"Strategic Marketing MBA | Empowering Data Insights"

Swapnil Bhole  • Peerlist

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

Project Overview:In the highly competitive world of online retail, understanding what customers like, how products perform, and market trends is critical for success. This project focuses on analysing a dataset with more than 1,000 Amazon product ratings and reviews to discover insights that can help boost product visibility, improve customer satisfaction, and ultimately increase sales.Problem Statement:The goal of this project is to dig into the dataset and find valuable insights to make smart business decisions. Here are the main questions we're exploring and what we've found so far:1. Effect of Discounts on Ratings: We've looked at whether offering discounts affects how customers rate products. Surprisingly, it seems that changing the discount percentage doesn't have a big impact on product ratings. Other factors matter more.2. Best-Rated Product Category: We've found that among all the types of products, Office Products have the highest average ratings. Customers really seem to like them.3. Price and Rating Connection: We've checked if a product's price is linked to how customers rate it. Interestingly, there's no clear connection between price and rating. Customers consider many things when they give ratings.4. Common Words in Reviews: Most reviews are positive, but we've also figured out the words people use most often when they're happy or unhappy with a product. This helps us understand what customers care about.5. Rating Distribution: We've looked at how ratings are spread across products. Most products get ratings between 4 and 4.5, which tells us about overall customer satisfaction.6. Most Reviewed Product and Its Rating: The product with the most reviews tends to get ratings between 4 and 4.5, showing that it's quite popular and well-liked.This project aims to help businesses make better decisions based on these insights, ultimately leading to happier customers and increased sales.

Amazon Sales Dataset

Project Description:The objective of this data analysis project is to explore the sales and order data for Northwind Traders, a fictitious gourmet food supplier. The dataset contains valuable information on customers, products, orders, shippers, and employees. By conducting a comprehensive analysis of this data, we aim to uncover key insights that can aid the company in making informed decisions, optimizing strategies, and enhancing overall business performance.The first aspect of the analysis will focus on identifying any noticeable sales trends over time. By closely examining the sales data across different periods, we will determine if there are any patterns or fluctuations in revenue. Understanding sales trends can help the company anticipate peak seasons, plan inventory management efficiently, and devise targeted marketing strategies to capitalize on high-demand periods.Another crucial aspect of the analysis is to determine the best and worst-selling products. By assessing sales data and revenue generated by individual products, we will identify which items perform exceptionally well and contribute significantly to the company's income. Likewise, we will pinpoint products that show weaker performance, enabling the company to focus on potential improvements or reconsidering their inclusion in the product portfolio.Identifying key customers is another critical objective of this analysis. By evaluating customer data and revenue contributions, we aim to identify customers who have a substantial impact on the company's sales. Recognizing these key customers allows the company to prioritize personalized services, maintain strong relationships, and enhance customer retention strategies, leading to long-term loyalty and sustained revenue growth.Furthermore, we will investigate the consistency of shipping costs across different providers. By comparing shipping expenses and freight charges for various providers and shipments, we will uncover any variations or inconsistencies in shipping costs. Understanding these cost discrepancies can assist the company in optimizing its logistics, selecting cost-effective shipping options, and improving overall operational efficiency.Overall, this data analysis project for Northwind Traders will provide valuable insights into sales trends, product performance, key customers, and shipping costs. Armed with these findings, the company can make data-driven decisions, allocate resources effectively, and implement targeted strategies to enhance profitability, customer satisfaction, and maintain a competitive edge in the gourmet food supply market. The recommendations derived from this analysis will empower Northwind Traders to optimize its operations and thrive in a dynamic business landscape.

Northwind Traders

Welcome to our Hotel Booking Data Analysis Project! In this data-driven journey, we embark on a comprehensive exploration of real-world hotel bookings from a city and a resort hotel spanning the years 2015 to 2017. Our main objective is to understand and visualize datasets from both the hotel's and customer's perspective.Defining the Problem and Objectives:Our project begins by defining the key problem areas we aim to address. We seek to uncover the reasons behind booking cancellations across various parameters, identify the best time to book a hotel, and determine peak tourism seasons.Data Collection and Preprocessing:We have meticulously gathered relevant data from kaggle, ensuring its completeness and suitability for analysis. Before delving into the data, we perform thorough data cleaning and preprocessing, handling missing values, outliers, and formatting issues. We normalize, scale, and encode categorical variables, priming the data for insightful analysis.Key Insights and Relationships:Through our analysis, we unravel intriguing relationships between key factors. We observe that as lead time increases, the ratio of cancellations to successful bookings rises, shedding light on customer booking behavior. Additionally, we discover that while the "no deposit" type is popular, it also experiences a higher number of cancellations, while the "non-refundable" type shows higher customer commitment. We also uncover the impact of market segments on cancellations and reveal a link between past cancellations and the likelihood of future cancellations.Price Variations and Tourism Peaks:By meticulously analyzing hotel prices over the years, we identify the peak tourism periods, particularly in the months of July and August. During these periods, average daily rates soar, reflecting the increased demand for hotel bookings.Conclusion and Recommendations:As we conclude our journey, we summarize our key findings and draw actionable insights from the data. Armed with these insights, hotel management and stakeholders can optimize booking strategies, minimize cancellations, and enhance customer satisfaction.

Hotel Booking Data Analysis

Our data analysis project focused on examining the trends and insights within the Google Play Store app dataset using Python, Pandas, and Matplotlib. We aimed to uncover valuable information about app categories, user ratings, and pricing to provide useful insights for app developers and stakeholders.Firstly, we analyzed the Android versions used by users, finding that 4.1 and up were the most prevalent. This information is crucial for developers as it highlights the need to ensure compatibility with this version and later ones to reach a significant portion of the user base. Next, we delved into app categories and discovered that 'Games' emerged as the most installed category. This finding emphasizes the popularity and demand for gaming apps among users. App developers looking to create successful and widely downloaded apps may consider exploring this category further. By identifying the 33 unique app categories, we showcased the diversity available in the Google Play Store. This knowledge is valuable for developers seeking to identify niche markets or explore new categories with untapped potential.Our analysis revealed that 271 apps received 5-star ratings, indicating a considerable number of highly rated apps in the dataset. This insight can serve as inspiration for developers, showcasing the characteristics that contribute to positive user experiences and high ratings. Examining content ratings, we found that most apps were classified as 'Everyone,' indicating that most apps cater to users of all ages. This knowledge can guide developers in creating apps that adhere to broad content guidelines and appeal to a wider audience. Furthermore, we discovered that the primary genre in the dataset was 'Tools,' indicating a significant focus on utility and functionality. This information provides developers with an understanding of the prevailing market trends and can inspire the development of innovative tools and utilities.Finally, our analysis of app pricing revealed that the 'Finance' category had the highest average price for paid apps. This finding suggests that finance-related apps are perceived to have higher value and may offer opportunities for developers to monetize their offerings effectively. Overall, this data analysis project using Python, Pandas, and Matplotlib provided valuable insights into app categories, user ratings, and pricing within the Google Play Store. The findings can guide developers in making informed decisions regarding app development, marketing, and targeting specific user segments.

Google Play Store App

Uber, a prominent transportation conglomerate, primarily offers taxi services through a mobile app, allowing individuals to easily request a ride. The primary objective of this investigation is to determine the busiest hour for both the number of ride requests and completed trips within the available dataset. The analysis aims to identify the time with the highest volume of unique ride requests, which can be valuable for optimizing driver schedules and ensuring sufficient coverage during peak demand. By comparing the supply of drivers with the demand for rides, the project aims to assess whether the number of available drivers increases in response to higher demand, ensuring an effective match between driver supply and fluctuations in customer demand. Furthermore, the study aims to identify the specific time of day that would be most appropriate to designate as the "end of the day" rather than the conventional midnight, considering the natural lowest points of both driver supply and ride demand.

Uber Case Study

Rotten Tomatoes is a renowned American website that aggregates reviews for movies and TV shows, serving as a widely trusted resource for quality entertainment recommendations. The Tomatometer score, which is derived from the viewpoints of numerous film and television critics, is considered a reliable measure of critical approval by millions of fans. The main goal of this project revolves around investigating the distribution of film ratings and determining the primary genres within the dataset. It delves into the analysis of the proportions of films classified as "Certified Fresh" and "Rotten" according to the Tomatometer ratings. Furthermore, it examines the historical pattern of film releases over time, focusing on the volume of releases per month, and aims to pinpoint the precise year and month of the most recent film releases. By comparing the Tomatometer ratings with audience ratings, the project seeks to identify the films that exhibit the most significant disparities in evaluations between critics and viewers. Additionally, it explores the linguistic diversity within critic's consensus ratings to identify the most frequently utilized languages.

Rotten Tomatoes Movies 2

In 1982, Inc. magazine introduced the Inc. 500 list, which showcased rapidly growing privately held companies in the United States. This prestigious list became a symbol of success for entrepreneurs, as it recognized promising ventures that would later gain widespread recognition. In 2007, the Inc. 500 list expanded into the Inc. 5000, offering readers a more comprehensive view of the entrepreneurial landscape by including a wider range of achievements and accomplishments.The objective of this case study is to utilize the available dataset, which contains detailed information about each company featured on the 2019 Inc. 5000 list. The dataset includes fields such as company name, industry classification, founding year, website, location, 2019 revenue, percentage growth, year-over-year change in the number of employees, and the number of years the company has been listed.By leveraging the powerful capabilities of Excel, researchers can conduct in-depth analyses based on factors like revenue, percentage growth, and workforce statistics. The specific analytical techniques employed will depend on the research questions and desired outcomes of the analysis. Excel enables researchers to explore relationships, trends, and patterns within the dataset, providing valuable insights into the performance, growth, and longevity of the companies on the Inc. 5000 list.

INC 5000 Companies 2019

<ul><li>The dataset provided contains information about the sales of a fictional pizza restaurant throughout a year. It is divided into four tables, each providing specific details about the orders, order details, pizzas, and pizza types. These tables include information such as the date and time of each order, the type and size of pizzas, the quantity, price, and ingredients of each pizza sold.</li><li>With this dataset, various research questions can be explored and analyzed. For instance, one can investigate the most popular pizza among customers, identify any seasonal trends in sales, or calculate the average number of pizzas ordered. To perform these analyses, tools like Excel can be utilized, leveraging its capabilities to examine factors such as price, order dates and times, and specific pizza information.</li><li>The specific analytical techniques and approaches employed will depend on the research questions at hand and the desired outcomes of the analysis. Excel provides a versatile platform for conducting these analyses, enabling the exploration of patterns, trends, and insights within the dataset.</li></ul>