![\"Tolinku](\"https:\/\/tolinku.com\/blog\/wp-content\/uploads\/2026\/03\/platform-ab-tests.png\")
\nThe A\/B tests list page showing test names, status, types, and variant counts.<\/em><\/p>\n\n\n\nWhy Ad-Hoc Testing Falls Short<\/h2>\n\n\n\n
Most teams start with ad-hoc testing. A product manager wants to try a new onboarding flow. A marketer suspects a different CTA would convert better. These are valid instincts, but without a roadmap they create problems:<\/p>\n\n\n\n
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- No prioritization.<\/strong> Tests compete for traffic and engineering time with no clear way to decide which runs first.<\/li>\n
- No sequencing.<\/strong> Teams run tests in isolation instead of building on previous learnings. A test on deep link destinations might inform a follow-up test on landing page copy, but without a plan, that connection is lost.<\/li>\n
- No capacity planning.<\/strong> Teams launch tests without checking whether they have enough traffic to reach statistical significance<\/a> in a reasonable timeframe.<\/li>\n
- No institutional memory.<\/strong> Results live in Slack threads and spreadsheets. Six months later, someone proposes a test that was already run.<\/li>\n<\/ul>\n\n\n\n
A roadmap solves all four problems. It is a living document that keeps your experimentation program focused, sequential, and accountable.<\/p>\n\n\n\n
Step 1: Build Your Test Backlog<\/h2>\n\n\n\n
Before you can prioritize, you need a list of potential tests. Start by auditing every touchpoint in your user journey and brainstorming hypotheses for each one. For a deep linking program, this includes:<\/p>\n\n\n\n
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- Link destinations.<\/strong> Which in-app screen should a campaign link open?<\/li>\n
- Fallback pages.<\/strong> What do users without the app see?<\/li>\n
- Banner messaging.<\/strong> What copy and CTA drive the most app opens?<\/li>\n
- Onboarding flows.<\/strong> How should the first session differ for users who arrived via a deep link versus organic?<\/li>\n
- Referral mechanics.<\/strong> What incentive structure generates the most shares?<\/li>\n<\/ul>\n\n\n\n
Capture each idea as a structured backlog item. Here is a simple format you can use in a JSON file, a spreadsheet, or a project management tool:<\/p>\n\n\n\n
{\n "backlog": [\n {\n "id": "TEST-001",\n "name": "Deep link destination: product page vs. category page",\n "hypothesis": "Sending campaign traffic directly to the product page will increase purchase rate by 15% compared to the category page.",\n "metric": "purchase_rate",\n "category": "quick-win",\n "ice_score": 8.0,\n "estimated_duration_days": 14,\n "min_sample_size": 5000,\n "status": "backlog",\n "dependencies": []\n },\n {\n "id": "TEST-002",\n "name": "Fallback landing page: single CTA vs. dual CTA",\n "hypothesis": "A single 'Install App' CTA will outperform a page offering both 'Install App' and 'Continue on Web' by reducing decision paralysis.",\n "metric": "app_install_rate",\n "category": "strategic-bet",\n "ice_score": 7.3,\n "estimated_duration_days": 21,\n "min_sample_size": 8000,\n "status": "backlog",\n "dependencies": []\n },\n {\n "id": "TEST-003",\n "name": "Smart banner copy: benefit-driven vs. action-driven",\n "hypothesis": "Benefit-driven copy ('See prices 40% lower in the app') will generate 20% more banner taps than action-driven copy ('Open in App').",\n "metric": "banner_tap_rate",\n "category": "quick-win",\n "ice_score": 8.7,\n "estimated_duration_days": 10,\n "min_sample_size": 3000,\n "status": "backlog",\n "dependencies": []\n }\n ]\n}\n<\/code><\/pre>\n\n\n\nAim for 15 to 30 backlog items to start. You will not run them all at once, but having a deep backlog means you always know what to test next.<\/p>\n\n\n\n
Step 2: Prioritize with ICE or PIE<\/h2>\n\n\n\n
A backlog without prioritization is just a wish list. Two widely used frameworks can help you rank tests objectively.<\/p>\n\n\n\n
ICE Scoring<\/strong> assigns each test a score from 1 to 10 on three dimensions:<\/p>\n\n\n\n
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- Impact.<\/strong> How much will this move the target metric if the hypothesis is correct?<\/li>\n
- Confidence.<\/strong> How confident are you that this will produce a measurable result (based on data, research, or past tests)?<\/li>\n
- Ease.<\/strong> How easy is it to implement and run this test?<\/li>\n<\/ul>\n\n\n\n
The ICE score is the average of all three. A test with Impact 9, Confidence 7, and Ease 8 scores 8.0.<\/p>\n\n\n\n
PIE Scoring<\/strong> uses a similar structure but with different lenses:<\/p>\n\n\n\n
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- Potential.<\/strong> How much room for improvement exists on this page or flow?<\/li>\n
- Importance.<\/strong> How much traffic or revenue does this touchpoint handle?<\/li>\n
- Ease.<\/strong> Same as ICE.<\/li>\n<\/ul>\n\n\n\n
Both frameworks work. Pick one and use it consistently. The value is not in the absolute scores; it is in forcing your team to evaluate tests against the same criteria instead of relying on whoever argues loudest.<\/p>\n\n\n\n
Here is a helper function for calculating and sorting your backlog:<\/p>\n\n\n\n
interface TestItem {\n id: string;\n name: string;\n impact: number; \/\/ 1-10\n confidence: number; \/\/ 1-10\n ease: number; \/\/ 1-10\n category: "quick-win" | "strategic-bet" | "infrastructure";\n}\n\nfunction prioritizeBacklog(tests: TestItem[]): TestItem[] {\n return tests\n .map(test => ({\n ...test,\n iceScore: (test.impact + test.confidence + test.ease) \/ 3\n }))\n .sort((a, b) => b.iceScore - a.iceScore);\n}\n\n\/\/ Quick wins (high ease, high confidence) go first in Q1.\n\/\/ Strategic bets (high impact, lower confidence) get scheduled\n\/\/ once you have learnings from quick wins.\n<\/code><\/pre>\n\n\n\nStep 3: Categorize Your Tests<\/h2>\n\n\n\n
Not all tests serve the same purpose. Categorizing them helps you balance your roadmap across three types:<\/p>\n\n\n\n
Quick wins<\/strong> are high-confidence, low-effort tests. Changing a CTA label, swapping a hero image, or testing a different deep link destination. These build momentum and generate early results. Aim to have 50-60% of your Q1 tests in this category.<\/p>\n\n\n\n
Strategic bets<\/strong> are higher-effort experiments with bigger potential payoffs. Redesigning the fallback landing page, testing a completely different onboarding flow, or running a new referral incentive structure. These take longer to implement and require more traffic to validate. Schedule 2 to 3 per quarter.<\/p>\n\n\n\n
Infrastructure tests<\/strong> improve your testing capability itself. Validating your analytics pipeline, confirming that your A\/B testing setup<\/a> is splitting traffic correctly, or establishing baseline metrics. These are especially important in Q1 when you are building your program.<\/p>\n\n\n\n
Step 4: Plan Test Capacity<\/h2>\n\n\n\n
Your roadmap is constrained by traffic. Running too many concurrent tests dilutes your sample sizes and extends the time to statistical significance. Running too few wastes your traffic.<\/p>\n\n\n\n
Calculate your weekly test capacity using this approach:<\/p>\n\n\n\n
interface CapacityConfig {\n weeklyTraffic: number;\n maxConcurrentTests: number;\n targetSignificanceLevel: number; \/\/ typically 0.95\n minimumDetectableEffect: number; \/\/ e.g., 0.05 for 5% lift\n}\n\nfunction estimateTestDuration(\n sampleSizePerVariant: number,\n weeklyTraffic: number,\n concurrentTests: number\n): number {\n const trafficPerTest = weeklyTraffic \/ concurrentTests;\n const trafficPerVariant = trafficPerTest \/ 2; \/\/ A\/B split\n const weeksNeeded = Math.ceil(\n sampleSizePerVariant \/ trafficPerVariant\n );\n return weeksNeeded;\n}\n\n\/\/ Example: 20,000 weekly visitors, 2 concurrent tests\n\/\/ Each test gets ~5,000 visitors\/week per variant\n\/\/ For a test needing 10,000 per variant: ~2 weeks\n<\/code><\/pre>\n\n\n\nA practical rule of thumb: if a test needs more than 6 weeks to reach significance, consider whether you can narrow the audience, increase the minimum detectable effect, or run it during a high-traffic period. The Google Developers guide to sample sizing<\/a> provides useful reference data for planning.<\/p>\n\n\n\n
- Importance.<\/strong> How much traffic or revenue does this touchpoint handle?<\/li>\n
- Confidence.<\/strong> How confident are you that this will produce a measurable result (based on data, research, or past tests)?<\/li>\n
- Fallback pages.<\/strong> What do users without the app see?<\/li>\n
- No sequencing.<\/strong> Teams run tests in isolation instead of building on previous learnings. A test on deep link destinations might inform a follow-up test on landing page copy, but without a plan, that connection is lost.<\/li>\n