Analyze sales data to identify trends, patterns, and areas for improvement in sales performance.
Data Collection: Obtain a dataset containing sales transactions, including dates, sales amounts, product categories, and sales regions.
Data Cleaning: Handle missing values, remove duplicates, and format the data for analysis
Exploratory Data Analysis (EDA): Generate descriptive statistics, and analyze sales trends over time, regional performance, and product category performance.
Visualization: Create visualizations such as line charts for sales trends, bar charts for regional performance, and pie charts for product category distribution.
Reporting: Summarize findings in a report, highlighting key insights and recommendations for improving sales performance.
Excel, Python (Pandas, Matplotlib/Seaborn), or BI tools like Tableau or Power BI.
Customer Behavior Analysis
Analyze customer behavior data to understand purchasing patterns and customer preferences.
Data Collection: Use a dataset with customer purchase history, including transaction dates, items purchased, and customer demographics.
Data Cleaning: Address missing or inconsistent data and format the data for analysis.
Behavior Analysis: Perform cohort analysis, RFM (Recency, Frequency, Monetary) analysis, and identify key customer segments based on behavior.
Visualization: Create visualizations such as histograms for purchase frequency, scatter plots for purchase value vs. frequency, and heatmaps for customer activity patterns.
Reporting: Compile findings into a report with actionable insights and suggestions for targeted marketing or personalized offers.
Excel, Python (Pandas, Matplotlib/Seaborn), or BI tools like Tableau or Power BI.
Website Traffic Analysis
Analyze website traffic data to evaluate the effectiveness of online marketing strategies and identify areas for improvement.
Data Collection: Obtain web analytics data, including page views, session duration, traffic sources, and user demographics.
Data Cleaning: Clean and preprocess the data, handling any missing values or anomalies.
Traffic Analysis: Analyze traffic trends, user behavior, and conversion rates. Identify the most popular pages, traffic sources, and user pathways.
Visualization: Create visualizations such as line graphs for traffic trends, pie charts for traffic sources, and bar charts for top-performing pages.
Reporting: Generate a report with key findings, including recommendations for improving website performance and optimizing marketing strategies.
Google Analytics, Excel, Python (Pandas, Matplotlib/Seaborn), or BI tools like Tableau or Power BI.
