Unit 04 (Making Better Decisions With Analytics)

What this unit covers

From data to action: Move from descriptive (what happened) to diagnostic (why), predictive (what’s likely), and prescriptive (what to do) using KPI deltas, funnels, and cohort trends to propose the next change.
Segmentation for decisions: Always segment by device, browser, new vs returning, channel, geo, and time to find where fixes pay off fastest; use GA4 Explorations or comparisons to focus on high‑impact cohorts.
Hypothesis and testing: Turn each observed gap into a precise hypothesis, define success and guardrail metrics, and choose A/B or multivariate tests; prioritize by expected impact × confidence × effort.
KPI to budget linkage: Use revenue per session (RPS) and margins to set CPC ceilings, scale channels with profitable unit economics, and cut underperformers; re‑invest where incremental RPS beats cost.
GA4 decision tools: Use Insights (automated anomalies), Explorations (ad‑hoc deep dives), segments/audiences, and calculated metrics to speed from question to answer.

Decision playbook

Diagnose quickly
- Scan KPI stack: traffic, conversion, value; flag biggest variance vs baseline.
- Segment the variance: device, browser, channel; confirm where the drop or opportunity concentrates.
Form a hypothesis
- Example: “Mobile checkout CR dropped after release; on Safari iOS, step 2 error spikes; fixing validation restores CR by 20%.”
Define success and safeguards
- Primary: session conversion rate or lead CR for affected cohort.
- Guardrails: AOV, engagement, error events, site speed.
Choose the intervention
- UX/content change, speed fix, copy/offer, funnel reorder, audience or bid shift.
Test and measure
- A/B where single change; multivariate when interactions matter; read with enough power and stable seasonality.
Decide and scale
- If uplift × traffic yields NPV positive against cost and margin, ship; else iterate or park.

High‑leverage use cases

Channel mix optimization
- Calculate RPS by channel and compare to CPC/CPM; scale email and paid search if RPS minus cost is highest; throttle display if cost exceeds RPS.
Funnel triage
- Compute micro‑conversion rates; improve the weakest step first to amplify total conversions; re‑check after release.
Cohort lift
- Returning vs new users: emphasize remarketing and email for fast wins; build dedicated new‑user onboarding to raise first‑visit engagement rate and CR.
Content and ASO decisions
- Use engagement time and scroll depth to prioritize topics/screens that correlate with conversions; test CTA placement and creative variants.

GA4 workflows that speed decisions

Insights and alerts: Set anomaly detection on CR, RPS, and key events; investigate flagged changes immediately.
Explorations with segments: Build segments for cart abandoners, high‑value users, and Chrome/Safari cohorts; analyze paths and friction, then create matching remarketing audiences.
Calculated metrics: Create decision metrics like net RPS (RPS × margin), checkout drop% by device, or LTV:CAC to evaluate campaigns uniformly.

In‑class activities

Decision lab: Given channel and KPI tables, propose reallocations with CPC ceilings using RPS and margin logic; defend trade‑offs.
Funnel fix workshop: Identify the leakiest step, write a hypothesis and test plan, and specify primary/guardrail metrics.
Segmentation sprint: Build two GA4 segments (e.g., iOS Safari cart abandoners and high‑value repeat buyers) and list one action per segment.

Expected outcomes

Convert analytics findings into prioritized tests and budget decisions with explicit KPI targets and guardrails.
Use GA4 Insights, Explorations, segments, and calculated metrics to accelerate time‑to‑action and reduce guesswork.
Demonstrate an evidence‑to‑deployment loop that repeatedly improves conversion and profitability.

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