The project topic is "Market Segmentation and Product Development Strategies." This project addresses a important component of modern business strategy, especially in the rapidly growing e-commerce sector. One way for online retailers to gain competitive advantage, increase customer satisfaction and drive profitability is by understanding how to efficiently segment markets and develop products that suit particular groups of customers. This implies that businesses must understand who their customers are exactly before designing a product or service for them. Furthermore, as big data and advanced analytics continue to rise, there are new opportunities for using customer data in making more informed decisions about markets. The aim of this research is bridging the gap between market segmentation insights and practical product development strategies that ultimately lead to better business outcomes.
In this notebook, we utilize the publicly available Online Retail dataset to explore customer segmentation and provide product development strategies based on the customer data.
The Online Retail 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.
Additional variable information
InvoiceNo:A unique identifier for the invoice. An invoice number shared across rows means that those transactions were performed in a single invoice (multiple purchases).StockCode:Identifier for items contained in an invoice.Description:Textual description of each of the stock item.Quantity:The quantity of the item purchased.InvoiceDate:Date of purchase.UnitPrice:Value of each item.CustomerID:Identifier for customer making the purchase.Country:Country of customer.
Import all the necessary libraries required for the analysis of customer data. Importing the Online Retail dataset from an Excel file into a pandas DataFrame. This will allow us to easily manipulate and analyze the data using Python's powerful data analysis libraries.
Before proceeding with any analysis, it's essential to check for missing values in the dataset. Missing values can affect the quality of analysis, so we need to identify and address them appropriately (e.g., by removing or imputing the missing data).
Exploratory Data Analysis (EDA) is a critical step in understanding the characteristics and underlying patterns of a dataset. This phase involves summarizing the main features of the data, often through visualizations and statistical measures, to uncover insights and guide further data preprocessing and analysis.
In this section, we will perform EDA on our dataset to gain a comprehensive understanding of the transaction data. By leveraging various data visualization techniques, such as plots and charts, we aim to identify trends, detect anomalies, and highlight key patterns. This process will help us to better grasp the data's structure, distribution, and relationships, setting the stage for more advanced analyses and modeling.
In this section, we will analyze the month-over-month (MoM) growth in sales and product quantity. Tracking MoM growth helps identify trends and patterns in customer purchasing behavior, allowing for a clearer understanding of sales performance over time. By evaluating the fluctuation in sales and product quantity across different months, we can gain insights into seasonal demand, promotional impacts, and potential growth opportunities for the business.
By analyzing customer data, we aim to gain insights into various demographic attributes, such as geographical location and purchasing behavior. This understanding will help identify key customer segments, tailor marketing strategies, and enhance customer experience based on their demographic profiles.
In this section, we will analyze the relationship between the number of orders and the total sales revenue generated by customers. It is common for a single order to include multiple products. By identifying the top 10 customers with the highest total number of orders, we can also determine the sales revenue they have generated. This analysis will provide insights into how order volume correlates with revenue and highlight key customers contributing significantly to sales.
In this section, we will explore Customer Segmentation through RFM (Recency, Frequency, and Monetary) analysis. RFM analysis is a powerful marketing technique that helps businesses categorize their customers based on their transaction behaviors. By analyzing the recency, frequency, and monetary value of customer interactions, we can identify key customer segments and tailor marketing strategies accordingly.
Recencymeasures how recently a customer has made a purchase. This helps in identifying customers who are still actively engaged.Frequencytracks how often a customer makes a purchase, indicating their loyalty and purchasing habits.Monetaryassesses how much money a customer spends, revealing their overall value to the business.
we will identify customers who have been classified as "lost" based on their RFM (Recency, Frequency, Monetary) scores. These are customers whose recent activity, purchase frequency, and spending are all low. By focusing on the RFM class '111', which signifies low values across all three metrics, we can pinpoint the customers who are least engaged with the business. This analysis helps in understanding which customers have become inactive and may benefit from targeted re-engagement strategies.
