Why Most "Test Ideas" Lists Are Useless
Every CRO blog has a "50 A/B test ideas" post. They're almost universally worthless because they give you a what without a why, without a hypothesis, and without the failure modes that will sink you.
After 100+ experiments across e-commerce, SaaS, and lead gen — tests that collectively moved $30M+ in measurable revenue — here's what I've learned: the difference between a program that generates wins and one that generates noise is whether your tests are rooted in behavioral mechanisms, not gut feelings about design.
Every experiment below follows this hypothesis format: If we do X for Y visitors, we expect Z because of behavioral principle Q. That format forces you to specify the mechanism, which makes it much easier to interpret results (especially losses) and decide what to test next.
Test 1: Remove Navigation from Landing Pages
Hypothesis: If we hide the main navigation on paid landing pages for visitors arriving from ads, we expect higher form submission rates because reducing exit pathways decreases decision friction and keeps users focused on the single conversion goal.
Optimizely build: Use the Element Change > Visibility control to hide your nav element on specific URL patterns. Apply to ad campaign traffic using UTM parameter audience conditions.
Primary metric: Form submissions or CTA clicks on that page.
Realistic lift expectation: 10-25% improvement in conversion rate. This is one of the most consistently high-yield tests in lead gen. The mechanism (removing distractions) is well-validated.
What to measure next to it: Bounce rate. In some categories, the navigation provides trust signals. If you see conversion go up but bounce rate also goes up, you may be converting fewer but better-qualified visitors.
Failure mode: The navigation carries trust signals (awards, "as seen in" logos) that your visitors need. If this happens, the variant will underperform. Fix: move those trust elements inline in the page body before re-running.
What to test after a win: Extend the nav-removal to your entire funnel. Test whether removing the footer also helps (it usually does a little more).
**Pro Tip:** Don't remove the nav everywhere — only on pages where the sole intended action is a single conversion. Your blog, product pages, and category pages still need navigation.
Test 2: Add Friction-Reduction Microcopy Near CTAs
Hypothesis: If we add reassurance copy directly below the primary CTA (e.g., "No credit card required," "Cancel anytime," "Takes 2 minutes") for all visitors, we expect higher CTA click-through because addressing anticipated objections at the moment of decision reduces loss aversion.
Optimizely build: Insert HTML block below the CTA button using the visual editor's Insert HTML feature. No developer needed.
Primary metric: CTA click rate.
Realistic lift expectation: 5-15%. Modest but almost always positive. This is one of those tests where even a "small" 5% lift on a high-volume CTA is significant revenue.
Behavioral mechanism: Loss aversion. The user's hesitation at the CTA is often not "I don't want this" but "I'm afraid of being trapped." Preemptively addressing the trap fear removes the barrier without requiring a design overhaul.
Failure mode: If the microcopy introduces information that creates new uncertainty (e.g., "Cancel anytime" on a product that has a complex cancellation process), it can backfire. Keep microcopy accurate.
What to test after a win: Test different objection-handling messages. "No credit card" vs. "30-day money-back guarantee" vs. "Join 10,000 users" — each addresses a different hesitation.
Test 3: Reposition Pricing to Lead with the Recommended Plan
Hypothesis: If we visually highlight a middle-tier plan and position it as "Most Popular" or "Recommended" for all pricing page visitors, we expect higher conversion to that tier because the anchoring effect and social proof of a recommendation simplifies choice and reduces decision paralysis.
Optimizely build: Use Element Change > Rearrange to reorder pricing columns, and Insert HTML to add a "Most Popular" badge above the target tier.
Primary metric: Clicks to the recommended plan's CTA.
Realistic lift expectation: 15-30% improvement in plan selection. Pricing page tests are among the highest-revenue-impact tests you can run because they directly affect AOV and revenue per user, not just conversion rate.
Behavioral mechanism: Anchoring + social proof. Displaying an expensive tier first makes the mid-tier feel reasonable. Social proof ("most popular") reduces decision anxiety by validating that the choice is already made by others.
Failure mode: If the "most popular" badge is perceived as inauthentic — particularly in markets where users are sophisticated and skeptical of dark patterns — it can decrease trust. A/B test the specific copy: "Most Popular" vs. "Recommended for Teams" vs. no badge.
What to test after a win: Test whether removing the lowest-tier option entirely improves conversion to the mid tier (framing effect: the low tier often serves as an anchor that makes mid-tier look expensive, not as a genuine option).
Test 4: Compress Checkout to a Single Page
Hypothesis: If we consolidate a multi-step checkout (separate pages for shipping, billing, review) into a single scrollable form for all checkout visitors, we expect lower cart abandonment because reducing perceived complexity and required interactions decreases checkout friction.
Optimizely build: This is a developer-required test. Use Optimizely's multi-page test to serve a custom single-page checkout to the variant cohort while maintaining the multi-step checkout for control.
Primary metric: Order completion rate (not checkout initiation rate — you want the macro conversion).
Realistic lift expectation: 10-20% reduction in cart abandonment. Checkout tests have outsized revenue impact because cart abandonment is typically 60-80% — even a 10% relative improvement on a $5M revenue stream is $500K.
Failure mode: Single-page checkouts can feel overwhelming on mobile. Always segment results by device type. A desktop win does not guarantee a mobile win.
What to test after a win: Test checkout progress indicators, payment option prominence (showing PayPal/Apple Pay prominently vs. buried), and shipping threshold messaging.
**Pro Tip:** See our guide on [A/B vs Multivariate vs Multi-Page Testing](/blog/posts/optimizely-ab-vs-multivariate-vs-multipage-testing) for why checkout tests specifically require the multi-page test type — a single-page A/B test on the checkout start URL will show inconsistent results because users mid-funnel may see mixed variants.
Test 5: Add Shipping Threshold Progress Notification
Hypothesis: If we display a dynamic "You're $X away from free shipping" message in the cart for visitors who haven't yet qualified for free shipping, we expect higher average order value because progress framing triggers completion motivation — users are more likely to add a low-friction item to unlock an already-perceived benefit.
Optimizely build: Requires minimal JavaScript to calculate cart total vs. threshold. Insert as a custom HTML block above the cart summary. Use a custom metric for AOV (average order value).
Primary metric: Average order value. Secondary: conversion rate (ensure adding-to-cart behavior doesn't lead to longer deliberation and higher abandonment).
Realistic lift expectation: 8-15% improvement in AOV among qualifying cart sessions.
Behavioral mechanism: The Endowed Progress Effect — users feel they've already started toward a goal (getting free shipping) and are motivated to complete it. The message frames the remaining gap as small, not the total as large.
Failure mode: If your free shipping threshold is far above most cart values (e.g., free shipping at $150 when average cart is $35), the progress message can backfire by highlighting how far away users are. Test with a threshold that the majority of carts can realistically reach with one additional item.
Test 6: Replace Generic Hero with Outcome-Focused Headline
Hypothesis: If we replace the homepage hero headline from a product-feature description ("The fastest project management tool") to an outcome statement ("Finish projects 30% faster — your team's first week is free") for all new visitor traffic, we expect higher signups or CTA engagement because outcome framing activates goal-relevance more directly than feature descriptions.
Optimizely build: Use the visual editor to change the H1 text and supporting subheadline. Target new visitors only (returning visitors are already past this awareness stage).
Primary metric: Hero CTA click rate. Secondary: scroll depth (does the new headline engage users enough to continue reading?).
Realistic lift expectation: 10-20% in CTA click rate. Homepage hero tests are high-variance — the range of outcomes is wide.
Behavioral mechanism: Jobs-to-be-done framing. Users don't hire your product for its features; they hire it to accomplish something. A headline that names that outcome resonates more deeply at the awareness stage.
Failure mode: Overpromising. If your outcome headline makes a claim your product can't reliably deliver, users who click through will have their expectations violated, leading to higher early churn. Keep outcome claims accurate and attributable.
What to test after a win: Test the specificity of the claim. "30% faster" vs. "complete projects on time" vs. "ship 2 weeks ahead of schedule" — specificity usually wins, but it needs to be credible.
**Pro Tip:** Pair headline changes with corresponding body copy changes. A mismatch between a bold outcome headline and a feature-focused body creates cognitive dissonance that actually reduces conversion.
Test 7: Show Social Proof in Proximity to the Primary CTA
Hypothesis: If we move customer testimonials or trust indicators (logos, review counts, star ratings) from the bottom of the page to directly adjacent to the primary CTA, we expect higher CTA click rates because social proof is most powerful at the moment of decision, not as passive page content.
Optimizely build: Use the visual editor to insert or relocate an HTML block containing testimonial content near the CTA. No developer required for basic implementations.
Primary metric: CTA click rate.
Realistic lift expectation: 5-12% improvement. Highly dependent on the quality and relevance of your social proof.
Behavioral mechanism: Social proof works through uncertainty reduction. The CTA moment is when uncertainty peaks. Placing proof exactly there addresses it at the peak — not 400px below the fold where most users never scroll.
Failure mode: Generic social proof ("Amazing product! — John D.") provides minimal signal. The most effective testimonials name a specific outcome ("Increased our trial-to-paid conversion by 18% in the first month — Sarah K., Head of Growth at Acme"). If your testimonials are generic, fix the content before testing the placement.
Test 8: Personalize CTA Text Based on Traffic Source
Hypothesis: If we change the hero CTA copy to match the intent of the traffic source (e.g., "See How It Works" for organic search visitors vs. "Start Your Free Trial" for paid traffic visitors), we expect higher conversion because message-intent alignment reduces the cognitive dissonance between what users expect from their click and what they receive.
Optimizely build: Use Optimizely audience conditions to segment by UTM source or referrer, and serve different hero variants to each segment. This is effectively multiple simultaneous A/B tests, one per traffic source.
Primary metric: CTA click rate, segmented by traffic source.
Failure mode: This test is only valid if your traffic sources are large enough to reach significance independently. Don't run this test if any individual source gets under 5,000 visitors per week.
Behavioral mechanism: Symmetric messaging — aligning the language of your landing experience with the intent implied by how the user arrived. Users who clicked an ad promising a free trial expect to be offered a free trial. Organic search visitors at the awareness stage often need more information before committing.
Test 9: Optimize Form Fields — Reduce to Required Only
Hypothesis: If we reduce our lead gen form from 6 fields to 3 fields (name, email, company) for all visitors, we expect higher form submission rates because reducing required effort decreases friction at the point of commitment.
Optimizely build: Use Element Change > Visibility to hide non-essential fields. You can also use this to hide field labels and rely on placeholder text for a cleaner visual appearance.
Primary metric: Form completion rate. Secondary: lead quality (ensure shorter form doesn't reduce lead quality — check with your sales team 30 days post-test).
Realistic lift expectation: 20-40% improvement in completion rate. Form optimization is consistently the highest-yield test category in lead gen.
Failure mode: If the removed fields were used by your sales team to qualify leads, shorter forms may increase submission volume but decrease lead quality. Always track downstream metrics (SQL conversion rate, deal size) before permanently shipping a form reduction.
What to test after a win: Progressive profiling — collect the removed fields in a follow-up email sequence after the initial conversion, rather than at the gate. Users have already committed; they're more willing to provide information.
Test 10: Add Urgency Indicators to Limited Offers
Hypothesis: If we add a countdown timer or inventory signal ("Only 3 left in stock," "Offer expires in 4:23:00") near promotional CTAs for visitors viewing sale or limited-offer pages, we expect higher conversion rates because scarcity and time pressure activate loss aversion and reduce deliberation.
Optimizely build: Insert HTML with a countdown timer (JavaScript) near promotional CTAs using the visual editor. Target specifically to promotion-period audiences using URL or query parameter conditions.
Primary metric: Clicks to the offer CTA. Secondary: conversion rate (ensure urgency doesn't create low-intent clicks that abandon at checkout).
Realistic lift expectation: 8-20% during the promotion window.
Behavioral mechanism: Loss aversion. The potential loss of a discount is more motivating than the potential gain of the same amount. Urgency indicators make the loss-potential concrete and time-bound.
Failure mode: Fake urgency. If your countdown timer resets when users refresh the page or reappears on their next visit, sophisticated users will notice and it destroys trust. Only use real urgency tied to real offers.
3 Bonus Tests the Standard Lists Miss
Bonus 1: Video autoplay muted vs. no video. On SaaS product pages, an autoplay muted product demo often outperforms both static images and full-screen video-with-sound. Test video presence vs. absence. The failure mode: video that doesn't clearly show the product's "aha moment" in the first five seconds performs no better than a static screenshot.
Bonus 2: Exit intent overlay with a secondary offer. Rather than a discount (which trains users to always wait for a deal), test an exit intent overlay offering a lower-commitment version of your primary CTA: "Not ready to start a trial? Read our 10-minute getting started guide instead." You preserve the primary conversion goal while capturing users who weren't ready to commit.
Bonus 3: Trust indicators on the payment page specifically. Most teams add trust badges to the homepage and product pages. The payment page — where users are entering credit card information — is where trust anxiety peaks and where trust signals are almost universally absent. A security badge, a real-time "10,000+ orders processed today" counter, or a visible money-back guarantee directly above the "Pay Now" button can produce surprisingly large lifts.
Common Mistakes
Testing aesthetics instead of behavioral mechanisms. "Let's test a blue button vs. green button" with no hypothesis about why color would change behavior produces meaningless results. Every test needs a mechanism.
Measuring only micro-conversions. CTA click rate is useful. Order completion rate is what pays salaries. Always include a macro conversion metric in your test, even if the test is only directly affecting a micro step.
Running all 10 of these tests simultaneously. If you have one testing resource, prioritize. Run the tests with the highest expected revenue impact and the highest likelihood of reaching significance in your available traffic window.
Launching without a pre-mortem. Before every test, spend five minutes asking: "If this test fails, what does that tell us?" If you can't answer that question, your hypothesis isn't tight enough.
What to Do Next
- Pick two tests from this list that match your site's biggest conversion bottleneck. Run them sequentially. Don't launch more tests than you can analyze well.
- Before launching, write out your hypothesis in the full format: "If we do X for Y visitors, we expect Z because of behavioral principle Q." If you can't complete the sentence, the test isn't ready.
- Read our guide on How Long Should You Run an A/B Test? and calculate your expected runtime before launching.
- Set up a test results log (a simple spreadsheet works) that tracks: hypothesis, primary metric, result, what you learned, what you'll test next. This is how programs compound learnings instead of spinning in place.