Purpose
BrandSignals was created to address a simple problem: financial performance alone does not fully explain business quality, yet the wider signals that shape it are often fragmented or overlooked.
Traditional financial analysis can show whether a company is growing, generating profit, or improving margins. But in highly consumer-facing sectors such as beauty and fashion, those numbers do not tell the full story. These businesses are also shaped by softer but equally important forces: customer loyalty, brand trust, employee sentiment, cultural relevance, product perception, and the strength of consumer engagement over time. These signals are visible, but they are often fragmented across different platforms and rarely analysed in a structured way alongside financial performance.
That gap is what BrandSignals is designed to address.
The project brings together financial and behavioural analysis into one framework, making it easier to evaluate not just how a business is performing on paper, but how it is being experienced in practice. The goal is to create a more complete view of real business quality: one that recognises that strong companies are not built on margins alone, but also on trust, execution, relevance, and consistency across the customer and employee experience.
Why Beauty and Fashion?
Beauty and fashion were chosen deliberately. They are sectors where perception and performance are unusually intertwined. Consumer behaviour, identity, aspiration, and brand meaning play an outsized role in shaping commercial outcomes. That makes them especially interesting from both a psychological and business-analytic perspective.
They are also sectors where behavioural signals are especially rich. Customer reviews, employee sentiment, brand engagement, search interest, and cultural momentum are not peripheral to performance; they are often part of what drives it. This makes beauty and fashion especially well suited to a framework that compares financial strength with behavioural business quality.
Why This Matters Now?
In an increasingly AI-driven world, storytelling, perception, and influence are becoming even more powerful. Businesses are no longer judged only by what they produce, but by how they are understood, experienced, and narrated across digital spaces. As AI accelerates the production and circulation of information, signals around trust, authenticity, reputation, and consumer sentiment become more important, not less.
This makes more thoughtful analysis essential. If markets are increasingly shaped by narrative, speed, and visibility, then there is growing value in frameworks that help distinguish durable quality from noise. BrandSignals is rooted in the belief that more ethical and responsible analysis begins with looking more carefully: not only at financial returns, but at the human and behavioural dimensions of how businesses operate.
In that sense, the project also speaks to a broader question of ethical investing. If capital shapes what kinds of businesses grow, then understanding companies more holistically matters. Looking beyond the balance sheet can help highlight questions of trust, treatment, quality, and long-term sustainability that are too often overlooked when attention is focused only on short-term financial outcomes.
Why This Project Is Personal to Me?
My name is Sufiyah Athena Sajan, and I am a recent graduate of New York University with a background in psychology and data science. Across my academic and professional experiences, I have become increasingly interested in the intersection between human behaviour, quantitative analysis, and commercial decision-making.
BrandSignals grew out of that interest. I wanted to create a framework that brings together the behavioural lens I developed through psychology, the analytical structure I developed through data science, and my growing interest in finance, business quality, and how companies create real and lasting value. For me, the project is both personal and practical: an attempt to understand consumer companies more holistically, beyond financial performance alone.
