Business Analytics with Information Technology & Finance major at Rutgers, exploring the intersection of fashion, data, consumer behavior, and strategy.
Finding stories where others see information
How aesthetics predict markets. Trend signals as financial data.
Forecasting, data modeling, and the stories numbers tell.
Equity markets, investor behavior, capital allocation and risk.
Why people buy, what they signal, how culture drives spending.
Business models, competitive positioning, and market thinking.
How platforms, algorithms, and online aesthetics shape the world.
Artificial intelligence in fashion, finance, and beyond.
The visual world — moodboards, fashion, travel, design, beauty.
This project investigates the relationship between fashion trend signals and their downstream impact on consumer demand and financial markets. The research combines Google Trends, publicly available stock market data, and time series modeling to examine how digital attention evolves before measurable business outcomes occur. Using Python for data collection, preprocessing, visualization, and predictive analysis, the study applies statistical and machine learning techniques to identify patterns, estimate trend lag, and evaluate whether early consumer interest can provide meaningful insights for forecasting, marketing strategy, and business decision making.
Finding signals where others see noise.
A monthly visual collection of fashion, travel, architecture, texture, and the images
Summer 2026
All the things I'm thinking about and writing through <3.
Questions, topic ideas, polls, comments, this is the part that talks back.
Contact me: Gmail - shahchelsy@gmail.com, LinkedIn - https://www.linkedin.com/in/chelsy-shah/, GitHub - https://github.com/shahchelsy
A little space to test your instincts, challenge your decisions, and discover how you think.
Hi, I'm Chelsy. Welcome to my little corner of the internet, where strategy, analytics, and curiosity come together to tell stories through data.
"Fashion changes, but style endures." — Coco Chanel
Fashion has always been more than clothing to me.
It has always been a language. Some people see an outfit. I see confidence, identity, culture, timing, and the choices people make before they ever say a word. Fashion has this quiet ability to change how someone carries themselves. It is never about fitting into a trend or becoming someone else. It is about feeling comfortable enough to become more of yourself. When you feel confident in what you wear, you move differently, speak differently, and often believe in yourself a little more.
As I became more interested in fashion, I also became fascinated by the business behind it. I found myself asking questions that went far beyond collections and runway shows. Why do some trends disappear within weeks while others influence an entire generation? Why does one campaign become part of popular culture while another is forgotten? What makes people suddenly want the same product at the same time?
Those questions slowly introduced me to another world. A world of consumer behavior, marketing, forecasting, finance, and data.
Behind every successful fashion house are thousands of decisions supported by numbers. There are analysts forecasting demand months in advance, marketers understanding exactly who they are speaking to, buyers predicting what customers will love next, and strategists deciding when a collection should launch and where it should be seen. Great fashion is creative, but great fashion businesses are incredibly analytical.
"Fashion is a language that creates itself in clothes to interpret reality." Karl Lagerfeld once said, I like to think this language can also be predicted through data and modified in a better way.
Every search trend, every social media conversation, every shift in consumer preference tells a story. Numbers are not just spreadsheets to me. They are evidence of changing behavior. They reveal how culture evolves, how markets react, and how brands can understand people before people fully understand themselves.
That curiosity is what led me to study Business Analytics with IT and Finance alongside my interest in brand strategy. I enjoy finding patterns, asking questions, and connecting creativity with commercial strategy. The intersection of these worlds is where I feel most inspired.
This website is my digital notebook, a place where I document observations, research ideas, opinions, visual inspiration, and projects that explore fashion through both a creative and analytical lens. Some pages may begin with a runway show, while others begin with a dataset. Both are equally exciting to me because they tell different parts of the same story.
As Miuccia Prada once said, "What you wear is how you present yourself to the world." I would add that understanding why people choose to present themselves that way is just as fascinating.
Beyond my coursework at Rutgers University, I enjoy exploring the business of fashion through research, data, and observation. My interests lie at the intersection of consumer behavior, trend forecasting, marketing strategy, and finance, where creativity is supported by evidence and every decision is backed by insight.
This portfolio is a reflection of that journey. It brings together the projects I've built, the research I'm pursuing, and the ideas that continue to shape my perspective. Whether I'm analyzing emerging trends, studying financial markets, or exploring how consumer behavior influences brands, I'm always looking for ways to connect creativity with commercial strategy.
My goal is to build a career where I can contribute to the future of the fashion industry by helping brands make smarter, more informed decisions through data, innovation, and strategic thinking.
Every project starts with a question. This is where I explore it, test it, and document what I learn along the way.
Analyzing spending, sales performance, and financial scenarios to support more efficient capital allocation.
How can a business decide where to invest its budget to generate stronger returns? This project examines the relationship between spending, sales performance, and financial outcomes through an Excel-based forecasting and scenario analysis model.
The objective was to evaluate how changes in spending and sales assumptions affect projected performance, compare alternative budget allocations, and identify opportunities for more informed capital allocation. Rather than relying on a single forecast, the model examines different financial scenarios to understand the potential consequences of each decision.
I used Microsoft Excel to organize and analyze financial data, explore relationships between spending and sales, and compare the results of alternative scenarios. Exploratory data analysis helped establish patterns in the underlying data before evaluating the model outputs.
The workbook brings together data analysis, forecasting, scenario comparisons, financial summaries, and model diagnostics. These components allow the results to be examined from different perspectives rather than relying on a single projected outcome.
Exploring historical patterns and relationships between spending and sales performance.
Comparing alternative assumptions to understand how financial outcomes may change.
Reviewing model outputs to support budget planning and capital allocation decisions.
Examining forecasting reliability and the limitations of model-based estimates.
The following worksheets document the analytical process, from exploring the underlying data to comparing scenarios and evaluating the model. Each addresses a different part of the financial decision-making process.
This worksheet brings the analysis together into a financial dashboard, making it easier to compare budget allocations, spending, sales, and projected outcomes.
The purpose is to translate model calculations into information that can support investment and budget planning decisions.
This worksheet provides the underlying scenario data used to compare different spending and sales assumptions.
It helps examine how changes in budget allocation can affect projected outcomes and illustrates the financial trade-offs between alternative decisions.
Before interpreting forecasts, it is important to understand the data behind them. This worksheet explores spending patterns, sales behavior, and relationships within the dataset.
These observations provide context for understanding model outputs and identifying patterns worth investigating further.
Forecasting results should not be interpreted without considering their reliability. The diagnostic analysis provides a way to examine how the model performs and where its estimates may be limited.
This matters because a financial decision should account for uncertainty rather than treating projected values as guarantees.
The analysis demonstrates how spending and sales assumptions can be evaluated together to compare financial scenarios. Bringing these comparisons into one model makes it easier to assess competing budget decisions and understand the implications of different allocation strategies.
Going forward, the model could be developed further by incorporating updated financial data, expanding the range of scenarios, and testing forecast performance against actual outcomes. This would help move the analysis beyond one-time projections toward a more continuous financial planning and decision-support process.
Financial forecasting creates value when it helps businesses make better decisions, not simply when it produces a projected number. The real advantage lies in understanding the trade-offs between spending, expected returns, risk, and the efficient use of available capital.
How AI-detected signals predict brand performance and investor response
This project investigates the relationship between fashion trend signals and their downstream impact on consumer demand and financial markets. The research combines Google Trends, publicly available stock market data, and time series modeling to examine how digital attention evolves before measurable business outcomes occur. Using Python for data collection, preprocessing, visualization, and predictive analysis, the study applies statistical and machine learning techniques to identify patterns, estimate trend lag, and evaluate whether early consumer interest can provide meaningful insights for forecasting, marketing strategy, and business decision making.
Understanding how regulation, investor psychology, and market behavior influence financial decision making.
This research examined how retail investors make financial decisions beyond traditional market fundamentals. Through survey analysis, regulatory research, and the study of SEBI's role in market development, I explored how investor psychology, financial literacy, and trust influence participation in India's capital markets. The project reinforced the importance of understanding human behavior alongside quantitative market data when evaluating investment decisions.
All the things I'm thinking about and writing through <3.
As a brand competing to be better than others and fighting for your space in the market, there’s one thing that always goes unnoticed. The intention.
Yes, the Red ocean strategy is very effective when a brand is competing in an existing market, it shows how a brand is better than the alternatives. When I think of it from a strategic point of view, the only question that arises is that Does it give you Credibility? Sustainable audience? I’ve said and thought this before, as a brand credibility becomes your priority to grow and customers follow. Red Ocean definitely can build credibility but it’s often hard to earn because customers already have alternatives set up and thus higher expectations.
In 2026, consumers, especially Generation Z aren’t just comparing brands and products anymore. They’re comparing values and experiences that they believe in. The question every brand is asking is “How do we become better than everyone else?”. My opinion is to ask “What makes the competition irrelevant?”
This is where the blue ocean strategy steps in. Instead of competing in the same saturated market place, create a new demand. Reveal a need that the consumers never realized had a better solution and how the brand makes it convenient for them to own. This strategy, be it in marketing or in innovation, gives the creators and the consumers a large scale to provide and accept respectively. It helps the brand expand by attracting consumers who weren’t a part of that category in the first place and get the best deliverables out of it while sustaining to the brand value. The strongest brands aren’t successful because they’re better, but because they created their own definition of “better” and consumers felt that story. Sometimes, this means solving a problem no one else is solving, giving people what has been ignored by everyone else or even changing customer value in the first place.
As a consumer, I rarely make any of my purchasing decisions based on how Brand A is better than Brand B but on what value the brand holds, its story and how authentic it feels to me. A brand may have the best marketing and strategy but if it does not relate to the consumers on a personal level, numbers are affected in the end. This is what I mean by intention, not the intention to sell or compete but one to solve, stand for something and to create meaning beyond the product. Strategy determines how the brands compete. Intention earns trust, builds brand relevance and determines why consumers choose to stay.
For years, the Victoria's Secret Fantasy Bra was almost a reward for the models who helped build the brand. Adriana Lima, Candice Swanepoel, Gisele Bündchen were women who had earned their place in VS history. So when the brand brought back the Fantasy Bra after eight years, why give its most expensive one yet to Kylie Jenner, someone who had never even walked its runway?
I think the answer is simple: Victoria's Secret wasn't just looking for a model. It was looking for an audience. In an interview with Vogue, Victoria's Secret's chief creative officer Adam Selman called Kylie a "cultural powerhouse." Someone who offered something beyond traditional runway recognition and had access to multiple consumer markets. Adding Jacob & Co. to the equation, the luxury jeweler created a record-breaking $50 million Fantasy Bra featuring 4,410 natural diamonds and a 75.54-carat yellow diamond. Suddenly, this wasn't just a lingerie show. It was a crossover between three powerful brands, each bringing its own identity, credibility, and audience.
From a business perspective, Victoria's Secret could potentially reach Kylie's beauty and lifestyle audience, Jacob & Co.'s luxury consumers, and its own existing customers through a single runway moment. It's a clever example of co-branding and audience expansion, with the potential to generate significant earned media without relying solely on conventional advertising.
But why not Adriana, Barbara, Candice, or the Hadid sisters?
My take is that choosing an established Angel would have celebrated the brand's history. Choosing Kylie challenged it. And that unexpected decision created an entirely different conversation. People weren't just discussing the return of the Fantasy Bra, they were debating who deserved to wear it.That doesn't mean controversy was necessarily the intention. But I do think Victoria's Secret understood that a surprise can introduce it to new audiences.
Of course, attention isn't the same as sales. Without post-show conversion or customer acquisition data, we can't know how commercially successful this decision actually was. Still, I find the shift fascinating. Victoria's Secret once used its runway to turn models into global celebrities. Now, it seems to be using global celebrities to make its runway culturally relevant again.
We've all been there. Wanting to be good at everything. The smartest person in the room, the most creative, the most successful, the one who somehow has everything figured out.
But at what point does wanting everything stop us from becoming something?
I came across an interesting concept in my strategic management class called "stuck in the middle."
In business, companies generally choose how they want to compete. Walmart focuses on keeping costs low with a broad target audience , while Nordstrom focuses on offering a more unique, premium experience. They aren’t trying to be everything to everyone. They understand what makes them valuable and gives them a competitive advantage in the long run.
Then there's the “best-cost strategy”, where a company tries to offer both quality (differentiation) and affordability (lower costs). If done well, this can be incredibly successful. But when a company can't deliver either effectively, it risks becoming stuck in the middle.
And I couldn't help but think about how similar this is to our lives.
We're constantly encouraged to do more. Learn five skills, pursue three careers, have multiple side hustles, build a personal brand, and somehow still find time to become the best version of ourselves.
Psychologically, I think a lot of this comes from comparison and the fear of missing out. We see someone succeeding at something and suddenly feel like we should be doing it too.
But being involved in four different worlds doesn't necessarily mean you're growing in all four. Sometimes, you're just dividing your attention four ways.
There's nothing wrong with having multiple interests. In fact, combining different skills can become your biggest strength. But there's a difference between strategically building on what you're good at and constantly starting something new because you're afraid of being left behind. It restricts you from mastering a skill or an interest to your maximum capacity if you keep quitting at an early stage.
Maybe the goal shouldn't be to master everything at once. Maybe it should be to become genuinely good at one thing, build confidence in it, and then expand.
Because just like businesses, we have limited resources. Ours happen to be time, energy, attention, and patience.
And if we try to give everything an equal share, what are we really giving our best to?
A trend is getting attention? I believe that doesn’t always mean it deserves a production order. Let me start with Google, TikTok and various social media trends. People look at a product, talk about it and some may even buy it. Does that mean the product guarantees a long term success? No, it does not.
Businesses often misconceive actual consumer demand and the consumer demand that a data sheet tries to feed it. In this generation consumer purchase attention and demand changes overnight. Talking about a product often means the consumer has barely made it to the early stages of the funner and that barely gives the brand a loyal sustainable customer in the long run.
What happens when a data sheet doesn’t match the actual demand? It creates a gap between the planned supply and the actual demand. This causes inventory accumulation, markdowns, holding costs increase, profit margins shrink and the working capital still is tied up. These fluctuations in demands may lead to an inefficient production schedule by the company.
What we fail to understand sometimes is that this forecasting error does not just mean entering a wrong number in the spreadsheet, it can change the companies’s profit percentage completely. According to The State of Fashion 2025 McKinsey and The Business of Fashion, the estimated sales value of excess fashion inventory in 2023 was $70-140B.
Lets take Zara’s example here. Instead of relying fully on seasonal forecasts, it uses smaller initial product runs, customer feedback and fast replenishment cycles to respond to actual demand. They don't predict what the consumers would want months before launching but they keep a flexible supply chain to quickly adapt to those changes. According to Inditex’s (Zara’s parent company) 2025 annual report, they reported a gross margin on 58.3% while emphasizing adapting its product offerings to consumer preferences.
Forecasting as important as it is, may not help in terms of accuracy alone, especially with such high fluctuating demands. The real problem begins when businesses become so committed to their intended production strategy that they leave little room for an emergent strategy when consumer preferences change.
In this situation, I believe investing in a supply chain unit might be an asset. In the end not knowing what the consumers want tomorrow may be okay, but not knowing how to respond to those demands may create a competitive disadvantage.
The places that shaped how I solve problems, work with people, and think about business.
Fashion · Travel · Moodboards · Architecture · Texture · Lifestyle · Inspiration
Questions, topic suggestions, polls, reader comments — this is the part that's built for conversation.
Contact me: Gmail - shahchelsy@gmail.com, LinkedIn - https://www.linkedin.com/in/chelsy-shah/, GitHub - https://github.com/shahchelsy