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Beyond the daily grind, I'm a relentless learner, diving deep into projects that push my technical and analytical skills, this is where I truly come alive. Learning, growing, tinkering and building a stronger, more versatile me.
Pet Pooja: Boosting Revenue and Customer Loyalty with Data-Driven Loyalty Programs
To address stagnant growth and reengage customers, I have developed a dynamic loyalty program feature for Pet Pooja's restaurant management software. Utilizing existing customer data, this solution automatically calculates and assigns personalized rewards, such as discounts or complimentary items, for repeat patrons. It seamlessly notifies restaurant owners of eligible rewards to streamline implementation. By incentivizing loyalty through tailored rewards and simplifying the process for businesses, this feature aims to increase customer lifetime value, retention rates, and revenue. Successful adoption by restaurant clients and measurable increases in customer lifetime value will demonstrate the revenue-driving power of data-driven loyalty strategies.
From Chaos to Parties: Streamlining Friend Group Gatherings via WhatsApp
To reignite post-pandemic social life, we propose a WhatsApp feature empowering users to effortlessly create and synchronise plans. From calendar chaos to carefree parties, imagine a WhatsApp where friend group gatherings flow effortlessly. No more endless "Who's free?" texts or frustrating schedule juggling. These new features empowers you to whip up plans, sync schedules in real-time, and land on the perfect time for everyone, all within the familiar comfort of your favorite messaging app. By seamlessly integrating with existing WhatsApp functionalities and prioritizing simple activities like dinners and movie nights, we aim to boost user engagement and strengthen WhatsApp's role in fostering real-world connections. These features leverages our small team's strengths and rapid iteration capabilities to deliver a user-friendly experience, revitalizing offline gatherings and strengthening social bonds in the post-pandemic era.
Urban Company: Leveraging New Features to Drive NPS in the Home Services Industry
Imagine Urban Company soaring to new heights of customer satisfaction, powered by innovative features that turn chores into joys. This case study chronicles my journey as a product manager, tasked with identifying and testing exciting additions to the platform, all with one goal in mind: skyrocketing Net Promoter Score (NPS) in the home services industry. By delving into user needs and exploring alternative solutions, we'll discover the perfect feature to transform tedious tasks into delightful experiences, boost business growth, and solidify Urban Company's position as the king of home comfort. This feature would transform stressed users into loyal advocates, and proving that innovation is the key to unlocking boundless potential in the home services industry, in this case Urban Company.
Building Trust on Meesho: Enhancing Buyer Confidence through Reseller and Product Information Posters
To bridge the trust gap on Meesho and empower confident purchasing decisions, I have explored the power of informative "posters" showcasing both resellers and their products with . Recognizing the platform's vast product selection yet limited transparency, I aim to injecting trust and value by presenting detailed information about sellers and their offerings. These posters, strategically placed within the Meesho experience, aim to illuminate buyer confidence, potentially driving increased sales and a more positive platform environment.
Incentivizing Learning on Udemy: Consequence-Based Approaches to Boost Course Completion Rates
To combat Udemy's abysmal course completion rate (hovering around 10%), I have proposed a features through consequence-based engagement strategies. This case study details my journey from pinpointing the challenge, meticulously analyzing user personas and data, to crafting and deploying innovative solutions. Through careful implementation, iterative feedback loops, and rigorous impact assessment, these features will bridge the gap between course content and real-world application, ultimately empowering learners to unlock their full potential and complete their Udemy journeys with newfound motivation.
CLTV Analysis using Python
In this project, I delved into Customer Lifetime Value (CLTV) analysis using Python, exploring the long-term value of customers to a business. By analyzing a dataset with customer acquisition data, I've employed Python's Pandas and Plotly libraries to assess acquisition costs, revenue, and conversion rates across various marketing channels. The project focuses on identifying profitable channels, calculating return on investment (ROI), and determining the CLTV for strategic customer retention and acquisition planning. This analysis is crucial for businesses seeking to optimize marketing efforts and enhance customer value over time.
A Customer Query resolution system for Google Pay
In this product case, I proposed the internal customer query system aimed at elevating customer satisfaction and experience. The core of this proposal included enabling the CX team to interact effectively with users through various channels and liaise with internal departments for advanced query resolutions. A critical aspect of the proposal was optimizing the CX team's efficiency, ensuring they could serve a larger customer base with streamlined resources. This project reflects my vision for integrating customer-centric strategies into existing business models, particularly in the digital payment sector.
Mining Sales Data for Insights: A Python-Based Framework to for Store Profitability Analysis
This python project entails a detailed analysis of store sales and profits using a dataset of sales, product, and customer data. Utilizing Python and libraries like Pandas and Plotly, the analysis covers various aspects, including sales and profit trends over time, performance by categories and sub-categories, and customer segment analysis. In this task, I've employed visualizations to interpret and present the data effectively, providing valuable insights for optimizing sales strategies and improving store profitability.
Engaging & Retaining: The LazyPay Rewards Program for Driving User Lifetime Value
This project focuses on LazyPay, India's leading 'pay later' (BNPL) platform, which is widely accepted across over 25,000 platforms. The project's goal was to create a rewards program to enhance user engagement and retention, increasing transaction frequency, and promoting good repayment habits. The proposed rewards scheme is straightforward, offering points based on spending and the number of transactions, incentivizing higher usage. The rewards are exclusive to users without pending dues, aligning with LazyPay's objective of responsible credit usage. This case study addresses the competitive BNPL landscape and LazyPay's strategic initiatives to maintain its market position and foster user loyalty.
Coding a C++ Powered Railway Ticket Reservation System for Enhanced Security and Passenger Convenience
In this project, I have developed a user-friendly C++ application for railway ticket reservations, aiming to make the booking process smoother and more secure. The application offers detailed train schedules, flexible seat selection, and robust security features like two-factor authentication. It's designed to overcome the existing system's limitations by providing comprehensive train information, a wider range of seat options, and enhanced transaction security. The code is organized into classes for easy management, and it incorporates advanced features like file handling and error management for a reliable user experience. This project enhances the railway ticket booking process, making it more efficient and user-centric.
Decoding WhatsApp Conversations: Sentiment Analysis of User Interactions with Python
In this project I analysed sentiment inclination of WhatsApp chats using Python, focusing on transforming raw chat data into an analyzable format. By employing Python libraries and custom functions, the chat data is parsed for time, date, author, and message content. This Sentiment analysis was performed by categorizing messages into positive, negative, or neutral sentiments. The findings predominantly showed positive sentiments, offering insights into the emotional nuances of digital conversations.
