You Should Test That!

Chris Goward’s conversion optimization philosophy is revolutionary because it challenges how most organizations make digital decisions. Instead of accepting expert opinions, design awards, or traditional usability testing as truth, Goward argues that real customer behavior—revealed through controlled, scientific experiments—is the only reliable guide for improving websites and marketing outcomes. You work in a world of HiPPOs (highest-paid person’s opinions) and Black Turtlenecks (persuasive gurus). They offer confident ideas but no proof. Goward’s answer is simple: treat every opinion as a hypothesis and test it.

From opinions to hypotheses

The book’s opening idea—“Test, don’t trust opinions”—defines the cultural shift you must make. Every design claim, marketing suggestion, or usability fix should be turned into a testable hypothesis. If an executive insists on a new home page layout, your reply isn’t argument but action: “Let’s test that.” When meetings stall under authority or charisma, testing becomes the tie-breaker that removes status from decision making.

(Note: this mirrors the scientific method popularized by Avinash Kaushik and Google’s data-driven culture—both advocate structured hypothesis testing instead of intuition.)

The controlled testing revolution

A controlled A/B/n or multivariate test randomly assigns visitors to variations and measures conversion differences under constant conditions. These tests provide statistically valid insight into what truly influences behavior. Without control, external factors like seasonality, competition, or marketing bursts distort results. Goward contrasts this with the “Pre & Post” fallacy—changing a page and comparing analytics before and after—which often yields false conclusions.

The LIFT and PIE frameworks

Goward builds a repeatable framework to analyze and prioritize experiments. The LIFT Model identifies six conversion factors—Value Proposition, Relevance, Clarity, Anxiety, Distraction, and Urgency—helping you pinpoint where a page persuades or fails. The PIE framework (Potential, Importance, Ease) directs which tests to run first, balancing opportunity with feasibility. Together, LIFT diagnoses problems and PIE decides order—forming a systematic roadmap for continuous optimization.

Optimization as learning loop

Testing is not about minor tweaks—it’s an organizational learning system. Each experiment adds insight to how visitors perceive your message, offers, and costs. Instead of guessing what “looks better,” you learn which changes actually move the metrics that define success. In practice, this means you rank pages by impact, draft hypotheses grounded in LIFT, create variations through wireframing, run controlled tests, and analyze not just conversion rates but revenue per visitor. Then you iterate.

Design, decision, and cultural transformation

The book’s deeper argument unfolds here: data-driven testing transforms not only your site but your organizational culture. ESR—Evolutionary Site Redesign— replaces risky “big bang” redesigns with stepwise, proven improvements. Testing keeps design accountable to results, not aesthetics. When integrated company-wide, testing matures into Strategic Marketing Optimization (SMO), where marketing, UX, and leadership align around evidence, not ego.

Real examples punctuate this journey: the failed “giant red button” experiment proving executives wrong; Tourism British Columbia’s 44% engagement lift through LIFT-based redesign; Electronic Arts’ 128% lift from a tested offer; and SAP’s organization-wide scaling that generated a 26% lift across multiple landing pages. As these stories show, learning replaces guessing and experimentation replaces debate.

Core argument

Conversion optimization succeeds when you view your website as a laboratory, not a canvas. Opinions are hypotheses, design is evidence-seeking, and data—not ego—drives every decision.

Across the book’s arc, you start with mindset (“Test, don’t trust”), master controlled experimentation, learn frameworks (LIFT and PIE), optimize value propositions, clarify usability, manage anxiety and distraction, induce urgency, and finally scale testing organization-wide. The outcome is more than higher conversions—it is a disciplined, experimental marketing culture built for continuous growth.

Testing is the scientific foundation of Goward’s method. You’re not simply comparing designs; you’re discovering causal truths about human behavior under consistent conditions. A controlled A/B/n test uses a valid control (the current version) and one or more challengers. Visitors are randomly assigned, exposed consistently, and tracked through cookies so each sees their version across visits. Conversions are measured to determine statistically significant winners.

Avoiding pseudo-experiments

Pre/Post comparisons—the “change it and reread analytics” approach—fail because uncontrollable market and timing noise confound results. Seasonality, competitor moves, and campaign intensity shift numbers without any relation to your change. Goward warns that such analysis often produces false confidence and expensive mistakes. Controlled concurrent experiments remove that risk.

Statistical rigor and duration

Every test needs sufficient sample size and time coverage (at least ten days, crossing weekends) to neutralize cyclical patterns. Significance thresholds—typically 80–95%—define how certain you must be. Declaring winners early wastes opportunity and builds false learning. Even a “clear winner” carries uncertainty, expressed as a range (say, 6–28%). That range demands judgment: act fast enough to capture value but cautiously enough to confirm confidence.

Representative sampling and tool reliability

You rarely test every customer. Instead, you sample website visitors and rely on randomization to generate reliable inference. Goward cautions against manual approaches like alternating URLs in ads—they violate randomization and often produce misleading outcomes. Dedicated tools manage cookies, random assignment, duplicate-content blocking, and goal attribution correctly.

Practical takeaway

Scientific discipline in testing creates trustworthy learning. Without controls, hypotheses remain opinions; with controls, they become evidence.

Testing is not just machinery—it’s your organization’s truth engine. Done correctly, it transforms meetings from debates into decisions driven by customer behavior. In Goward’s world, the site becomes your lab, and conversion rate is not a static number—it is continuously improved knowledge.

To know what to test, you must first know what’s broken. Goward’s LIFT Model gives you six diagnostic lenses that capture both the boosters and blockers of conversion. It builds a vocabulary teams can share to evaluate pages consistently: Value Proposition, Relevance, Clarity, Anxiety, Distraction, and Urgency.

Value Proposition as core

Value Proposition anchors everything. Conversion happens when perceived benefits outweigh perceived costs. If people don't see clear benefits or perceive high friction, they leave. You can increase benefits through offers, credibility signals, and emotional resonance—or reduce costs by simplifying payments, clarifying prices, or offering risk reversal. Each change becomes a hypothesis you can test.

Drivers: relevance, clarity, urgency

Relevance ensures the page matches visitor intent; Clarity makes your message instantly understandable; Urgency motivates action now. These fill the marble jar (Goward’s metaphor) until conversion tips over. You test relevance through unified ad/landing page messaging; clarity through design and copy simplification; urgency through scarcity or limited-time offers. Tourism BC’s thank-you-page experiment, built from LIFT analysis, improved engagement 44%—proof that structured diagnosis leads to measurable outcomes.

Inhibitors: anxiety and distraction

Anxiety is the fear that stalls action—concerns about privacy, payment, data use, or delivery. Distraction splits focus with unnecessary navigation, overly decorative imagery, or competing calls to action. Ballard.com’s rotating banners, Forever21’s carousels, and BlackBerry’s slow “speed” page show typical cases: promise and presentation mismatch produces anxiety and confusion.

From analysis to hypotheses

You use the LIFT factors to write hypotheses. “We believe that reducing distraction by removing the rotating banner will increase conversions.” Or “Adding delivery guarantees above the fold will reduce fulfillment anxiety.” This structured thinking transforms vague design conversations into measurable learning cycles.

The discipline

LIFT provides a diagnostic language. Instead of arguing aesthetics, you identify which factors leak persuasion energy and craft tests to plug them.

LIFT converts design debate into empirical analysis—building a bridge from intuition to insight. It’s the foundation for every experiment that follows.

Even if you understand what drives conversion, you can’t test everything immediately. Goward’s PIE framework—Potential, Importance, Ease—helps you allocate your testing resources for maximum return. Each page or idea receives a 1–5 score in these categories, guiding which experiments to run first.

Potential: where big lifts lurk

Potential measures how much improvement you can reasonably expect. Underperforming pages with high bounce rates, poor clarity, or weak value propositions often promise large gains. If your landing page is cluttered or mismatched to ads, its LIFT symptoms reveal untapped potential.

Importance: where money flows

Importance relates to traffic volume and acquisition cost. Pages carrying paid-search or email campaign traffic deserve priority since improvements multiply ROI. Goward’s template aggregation technique—grouping similar pages to view total entrances—exposes hidden high-impact targets. On one jewelry site, product detail templates had triple the entrances of the home page but had never been tested.

Ease: balancing effort and politics

Ease considers technical feasibility and organizational difficulty. Some pages require complex backend work or stakeholder alignment. Choose the ones that are simplest first unless the potential lift justifies complexity. LemonFree’s major site-template test demanded technical investment but netted a 19% lift in revenue per visit—a high-effort, high-payoff success.

Turning scoring into action

A transparent PIE scoring table diffuses politics—any stakeholder can see why a page ranks first. It replaces persuasion with math. Over time, periodic re-scoring keeps your roadmap dynamic.

Testing roadmap insight

PIE ensures you spend energy where improvement is both likely and lucrative. It turns testing prioritization into a rational, repeatable process.

Combined with LIFT, PIE gives you the operational structure that moves optimization from random testing to disciplined experimentation.

Design is often treated as art—Goward transforms it into a conversion engine. He warns against ‘award designs’ that look stunning but fail to drive sales. Choosing style over clarity or relevance costs money. The cure is to design for conversion, not for applause.

Evolutionary, not revolutionary

Revolutionary redesigns (RSRs) overhaul everything at once, hiding which changes help or hurt. Evolutionary Site Redesign (ESR) evolves components through sequential tests so every change has proof. Instead of one risky rollout, you build the next site as a series of validated improvements. ESR minimizes business risk and makes beauty measurable.

Clarity and eyeflow

Design clarity reduces cognitive load. Colonial Candle lifted conversions 20% by improving information hierarchy. WiderFunnel’s catalog test repositioned columns and lifted response 16%. Minimalist layouts often outperform complex designs, as Safe Software discovered with professionals allergic to marketing fluff. Contrast, legibility, and straightforward copy writing drive comprehension and trust.

Match messaging and minimize noise

Your ad promises establish expectations; your landing page must mirror them. A “create a will” ad should land on a “create a will” page—not a generic homepage. Banana Republic lost conversions when landing pages dropped offer details. Walmart’s mobile flow failed by removing browsing options. Matching scent trails preserves relevance and momentum.

Distraction also undermines message focus. Simplifying headers, reducing competing visuals, and guiding attention to one clear call to action improves conversions dramatically.

Design principle

Good design is invisible—it serves clarity, relevance, and urgency. When you design for conversion, aesthetics become evidence-backed advantages.

Testing design elements iteratively transforms subjective visual decisions into learning instruments. You build sites that not only win awards for looks—but more importantly, win customers.

Three psychological barriers define visitor hesitation: anxiety, distraction, and lack of urgency. Goward’s methodology teaches you to mitigate or leverage each through data-driven experiments.

Anxiety: reduce friction and fear

Privacy, usability, and fulfillment fears keep visitors from converting. You can test smaller forms, friendly error handling, visible guarantees, and speed improvements. WineExpress increased signups by moving optional fields to a thank-you page. Elastic Path lifted sales 21.8% by simplifying checkout steps. Even reassurance placement—plain-language privacy promises above the fold—bridges trust faster than legal copy blocks.

Distraction: narrow the focus

Above-the-fold chaos kills relevance within 0.2 seconds. Replace rotating carousels with singular focal points—a headline and one CTA. The “jam study” shows that more choices create interest but reduce purchases. Focused simplicity, confirmed by tests like FaveCrafts (22% lift) and Hair Club (20% lift), usually wins.

Urgency: trigger action

Urgency compels immediate decisions by conveying scarcity or time sensitivity. You can emphasize low stock, expiring offers, or invite-only access. For Environics Analytics, redesigning webinar promotions quadrupled preregistrations (290% lift). Internal urgency rises near deadlines; external urgency generates motivation when none exists. Even operational speed matters—responding to leads within five minutes dramatically increases outcomes (Oldroyd, InsideSales.com).

Testing insight

Each emotional factor is testable. When you reduce anxiety, eliminate distraction, or amplify urgency, you don’t just improve UX—you unlock latent demand.

Rather than guessing emotional impact, you validate behavioral effects through measurable change. The emotional science of conversion becomes empirical marketing intelligence.

In the final chapters, Goward expands testing into a full organizational culture called Strategic Marketing Optimization (SMO). Testing individual pages is tactical; testing systematically across departments is strategic. This shift creates a feedback loop of validated learning.

From isolated tests to organizational learning

You start by running small experiments to demonstrate ROI. A single validated test—like SAP’s 32.5% lift—can generate internal momentum. Once leadership links results to revenue metrics and sees transparent proof, testing spreads beyond UX teams into product, media, and analytics.

Analyzing results for strategic value

The goal is not simply to “find a winner” but to build insight. Every test—win, loss, or neutral—adds knowledge about audience psychology, offer elasticity, and design perception. Revenue-weighted metrics rather than raw conversions show true value. As WineExpress learned, a smaller conversion lift can hide larger revenue-per-visitor growth.

Championing change

Testing evangelists inside companies must connect experiments to strategic KPIs and story-driven communication. Goward recommends writing an “Optimization Manifesto,” securing executive sponsorship, running skunkworks experiments for quick wins, and scaling success data to stakeholders. When testing becomes routine, your organization evolves from marketing by intuition to marketing by learning.

Final message

Conversion optimization is bigger than A/B testing—it is a disciplined, learning-driven culture that makes every marketing decision an experiment and every outcome an improvement.

When you scale testing into culture, you transform digital marketing from art and opinion into science and progress. It’s not just about optimizing pages—it’s about optimizing how your company learns.