Avelize - Shopify Expert Agency

Stop leaking revenue. Turn traffic into buyers.

Turn more visitors into buyers. We apply behavioral psychology, A/B testing, and data-driven UX to maximize your eCommerce conversion rate.

What this page covers

Avelize improves conversion rate by diagnosing the parts of product discovery, merchandising, trust, checkout, pricing, and mobile UX that stop qualified visitors from buying.

When this matters

CRO matters when traffic exists but revenue does not follow, especially on Shopify Plus stores with complex products, high AOV, B2B buyers, or multi-step purchase decisions.

What Avelize improves

  • Conversion diagnostics
  • PDP and collection improvements
  • Checkout friction reduction
  • Measurement and testing roadmap

How Avelize works

  1. Diagnose: We review the current storefront, crawl paths, analytics, customer journey, and business constraints before recommending changes.
  2. Prioritize: We rank fixes by revenue impact, crawlability, conversion risk, implementation effort, and launch safety.
  3. Implement: We ship controlled improvements in code, content, UX, schema, internal links, or integrations depending on the page goal.
  4. Measure: We validate the outcome through Search Console, analytics, QA checks, and follow-up optimization.

The Scientific Method for Revenue

CRO is not about blindly changing button colors. We build a highly structured experimentation backlog based on real user data, prioritize tests using the ICE framework (Impact, Confidence, Ease), and deploy rigorous statistical analysis to ensure every "win" translates to actual bottom-line growth.

Why most Shopify A/B tests never conclude

Split testing needs enough conversions to separate a real effect from noise, and the required sample is larger than most stores expect. Detecting a 10% relative improvement on a 2% baseline conversion rate takes roughly eighty thousand sessions per variant at 95% confidence and 80% power — about 160,000 sessions for one two-variant test.

A store doing 5,000 sessions a week would need more than six months for a single test, during which seasonality, campaigns and pricing all change underneath the experiment. Most teams stop early, see a number they like, and ship a result that was noise.

So the first thing we establish is whether your traffic supports testing at all. It is an arithmetic question with a definite answer, and the answer determines the entire approach.

What to do when you do not have test traffic

Below the testing threshold, the productive path is to fix things that are known to be broken rather than to run underpowered experiments on things that might be.

  • Session recordings and funnel analytics to find where people actually stop, rather than where you suspect they do.
  • Device and browser segmentation. A conversion rate that looks acceptable in aggregate often hides a mobile Safari figure that is half the desktop number.
  • Diagnosing the known friction: unexpected shipping cost revealed late, forced account creation, slow or broken variant selection, product pages that do not answer the question that stops the purchase.
  • Search and filtering behaviour, including what people search for on site and find nothing for.
  • Customer interviews and post-purchase questions, which surface reasons no analytics tool records.

These produce changes with a clear rationale and an obvious before-and-after in the funnel, which is the honest version of optimisation at that traffic level.

How we report results

We report the confidence interval, not just the point estimate, and we state the minimum detectable effect the test was powered for before it starts. A test that shows a 4% lift with an interval spanning minus 6% to plus 14% has not shown anything, and we say so rather than presenting the midpoint as a result.

We also record what was rejected. A conversion programme that only reports wins is not measuring; it is selecting.

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