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https://github.com/k-bloch/cafe-rewards-offers-analysis

This project explores customer interaction patterns with a café rewards program using Python and Jupyter Notebook, focusing on offer completion rates, demographic trends, and visualizations to enhance marketing strategies.
https://github.com/k-bloch/cafe-rewards-offers-analysis

data-analysis-python jupyter-notebook seaborn sql

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This project explores customer interaction patterns with a café rewards program using Python and Jupyter Notebook, focusing on offer completion rates, demographic trends, and visualizations to enhance marketing strategies.

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README 

        
# Cafe Rewards Program: Interaction Patterns and Completion Rates



All technical steps and visualizations are documented in the accompanying [Jupyter Notebook](notebooks/cafe-rewards-program-interaction-patterns-and-completion-rates.ipynb).



## Project Background



Beanie Coffee is a popular coffee shop chain that runs a loyalty program called **Cafe Rewards**, offering members (registered customers) access to exclusive offers. These offers fall into three types:

- **BOGO**: Buy one, get one free.

- **Discount**: Direct monetary discounts on purchases.

- **Informational**: No direct rewards, just product or promotional information.



The data from a recent 30-day campaign is now ready for analysis. Executives have requested a review on the following areas:

- **Completion Rates**: How many reward offers were completed, and which ones had the highest completion rates?

- **Customer Demographics**: What are the key demographic patterns, especially regarding offer completion?



## Executive Summary



Key findings include:

- A campaign-wide completion rate of ~63%.

- Informational offers showed both extreme successes (93% completion for one campaign) and weaknesses (50% completion for another).

- The success of informational offers suggests significant customer interest, but the lack of transaction-level data makes it difficult to gauge their true impact.

- Demographic trends reveal younger customers are least engaged overall, while older customers show high engagement with discount and BOGO offers.

- Older customers are consistently more engaged across most offer types.

- Younger customers engage more with informational offers but represent the smallest proportion of customers.



### Completion Rates



- **Total Interactions**: 66,496 offers were received, and 41,567 were completed (~63% completion rate).

- **Informational Offers**:

  - Informational_2 excelled with a 93% completion rate.

  - Informational_1 performed poorly, with a 50% completion rate.

  - Informational offers have a short 3-day window for completion, therefore their long-term impact on purchases can't be reliably measured. Customers may interact with these campaigns beyond the 3-day window.

- **Discount Offers**: 

  - Discount_3 achieved the highest success at 75% completion.

  - Discount_1 was weaker at 49%.

- **BOGO Offers**:

  - These had the overall lowest completion rate (57%), with Bogo_2 standing out as particularly weak (50%).



### Customer Demographics



1. **BOGO Offers**:

   - Weakest engagement in the 18–34 age group.

   - Best performance with customers aged 55–64.

   - Most completions came from the 45–54 age group.

2. **Discount Offers**:

   - Lowest engagement from customers aged 34 and younger.

   - High and consistent redemption rates from customers aged 45 and older.

3. **Informational Offers**:

   - Highest completion rates in customers aged 18–34.

   - The 45–54 age group were the biggest group who interacted with this offer.



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## Recommendations



1. **Informational Offers**:

   - Consider capturing transaction-level data to verify their true impact.

   - Investigate why Informational_2 was so successful and replicate its elements in future campaigns.



2. **BOGO Offers**:

   - Reassess the products included in these campaigns. They may not align with customer preferences.

   - Test alternative product selections or incentives on a small scale, borrowing successful elements from discount and informational campaigns.

   - Examine other factors such as redemption time limits, minimum spend thresholds, and marketing channels.



3. **Demographics**:

   - Increase outreach to the 18–34 demographic, who represent the smallest customer base but are responsive to informational offers.

   - Incorporate products and patterns from successful informational campaigns into BOGO and discount offers.

   - Explore whether income levels or other demographic traits influence engagement and how.