We transform campaigns into data-driven systems using advanced mathematics, machine learning, and quantitative analysis
What separates us from traditional agencies and in-house teams
Founder & CEO
I founded Bayswater after working as a quantitative trader at leading hedge funds, where I designed and managed algorithmic trading systems for portfolios of up to $250 million. That experience revealed a clear opportunity: to apply the same mathematical frameworks used in finance to digital marketing, bringing measurable, model-driven performance to an industry still largely driven by intuition.
My academic background spans Imperial College London and Queen's University Belfast, with research at the University of Oxford and UCL focused on computational methods. Today, we apply those same data-driven techniques to help brands scale more intelligently and spend more efficiently.
At Bayswater, we turn guesswork into precision. Every customer interaction becomes data. Every decision is a strategic edge.
We provide a fully-managed, end-to-end solution. Our engagement covers strategy, creative, technology, and reporting - all unified under a single quantitative framework.
We engineer probabilistic systems that maximise engagement and ROI through advanced mathematics and machine learning.
Continuously updating models based on real-time data, using Bayesian inference to optimise every campaign parameter for maximum impact.
Proprietary algorithms model customer behavior as stochastic processes, predicting engagement patterns with institutional-grade accuracy.
Mathematical models create individualized customer experiences at scale, optimising content, timing, and frequency for each subscriber.
Advanced models trained on millions of interactions identify micro-patterns, driving 3-5x performance improvements.
Multi-armed bandit algorithms replace traditional A/B testing, maximizing learning speed and campaign performance.
Institutional dashboards provide real-time insights with predictive analytics forecasting future outcomes.
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