | 1 |
| SHIP0020 | failed | blank | false | address_not_found | ORD100020 | CUST0018 | 27 Queen's Road | Hong Kong | HK | 999077 | Hong Kong | 2024-05-09 | blank | 0 | CR015 | Chun Wong | blank | 1.1 | 0 | 2 |
What the 200 rows show
from the 200-row sampleDelivered (delivery status) stands out: mean customer_
- 93%delivery_
accuracy = true - 4.7median customer_
rating - 20delivery address countries
- 27delivery address cities
- 29delivery address states
- 4.8median delivery_
speed_ hours
Median 4.7, from 2.1 to 5.0.
- string 13
- integer 1
- float 4
- date 1
- datetime 1
- boolean 1
Columns
21 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 13 columns | |||
shipment_id | string | Unique identifier for each shipmentunique | SHIP0001 |
order_id | string | Unique identifier for the customer order associated with the shipment | ORD100001 |
customer_id | string | Unique identifier for the customer receiving the shipment | CUST0001 |
delivery_address_street | string | Street address for the delivery location | 123 Main St |
delivery_address_city | string | City for the delivery location | New York |
delivery_address_state | string | State or region for the delivery location | NY |
delivery_address_postal_code | string | Postal code for the delivery location | 10001 |
delivery_address_country | string | Country for the delivery location | USA |
delivery_status | string | Status of the delivery (e.g., delivered, delayed, failed, returned)delivered · delayed · failed · returned · in_transit | delivered |
courier_id | string | Unique identifier for the courier or delivery agent | CR001 |
courier_name | string | Name of the courier or delivery agentoptional | Tommy Lee |
customer_feedback | string | Textual feedback provided by the customer about the deliveryoptional | Quick and easy process. |
delivery_issue_code | string | Code representing any issues encountered during delivery (e.g., address_not_found, recipient_unavailable)9 codes · optional | traffic_delay |
| Numbers 5 columns | |||
delivery_speed_hours | float | Time taken (in hours) from shipment dispatch to actual delivery0 or more | 3.5 |
customer_rating | float | Customer satisfaction rating for the delivery (1.0 to 5.0)1 to 5 · optional | 4.7 |
shipment_weight_kg | float | Weight of the shipment in kilograms0 or more · optional | 2.1 |
shipment_value | float | Monetary value of the shipment in local currency0 or more · optional | 120.5 |
delivery_attempts | integer | Number of delivery attempts made for this shipment1 or more | 1 |
| Dates and times 2 columns | |||
scheduled_delivery_date | date | Date the delivery was scheduled to arrive | 2024-05-12 |
actual_delivery_datetime | datetime | Timestamp when the shipment was actually delivered | 2024-05-12T16:35:00 |
| True or false 1 column | |||
delivery_accuracy | boolean | Indicates if the delivery was made to the correct address | true |
Use it for
A logistics dashboard
The delivery_
accuracy rate, customer_ rating by delivery_ status and a breakdown of delivery_ issue_ code. Excel, Power BI or Tableau. Why do the 127 delivered rows have a mean customer_
rating of 4.8? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Shipments200SHIP00014.7deliveredSHIP00023.9delayedSHIP0014failed
A software demo
Believable shipments with order_
id, customer_ id and delivery_ address_ street to fill a screen in front of a buyer.
This dataset200 rows21 columns
Yours1,000 rows21 columnsdelivery_address_street: UK only
blueprint · last-mile-delivery-performance
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 21 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Log delivery location and time
- Include customer feedback score
- Track delivery vehicle and route
- Flag late or incomplete deliveries
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- last-mile-delivery-performance