MarTech and the business of demand
Byline
Understand how third-party data deletion pipelines work across data, privacy, and consent operations.
Discover overlooked MarTech costs that can affect budgeting, stack management, and growth operations.
Learn what data clean rooms actually solve for B2B marketers, including privacy-safe matching, measurement, audience analysis, and partner data collaboration.
Explore how lead forms embedded inside ad units change conversion paths, data capture, campaign measurement, user experience, and marketing workflows.
Explore why analytics models built around human activity struggle when automated agents dominate traffic, actions, workflows, and digital interactions.
Understand the sender requirements now shaping B2B email, including authentication, unsubscribe rules, spam controls, and deliverability standards.
A practical method for auditing a martech stack, why the tool count matters less than the overlap between them, and what to do with the findings.
What GDPR and CCPA actually require of B2B marketers, the record-keeping most programmes get wrong, and what good practice looks like day to day.
Which marketing tasks an AI agent can run without supervision, judged by how reversible the action is and how much is exposed when it goes wrong.
ABM software does three different jobs. How to tell selection, activation and measurement apart before comparing tools that are not substitutes.
What has genuinely changed as search shifts to synthesised answers, what has not, and where the honest position is that nobody yet knows.
A definition of first-party data that survives scrutiny, why the distinction is not academic, and four questions to ask of any dataset you hold.
Four conditions decide whether a retail media network outside retail is viable, and the questions to ask before committing advertising budget.
Where webinar value leaks between registration and follow-up, what a functioning programme looks like, and the measurement that actually matters.
Rules-based, model-driven and real-time orchestration compared by what each demands as an input, plus the complication B2B data adds to all three.
Why published demand generation benchmarks mislead, the comparison that works instead, and the pipeline metrics worth tracking in their place.
What marketing mix modelling actually does, the four things it requires to work, and an honest answer on the spend scale it needs to be reliable.
Three stack problems that look identical and are not, what the suite pitch leaves out, and a practical test for whether to consolidate at all.
How to test an AI marketing tool on your own messy data, what to ask about its failure cases, and how to design a pilot that is able to fail.
Who does the work in a B2B creator programme, how it is budgeted, who owns the audience, and how to measure it without pretending it is precise.
