I’ve taught digital analytics and marketing technology at George Brown College for nearly fifteen years — courses built around campaign measurement, attribution models, and the statistical methods that make reporting honest rather than convenient.
During that time, several of the tools those courses covered were deprecated or significantly changed. The most consequential was GA4’s removal of rule-based attribution models in 2023, which eliminated a straightforward way for marketing teams to understand which channels were actually driving conversions. The data-driven model that replaced it is not the same thing, and for most marketing teams at small and mid-market companies, it does not solve the same problem.
CheckSuite is the result of deciding to build alternatives rather than work around the gaps — a suite of focused marketing operations tools grounded in the same applied statistics and machine learning the courses covered. The tools are not trying to replicate GA4 or compete with enterprise analytics platforms. They address specific, bounded problems that those platforms have either abandoned or never addressed well.
Corner Store Games is a separate project: small games built around ideas from statistics, decision theory, and reasoning under uncertainty. The connection to the other work is real, though the games are meant to stand on their own.
My research background is in machine learning and natural language processing. My MSc thesis at Athabasca University produced TutorAlert, an NLP system for detecting student difficulty in online learning environments, with results published in IEEE and Springer venues. That work taught me something that has shaped everything since: prediction is not the same as understanding. Most of the ML and analytics world is built on correlations. Causal thinking changes the questions — not just what happened, but why, and what would happen if we intervened. That shift matters in any field that uses data to make decisions, and it is the thread running through the research, the products, and the teaching.
I’m pursuing a formal research affiliation and PhD work in applied causal inference and NLP, which is the natural next step for that thread.
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