Exploring Customer Segmentation and Customer Lifetime Value for Sales Forecasting

---

Background

Welcome to the data exploration journey of understanding customer behavior and enhancing sales forecasting for a UK-based company specializing in unique all-occasion gifts. Our goal is to unlock valuable insights from customer data and historical sales, laying the foundation for effective customer segmentation and improved sales predictions.

Objectives

• Understand the Data:

  • Dive deep into the provided Online Retail II dataset to comprehend its intricacies and features.

• Exploratory Data Analysis (EDA):

  • Perform comprehensive exploratory data analysis to uncover hidden patterns, trends, and anomalies within the dataset.

• Data Preparation:

  • Preprocess and prepare the data for subsequent analyses, ensuring its suitability for modeling.

• Customer Segmentation:

  • Utilize advanced segmentation techniques to categorize customers based on their behavior, preferences, and historical interactions.

• Forecasting Models:

  • Develop and implement tailored forecast models for each customer segment, aiming for accurate sales predictions.

• Results Presentation:

  • Present the findings, insights, and actionable recommendations in a clear and concise manner.

Data Description

The heart of our exploration lies in the Online Retail II dataset, offering a real-world snapshot of online retail transactions. The primary data elements include:

online_retail_II.xlsx
This comprehensive table captures records for all created orders, boasting 1,067,371 rows and 8 columns. With a size of 44.55MB, it serves as a rich source of information for our analysis.

Data Element	Type	Description
Invoice	object	Invoice number, uniquely assigned to each transaction. If starting with 'c', it signifies a cancellation.
StockCode	object	Unique product (item) code assigned to each distinct product.
Description	object	Descriptive name of the product (item).
Quantity	int64	Quantities of each product (item) per transaction.
InvoiceDate	datetime	Date and time of the invoice generation.
Price	float64	Unit price of the product in pounds (£).
Customer ID	int64	Unique customer identifier with a 5-digit integral number.
Country	object	Country name where the customer resides.

File Tree

.
├── data
│   ├── 2009-2010.csv
│   └── 2010-2011.csv
├── models
│   └── t2v
├── notebooks
│   ├── ds4a_retail_challenge.ipynb
│   ├── gensim_lda.py
│   └── utils.py
├── README.md
└── requirements.txt

References

1. https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/
2. https://github.com/nicodv/kmodes
3. https://towardsdatascience.com/understanding-topic-coherence-measures-4aa41339634c
4. https://medium.com/@thomas.shawcarveth/market-segmentation-and-predicting-marketing-success-with-data-science-f48c99e3b4e1
5. https://www.geeksforgeeks.org/rfm-analysis-analysis-using-python/
6. https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python/