https://github.com/streamlit/example-app-cohort-analysis
https://github.com/streamlit/example-app-cohort-analysis
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/streamlit/example-app-cohort-analysis
- Owner: streamlit
- License: apache-2.0
- Created: 2022-06-16T10:26:22.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-06-20T11:32:34.000Z (about 3 years ago)
- Last Synced: 2025-01-29T10:36:27.247Z (5 months ago)
- Language: Python
- Size: 1.84 MB
- Stars: 5
- Watchers: 4
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 👥 Cohort Analysis App
[](https://cohort.streamlitapp.com/)
This Cohort Analysis App calculates the `retention rate` (the percentage of active customers compared to the total number of customers, split by month). This `retention rate` is then visualized and interpreted through a heatmap.
These 2 demos are based on the following tutorials:
---
### First app (`Bikes` dataset)
This demo is inspired by this [Cohort Analysis Tutorial](https://github.com/maladeep/cohort-retention-rate-analysis-in-python).
This dataset came from the hypothetical `Sprocket Central Pty Ltd`, a medium size bikes & cycling accessories organisation.
The data was collected from `January 1, 2017` to `December 31, 2017`, and available in `CSV` format, downloadable [here](https://www.kaggle.com/datasets/archit9406/customer-transaction-dataset).
Each row in the dataset contains information about an individual bike purchase:
- Who bought it
- How much they paid
- The bike's `brand` and `product line`
- Its `class` and `size`
- What `day` the purchase happened
- The `day` when the product was first sold
The underlying code takes those purchases and groups them into cohorts and calculates the `retention rate`, split by month, so that one can answer the question:
The underlying code groups those purchases into cohorts and calculates the `retention rate` (split by month) so that one can answer the question:
*if I'm making weekly changes to my store to get people to come back and buy more bikes, are those changes working?"*
These cohorts are then visualized and interpreted through a heatmap [powered by Plotly](https://plotly.com/python/).
---
### Second app (`Food` dataset)
This demo is inspired by this [Cohort Analysis Tutorial](http://www.gregreda.com/2015/08/23/cohort-analysis-with-python/)
This dataset comes from the hypothetical `Relay Food` company. The data spans from `June 1, 2009` to `September 3, 2010` and is available in CSV format (downloadable [here](https://github.com/springcoil/marsmodelling/blob/master/relay-foods.xlsx)).
Each row in the dataset contains information about an individual food order:
- Who bought it
- How much they paid
- The pick-up date
The underlying code groups those purchases into monthly cohorts (with the user's cohort group based on their first order) and calculates the `retention rate` so that one can answer the question:
*if I'm making monthly changes to my shop to get people to come back and order more, are those changes working?"*
These cohorts are then visualized and interpreted through a heatmap [powered by Plotly](https://plotly.com/python/).
---
### Process
Here are the steps we undertook to create the cohort analysis app:
1. Load the data
2. Create the cohort
3. Calculate the retention rate
4. Visualize and interpret the retention rate via the heatmap
---
### About the app
- App created using 🎈[Streamlit](https://streamlit.io/) and [Plotly Heatmaps](https://plotly.com/python/heatmaps/)
- Deployed on [Streamlit Cloud](https://streamlit.io/cloud) ☁️
---
### Questions? Comments?
Please ask in the [Streamlit community](https://discuss.streamlit.io).