Research
Most digital marketing consultants read research. This practice writes it | and applies it.
Three international research papers currently under peer review. An active MPhil/MS Data Science (AI) journey building directly on a Master’s in Computer Science. A five-domain research roadmap already shaping the SaaS products being built for 2027 and beyond.
This is not academic work happening separately from client work. Every research question originates from real patterns observed across 12+ years and 100+ client engagements | and every finding feeds directly back into the Cognitive Marketing Engine applied in active client strategies.
This page is the hub connecting all of it.
By The Numbers
3 International Research Papers | Under Peer Review (2026)
2 Academic Degrees | MCS (Completed) + MPhil/MS Data Science (AI)
5 Research Domains | Actively Shaping Future Direction
3 SaaS Products | Directly Derived From Research
1 PhD Track | Targeted 2027
The Research Philosophy
“Data over opinion.”
This is one of the core values driving this entire practice | and research is where that value is most rigorously tested.
A practitioner can claim “this strategy works” based on a handful of client results and call it expertise. A researcher has to prove it | with methodology, statistical validation, and peer review from people whose job is to find the flaws in the argument.
This practice operates at the intersection of both.
12+ years of practitioner experience generates the questions.
MPhil/MS Data Science (AI) provides the rigorous methodology to answer them.
Peer review validates the answers.
Client engagements apply the validated answers | and generate new questions.
This loop | practice to research to validation to practice | is what makes the strategic recommendations in this practice mathematically defensible, not just experientially confident.
Explore the Research
Three pages. One continuous body of work.
Research Papers
Three international papers | currently under peer review (2026)
AI-Driven Lead Scoring for Digital Marketing | moving B2B lead scoring from static rules to machine learning probability prediction.
Predicting High-Value Leads in E-Commerce Using Deep Learning on Sequential User Behavior | applying sequential deep learning to identify high-lifetime-value customer journeys.
AI-Driven Multi-Touch Attribution Using Deep Learning | replacing platform-biased attribution with causally validated incremental lift measurement.
Each paper originated from a real client pattern and directly informs specific loops of the Cognitive Marketing Engine.
→ Read the Full Papers (link to /research/papers)
MPhil/MS Data Science (AI) Journey
The academic path connecting 12+ years of practice to graduate-level AI research
From a Master of Computer Science (2015–2017) that quietly laid the technical foundation, to the 2025 decision to return to graduate study | this page documents why the MPhil/MS Data Science (AI) program was necessary, what it actually involves, and how every concept learned in coursework has a traceable application in client engagements within weeks.
Includes the direct mapping from coursework (Advanced NLP, Advanced Machine Learning, Algorithm Analysis, Data Tools & Techniques) to specific components of the Cognitive Marketing Engine | and the path toward PhD applications in 2027.
→ Explore the Academic Journey (link to /research/ms-ds-journey)
Research Interests & Future Direction
The five-domain roadmap for the next 5–10 years
Predictive & Causal Marketing Intelligence | extending lead scoring and attribution research into transfer learning and privacy-safe causal models.
NLP & Content Integrity | using transformer models to detect dark patterns and policy-violation risk in marketing content before publication.
AEO / GEO / LLMO | one of the most underexplored areas in marketing research today: how content gets cited and represented by AI answer engines, not just ranked by traditional search.
Computational Marketing Infrastructure | making the Cognitive Marketing Engine itself a subject of ongoing research and improvement.
Marketing-Focused SaaS Products | where research becomes commercial product. Three tools currently mapped directly to research domains above, targeted for 2027 and beyond.
→ See the Full Research Roadmap (link to /research/interests)
How This Research Shapes Client Work
This is the connection most consulting practices cannot make | because most consultants aren’t doing original research in the first place.
Every loop of the Cognitive Marketing Engine has a research foundation:
Loop 1 | Empirical Diagnostics
SBERT semantic embedding tracking and Isolation Forest anomaly detection | directly informed by Advanced NLP and Advanced Machine Learning coursework, and extended in Research Interests Domain 1 and 2.
Loop 2 | Causal Strategy
Bayesian Media Mix Modeling and Markov Chain multi-touch attribution | the direct output of Research Paper 3, with active extensions explored in Research Interests Domain 1 and 4.
Loop 3 | Programmatic Execution
Built on the Python data science stack and algorithmic efficiency principles from Algorithm Analysis and Data Tools & Techniques coursework.
Loop 4 | Continuous Optimization
XGBoost propensity modeling and BG/NBD customer lifetime value tracking | the direct output of Research Papers 1 and 2, with adaptive retraining research ongoing in Research Interests Domain 4.
The framework is not static because the research is not static. As the MPhil/MS Data Science (AI) program progresses and new papers move through peer review, the Cognitive Marketing Engine itself evolves.
Why This Matters
There are thousands of digital marketing consultants who will tell you they “use AI” or “are data-driven.”
For most of them, this means using ChatGPT for content ideas and looking at a dashboard with “AI-powered” in its marketing copy.
For this practice, it means:
- Original research currently under international peer review
- Active graduate-level coursework in the mathematical foundations of the models being used
- A direct, traceable line from academic research to client-facing strategy
- A long-term trajectory toward PhD-level expertise in this exact intersection
- SaaS products already being designed based on validated research findings
This is a fundamentally different category of expertise | and it is documented, verifiable, and continuously growing.
Ready to see how this research applies to your business?
→ Understand the Framework (link to /approach/my-framework)
→ Start With the Audit (link to /work-with-me)
→ My Academic Story (link to /about/my-story)