https://github.com/tknishh/case-study-ueats-ghub-sql
Analyzing the Impact of Business Hour Mismatch on Order Volume in the Food Delivery Industry: A Case Study of UEats and Ghub
https://github.com/tknishh/case-study-ueats-ghub-sql
assignment bigquery case-study-analysis loop product-analyst sql
Last synced: 8 months ago
JSON representation
Analyzing the Impact of Business Hour Mismatch on Order Volume in the Food Delivery Industry: A Case Study of UEats and Ghub
- Host: GitHub
- URL: https://github.com/tknishh/case-study-ueats-ghub-sql
- Owner: tknishh
- Created: 2023-09-05T07:03:17.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-09-08T21:16:00.000Z (over 2 years ago)
- Last Synced: 2025-03-18T04:42:33.583Z (about 1 year ago)
- Topics: assignment, bigquery, case-study-analysis, loop, product-analyst, sql
- Homepage:
- Size: 943 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Analyzing the Impact of Business Hour Mismatch on Order Volume in the Food Delivery Industry: A Case Study of UEats and Ghub
**Prompt**
- It is important for a store to have consistent business hours across all food delivery platforms to avoid operational inconsistencies.
- If a store's business hours on one platform do not match the hours listed on another platform, it can create potential problems.
- Ensuring that a store's business hours are the same across all food delivery platforms is a key operational metric.
**Example** of business hours in Doordash
**Goal**
The goal of this interview is to come up with a query to understand differences in the business hours for a store across all platforms. You will compute a metric called business hour mismatch between a store on Grubhub and a store on UberEats
For this problem statement, we will only focus on Grubhub and UberEats.
1. Write a SQL query/set of queries that
1. Computes Business Hours Mismatch between a restaurant on two platforms. For the sake of simplicity, we will assume UberEats as the **ground truth.** We will then try to find the issues in Grubhub store hours.
1. Identify if Grubhub Hours are within the range of UberEats hours (column:is_out_of_range: “In Range”, “Out of Range with 5 mins difference”, “Out of Range”)
1) **Computing Business Hour Mismatch**
**Data**
Note all the data is sample data available in BigQuery. (To view the data, open your personal BigQuery console and run these queries).
| UberEats | SELECT * FROM arboreal-vision-339901.take_home_v2.virtual_kitchen_ubereats_hours LIMIT 1000; |
| --- | --- |
| Grubhub | SELECT * FROM arboreal-vision-339901.take_home_v2.virtual_kitchen_grubhub_hours LIMIT 1000; |
Note: The (b_name,vb_name) tuple can be used as a key to identify the same store and join across the two tables.
Input
**Uber Eats Business Hours**
Take the first key value pair in the menu dictionary and the first section and assume that as the store business hours.
Note: daysBitArray starts with Monday and indicates the days of the week for this time window is applicable. The might be more than element in the regularHours array.
**Grubhub Business Hours**
Grubhub
| Virtual Restaurant ID (slug) | JSON response (response) | Link to Block |
| --- | --- | --- |
| johnspizz_sicilianpi_gh | SELECT response FROM arboreal-vision-339901.take_home_v2.virtual_kitchen_grubhub_hours LIMIT 1000; | |
| | | |
**Output**
| Grubhub slug | Virtual Restuarant Business Hours | Uber Eats slug | Uber Eats Business Hours | is_out_range (expected output) |
| --- | --- | --- | --- | --- |
| | | | | In Range |
| | | | | Out of Range |
| | | | | Out of Range with 5 mins difference between GH and UE |
**Submission instructions**
1. Send us a SQL or sets of SQL that ouputs data in this format.
## Tips
1. Please use your personal BigQuery, it is free. ( https://cloud.google.com/bigquery/public-data/ ⇒ click on Go to analytics hub ,
https://cloud.google.com/blog/products/data-analytics/query-without-a-credit-card-introducing-bigquery-sandbox , https://towardsdatascience.com/bigquery-without-a-credit-card-discover-learn-and-share-199e08d4a064 )
2. You can view the data once you open BigQuery and run from this table.
3. Please use json parsing and do not use regex.
4. There is a way to write the SQL without brute forcing all day combinations etc, we would strongly prefer that.
1. Some starting point that might help https://stackoverflow.com/questions/34890339/how-to-extract-all-the-keys-in-a-json-object-with-bigquery
5. Slug is a unique idenfitier for a store
6. If there are multiple entries for a store, use the one with the latest timestamp.
7. You can use unnest to flatten business hours array in JSON