Customer Purchasing Patterns with Market Basket Analysis
Identifying Key Associations
By [source]
About this dataset
This dataset contains customer purchase patterns from a retail retail company that was used to identify key associations using the Market Basket Analysis model. In particular, this dataset provides insights into loyalty programs, customer segmentation, product recommendations and even cross-selling opportunities. The records contain customer demographic information such as age, gender and income, as well as details about their purchasing history such as payment methods, product quantity purchased and shipping status. Through a deep analysis of these data points using Market Basket Analysis we can gain insights into how customers interact with the products in order to increase sales and optimize loyalty incentives. The dataset is composed of two files: the prepared_dataset file containing aggregate customer purchase data; and teleco_market_basket which contains individual level customer purchase information. With these datasets we can start tracking important itemsets or combinations of items purchased together by customers and use them in powerful ways to provide better service levels while increasing overall satisfaction
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How to use the dataset
To use this dataset, start by exploring the available variables by looking at descriptive statistics such as mean, median and standard deviation for each variable. This will allow you to gain a better understanding of how customers are utilizing different products or services. Additionally, you can look for correlations between variables to identify associations between different variables or products purchased in tandem.
Once you’ve determined which associations are most meaningful in terms of predicting customer behavior, you can then utilize these insights to inform new marketing strategies or other business decisions. For instance, if a certain product category is often bought with another product category in tandem by your customers then that insight could be used to drive sales of both products simultaneously.
Additionally, using this dataset offers an opportunity to compare and analyze sales figures against time periods or particular seasons (excluding dates), which allows managers to anticipate future trends more confidently without relying on gut intuitions alone!
Research Ideas
- Identifying which customer segments are most likely to purchase complementary products based on which items have been purchased together.
- Suggesting potential discounts and incentives for customers based on their previous purchases or purchase patterns.
- Identifying whether customer loyalty programs have an effect on the purchasing habits of customers by analyzing changes in their purchase patterns over time
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: prepared_dataset.csv
| Column name |
Description |
| 0 |
Customer ID (Integer) |
| 1 |
Product ID (Integer) |
| 2 |
Price of product purchased (Float) |
| 3 |
Quantity Purchased (Integer) |
| 4 |
Payment Method (String) |
| 5 |
Shipping Cost (Float) |
| 6 |
Shipping Method (String) |
| 7 |
Order Date (Date) |
| 8 |
Delivery Date (Date) |
| 9 |
Delivery Status (String) |
| 10 |
Customer Name (String) |
| 11 |
Customer Address (String) |
| 12 |
Customer City (String) |
| 13 |
Customer State (String) |
| 14 |
Customer Zip Code (Integer) |
| 15 |
Customer Country (String) |
| 16 |
Customer Phone Number (String) |
| 17 |
Customer Email (String) |
| 18 |
Customer IP Address (String) |
| 19 |
Item20 (String) |
File: teleco_market_basket.csv
| Column name |
Description |
| Item01 |
Customer ID (Integer) |
| Item02 |
Product ID (Integer) |
| Item03 |
Price of product purchased (Float) |
| Item04 |
Quantity Purchased (Integer) |
| Item05 |
Payment Method (String) |
| Item06 |
Shipping Cost (Float) |
| Item07 |
Shipping Method (String) |
| Item08 |
Order Date (Date) |
| Item09 |
Delivery Date (Date) |
| Item10 |
Delivery Status (String) |
| Item11 |
Customer Name (String) |
| Item12 |
Customer Address (String) |
| Item13 |
Customer Phone Number (String) |
| Item14 |
Customer Email (String) |
| Item15 |
Customer Gender (String) |
| Item16 |
Customer Age (Integer) |
| Item17 |
Customer Income (Integer) |
| Item18 |
Customer Education Level (String) |
| Item19 |
Customer Occupation (String) |
| Item20 |
Purchase History (String) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .