Analytics, Measurement, and Ethics
Core concepts
Analytics turns data into decisions. Know the difference between descriptive (what happened), diagnostic (why), predictive (what will happen), and prescriptive (what to do) analytics. Understand KPIs versus metrics (a KPI is a metric tied to a strategic objective), dashboards, attribution models (first-touch, last-touch, linear, time-decay), and the limits of correlation.
Measurement requires valid, reliable data. Candidates should know the marketing ROI formula: (gain from investment − cost of investment) / cost of investment. Also understand A/B testing, statistical significance, and sample bias. Ethics covers informed consent, data privacy (e.g., GDPR-style principles), truth in advertising, avoiding deceptive claims, and respecting vulnerable audiences. Recognize conflicts of interest and the duty to protect consumer data.
Traps in questions
- Confusing a metric with a KPI; not every number is a KPI.
- Assuming last-touch attribution credits the true driver; it overweights the final click.
- Treating a small, biased sample as conclusive.
- Overlooking ethical duties when a tactic is legal but misleading.
Worked example
A campaign costs $20,000 and generates $50,000 in incremental profit. ROI = (50,000 − 20,000)/20,000 = 150%. If the $50,000 is revenue rather than profit, label it ROAS, not ROI.
Rule of thumb: Define the KPI and the attribution model before you launch; otherwise results are unfalsifiable.