ANALYSIS REPORT
Analyst: Joy Ikwue
Date: May, 2026
Programme: TaTa (Forage)
Dataset: 541,910 Sales
Tools: Excel
| $10.4M Total Revenue(Dec 2010-2012) | $9.6MTotal Revenue (2012) | 5.6M Quantity Sold | $521.20 AOV | 375 Total Customer | 37 Total Country | 536,642 Row-Analyzed | | --- | --- | --- | --- | --- | --- | --- |
This report presents a data-driven analysis of the Online Retail Store's sales performance for the period December 2010 to December 2011. The dataset was cleaned to remove invalid entries, including negative quantities, zero-value unit prices, missing customer records, and unknown descriptions, to ensure data integrity. Four key business questions were analysed using pivot tables and visualisations in Excel. The analysis revealed that revenue peaked in November 2011 at $1.5M, driven by pre-Christmas seasonal demand. The Netherlands emerged as the strongest international market by both revenue and quantity sold. Customer 14646 was identified as the highest revenue-generating customer at $280.2K. Regional demand analysis confirmed European countries as the dominant market outside the United Kingdom, with the Netherlands, EIRE, and Germany leading in product demand. These findings provide actionable insights to support the CEO and CMO in making informed decisions around forecasting, customer retention, and international market expansion.
The CEO and CMO of the Online Retail Store require data-driven insights to support strategic business decisions around revenue forecasting, international market expansion, and customer retention. The existing transactional dataset for the period 2010-2011 contained data quality issues, including negative quantities, zero-value unit prices, missing customer records, and unknown descriptions, that needed to be addressed before meaningful analysis could be conducted. This report cleans the dataset, analyses four key business questions, and presents visual insights that enable both stakeholders to make informed decisions on seasonal revenue trends, top-performing markets, highest-value customers, and regions with the greatest product demand.
The dataset used for this analysis is the Online Retail Store's transactional data, spanning December 2010 to December 2011. The raw dataset contains 541,910 rows and 8 variables: Invoice Number, Stock Code, Description, Quantity, Invoice Date, Unit Price, Customer ID, and Country. The data captures customer transactions across multiple countries, with the United Kingdom representing the majority of sales. Prior to analysis, the dataset underwent a thorough cleaning process, reducing it to 536,642 rows used in the final analysis.
| Variable | Type | Description | Used in Analysis |
|---|---|---|---|
| Invoice No | INT | Unique identifier assigned to each transaction or order | Yes |
| Stock Code | INT | Unique identifier assigned to each product in the inventory | No |
| Description | Varchar | Name or description of the product purchased | Yes |
| Quantity | INT | Number of units purchased per transaction | Yes |
| Invoice Date | Datetime | Date and time the transaction was recorded | Yes |
| Unit Price | Decimal | Price per unit of the product in GBP | Yes |
| Customer ID | INT | Unique identifier assigned to each customer | Yes |
| Country | Varchar | Country where the customer is located | Yes |
| Issue | Volume | Decision |
|---|---|---|
| Duplicate Value | 1% (5268) | Remove duplicate across all columns |
| Anomalies | 2% (10589) | Exclude from the dataset |
| Missing value | (5) | Dropped, not relevant to the analysis |
| Datatype | All column | Assigned appropriate data type to each column |
| Unspecified Name | - | Excluded rows marked 'Unspecified' (no price or customer ID given) |