It's Front Porch’s Post

📊 Choosing the Right Chart for Your Data: A Quick Guide! Ever looked at data and wondered, “What’s the best way to show this?” Choosing the right chart can make or break your data story. Here’s a quick rundown to help you pick the perfect visual every time: 🔹 Bar Charts: Great for comparing categories. Use them to show differences in sales, demographics, or survey results. Horizontal or vertical, these work best with a few distinct categories. 🔹 Line Charts: Ideal for trends over time. If you’re tracking monthly sales, website visits, or stock prices, line charts make patterns over time easy to spot. 🔹 Pie Charts: Use with caution! They work well for displaying parts of a whole, but only if you have 4 or fewer categories. Anything more, and it’s better to opt for a bar chart. 🔹 Scatter Plots: Perfect for showing relationships between two variables. For example, visualize how marketing spend affects revenue, or analyze the correlation between age and income. 🔹 Heatmaps: These make it easy to spot patterns and intensities across two dimensions. Think of customer satisfaction scores by department and month, or conversion rates by platform and region. 🔹 Histograms: Best for frequency distributions. They show how often each range of values occurs, making them ideal for analyzing age ranges, income brackets, or grades. 🔹 Box Plots: Go-to for distribution insights and outliers. Box plots are fantastic for summarizing data distributions, like salaries within departments or scores on an exam. Choosing the right chart helps your audience quickly understand and retain the information you're sharing. Remember, data visualization is all about clarity and impact! 💬 Which chart do you rely on most? Share below! #DataVisualization #DataAnalysis #ChartTips #DataStorytelling #DataScience #DataInsights #Analytics

🍎 In the world of data science, understanding relationships between variables is crucial, and that's where the Chi-Squared Test comes into play! It’s a fundamental tool that helps us make sense of categorical data, revealing hidden patterns that can drive business decisions. Here’s how Chi-Squared tests show up in data science and business: 👉🏾 Market Research: Assess if consumer preferences differ by demographics, helping businesses tailor their marketing strategies effectively. 👉🏾 Quality Control: Analyze whether defects in manufacturing processes are independent of different production lines, ensuring product consistency. 👉🏾 A/B Testing: Evaluate the effectiveness of different marketing campaigns or website designs by comparing user engagement across categories. Harnessing the power of Chi-Squared tests can lead to actionable insights and data-driven decisions that elevate business performance! ------------------------- Comment below for Part 2 of Chi-Squared Test or if you learned something :) Repost ♻️ Follow ➕ #DataScience #Statistics #ChiSquared #BusinessIntelligence #Analytics #DataDrivenDecisions

Excited to share my latest project! 🚀 I just completed the ‘Case Study: Analyzing Customer Churn in Excel’ course on DataCamp. As part of the course, I created a dashboard to investigate a dataset from an example telecom company, Datable, and analyze their churn rates. The dashboard showcases essential KPIs such as: Total number of customers Number of churned customers Churn rate It also provides insights into: Reasons for customer churn, with a focus on competitor activities Churn rate differences by customer demographics Breakdown of churn rate based on average data usage and international data plan usage Top 25 states with the highest churn rates check the excel file on github https://lnkd.in/dHdQuiBM Check out my dashboard below and let me know your thoughts! #DataAnalysis #Excel #DataCamp #CustomerChurn

In this Power BI project, sales data from an online retail store was analyzed to gain insights into customer behavior and revenue performance. Data from sources like Excel and SQL Server was imported and transformed, with steps including data cleaning, normalization, and merging tables to create a comprehensive dataset. Key metrics, such as total revenue, monthly sales growth, and customer demographics, were visualized using dynamic charts and tables. DAX calculations were implemented to derive additional insights, such as average order value and customer lifetime value. The final dashboard enabled stakeholders to interact with filters, drill down into product categories, and identify trends, aiding data-driven decision-making on marketing and inventory strategies. The finished work will be here in few hours to grace your screen, a snapshot is here just for you to take a bite TheData Immersed(TDI) Anne Nnamani

🚀 Excited to share my recent Power BI project on Churn Data Analysis! 🚀 As part of my continuous learning and passion for data analytics, I've worked on a project that dives deep into understanding customer churn behavior. 📊 Project Overview: Customer churn analysis is crucial for businesses to understand why customers are leaving and how to retain them. Using a dataset with various customer attributes, I explored patterns and trends to identify key factors contributing to churn. Key Insights: 🔍 Analyzed customer demographics, usage patterns, and service-related factors. 📉 Identified that the highest churn rate was among customers with shorter service tenures. 📈 Found that customers who used certain features more frequently were less likely to churn. #PowerBI #DataAnalysis

🚀 New Project Alert!🚀 I’m excited to share my latest project on E-commerce Customer Segmentation! This project leverages data analysis and unsupervised machine learning to segment customers based on their transactional behaviors, allowing businesses to optimize their coupon strategies and improve customer retention. 🔍 Key Features: - Customer Demographics analysis by gender and city. - Coupon Usage Insights: Tracking transactions and burn rates over time. - Top-performing Cities & Branches: Identifying the top locations with the most successful coupon burns. - Customer Retention & Loyalty Trends: Visualizing customer retention patterns using powerful data insights. 📊 The project also includes a dynamic, interactive dashboard built with Power BI to help stakeholders make data-driven decisions. Check out the full project on GitHub! 👇   [GitHub Repository](https://lnkd.in/dCKYCDPp) #MLSC #DataScience #Ecommerce #PowerBI #MachineLearning #CustomerSegmentation #Python #GitHub --- You can now copy and paste this into LinkedIn to share your project!

🔍 Unlock the Secrets of Your Data with Power BI's Decomposition Tree 📊 Ever wondered how to truly get to the bottom of your data? Meet the Decomposition Tree in Power BI a game changing tool for data enthusiasts and business leaders alike. Why is this a big deal? The Decomposition Tree doesn’t just show you the data; it analyzes and breaks it down like a recursive algorithm, digging deeper with each iteration. Whether you’re analyzing sales performance across regions or diving deep into customer demographics, this tool helps you pinpoint exactly where the magic or the problem happens. When should you use it? - Identifying Root Causes: Struggling to understand why sales dipped in Q3? Drill down from total sales to regions, states, and even cities to find the answer. - Spotting Patterns: Want to know which territories are your star performers? The Decomposition Tree lays it out clearly, helping you see patterns and trends at a glance. Making Data-Driven Decisions: Whether you're in finance, marketing, or operations, this tool equips you with the knowledge to make smarter, data-backed decisions. In essence, the Decomposition Tree is your go to when you need to dissect complex data into simple, actionable insights. Don’t just analyze your data understand it. #PowerBI #DataAnalysis #BusinessIntelligence #DataInsights #DecompositionTree

🚀 Unlock Business Success with Data Analytics 🚀 Here’s how to get started: 1. Identify Key Metrics 2. Collect Data 3. Analyze the Data 4. Implement Insights 5. Monitor and Adjust Do you want to unlock valuable insights, drive growth, and achieve sustainable success in today’s dynamic marketplace?? 🔗 Read the full guide: https://hubs.li/Q02B2ZcY0

Day twenty-two(22) of my ninety(90) days of posting consistently on LinkedIn. Today i took up a project to analyze churn customers in a telecommunication company using Excel. Here’s a step-by-step guide on how I created the customer churn dashboard : 1. Data Collection: This data was sourced from AMDARI. 2. Data Cleaning: Next, I cleaned the data to ensure accuracy and consistency. This involved handling missing values, standardizing formats, and removing duplicates. 3. Data Analysis: I performed exploratory data analysis to identify key factors influencing churn. This included calculating churn rates, segmenting customers based on demographics like age, gender and analyzing usage patterns. 4. Dashboard Design: I designed the dashboard to provide a comprehensive view of churn metrics. Key components included: General Churn Rate Overview Customer Segmentation: Visualizations showing churn rates across different customer segments. Usage Patterns: Charts depicting usage patterns of churned customers. 5. Data Visualization: Using Excel’s powerful charting tools, I created interactive charts and graphs. PivotTables were utilized to summarize data. 6. Insights and Actionable Recommendations: Finally, I derived actionable insights from the dashboard such as: •Higher number of churns were observed in Females compared to males. •Customers with post paid plan and also lower data usage churn than those with prepaid plan and higher data usage. •And also younger customers compared to senior citizens. Recommendations includes enhancing engagement strategies for the younger age groups as well that customers with post paid plans. Creating this churn dashboard not only provided valuable insights but also showcased the power of Excel in data analysis and visualization. #Excel #DataAnalytics #CustomerChurn #Dashboard #DataVisualization

🚀 Excited to unveil my latest data analytics endeavor using Excel! Introducing: Marketing Campaign Analysis 📊 Our project dives deep into insights from Maven Marketing's campaign, aiming to uncover key findings that will drive actionable recommendations for optimizing our strategies and achieving superior results. Stay tuned for the key takeaways! Here’s what we're exploring: 🎇Customer Segmentation🎇: Delving into demographics (birth year, education, marital status, income) to refine our ideal customer profile. 🎇Campaign Performance Analysis🎇: Identifying our most successful campaign to replicate its success across other initiatives. 🎇Product Performance Optimization🎇: Leveraging insights from top-selling products to shape our future strategies. 🎇 Channel Optimization 🎇 : Pinpointing underperforming channels and exploring strategies for enhancement. Stay tuned as we unravel insights that will shape the future of our marketing efforts. Let's drive excellence together! 🌟 Mentor: Shiva Vashishtha (Data Science Trainer) Project Link: https://lnkd.in/dVp2GzeG #DataAnalytics #MarketingStrategy #ExcelAnalysis #marketing #customerinsights