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PORTFOLIO
Explore a selection of my work, highlighting my expertise in business analysis, product optimization, and strategic pricing to drive growth and enhance performance.
Boosting Sales with PPC!
This project focused on a clothing brand that struggled with inefficient ad spend and low conversions, requiring a more effective strategy to boost sales and ROI.
Insights:
-Analyzed audience demographics and behavior.
-Launched targeted Google Ads and Facebook Ads campaigns.
-Used retargeting strategies and continuously optimized campaigns based on data.
Result:
-50% increase in sales and 45% growth in website traffic.
-Achieved a 4:1 ROAS.
-Reduced customer acquisition cost by 20%.
This campaign proved the power of targeted PPC in driving business growth and improving ROI.
Insights:
-Analyzed audience demographics and behavior.
-Launched targeted Google Ads and Facebook Ads campaigns.
-Used retargeting strategies and continuously optimized campaigns based on data.
Result:
-50% increase in sales and 45% growth in website traffic.
-Achieved a 4:1 ROAS.
-Reduced customer acquisition cost by 20%.
This campaign proved the power of targeted PPC in driving business growth and improving ROI.
CUSTOMER WEBSITE BEHAVIOUR
This project focuses on analyzing customer behavior on the Wolf & Badger e-commerce platform by applying data analytics and machine learning techniques. The goal is to gain actionable insights that will drive informed business decisions, enhance marketing strategies, and improve cross-category promotions. By using tools like Random Forest models, association rule mining, and A/B testing, the project aims to understand customer demographics, shopping behaviors, and website performance.
The ultimate objective is to optimize customer engagement, improve sales strategies, and refine website features for better user experiences, ultimately supporting long-term growth and operational efficiency.
The ultimate objective is to optimize customer engagement, improve sales strategies, and refine website features for better user experiences, ultimately supporting long-term growth and operational efficiency.
VR- MARKET ANALYSIS
This project was designed to expand VR Space Avenue's market globally by launching our innovative VR interior design services in London. Originating in India, VR Space Avenue has built a reputation for revolutionizing the design process with virtual reality technology, enabling clients to visualize and personalize their interiors in real time.
With London’s high-income, tech-savvy population and its demand for luxury services, the city offers a prime opportunity to introduce our VR-powered design solutions. This expansion allows us to tap into the affluent clientele in areas like Chelsea, who value cutting-edge technology and exclusive design experiences. By combining traditional design expertise with VR innovation, VR Space Avenue aims to set a new standard in interior design, offering unparalleled customization and an immersive customer experience.
With London’s high-income, tech-savvy population and its demand for luxury services, the city offers a prime opportunity to introduce our VR-powered design solutions. This expansion allows us to tap into the affluent clientele in areas like Chelsea, who value cutting-edge technology and exclusive design experiences. By combining traditional design expertise with VR innovation, VR Space Avenue aims to set a new standard in interior design, offering unparalleled customization and an immersive customer experience.
Data-Driven Insights
This project examines global economic indicators during the COVID-19 pandemic through data handling and visualization.
Key components include:
1. ER Diagram for Estate Agency Database: Visualizes relationships between properties, vendors, buyers, and contracts.
2. Data Cleaning: Corrected errors and imputed missing values to ensure data accuracy.
The analysis compares 2019 and 2020 data, revealing a decline in GDP per capita, a slight drop in life expectancy, increased healthcare spending, and stable CO2 emissions. The findings show the pandemic's mixed impact on the global economy, with a shift toward higher healthcare spending.
Key components include:
1. ER Diagram for Estate Agency Database: Visualizes relationships between properties, vendors, buyers, and contracts.
2. Data Cleaning: Corrected errors and imputed missing values to ensure data accuracy.
The analysis compares 2019 and 2020 data, revealing a decline in GDP per capita, a slight drop in life expectancy, increased healthcare spending, and stable CO2 emissions. The findings show the pandemic's mixed impact on the global economy, with a shift toward higher healthcare spending.
OPTIMIZING PAID AD CAMPAIGNS
Space Avenue, an interior design company, aimed to optimize its paid ad campaigns on Google Ads and Facebook Ads to improve performance and reduce costs.
The task involved refining audience targeting, testing ad creatives, and optimizing ad copy and landing pages to boost engagement and conversions.
Strategies Implemented:
-Data-driven audience refinement
-Testing various ad designs
-Improving ad copy
Results Achieved:
-30% increase in user engagement
-25% rise in conversion rates
-27% reduction in cost-per-click (CPC)
The task involved refining audience targeting, testing ad creatives, and optimizing ad copy and landing pages to boost engagement and conversions.
Strategies Implemented:
-Data-driven audience refinement
-Testing various ad designs
-Improving ad copy
Results Achieved:
-30% increase in user engagement
-25% rise in conversion rates
-27% reduction in cost-per-click (CPC)
PRODUCT ROLLOUT ANALYSIS
This project examines the rollout strategy for BevZ, a zero-sugar energy drink, by comparing two distinct approaches:
1. Tony’s Uniform Strategy
2. Sandy’s Flexible Plan
The analysis incorporates data analysis, market trends, and financial insights to evaluate the effectiveness of each approach. The goal is to recommend a strategy that combines the strengths of both plans, optimizing sales performance and operational efficiency for BevZ's successful market entry.
1. Tony’s Uniform Strategy
2. Sandy’s Flexible Plan
The analysis incorporates data analysis, market trends, and financial insights to evaluate the effectiveness of each approach. The goal is to recommend a strategy that combines the strengths of both plans, optimizing sales performance and operational efficiency for BevZ's successful market entry.
Pricing Model
This project utilizes advanced Excel to create a dynamic dashboard for optimizing car insurance premiums. By analyzing factors like income, gender, occupation, vehicle type, and mileage, the model identifies personalized pricing strategies to enhance profitability, improve risk assessment, and boost customer satisfaction.
ALUMNI DONATION ANALYSIS
This project leverages analytics to enhance the University’s outreach program by identifying the top 10,000 alumni most likely to donate. Through the analysis of alumni behavior across various networking events, industry meetups, and social activities, the project aims to refine donor engagement strategies. By developing a predictive model, the goal is to boost donation rates, strengthen engagement, and foster a lasting relationship between the alumni and the University.
Objective/Key Insights and Results:
Objective: Utilize data analytics to identify and target the 10,000 alumni with the highest potential to donate.
Key Insights:
Analyzing alumni participation and behavior in key events (networking, meetups, social activities).
Recognizing patterns in engagement that correlate with increased likelihood of donations.
Results & Conclusion:
-The predictive model effectively identified the most promising alumni for donations.
-Targeting these individuals leads to improved outreach efforts, increased donation rates, and stronger alumni connections with the university.
Objective/Key Insights and Results:
Objective: Utilize data analytics to identify and target the 10,000 alumni with the highest potential to donate.
Key Insights:
Analyzing alumni participation and behavior in key events (networking, meetups, social activities).
Recognizing patterns in engagement that correlate with increased likelihood of donations.
Results & Conclusion:
-The predictive model effectively identified the most promising alumni for donations.
-Targeting these individuals leads to improved outreach efforts, increased donation rates, and stronger alumni connections with the university.
MOVANO RINGS
This Project evaluates the Movano Evie Ring, a wearable health device designed specifically for women. By leveraging biometric sensors for real-time health tracking, the device offers valuable insights into personal health management. The project also integrates the UTAUT framework to assess the key factors driving adoption and its market potential.
Objective: Assess the adoption drivers and market potential of the Movano Evie Ring, focusing on innovation and usability.
Key Insights:
1. The integration of biometric sensors provides personalized health insights, appealing to health-conscious women.
2. UTAUT analysis reveals key factors driving adoption, including performance expectancy and social influence.
Objective: Assess the adoption drivers and market potential of the Movano Evie Ring, focusing on innovation and usability.
Key Insights:
1. The integration of biometric sensors provides personalized health insights, appealing to health-conscious women.
2. UTAUT analysis reveals key factors driving adoption, including performance expectancy and social influence.
MACHINE LEARNING CLASSIFIERS
The project aims to compare three machine learning classifiers—Naive Bayes, Random Forest, and Support Vector Machines (SVM)—to evaluate their accuracy, training time, and efficiency on a given dataset. The goal is to identify the most suitable classifier based on the dataset's characteristics.
Recommendation:
-For complex datasets with non-linear relationships, Random Forest or SVM are preferable.
-Naive Bayes is ideal for faster execution on smaller datasets.
The choice of classifier depends on the dataset’s complexity, with Random Forest offering the highest accuracy but a risk of overfitting, while Naive Bayes is the most time-efficient for simpler tasks.
Recommendation:
-For complex datasets with non-linear relationships, Random Forest or SVM are preferable.
-Naive Bayes is ideal for faster execution on smaller datasets.
The choice of classifier depends on the dataset’s complexity, with Random Forest offering the highest accuracy but a risk of overfitting, while Naive Bayes is the most time-efficient for simpler tasks.
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