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Retail-Customer-Segmentation

image

Problem Description

In this project, your task is to identify major customer segments on a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

Data Description

Attribute Information:

  • InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation.

  • StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.

  • Description: Product (item) name. Nominal.

  • Quantity: The quantities of each product (item) per transaction. Numeric.

  • InvoiceDate: Invice Date and time. Numeric, the day and time when each transaction was generated.

  • UnitPrice: Unit price. Numeric, Product price per unit in sterling.

  • CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer.

  • Country: Country name. Nominal, the name of the country where each customer resides.

Business Use Case:

  • Targeted Marketing: By segmenting customers based on their behavior, interests and demographics, businesses can tailor their marketing campaigns and promotions to specific groups, resulting in higher response rates and conversions.
  • Cross-selling and Upselling: Customer segmentation can help businesses identify customers who are likely to purchase complementary or premium products, and target them with personalized recommendations.
  • Customer Retention: By understanding customer behavior and preferences, businesses can design and implement strategies to reduce churn and increase customer loyalty.
  • Personalized Customer Experience: Segmentation can help businesses understand customer needs and tailor their offerings, such as product recommendations, customer service, and overall experience, to meet those needs.
  • Improved Customer Insights: Customer segmentation provides valuable insights into customer behavior and preferences, which can inform future business decisions and strategies.
  • Optimization of Resource Allocation: By prioritizing and focusing resources on high-value customer segments, businesses can optimize their marketing and sales efforts, reduce costs, and increase ROI.

Data Pre-Processing

  • Implemented libraries such as pandas, numpy,matpplotlib,seaborn,Scikit-learn
  • Got the descriptive information of the data.
  • checked for null values if any
  • Checked for Outliers and duplicated values if any.

Data Cleaning

  • got rid of rows with null customerID
  • got rid of duplicated values
  • Dropped the id column since it is of no significance to our model training
  • Imputed null values with knn imputer and simple imputer
  • treated outliers

Exploratory Data Analysis

  • visualized maximum ordering countries.
  • Visualized month wise orders
  • checked the distribution of Revenue with the help of boxplot.

Feature Engineering

  • Applied RFM analysis which allows you to segment customer by the frequency and the value of purchases and how recent was the purchase.
  • labelled customer based on the RFM score range 1-4
  • Performed log transforamtion on RFM features to noramalize them.

Model training :

  • Used Elbow method, silhouete score, davies bouldin score to come up with optimum value of cluster for K-means
  • plotted dendogram baased on agglomerative hierrachical clustering.
  • found optimum cluster value to be 2 in both the clustering methods.