Age Segmented Sales Performance Data

Data centres on understanding customer preferences, needs, and behaviours through age segmentation. The intention is to enable analysis that reveals how different age categories influence key sales metrics such as revenue and profit. By utilising this information, users can refine marketing strategies to better resonate with each group, driving sales and enhancing customer loyalty. The core product focus within the transactions is Clothing and Accessories.

The dataset contains sixteen distinct fields covering transaction details, customer demographics, and financial outcomes. Key variables include the Date, Day, Month, and Year of the sale. Customer demographic information is provided via Customer_Age (the individual age), the categorised Age_Group (which segments customers into groups like Adults 35-64 and Young Adults 25-34), and Customer_Gender. Transactional metrics include the transacting Country, Product_Category (Bikes, Accessories, or Clothes), Order_Quantity, Unit_Cost, and Unit_Price. Financial performance is quantified through Profit, Cost, and Revenue.

The dataset is provided as a CSV file named PRODUCT SALES.csv, with a size of 11.77 MB. It records 113,000 valid entries across all fields, featuring zero missing data points or mismatched records. The dataset maintains a high usability score of 10.00. Updates are expected to occur quarterly.

Ideal applications include identifying sales trends based on customer age demographics, optimizing targeted marketing campaigns to increase conversion rates, analysing the differential impact of age groups on revenue generation and profitability, and tailoring product strategies to meet the specific purchasing behaviours and needs of segmented customer groups.

Geographic: Sales transactions involve six unique countries, with the United States being the location for 35% of the entries. Time Range: The records span transactions occurring between 2017 and 2021. Demographics: Customer ages range significantly, from 17 up to 87, with an average age of 35.9. The most prominent segments are Adults (35-64), representing 49% of the data, and Young Adults (25-34), accounting for 34%. Gender distribution is nearly balanced, with 52% Male and 48% Female entries. Products: The data primarily tracks sales of Accessories (62%) and Bikes (23%).

CC0: Public Domain

Marketing Analysts: To develop refined segmentation models and execute effective age-targeted advertising efforts. Sales Strategists: To evaluate product success and allocate resources based on purchasing patterns across different age brackets. Retail Managers: To optimize stock levels and promotional efforts for items, particularly Clothing and Accessories, in response to demographic demand. Data Scientists: For developing predictive models focused on customer behaviour and value linked to age segmentation.

Original Data Source: Age Segmented Sales Performance Data