ads-test

SKILL.md

A/B Test Design & Experiment Planning

Process

Understand what the user wants to test (creative, audience, bidding, landing page)

Build structured hypothesis using the framework below

Calculate required sample size and estimated duration

Recommend platform-specific test setup

Define success criteria and measurement plan

Hypothesis Framework

Every test must start with a structured hypothesis:
IF we [change/action]
THEN [metric] will [increase/decrease] by [estimated %]
BECAUSE [reasoning based on data or insight]

Example:
IF we replace polished product shots with UGC creator videos
THEN Meta CTR will increase by 25-40%
BECAUSE Andromeda prioritizes diverse creative formats and UGC consistently outperforms polished in 2025-2026 benchmarks

Hypothesis Quality Checklist

 Single variable being tested (isolate the change)

 Specific metric defined (not "performance")

 Estimated effect size stated (needed for sample size calculation)

 Timeframe defined

 Success/failure criteria clear before launch

Statistical Significance Calculator

Required Sample Size (per variant):

n = (Z_alpha + Z_beta)^2 × 2 × p × (1-p) / MDE^2

Where:
- Z_alpha = 1.96 (for 95% confidence)
- Z_beta = 0.84 (for 80% power)
- p = baseline conversion rate
- MDE = minimum detectable effect (relative %)

Simplified lookup:

Baseline CVR
5% MDE
10% MDE
20% MDE
30% MDE

1%
612,000
153,000
38,300
17,000

2%
302,400
75,600
18,900
8,400

5%
116,800
29,200
7,300
3,200

10%
55,200
13,800
3,450
1,530

20%
24,600
6,150
1,540
680

Per variant, 95% confidence, 80% power

Test Duration Estimator

Duration = Required Sample Size / Daily Traffic per Variant

Minimum duration: 7 days (capture weekly patterns)
Maximum recommended: 28 days (avoid seasonal drift)
Learning phase: Google 7-14 days, Meta 3-7 days, LinkedIn 7-14 days

Inputs needed:
- Daily impressions or clicks
- Number of variants (2 = A/B, 3+ = multivariate)
- Baseline conversion rate
- Minimum detectable effect desired

Duration Quick Estimates

Daily Clicks
2% CVR, 20% MDE
5% CVR, 20% MDE
10% CVR, 20% MDE

100
189 days
73 days
35 days

500
38 days
15 days
7 days

1,000
19 days
7 days
4 days*

5,000
4 days*
2 days*
1 day*

*Minimum 7 days recommended regardless of sample sufficiency

Platform-Specific Test Setup

Meta Experiments

Use Ads Manager > Experiments tab (not manual ad set duplication)

Automatic audience splitting ensures no overlap

Supported test types: A/B (creative, audience, placement), Holdout, Brand Survey

Meta's Incremental Attribution (April 2025) provides AI-powered holdout testing for measuring real causal impact

Budget: split evenly across variants; minimum $100/day per variant recommended

Duration: 7-14 days typical; Meta auto-determines winner at 95% confidence

Google Experiments

Campaign Experiments (custom experiments) or Ad Variations

Create experiment from existing campaign > select experiment type

Traffic split: 50/50 recommended for fastest results

Supported: bidding strategy, ad copy, landing page, audience

Metrics: choose primary metric (conversions, CPA, ROAS) before launch

Duration: 14-30 days recommended; minimum 2 weeks for bidding tests

LinkedIn A/B Testing

Built into Campaign Manager for Sponsored Content

Duplicate ad set with single variable change

Target: same audience segment with automatic rotation

Minimum budget: $50/day per variant

Key metrics: CTR (>0.44% benchmark), CPL, Lead Form CVR (13% benchmark)

Duration: 14-21 days (LinkedIn's smaller daily volumes require longer tests)

TikTok Split Testing

Available in TikTok Ads Manager > Create A/B Test

Test types: targeting, bidding, creative

Auto-splits audience to avoid contamination

Minimum 7 days, recommended 14 days

Budget: minimum $20/day per ad group

Creative tests: isolate hook (first 2-3 seconds) as the primary variable

TikTok's enhanced split testing supports modular test variables (targeting, creative, budget, placement) via Smart+ since 2025

What to Test (Priority Order)

High Impact (test first)

Creative concept (different messaging angles, not just color changes)

Hook/first 3 seconds (video opening on Meta, TikTok, YouTube)

Offer structure (pricing, discount type, free trial length)

Landing page (headline, CTA, form length)

Bidding strategy (tCPA vs tROAS vs Maximize Conversions)

Medium Impact

Audience targeting (interest vs lookalike vs broad)

Ad format (static vs video vs carousel)

CTA button (Learn More vs Sign Up vs Shop Now)

Campaign structure (CBO vs ABO, consolidated vs segmented)

Low Impact (test last)

Ad scheduling (time of day, day of week)

Device targeting (mobile vs desktop)

Minor copy variations (word substitutions without concept change)

Common Testing Mistakes to Avoid

Testing too many variables at once (no clear winner attribution)

Ending tests too early (before statistical significance)

Testing during atypical periods (holidays, launches, incidents)

Comparing unequal time periods

Not documenting learnings (build institutional knowledge)

Testing small changes when big changes are needed (optimize vs innovate)

Ignoring learning phase on automated platforms

Output Format

## A/B Test Plan

### Hypothesis
IF [change]
THEN [metric] will [direction] by [amount]
BECAUSE [reasoning]

### Test Design
| Parameter | Value |
|-----------|-------|
| Platform | [platform] |
| Test Type | [A/B / Multivariate] |
| Variable | [what's being changed] |
| Control | [current state] |
| Variant | [proposed change] |
| Primary Metric | [KPI] |
| Traffic Split | [50/50 / other] |

### Sample Size & Duration
| Metric | Value |
|--------|-------|
| Baseline CVR | [X%] |
| MDE | [X%] |
| Required Sample | [N per variant] |
| Daily Traffic | [N clicks/day] |
| Est. Duration | [X days] |
| Min Duration | 7 days |

### Success Criteria
- Winner declared at 95% confidence
- [Primary metric] improvement of [X%]+ sustained over [Y] days
- No negative impact on [secondary metric]

### Setup Instructions
[Platform-specific step-by-step]

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