Reference no: EM134025762
Problem
Zara, a global fashion retailer, is analyzing its product sales data to understand customer buying behavior and improve profitability. The dataset includes 226 product entries with details such as product category, seasonal tags, promotions, sales volume, ratings, price, and revenue. The company's management is particularly interested in:
1) How promotions influence sales and revenue.
2) Which product categories (e.g., shoes, jackets, etc.) perform better across different seasons?
3) Differences in performance between men's and women's products.
4) The role of product ratings and placement (aisle position) in customer purchases. A Power BI dashboard has been created with KPI cards, bar charts, pie charts, and tables.
Key insights from the dashboard show that:
1) Promo products generate 50% higher revenue than non-promo products.
2) Shoes and jackets dominate sales. Get the instant assignment help.
3) Higher-rated items are more often discounted.
4) Men's products provide higher profits than women's products.
5) Products positioned in aisles perform better than those placed elsewhere.
Management has asked your team (as data analysts) to analyze the dataset, interpret the insights, and recommend strategies to boost underperforming areas while leveraging strong-performing products.
Task
(1500-word limit). Explain the types of analytics with example. Explain the impact.