Practical notes on marketing, AI workflows and the decisions behind the work.
Why cheaper leads can mean more expensive pipeline. Define funnel stages and metrics precisely, compare cohorts of the same age, check the sales handoff, and use AI for competing explanations, not verdicts.
Read the articleDesign a marketing experiment that can change a decision: an observation, a falsifiable hypothesis, a fair comparison, guardrails and a volume check before launch. Includes what to do when B2B traffic is too low.
Evaluate an AI workflow honestly: time accepted output, not generated output, count setup, review and maintenance, and keep released capacity, cash savings and commercial gains apart. A worked model with a sensitivity check.
How to decide whether an AI-assisted draft deserves publication: audit claims and implied experience first, then added value, then voice. An annotated edit and a publish, revise or reject rubric.
What SEO, AEO and GEO mean, where they overlap, which tactics Google’s documentation supports and which are speculation, and how to measure the path from being cited to receiving an enquiry.
Build a B2B content plan from the questions that slow buying decisions: problem recognition, option evaluation, internal approval and implementation. A worked content map and a blank template.
Positioning decides who you are for, against which alternative, why you are better and what proves it. Messaging comes after. A worked example from generic headline to defensible claim, and where AI helps.
How to build a B2B ideal customer profile from won, lost and churned accounts and real customer language, using AI to sort the evidence while unsupported traits stay marked unknown.
A practical method for choosing a marketing team’s first AI project: start from the bottleneck, screen tasks on value, frequency, inputs, reviewability and error impact, then run one bounded pilot.
Fixed rules, a reviewed AI step, a controlled workflow or an agent: what each is, what added autonomy costs in checking and reliability, and a decision tree for choosing the simplest approach that works.
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