Data Analytics professional at Schenider Electric having 2+ years of experience with Excel,SQL, Python,Tableau

Shiva Vashishtha  • Peerlist

Aspiring Data Scientist with a UI/UX Designer and Video Editor background, passionate about data.

The banking industry faces significant challenges in improving conversion rates for term deposit products. Traditional marketing strategies result in low response and success rates. Understanding client behaviors, preferences, and historical interaction data can offer targeted insights to refine marketing efforts.What is the problem? Clients are contacted without clear segmentation, leading to wasted resources and low subscription success. There is no robust profile of an ideal client, which makes targeting inefficient.Strategic or Tactical? This is a strategic project aimed at enhancing long-term client acquisition and engagement strategy by leveraging data-driven decisions.Data/Customer Insight: A dataset of 45,211 records provides detailed demographic, financial, and interaction history data. Insights from this data can directly shape marketing communication strategies.

BankOpt Targeted Marketing Analysis

Telecom Customer Churn Report – Q4 2022Executive Summary: This report shows the churn situation of our telecom company for the last quarter of 2022. It gives a quick overview of the data, explains the main reasons why customers are leaving, and shares ideas on how we can reduce churn in the future.Data Overview:In Q4 2022, 7,044 customers left the company, which is 27% of our total customer base. Compared to the industry average churn rate of 4.5%, our churn rate is much higher, meaning we are losing more customers than other telecom companies.Trends and Reasons for Churn:● Most customers left because of competition and price.● Many said they found cheaper alternatives.● Customers who had been with us for less than a year were more likely to leave than long-term ones.Insights:● Customer service matters a lot. People stay when their issues are solved quickly and properly.● Price is a big factor. Customers often leave if they think our service is too expensive.Recommendations:To reduce churn:● Improve customer service: Hire more support staff, reduce wait times, and train the team better.● Offer discounts or promotions to loyal customers or those considering leaving.● Reward long-term customers to encourage them to stay.Conclusion:Our churn rate in Q4 2022 is higher than the industry average. The main reasons are price concerns and service issues. To keep more customers, we should focus on better support and attractive offers to build loyalty.

Telecom Customer Churn Report – Q4 2022

This report analyzes a year’s worth of sales data from a pizza restaurant. The goal is to identify key trends in customer behavior, order patterns, revenue generation, and menu performance. By understanding these aspects, we can make data-driven decisions to optimize sales, improve customer satisfaction, and enhance profitability.

Pizza-Sales-Analysis

Marketing Campaign Report By Afnan AliExecutive Summary: We looked at what makes people buy things online, which marketing campaign worked best, who the usual customer is, the most popular product, and which way of selling is not working well. We also suggest ways to help improve online sales and make more money.Insights:<ul><li>The most important things that affect online buying are age, education, marriage status, and income.</li><li>Most buyers were born between 1963 and 1982, have a graduate degree, and earn between $41K and $82K per year.</li><li>The "Last Campaign" had the most success in getting people to buy.</li><li>The best-selling product was Wines.</li><li>The web purchase channel was not doing as well as others.</li></ul>Recommendations: To make online sales better, we suggest:<ol><li>Make the website easier to use so people can buy things quickly. We can make a user friendly UiUx.</li><li>Use SEO (Search Engine Optimization) and SEM (Search Engine Marketing) to show up more on Google and other search engines.</li><li>Give special offers and suggestions based on what customers bought before.</li><li>Use social media (Facebook, Instagram, etc.) to show people the products and talk to them.</li><li>Provide safe and easy payment options so people feel safe when buying online.</li></ol>Conclusion: By knowing what makes people buy online, focusing on the best-selling products, and making the online shopping experience better, we can increase sales and make more money. With the right strategies, online sales can be a big success for the company!&nbsp;Project ownerName: Afnan Ali Iqbal AliDate: March 27th, 2025&nbsp;&nbsp;

Marketing-Campaign-Analysis

"Data enthusiast decoding insights, using stats, tech tools for smarter decisions. Unveiling patterns,in analytics.

<h3>📊 Coffee Shop Sales Dashboard</h3>This Power BI dashboard provides a comprehensive analysis of a coffee shop's sales performance, designed to offer actionable insights into customer behavior, product trends, and business growth.<h3>🚀 Features</h3><ul><li>Overview Page Get a snapshot of key metrics such as total sales, number of transactions, and average revenue per order.</li><li>Sales Analysis Break down sales by product categories (e.g., coffee, pastries, tea), identify top-selling items, and compare performance over time.</li><li>Time-Based Trends Visualize hourly, daily, and monthly sales patterns to optimize staffing and inventory decisions.</li><li>Location Insights Compare performance across multiple store locations or regions.</li><li>Customer Preferences Understand which products are most popular and identify customer buying patterns.</li></ul><h3>🛠️ Tools Used</h3><ul><li>Power BI Desktop For data visualization and dashboard creation.</li><li>DAX For calculated metrics and measures.</li><li>Power Query (M Language) For data transformation and modeling.</li></ul><h3>📌 Use Case</h3>Perfect for coffee shop owners, retail managers, or analysts looking to:<ul><li>Monitor daily performance</li><li>Identify best-selling products</li><li>Optimize business operations</li><li>Improve decision-making with data</li></ul>

Coffee Shop Sales

Full Stack (MERN) and Data Analayst Fresher, experienced in Internship Trainee at Nutan Capital Partners LLP

The Sales by Product Dashboard provides a comprehensive view of sales performance across individual products or product categories. It enables businesses to identify top-selling items, underperforming products, and sales trends over time. By visualizing key metrics such as total revenue, units sold, average selling price, and contribution to overall sales, stakeholders can make data-driven decisions about inventory, marketing, and pricing strategies. This dashboard is essential for understanding customer preferences, optimizing product portfolios, and driving profitable growth.

Sales by product

Highly motivated and analytical professional ready to embark on an exciting career as a data analyst

Tele-Churn2-Analysis using Python

Insightful Data & Business Analyst | Skilled in Python, SQL, Excel, Tableau, Google lookerstudio & Power BI

<h3>BlinkIt-Enhancing Grocery Retail and personal growth with Data-Driven Strategies .</h3><ul><li>Blinkit, a grocery retail chain operating across multiple cities, is experiencing inconsistent sales performance across its outlets and product categories. The CEO has tasked you, as a data analyst, with developing a comprehensive dashboard to identify key trends, uncover performance gaps, and highlight growth opportunities.</li><li>This dashboard will serve as a strategic decision-making tool, providing clarity on what’s working, what’s not, and what requires immediate attention. By analyzing sales, outlet performance, and product dynamics, our insights will drive profitability, operational efficiency, and long-term success.</li></ul>Get more details from : <a target="_blank" rel="noopener noreferrer" class="break-all link-break text-gray-500 hover:underline" href="https://github.com/DiyaSurana/blinkit_Project-">DiyaSurana/blinkit_Project</a>

BlinkIt

Business Analyst at IBM India Pvt. Ltd.| Data Science |Data Analyst| Python| PowerBI| Machine Learn| SQL|

This report explores key factors influencing online purchases, identifies the most effective marketing campaign, profiles the average customer, and assesses both top-performing products and underperforming sales channels. Based on our findings, we propose strategic actions to enhance online sales and drive revenue growth.

Marketing Campaign

Problem &amp; backgroundProblem: - Pizza restaurant has recently seen a decline in sales and plans toincrease them by looking at customer and order data. To do this, themanagement plans to perform a thorough analysis of order data andconsumer behaviour in order to spot important trends and areas forimprovement.Background: - An overview of pizza sales data from January 2015 toDecember 2015 is given in this report. To find trends and patterns in pizzasales, data was gathered from pizza joints across the United States andanalysed.Solution - In response to declining sales, we propose an integrated solution thatcombines data analysis, data-driven insights, and stakeholder engagement.

Pizza Sales

I'm a data analytics professional with a knack for finding patterns and stories in data.

January 2015 to December 2015 sales data is given in the report. The analysis focuses on order data, including date, time, pizza types, sizes, quantities, prices, and ingredients.The Pizza Sales Project will leverage data analysis to gain a comprehensive understanding of sales trends and identify strategic opportunities for growth. By examining seasonal variations, we can pinpoint factors influencing monthly fluctuations and optimize resource allocation.Analyzing sales by time of day will reveal peak periods and potential for targeted promotions, while evaluating category performance will highlight customer preferences and inform menu innovation. Ultimately, this project aims to provide data-driven recommendations for marketing, promotional strategies, and menu adjustments, enabling the business to maximize sales and achieve sustainable expansion.This analysis reveals key insights into the pizza place's sales, identifying peak hours, popular items, and customer preferences. Key recommendations include optimizing menu offerings, implementing targeted marketing campaigns, and improving operational efficiency. By leveraging these findings and implementing the recommended strategies, the pizza place can enhance customer satisfaction, increase sales, and achieve sustained growth.

Pizza Place Sales

<h3>Credit Card Sales Analysis – Power BI Dashboard</h3>This Power BI project provides an in-depth analysis of credit card sales and customer transactions, offering valuable insights into revenue trends, transaction behaviors, and customer demographics. The dashboard is designed to help businesses understand key performance metrics related to credit card usage.🔹 Key Features &amp; Insights:<ul><li>Revenue &amp; Transaction Trends: Weekly, quarterly, and yearly revenue comparisons with detailed transaction counts.</li><li>Customer Segmentation: Analysis by job type, income level, education, and age group.</li><li>Spending Patterns: Breakdown of revenue by expenditure type, including grocery, fuel, entertainment, and travel.</li><li>Card Category Performance: Insights into revenue generated by different card types (Blue, Silver, Gold, Platinum).</li><li>Transaction Methods: Comparison of chip, swipe, and online transactions.</li><li>Geographical Insights: Revenue distribution across the top-performing states.</li><li>Customer Behavior: Insights into account delinquency rates, activation trends, and income-based spending.</li></ul>📊 Tools &amp; Technologies Used:<ul><li>Power BI Desktop for data visualization and report generation.</li><li>Data Modeling &amp; DAX for calculated insights.</li><li>Interactive Filters &amp; Drilldowns for deep analysis.</li></ul>This dashboard helps businesses optimize credit card offerings, enhance customer targeting strategies, and drive better decision-making through data-driven insights.

Credit Card Sales Analysis

I'm excited to embark on my journey as a data analyst and bring my analytical skills to the forefront.

I built an interactive Excel dashboard that dives deep into hospital admission data, uncovering key patterns behind readmissions. This analysis highlights targeted opportunities to reduce readmission rates, leading to improved patient care, optimized resources, and significant cost savings.Lowering high readmission rates is an important challenge. As a data analyst, you can tackle this problem with a clear and structured approach. Here's a simple step-by-step plan:1. Data Acquisition &amp; Integration2. Exploratory Data Analysis (EDA) &amp; Insights Generation3. Risk Stratification &amp; Predictive Analytics4. Identifying Key Drivers &amp; Root Cause Analysis5. Personalized Intervention Strategies6. Outcome Measurement &amp; Performance Tracking7. Regulatory Compliance &amp; Continuous Monitoring

Hospital Administration Analysis

Data Analytics professional at Nishtya Infotech Pvt. Ltd. having 6+ months of experience with Excel,SQL,Python,Tableau

"Google Play Store app analysis uncovers key trends, user preferences, and performance metrics using data-driven insights. Leveraging Python for statistical analysis and Power BI for interactive visualizations, this project explores app ratings, reviews, pricing, and installs to identify patterns that influence app success. Correlation analysis and advanced Power BI tooltips enhance the depth of insights, providing a comprehensive understanding of the app ecosystem."

GooglePlayStoreApp

This project aims to analyze sales data from Superstore to identify key trends, customer purchasing behavior, and areas for revenue growth. By leveraging data analytics, we seek to enhance product performance, optimize pricing strategies, and improve overall sales efficiency.Objectives<ul><li>Identify top-selling and underperforming products.</li><li>Analyze customer purchasing patterns and preferences.</li><li>Optimize pricing, discounts, and promotions for higher revenue.</li><li>Improve inventory management to reduce stockouts and overstocking.</li><li>Enhance marketing strategies for better customer engagement.</li></ul>Methodology<ul><li>Data Collection: Gather historical sales, customer demographics, and product performance data.</li><li>Exploratory Data Analysis (EDA): Identify trends, seasonality, and correlations.</li><li>Sales Forecasting: Use predictive analytics to estimate future sales performance.</li><li>Market Basket Analysis: Understand product relationships for better cross-selling strategies.</li><li>Customer Segmentation: Group customers based on purchase behavior to tailor marketing efforts.</li></ul>Expected Outcomes<ul><li>Increased revenue through data-driven decision-making.</li><li>Better inventory control to meet demand efficiently.</li><li>More effective marketing strategies targeting the right customer segments.</li><li>Improved customer satisfaction and loyalty through personalized promotions.</li></ul>This project ultimately helps Superstore optimize its sales strategies, boost profitability, and enhance customer experience.

Improving sales performance at superstore

Pfizer aims to leverage genomics and clinical trial data to analyze drug efficacy and patient response variability. The goal is to identify key genetic markers, demographic factors, and clinical characteristics that influence how patients respond to treatments. By detecting patterns in side effects and progression-free survival (PFS) rates, this analysis will contribute to personalized medicine, enabling tailored treatment plans for improved patient outcomes.Objectives<ol><li>Analyze Drug Efficacy Across Patient Demographics<ul><li>Investigate how age, sex, ethnicity, and comorbidities affect treatment outcomes.</li></ul></li><li>Identify Genomic Biomarkers for Drug Response<ul><li>Detect genetic mutations, SNPs, and gene expression patterns that correlate with positive or negative treatment responses.</li></ul></li><li>Detect Patterns in Side Effects<ul><li>Analyze which genetic and clinical factors contribute to higher risk of adverse effects.</li></ul></li><li>Predict Progression-Free Survival (PFS)<ul><li>Use statistical and machine learning models to forecast patient survival rates based on their genomic and clinical profiles.</li></ul></li><li>Enhance Personalized Medicine and Clinical Trial Optimization<ul><li>Develop models that stratify patients into optimal treatment groups and improve clinical trial recruitment.</li></ul>Expected Outcomes✔ Personalized Treatment Plans ✔ Reduced Side Effects ✔ Optimized Clinical Trials</li></ol>

Pfizer’s Personalized Medicine Initiative Case Study

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Adecco India, a mid-sized tech company, is facing an increase in employee turnover, especially among junior staff in the sales department. To address this, the company aims to understand the underlying factors affecting employee satisfaction and retention, and develop strategies to improve employee engagement.

Adecco HR Analysis

Porter Delivery, once a successful food delivery service, is facing declining customer satisfaction. To address this issue, a comprehensive analysis is being conducted using various analytical tools to identify key factors affecting the customer experience. The study aims to uncover underlying problems and develop effective solutions to restore Porter's reputation as a reliable and customer-focused service.

Porter Delivery Analysis

Music Store Analysis is a data-driven project using SQL to explore and analyze key business aspects of a music store. The project focuses on predefined questions to uncover insights into sales trends, artist trends and many more. By querying and analyzing structured data, we identified top-selling genres, high-value customers, revenue distribution, and stock efficiency. This study helps understand market trends and optimize business strategies. The project demonstrates the power of SQL in handling large datasets, extracting valuable insights, and aiding data-driven decision-making in the music retail industry, ultimately improving sales performance and customer satisfaction.

Music Store Analysis

Walmart faces inventory management challenges, struggling to balance stockouts and overstocking. The problem requires a data-driven solution that dynamically adjusts inventory levels by forecasting demand based on past sales and real-time insights.To solve this problem we follow the steps given below:<ol><li>Data Collection and integration</li><li>Demand forcasting and Model Development</li><li>Real time data Processing</li><li>Agile Project Management</li><li>Actionable insights and recommendations</li><li>Performance monitoring and optimization</li></ol>