Purpose
To define a structured process for testing ad creatives, audiences, targeting, and landing pages in order to maximize performance and ROI.
This ensures decisions are data-driven, not guesswork.
Scope
- Applies to all paid campaigns (Google, Meta, LinkedIn, YouTube, TikTok, Display).
- Used by Paid Ads Specialists, Performance Marketing Managers, and Analysts.
- Covers ad-level, audience-level, and landing page experiments.
Objectives
- Standardize A/B testing methodology across all channels.
- Improve campaign performance by identifying winning variables.
- Ensure tests are conducted scientifically (one variable at a time).
Step-by-Step Process
Step 1 – Define Test Hypothesis
- Clearly state what you are testing and expected outcome.
- Example SaaS: “If we add proof (case study stat) in the headline, CTR will increase.”
- Example B2C: “If we use video instead of static, conversion rate will improve.”
Step 2 – Select Test Variable
- Only test one variable per A/B experiment.
| Test Type | Variables | Example (B2B SaaS) | Example (B2C Service) |
| Creative Test | Image vs Video, Carousel vs Static | Explainer Video vs Carousel | Cleaning Reel vs Static Offer |
| Copy Test | Headline, CTA text, length | “Cut churn by 30%” vs “Boost adoption” | “Book Now” vs “Get 20% Off Today” |
| Audience Test | Geo, interest, lookalike, job title | US SaaS CTOs vs UK SaaS PMs | Moms 28–40 vs Dual Income Families |
| Landing Page Test | Layout, CTA placement, form length | Demo page with case study vs without | Booking page with WhatsApp CTA vs normal form |
Step 3 – Set Test Parameters
- Audience Size: Minimum 5,000–10,000 per variation (for statistical significance).
- Budget: Allocate 10–20% of campaign budget to testing.
- Duration: Run tests for at least 7–14 days (avoid premature conclusions).
- Success Metric: CTR, CPL, CPA, ROAS (must be defined upfront).
Step 4 – Execute Test
- Duplicate campaign/ad set with only one variable changed.
- Label clearly (e.g., “Demo_Headline_A” vs “Demo_Headline_B”).
- Launch under same budget & timeframe conditions.
Step 5 – Analyze Results
- Compare performance on primary KPI (defined in Step 3).
- Confirm statistical significance (>95% confidence preferred).
- Log results in Experiment Tracker: Hypothesis → Variation A → Variation B → Winner → Next Step.
Step 6 – Apply Learnings
- Scale winning variation → move into main campaigns.
- Archive losing variation → keep notes for future reference.
- Feed insights back into Creative & Copy Guidelines (Doc 3) and Campaign Optimization (Doc 8).
Roles & Responsibilities
| Role | Responsibility |
| Performance Marketing Manager | Defines test hypothesis, approves test plan |
| Paid Ads Specialist | Sets up variations, executes campaign |
| Designer/Copywriter | Creates alternate creatives/copies |
| Analytics Lead | Validates results, ensures significance |
| Client POC | Informed on results & next actions |
Governance
- Testing Budget: Min 10% of total paid budget reserved for A/B tests.
- Test Limit: Max 2 parallel A/B tests per channel to avoid dilution.
- Review Frequency: Weekly review of live tests, monthly insights report.
- Experiment Tracker: Every test logged with hypothesis, setup, result, and learnings.