High-performing stores do not guess their way to growth. Rather, the best practice for sustainable growth is to increase website conversion with experiments. Drawing on successful cases, this article shows how top teams choose the right experiments to turn insights into long-term conversion across their stores.
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5 Types of Experiments That Can Improve Your Website Conversions
Not all experiments solve the same conversion problem. Winning stores choose experiment types based on decision scope and risk. Some tests are designed to validate a single change, while others are built to understand how multiple variables interact across the funnel. It is essential to understand the differences between experiments to select the right one for your business.
1. A/B Tests
Best for: Validating Single Changes
A/B testing is one of the most commonly-used experiments. This test compares two versions of a web page or app against each other to determine which one performs better. Winning stores use A/B tests for a fast and confident answer to a focused business question, such as whether a new headline increases add-to-cart rate.
In practice, the best A/B tests are built around a clear hypothesis tied to revenue. Instead of a generic question like “Which button color wins?”, a clearer question like “Will a more benefit-driven CTA increase checkout starts?” ensures the clarity and relevance of test outcomes. A/B tests support risk management, especially when stores are going for impactful changes, such as a new pricing layout. A/B testing is employed to validate performance before rolling out changes across channels.
2. Multivariate Tests
Best for: Optimizing Complex Pages
Multivariate testing helps to analyze how several elements work together. Winning stores use this test when they assume that multiple components, such as headline, image, and CTA, can jointly influence conversion. However, as multivariate tests require more traffic, it is only deployed on high-volume landing pages where even small changes translate into great revenue.
For example, a store might test product imagery, benefit statements, and trust badges together to see which combination produces the highest conversion rate. The experiment result reveals what works well together and is valuable when pages are dense or when branding and design must be aligned.
3. Funnel Experiments
Best for: Drop-off Diagnosis
Funnel experiments focus on how users move through the full channel, from landing page to checkout to purchase. This test is used when conversion problems are not caused by a single page but by the flow between pages.
For instance, a store might test different checkout sequences, shipping disclosure timing, or login requirements to see how they affect completion rates. These experiments identify friction points to clarify which changes reduce abandonment.
Funnel experiments are especially powerful for identifying hidden bottlenecks. A landing page may convert well, but if users drop off during payment, revenue still suffers. By experimenting across steps, stores gain a more complete picture of how to improve conversion.
4. Quasi-xperiments
Best for: Real-world Impact Measurement
Not every business decision can be tested with A/B tests. Quasi-experiments allow stores to measure real impact after experiments are run. For example, merchants can compare performance before and after changing their homepage to see if traffic and conversion improve or not.
High-performing stores tend to be careful when running quasi-experiments. They control for seasonality, marketing spend, and traffic sources so results remain valid. While not as simple and fast as A/B tests, quasi-experiments provide directional insight for large operational changes.
5. Personalization
Best for: Traffic at Scale
Personalization experiments test how adapting content to user segments affects conversion. However, stores should only apply this experiment if they have enough traffic to validate multiple versions, such as enough new vs. returning customers. This test will show different offers, headlines, or product recommendations based on visitor context. Personalization experiments help stores move from a one-size-fits-all experience to a more tailored one to boost engagement and conversion rates.
Key Factors to Test and Boost Conversion Rate
High-performing stores do not test randomly. They organize their experimentation around core factors that influence how customers perceive value, trust the brand, and complete purchases. Each group of test factors represents elements that can greatly impact conversion.
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Message and relevance: Determine whether visitors immediately understand what is being offered and why it matters to them. If users feel confused or unsure, they leave before even considering a purchase.
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Trust and confidenc: Influence whether people feel safe enough to buy. Even a strong product will not convert if shoppers are unsure about credibility, pricing, or policies.
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Action and flow: Shape how easily users can move from interest to purchase. A weak call to action or a complicated checkout can block otherwise motivated buyers.
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Experience quality: Defines how smooth and comfortable the journey feels. Speed, mobile usability, and navigation strongly affect conversion even when messaging is strong.
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Adaptation: Allows stores to tailor experiences to different visitors. When content matches a user’s context or intent, conversion rates rise without needing more traffic.
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Factor |
Description |
What to Test |
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1. Message & relevance |
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Value proposition |
How clearly the product or offer is demonstrated |
Headlines, sub-headlines, benefit statements |
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Content relevance |
How well the page matches what the visitor is searching for or expecting |
Page copy, keyword alignment, product descriptions, use-case sections |
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2. Trust & confidence |
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Social proof |
Signals that build trust and reduce risk |
Reviews, ratings, testimonials, logos |
|
How easy it is to understand the cost |
Discounts, bundles, shipping fees, bonuses |
|
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3. Action & flow |
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Call-to-action (CTA) |
How users are encouraged to take action |
Button text, color, placement, size |
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Checkout flow |
How smooth the buying process feels |
Number of steps, guest checkout, form fields |
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3. Experience quality |
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Page speed |
How fast the pages load |
Image compression, scripts, and hosting |
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Mobile experience |
How well the page works on small screens |
Button size, layout stacking, and page speed |
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Navigation |
How easily users find what they need |
Menus, filters, internal links |
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4. Adaptation |
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Personalization |
How content adapts to the visitor |
Location-based offers, past behavior |
Experiment Ideas From Winning Stores
High-performing stores do not rely on generic ideas. They draw from patterns that have repeatedly shown impact across different traffic sources and past experiments. This is how merchants increase website conversion with experiments in the most effective ways.
#1. Messaging and Headline Experiments to Improve Relevance
Messaging experiments are used to test how clearly and accurately a page communicates to its right audience. When relevance fails, traffic suffers accordingly, regardless of design quality or pricing strategy.
Weak messaging often sounds impressive but says very little to the buyer. For example, a statement like “Powerful ecommerce solutions for modern brands” doesn’t clarify the benefits or information for customers. This may lead to higher bounce rates.
A stronger message, such as “See where shoppers abandon your store to boost revenue,” immediately signals usefulness, real business problems, and measurable value. This outcome-driven messaging improves engagement and conversion as visitors resonate with the message. Winning stores continuously test headlines and content to reduce ambiguity across the entire funnel.
Learn more: GemX Use Case Series: How to A/B Test Multiple Headlines
#2. Call-to-action Experiments for Clearer Next Steps
Conversion may stall even when visitors understand the offer and trust the brand, if the next step is unclear or complex. Therefore, call-to-action experiments are employed to remove friction at the decision point by clarifying what happens after the purchase.
A poor CTA often uses generic language such as “Submit” or “Get started.” These introduce anxiety and hesitation as customers are clueless about what they need to do next.
A good CTA explicitly sets expectations and reduces perceived risk. A phrase like “Sign up for free” explains both the action and the consequence. Winning stores often test CTA language, placement, and color to align on-page elements with user intent. When CTAs remove uncertainty, click-through rates increase, cart abandonment decreases, and conversion improves significantly.
#3. Trust and Social Proof Experiments to Reduce Anxiety
Trust experiments can test how credibility signals influence a visitor’s willingness to proceed. Even with strong messaging and clear CTAs, conversion will stall if users fear making the wrong decision or being misled.
A low-performing store may hide trust indicators. For example, they show product pages without reviews, testimonials, or recognizable signals. For new visitors, this creates hesitation and distrust, which discourages them from scrolling.
A high-converting store makes trust signals visible. By displaying reviews, trust badges, and ratings, they greatly reduce perceived risk. Winning stores continuously experiment with the type, placement, and framing of trust signals to understand what reassures their audience. When trust is reinforced, add-to-cart rates and checkout completion consistently rise.
#4. Layout and Visual Experiments to Guide Attention
Top stores use layout experiments to test how information and visual assets placement influences engagement and decision-making. As visitors tend to scan through pages, they may miss important information if the page structure is too complex or confusing.
A poor layout often overwhelms users with dense text, low-quality images, and complex elements. This increases cognitive load and leads to disengagement.
High-performing stores use a layout with simplified visual flow. Key messages appear first, followed by social proof, then more details. Spacing, contrast, and section order are adjusted to lead users toward action.
#5. Offer and Price Experiments to Increase Perceived Value
Offer and price experiments test how value is framed rather than how much something costs. As many stores mistakenly assume that conversion issues stem from pricing rather than unclear value justification, offer experiments provide insight into how merchants can improve price clarity to encourage purchase.
A weak offer might state a price with no context, such as “$99 per month.” This forces users to calculate value on their own and encourages them to switch to alternatives.
A strong offer message reframes the offer around outcomes. For example, “$3.30 per day to increase your store’s revenue” is a good offer as it adds guarantees and bonuses to increase perceived value. Winning stores experiment with offer framing, bundling, and incentives to understand what impacts their audience. These experiments often lift both conversion rate and average order value, proving that value perception is as important as price setting.
Learn more: 15+ Split Testing Examples for Shopify Stores (Real Data, CRO Insights & Easy Wins)
5 Practical Tips to Increase Website Conversion with Experiments
High-performing stores do not win because they run more A/B tests. Rather, they win because they use experiments as a decision system that connects user behavior to business outcomes. Our study on the top stores on Shopify reveals five winning principles that are core to long-term success.
Tip 1. Match Experiments to Business Questions, Not Page Elements
Winning stores always start experiment with a clear business question. This can be “Why is our paid traffic converting 40% worse than organic?” or “Why do mobile users abandon checkout more than desktop?” These questions define the scope and purpose of the experiment before any change is considered.
Weak experimentation begins with questions such as “Let’s test the CTA color” or “Let’s change the hero image.” These tests may produce statistical winners, but they do not explain anything about the success. As a result, every future test remains disconnected.
With close alignment between the experiments and clear business goals, merchants document and learn from past experiments. This is how high-performing stores increase website conversion with experiments that scale beyond a single page.
Pro Tip: Learn how to choose the right experiment methods with GemX that fit your real business issues and questions.
Tip 2. Designing Business-oriented Experiments, Not Clicks-oriented
Clicks are not revenue. Winning stores design experiments that measure revenue outcomes rather than surface-level metrics. A test that yields more clicks but fewer checkout completions is not a success.
A weak experiment tends to optimize for micro-metrics such as click-through rate or scroll depth without boosting revenue. This drives small lifts while stagnant total sales and leads to misguided rollouts.
Agood experiment ties changes to meaningful outcomes such as conversion rate or revenue per visitor. When testing a headline, it is wrong to ask “Did more people click?” Merchants should ask, “Did the people who clicked buy more and stay longer?” This ensures that experiments provide consistent results, drive real growth, and support profitability.
Learn more: CRO Framework for Shopify: A Structured Path for Conversion Lift
Tip 3. Running Consistent and Continuous Experiments
The highest-performing stores are stores with consistent execution. Conversion varies between traffic sources, campaigns, and customer segments. A store that runs one experiment for all often fails.
Many stores treat experiments as short-term projects. They launch a test, wait for a result, implement a change, then stop. While winning stores treat experimentation as infrastructure. There is always a test running. Learnings from one experiment feed the next. Over time, this creates a compounding knowledge base about what actually drives purchase and engagement. This is how brands increase website conversion with experiments in a way that compounds instead of resets.
Tip 4. Reading Experiment Results to Guide Long-term Optimization
Test result is a data points about how customers think, decide, and respond to risk. Reading experiment results is important as this informs big decisions and guides future experiments.
For example, instead of analyzing the winning version and then moving on, top stores extract patterns. If three messaging experiments show that customers respond to risk-reduction more than feature lists, merchants may consider reshaping landing pages, ad messages, and product descriptions to match customer preferences. This is how experimentation turns into a growth engine rather than a series of isolated optimizations.
Tip 5. Support Your Experiments with Tools
No matter how strong the strategy is, execution depends on tools. Winning stores use experimentation platforms, analytics, and behavioral tracking systems to see what users do, not what they say. Tools like GemX enable teams to deploy experiments quickly, measure lift accurately, and segment results by traffic source, device, and customers.
Weak setups rely on basic analytics that cannot separate noise from true impact. Strong setups give teams confidence to make revenue-affecting decisions based on real data. This is essential when scaling experimentation across multiple campaigns, funnels, and customer segments.
Conclusion
Winning stores do not rely on assumptions or one-off optimizations. They build systems to continuously learn and turn insights into measurable growth. When experiments are designed around real business goals and supported by reliable data, they become the most powerful growth engine. This structured approach also allows merchants to increase website conversion with experiments that scale across channels, products, and customer segments, creating sustainable revenue growth rather than short-term wins.
FAQs about Increase Web Conversion with Experiments
