How to evaluate marketing decisions, define a useful baseline and interpret the evidence.
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.
Design 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.
Use a baseline, review false positives and test whether the score improves the decision your team actually makes.
Current preference: off
