/Customer-Segmentation

Clean and Apply RFM technique to rank and group clusters to identify the best customers and perform targeted marketing campaigns, using real online transaction data

Primary LanguageJupyter Notebook

Customer-Segmentation

Clean and Apply RFM technique to rank and group clusters to identify the best customers and perform targeted marketing campaigns, using real online transaction data

Description

Apply RFM technique to derive our business goals!

Marketing Technique: Using RFM (Recency, Frequency, Monetary) Marketing model

  • Recency : last time customer made a purchase, more likely to repeat than a old customer
  • Frequency : how many times a customer made a purchase, who purchases often is likely to come back
  • Monetary Value : amount of money a customer has spent within that timeframe, large purchases likely to return than a customer who spends less

Business Benefits:

  1. Personalization: By creating effective customer segments, you can create relevant, personalized offers.
  2. Improve Conversion Rates: Personalized offers will yield higher conversion rates because your customers are engaging with products they care about.

Challenges:

  • Working with a skewed Probability distribution
  • Making good sense of Marketing Knowledge

Install Setup

This project requires Python 2.7 and the following Python libraries installed:

You will also need to have software installed to run and execute a Jupyter Notebook

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.

Code

Template code is provided in the notebook retail-customer-data-segmentation.ipynb Jupyter Notebook file.

Run

In a terminal or command window, navigate to the top-level project directory (that contains this README) and run one of the following commands:

jupyter notebook retail-customer-data-segmentation.ipynb.ipynb

or

ipython notebook retail-customer-data-segmentation.ipynb.ipynb

This will open the Jupyter Notebook software and project file in your web browser.

Data

The dataset used in this project will be included as retail_data.csv. This dataset is sourced from public domain and contains the following attributes:

Description

This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.

Features

  • Invoice : A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation.
  • StockCode : Product (item) code. A 5-digit integral number uniquely assigned to each distinct product.
  • Description : Product (item) name.
  • Quantity : The quantities of each product (item) per transaction.
  • InvoiceDate : Invoice date and time. The day and time when a transaction was generated.
  • Price : Unit price. Product price per unit in sterling (£).
  • Customer ID : Customer number. A 5-digit integral number uniquely assigned to each customer.
  • Country : Country name. The name of the country where a customer resides.

Transformed Features

  • most_recent_txn : date of their most recent transaction
  • num_orders : number of transactions they’ve made within a consistent time frame (a year will work best)
  • total_revenue : total amount they’ve spent during that same timeframe.

Target Variable

  • user_segment: Customer Segment (111, X1X, XX1, X13, X14, 14X, 44X)

  • 111: Core or Best Customers : Focus on loyalty programs and new product introductions. Proven to have a higher willingness to pay, so don't use discount pricing to generate incremental sales. Instead, focus on value added offers through product recommendations based on previous purchases

  • X1X: Loyal Customers : Loyalty programs are effective for these repeat visitors. Advocacy programs and reviews are also common X1X strategies. Lastly, consider rewarding these customers with Free Shipping or other like benefits.

  • XX1: Whales or Highest Paying Customers : These customers have demonstrated a high willingness to pay. Consider premium offers, subscription tiers, luxury products, or value add cross/up-sells to increase AOV. Don't waste margin on discounts

  • X13 OR X14: Promising - Faithful customers : You've already succeeded in creating loyalty. Focus on increasing monetization through product recommendations based on past purchases and incentives tied to spending thresholds (pegged to your store AOV).

  • 14X: Rookies - Your Newest Customers : Most customers never graduate to loyal. Having clear strategies in place for first time buyers such as triggered welcome emails will pay dividends.

  • 44X: Slipping - Once Loyal, Now Gone : Customers leave for a variety of reasons. Depending on your situation price deals, new product launches, or other retention strategies.