Supraja Bala https://suprajabala.com/ A Market Analyst Thu, 21 Nov 2024 03:43:49 +0000 en-US hourly 1 https://wordpress.org/?v=6.7.1 https://suprajabala.com/wp-content/uploads/2024/11/cropped-Screenshot-2024-11-05-162434-32x32.png Supraja Bala https://suprajabala.com/ 32 32 About – Supraja Bala https://suprajabala.com/about/?utm_source=rss&utm_medium=rss&utm_campaign=about https://suprajabala.com/about/#respond Tue, 05 Nov 2024 20:02:51 +0000 https://suprajabala.com/?p=2436 About Supraja Bala Hello! I’m Supraja Bala, a creator at heart and a marketer by ambition. I’m currently pursuing my Master’s in Business Analytics and Project Management with a specialization in Marketing at the University of Connecticut. I’m passionate about combining data-driven insights with creativity to build impactful brands, and I have big dreams of […]

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About Supraja Bala

Hello! I’m Supraja Bala, a creator at heart and a marketer by ambition. I’m currently pursuing my Master’s in Business Analytics and Project Management with a specialization in Marketing at the University of Connecticut. I’m passionate about combining data-driven insights with creativity to build impactful brands, and I have big dreams of launching my own brand, "Supraja Bala," in the future (yes, people tell me my name sounds perfectly brandable!).

My Journey in Marketing
Marketing has always fascinated me, both as a discipline and as a means to connect with people on a deeper level. I consider myself a full "Marketer package"—with skills spanning from the creative front end to the analytical backend.

Creative & Customer-Centric: I enjoy designing and creating customer experiences that resonate. From using tools like Figma for UI/UX to crafting websites, I approach marketing with empathy, always considering the customer's perspective.

Analytical & Strategic: I’m skilled in market research, targeting, segmentation, and positioning to build targeted strategies. My knowledge of tools like Google Analytics, predictive modeling, and data visualization enables me to turn raw data into valuable insights, powering social media and SEO-driven campaigns with a data-backed edge.

Values & Vision
I'm a dedicated worker, and I love challenging myself. Anytime self-doubt arises, I push through to prove myself wrong—and that’s helped me build confidence and resilience. Whether it’s being the most overdressed at a party or building a strong online presence on Instagram, I’m all about embracing my unique style without letting external critiques hold me back.

Personal Interests
Beyond marketing, I’m drawn to creative pursuits like painting, crocheting, and making ceramics. I also love fashion and exploring how design intersects with branding—another reason I'm excited to start my own brand one day. If you’re interested in connecting, collaborating, or just chatting about all things marketing, feel free to reach out. I’m always open to learning and sharing ideas with others who are as passionate about building brands as I am.

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Customer Churn https://suprajabala.com/churn_prediction/?utm_source=rss&utm_medium=rss&utm_campaign=churn_prediction https://suprajabala.com/churn_prediction/#respond Tue, 05 Nov 2024 14:36:16 +0000 https://suprajabala.com/?p=2366 Credit Card Customer Churn Prediction In today’s highly competitive financial services industry, retaining customers is more critical—and challenging—than ever. For credit card companies, predicting customer churn (when a customer stops using a service) is essential for maintaining profitability and growth. With this in mind, I embarked on a data science project to predict credit card […]

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Credit Card Customer Churn Prediction

In today’s highly competitive financial services industry, retaining customers is more critical—and challenging—than ever. For credit card companies, predicting customer churn (when a customer stops using a service) is essential for maintaining profitability and growth. With this in mind, I embarked on a data science project to predict credit card customer churn, utilizing machine learning techniques and Kaggle's credit card customer dataset.

Project Overview

Customer churn prediction uses historical customer data to identify patterns indicating a likelihood of departure. My project involved analyzing a wide range of customer attributes and transaction data to build a predictive model that helps pinpoint potential churners. This model allows credit card companies to proactively reach out to high-risk customers with targeted retention strategies.

The Dataset

The dataset, sourced from Kaggle, included a variety of features describing customer demographics, account details, transaction history, and credit card usage patterns. Here’s a brief look at some of the key features:
Customer Demographics: Age, gender, education level, and marital status.
Account Details: Credit limit, card category, months of inactivity, total revolving balance.
Transaction Patterns: Total transactions in the last year, transaction amounts, and the ratio of transaction types.

Approach and Methodology

1. Exploratory Data Analysis (EDA): Before diving into model building, I conducted EDA to gain insights into the data’s structure and distribution. Visualizations helped reveal trends in customer behavior, such as spending habits, credit limit utilization, and account activity levels. I also looked for correlations between features to identify the most significant indicators of churn.

2. Data Preprocessing: Data preprocessing was crucial to ensure accuracy. This included handling missing values, encoding categorical variables (like gender and card category), and scaling numerical features to ensure uniformity. Additionally, I balanced the dataset to mitigate any impact from class imbalance, which often skews predictions in favor of the majority class.

3. Feature Engineering: I created new features that could capture more nuanced aspects of customer behavior, such as the ratio of inactive months to active months and a normalized transaction frequency. These engineered features allowed the model to capture additional patterns that weren’t obvious in the original data.

4. Model Selection: After prepping the data, I experimented with various machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting. I chose the best-performing model based on evaluation metrics like accuracy, precision, recall, and F1-score. The goal was to balance predictive accuracy with interpretability, ensuring the model’s insights could guide real-world decisions.

5. Model Evaluation: Beyond accuracy, I evaluated the model using precision and recall metrics to understand its effectiveness in identifying actual churners without too many false positives. The confusion matrix provided a breakdown of correct and incorrect classifications, helping fine-tune the model for optimal performance.

Key Findings

High Churn Predictors: Features like the number of inactive months, credit limit utilization, and total revolving balance were strong indicators of potential churn. Customers with higher inactivity periods and lower engagement with their credit cards were more likely to churn.

Importance of Account Activity: The model showed that customers with a consistently high number of transactions and regular card usage were less likely to churn. This insight highlights the importance of encouraging regular card usage to retain customers.

Customer Profile: Younger customers with lower income brackets and limited credit histories showed higher churn rates. This finding suggests that tailored retention efforts for these segments could help improve customer loyalty.

Business Implications and Recommendations

The predictive model and analysis provided actionable insights that could guide credit card companies in their customer retention efforts:

Targeted Retention Campaigns: Using the churn predictions, companies can identify high-risk customers and create personalized retention offers, such as loyalty points, reduced fees, or exclusive promotions, to encourage ongoing engagement.

Inactivity Alerts: The model identified inactivity as a significant churn predictor. Credit card companies could introduce inactivity alerts or reminders for customers who haven’t made recent transactions, encouraging them to re-engage with their accounts.

Customized Services for At-Risk Segments: Certain customer demographics, such as younger, lower-income users, were more prone to churn. Companies can design customized financial products or loyalty programs to appeal to these segments, fostering long-term relationships.

Data-Driven Decision Making: With an accurate churn model, credit card providers can make data-backed decisions, allocating resources toward retaining the most at-risk customers while maintaining a focus on profitable customer segments.

Conclusion

This project demonstrates the power of data science in customer relationship management within the credit card industry. By predicting customer churn, companies can take a proactive approach to customer retention, strengthening their competitive position and improving overall profitability. As businesses continue to leverage machine learning and predictive analytics, the ability to understand and address churn at an individual level will become a core component of customer-centric strategies.

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Predicting Grains https://suprajabala.com/price_prediction/?utm_source=rss&utm_medium=rss&utm_campaign=price_prediction https://suprajabala.com/price_prediction/#respond Tue, 05 Nov 2024 13:31:37 +0000 https://suprajabala.com/?p=2354 Predicting Grain and Cereal Prices In today’s world, where global food security and economic stability are increasingly intertwined, understanding the future prices of essential grains such as corn, oats, and rice is critical. These commodities face price volatility due to factors like weather, geopolitical tensions, and demand-supply dynamics. Our recent project sought to bring clarity […]

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Predicting Grain and Cereal Prices

In today’s world, where global food security and economic stability are increasingly intertwined, understanding the future prices of essential grains such as corn, oats, and rice is critical. These commodities face price volatility due to factors like weather, geopolitical tensions, and demand-supply dynamics. Our recent project sought to bring clarity to this complex landscape using advanced data mining and predictive modeling techniques.

Project Overview

Our team embarked on a comprehensive time series forecasting project, aiming to predict the futures prices of major grains. We focused on analyzing and forecasting the daily and monthly closing prices, leveraging data from Yahoo Finance that included over two decades of historical price information. This data provided valuable insights for stakeholders, from farmers and traders to policymakers, to make informed decisions.

Data and Methodology

The dataset included daily data for various grains such as corn, oats, wheat, rice, and soybeans, with attributes like opening price, high, low, close, and trading volume. Here’s an overview of our analytical approach:

Data Segmentation and Aggregation: We split the data by commodity type and aggregated it monthly to capture long-term patterns and smooth out daily volatility.

Time Series Exploration: For each grain, we examined trends, seasonality, and potential autocorrelations in variables like opening and closing prices. This step helped us identify patterns and anomalies, such as seasonal spikes, indicating high trading volume or price fluctuations in specific months.

ARIMA and ARIMAX Modeling: Based on our exploratory analysis, we employed ARIMA (AutoRegressive Integrated Moving Average) models for individual grain price prediction. We selected the best model for each grain by comparing metrics like Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC). To understand the impact of external factors (such as opening price and trading volume), we enhanced our ARIMA models to ARIMAX, which allowed for additional explanatory variables.

Key Findings

Our predictive models revealed intriguing insights:

Corn: Forecasts indicated a price decline from March 2023 to February 2024, suggesting a potential profit squeeze for farmers. Traders could consider this a buying opportunity, as prices might later rebound.

Oats: Prices displayed a slight decrease followed by a significant increase, making May 2023 an optimal period for investment. However, from September to November, a marked price drop was predicted, followed by stability—a potential buying window for traders.

Rice: Rice prices were characterized by high volatility, with a mid-year peak in June followed by a steady increase toward the end of the year. Farmers might benefit by timing their sales toward year-end to capitalize on higher prices.

Business Insights and Recommendations

Our analysis of grain futures prices offers valuable insights tailored to farmers, traders, and policymakers, helping them navigate market volatility with strategic foresight.

For Farmers:

Optimizing Sales Timing: By leveraging our model's predictions, farmers can make informed decisions about when to sell their crops to maximize revenue. For example, in the case of oats, the model forecasts a substantial price increase post-May, peaking around September, followed by a sharp decline. Selling oats in late summer could yield higher profits, while holding until later in the year might reduce revenue potential.
Minimizing Risks in Downward Markets: For grains like corn, which shows a predicted price decline throughout 2023, farmers might face tighter profit margins. To mitigate losses, farmers can explore options like advanced purchasing agreements, price hedging strategies, or storing grains to sell when the market rebounds.

For Traders:

Strategic Buying and Selling Windows:Traders can capitalize on price fluctuations by planning purchases and sales aligned with peak and low periods. For instance, in the case of rice, which is expected to rise toward the end of the year, traders could buy during the mid-year dip and hold until prices climb again, maximizing their return on investment.
Risk Diversification Across Commodities: Given that different grains exhibit varying trends (e.g., corn's consistent decline, oats' mid-year peak), traders could diversify their portfolios by investing in multiple grains with complementary price movements. This approach can provide stability and reduce exposure to risk associated with any single commodity's price trend.
Leverage Seasonal Patterns: Seasonal cycles revealed in the data allow traders to plan for recurring price increases or decreases. For instance, if historical patterns show oats rising after summer each year, traders can leverage this pattern for consistent profits by timing trades with these seasonal fluctuations.

For Policymakers:

Market Stabilization and Support Programs: Knowing when prices are expected to drop, policymakers can consider market support mechanisms, such as subsidies or price guarantees, to protect farmers' incomes during low-price periods. For example, if the corn market is anticipated to experience prolonged low prices, interventions like financial assistance or purchasing programs could stabilize the market and help farmers cover their costs.
Ensuring Food Security and Reducing Price Volatility: Seasonal price spikes, such as those predicted for oats around September, could lead to temporary food shortages if demand surpasses supply. Policymakers can use such insights to adjust import/export quotas or implement temporary trade restrictions, ensuring stable food availability and affordable pricing for consumers.
Long-Term Investment in Agricultural Infrastructure: Observing patterns like rice's price volatility may indicate underlying issues in the supply chain or market sensitivity to external factors. Policymakers can invest in infrastructure improvements, like storage facilities or transportation networks, to reduce these vulnerabilities, minimize seasonal price swings, and create a more resilient agricultural market.

Conclusion

This project underscored the power of data-driven forecasting in the agricultural sector, helping stakeholders make decisions based on predictive insights rather than reacting to market volatility. Moving forward, we hope our model can contribute to a more stable, predictable agricultural market, ultimately aiding in food security and economic resilience.

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Neighbour Aunt https://suprajabala.com/neighbour_aunt/?utm_source=rss&utm_medium=rss&utm_campaign=neighbour_aunt https://suprajabala.com/neighbour_aunt/#respond Fri, 01 Nov 2024 23:34:49 +0000 https://suprajabala.com/?p=2277 Neighbour Aunt: Market Feasibility Study to Meme-ified Pitch When I started working on Neighbour Aunt, our goal was clear: create a platform that connects home cooks with individuals craving nutritious, homemade meals. But every innovative idea needs more than enthusiasm to thrive—it needs a solid foundation in market feasibility. As the designated Market Analyst for […]

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Neighbour Aunt: Market Feasibility Study to Meme-ified Pitch

When I started working on Neighbour Aunt, our goal was clear: create a platform that connects home cooks with individuals craving nutritious, homemade meals. But every innovative idea needs more than enthusiasm to thrive—it needs a solid foundation in market feasibility. As the designated Market Analyst for this project, my role was to dive deep into the data, understand market trends, and validate the idea’s viability. Here’s how I not only analyzed the numbers but also delivered a memorable pitch using a tool of our time—memes.

The Role of Market Analysis: Validating the Idea

To understand if Neighbour Aunt could succeed in today’s fast-paced, convenience-focused world, I needed to get specific. My analysis started with these core questions:

Who needs Neighbour Aunt?
I identified two main groups: Busy professionals seeking convenience without sacrificing health, and local cooks eager to share their skills.

How big is this market?
I discovered that a significant portion of the population is health-conscious, with 63% of Americans actively trying to eat healthier. Additionally, the rising popularity of local culinary experiences suggested a growing demand for homemade, authentic meals.

What is the competition?
Comparing Neighbour Aunt to restaurants and meal kits like Hello Fresh and Blue Apron helped highlight our unique positioning—an affordable, community-focused alternative for home-cooked meals.

From understanding user segments to analyzing competitive pricing, I ensured every data point underscored Neighbour Aunt’s relevance and feasibility.

From Data to Insight: Building a Strategy

Data is essential, but what matters most is what you do with it. Armed with statistics and insights, I worked on refining Neighbour Aunt’s positioning:

Competitive Advantage: Neighbour Aunt could bridge a market gap by offering quality, home-cooked meals at a price point significantly lower than dining out.

Value Proposition: Our unique selling points—community connection, affordability, and authentic home-cooked flavor—are values that resonate with our audience.

User Persona Insights: I created profiles for our target users, including health-conscious professionals and culinary enthusiasts, which helped align our strategy with their needs. This analysis was critical for defining Neighbour Aunt’s brand identity and cementing its value in the minds of potential users.

The Meme-ification of My Pitch: Making Market Research Fun and Engaging

When it was time to present my findings, I knew I needed to do something different to capture my audience’s attention. Instead of a traditional slideshow, I decided to take an unconventional approach: using memes to tell the story. Here’s why it worked:

Complexity in Simplicity: Memes helped me break down complex market insights into digestible snippets.

Engagement Factor: Memes naturally draw people in. Each slide had a meme to lighten the mood and emphasize a key point, making the pitch more engaging and memorable.

Visualizing Market Data: I used funny, relatable imagery to convey our data, like a chart meme comparing the affordability of Neighbour Aunt to traditional restaurants. The humor served as a bridge to more serious insights, making the presentation both enjoyable and informative.

What I Learned: Analyzing Markets and Pitching with Purpose

Working on Neighbour Aunt was a crash course in translating market research into actionable strategy and a memorable pitch.

This project taught me how to conduct in-depth market research, using facts and data to assess whether my idea could meet real-world demands. I had the chance to use tools like Google Trends, Ubersuggest, and SEMrush to analyze audience preferences, study competitors, and evaluate market potential. These tools provided the backbone for a data-driven approach, enabling me to see the feasibility of Neighbour Aunt from multiple perspectives.

Additionally, this project allowed me to implement the marketing concepts of segmentation, targeting, and positioning that I learned in my Marketing Management class. Identifying core user segments and positioning Neighbour Aunt as an affordable, community-driven alternative to traditional meal options was both strategic and rewarding. Stepping into the shoes of a marketer gave me insight into building a brand that resonates with its audience.

And when it came time to present my findings, I took a creative leap with a meme-based approach to make the data engaging and relatable. By combining analytical depth with a playful presentation style, I not only highlighted Neighbour Aunt’s potential but also made the pitch enjoyable and impactful.

This experience has been a powerful lesson in balancing research and creativity, ensuring that solid data and a compelling narrative can make any idea shine.

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Hello world! https://suprajabala.com/hello-world/?utm_source=rss&utm_medium=rss&utm_campaign=hello-world https://suprajabala.com/hello-world/#comments Thu, 24 Oct 2024 15:31:39 +0000 https://suprajabala.com/?p=1 Welcome to WordPress. This is your first post. Edit or delete it, then start writing!

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Welcome to WordPress. This is your first post. Edit or delete it, then start writing!

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