Analyzing Amazon Subcategory Sales Data

I created this interactive dashboard to analyze product sales performance across categories, sub-categories, regions, customers, and top-selling products — using Excel 📊✨ What makes this dashboard unique? 👉 A dynamic “Select Measure” slicer that lets you switch between Revenue, Profit, Quantity, and Orders — and the entire dashboard updates instantly (including chart titles). This makes analysis faster, clearer, and business-friendly. 🔍 Key Insights: • Monthly sales quantity trends • Best-selling categories & sub-categories • Region-wise performance • Top 10 customers & cities • Top 20 highest-demand products • KPIs: Total Revenue | Total Profit | Total Orders | Total Customers 🛠 Skills strengthened through this project: ✔ Excel Data Cleaning ✔ Pivot Tables & Pivot Charts ✔ Dynamic Measures (Revenue, Profit, Orders, Quantity) ✔ Slicers, Timelines & Interactive Filters ✔ Data Visualization & Storytelling I’d love your thoughts, suggestions, or feedback! 😊 #ExcelDashboard #DataAnalytics #ExcelSkills #DashboardDesign #SalesAnalytics #DataVisualization

Research like a pro before jumping into a category. We have many dashboards and tools for analyzing products, but what if we need to evaluate categories that are new to us? 🤔 That is what the Category Insights dashboard is about. This tool helps us understand how products perform within specific categories across Amazon US, UK, Germany, and Japan marketplaces. You can see what's hot and analyze data like units sold, customer views, returns ratio, and how much sellers are spending on ads. All this info spans from the past 30 days, 90 days, 6 months, or even a whole year! All the first-party data is more useful now than ever since the low inventory fee makes it more expensive to "test" new products. But it doesn't stop there. You can use it to learn about your competition, see how many sellers offer similar products, and see the average number of offers per item. It also allows you to discover what features, such as durability and color drive most of the sales within a category. This intel helps you find gaps in the market where you can stand out. And once you have all that information, you can create a "favorite features" list to keep track of the in-demand features you want to focus on⭐️ Do you see yourself using this new dashboard soon?

🔍 Exploring PySpark: Advanced Data Analysis⚙️ 🌱 Scenario: Analyzing Multi-Dimensional Sales Data📊 Imagine being tasked with analyzing sales data that spans multiple dimensions, including time, regions, and product categories. To unlock insights from this complex dataset, PySpark's powerful capabilities come into play. 🔑 Step 1️⃣: Defining the Challenge Your goal is to gain a comprehensive understanding of sales performance by considering multiple dimensions such as time periods (months), regions (North, South, East, West), and product categories (electronics, clothing, appliances). You want to uncover patterns, trends, and identify the top-performing category in each region for each month. 🛠️ Step 2️⃣: PySpark's Multidimensional Solution Code snippet to get you started. from pyspark.sql import SparkSession from pyspark.sql.window import Window import pyspark.sql.functions as F # Create a Spark session spark = SparkSession.builder.appName("MultiDimensionalSalesAnalysis").getOrCreate() # Sample sales data data = [   ("2023-01", "North", "electronics", 15000),   ("2023-02", "South", "clothing", 10000),   ("2023-03", "East", "appliances", 12000),   # ... (more data) ] # Create DataFrame columns = ["month", "region", "category", "sales_amount"] df = spark.createDataFrame(data, columns) # Define window specifications window_spec = Window.partitionBy("month", "region").orderBy(F.desc("sales_amount")) # Use PySpark functions to rank categories by sales df_ranked = df.withColumn("rank", F.rank().over(window_spec)) # Filter top-performing categories top_performing_categories = df_ranked.filter(F.col("rank") == 1) https://lnkd.in/dVZ3dwTZ() 🎉 Step 3️⃣: Understanding the Insights This PySpark challenge dives deep into multidimensional data analysis, allowing you to rank and identify the top-performing product category for each region in each month. Insights from such analysis can be a game-changer in decision-making and strategic planning. 📈🔍 #onestepanalytics #PySpark #AdvancedDataAnalysis #SalesPerformance #MultiDimensionalData #DataInsights #StrategicPlanning #BigDataAnalytics #DataScience #MachineLearning #DataVisualization #DataMining #BusinessIntelligence #DataEngineering #DataWrangling #PredictiveAnalytics #DataDrivenDecisions