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Discover what makes us different
Our average client sees a 35% lift in conversion rate across 150+ CRO projects.
500+ tests run across landing pages, checkouts and lead capture forms.
Every change is backed by analytics, heatmaps and real session data.
Certified VWO partners running enterprise-grade experimentation programmes.
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Find answers to common questions
Prebo integrates CRO work with GA4, Google Tag Manager, server-side tracking, and common ecommerce platforms like Shopify and WordPress to ensure accurate event capture and attribution. We also set up ETL or data-layer solutions where needed so test results feed into a single source of truth for decision making.
Common experiments include A/B tests, multivariate tests, funnel experiments, UX and checkout performance optimizations, and technical fixes that reduce friction. Test duration depends on traffic volume and required statistical power but typically ranges from several weeks to a few months per experiment cycle.
We prioritise revenue-per-visitor, conversion rate, average order value, customer acquisition cost (CAC), lifetime value (LTV), and marketing efficiency ratio (MER), alongside statistical significance for experiments. Clean attribution and centralized data pipelines ensure those metrics reflect true business impact rather than platform-reported figures.
CRO is designed to improve conversion efficiency and attribution accuracy, which can increase revenue and profitability when combined with product-market fit and adequate traffic. Outcomes vary by business and depend on test quality, funnel issues identified, and downstream economics, so results are not assured and are measured against revenue-focused KPIs rather than vanity metrics.
CRO is the systematic process of improving a website or funnel to increase revenue per visitor. Prebo Digital uses a technical-first, analytics-driven approach with hypothesis-driven experiments, server-side tracking, and funnel-level optimization focused on measurable revenue outcomes.
In This Article
Measure first
Prioritise revenue
Systemise tests
For US-based eCommerce brands, SaaS companies, and service businesses, improving landing page conversion rates is one of the fastest ways to reduce CAC and increase profitability. This guide explains how to improve landing page conversion rates using a systemized approach: define intent, instrument accurate measurement, design experiments, and iterate with data-driven decisions. The steps below assume you optimise for revenue and clean attribution rather than vanity metrics.
Before you change layouts or copy, define a single, measurable conversion for the page (purchase, trial signup, lead form completion). Map the page into a simple funnel: top-of-funnel (TOF) traffic source → page engagement → micro-conversions (add-to-cart, form interaction) → bottom-of-funnel (BOF) conversion. Use this TOF → MOF → BOF model to prioritise which pages to optimise first.
| Stage | Signal | Metric |
|---|---|---|
| TOF | Ad click / organic entry | Sessions, CTR |
| MOF | Engagement events (scroll, video play) | Engagement rate, time on page |
| BOF | Form submit / purchase | Conversion rate, $ per session |
Measurement quality drives confident decisions. Use GA4 events, Google Tag Manager, and server-side tracking to capture the funnel signals above. Server-side tracking reduces attribution loss from ad blockers and browser restrictions, which is critical for US ad platforms like Google Ads and Meta. If your stack includes Shopify or WooCommerce, ensure server-side events align with ecommerce purchases and that order IDs are deduplicated in your analytics layer.
Quick note: small sample sizes can mislead A/B tests. For statistically reliable results in the US market, plan tests to run until they reach both statistical confidence and business-relevant impact (e.g., changes that move $/session by >5%).
If you need a checklist for initial setup, see our Services Overview where we document common tracking and CRO inclusions for retainers. To understand how this work ties into overall strategy, visit our homepage for framework context.
Use quantitative and qualitative data to locate the biggest conversion leaks. Start with session recordings, heatmaps, and event funnels to see where users drop off. Pair that with survey feedback and UX audits to learn why they drop. Prioritise fixes that impact revenue fastest: broken checkout flows, slow page speed, unclear CTA, or mismatched ad-to-page messaging.
Run hypothesis-driven A/B tests with one variable change per test when possible. Examples of high-impact tests: headline clarity, CTA prominence, price presentation (monthly vs annual), and form length. Use holdback groups to validate lifts against an untested baseline and measure outcomes in revenue terms (e.g., $ per visitor) in addition to relative conversion lift.
Move reporting beyond platform-reported conversions. Build a consolidated dataset-GA4 + server-side events + ad platform costs-to calculate MER and CAC at the campaign or creative level. This allows you to answer questions like: "Which landing page variant produced the highest $/session for paid search in Q4?" and to avoid over-optimising to noisy platform metrics.
In the United States, state-level privacy laws and consent management are evolving. Implement consent banners that respect user choices and ensure server-side events respect consent flags to avoid legal and measurement conflicts. Common pitfalls include sending PII in clear-text events and failing to honour opt-out signals from consent management platforms (CMPs). For ecommerce stores on Shopify or WooCommerce, check that your checkout customisations retain compliance and do not block required disclosures.
Example: a mid-market Shopify brand in the US has 3% landing page conversion rate from paid social at $50 average order value (AOV) and $20 CPA. A focused CRO program reduces friction and raises conversion to 3.6% (20% relative lift). If traffic and AOV remain constant, CAC effectively drops to ~$16.67 (estimated), improving unit economics and freeing budget to scale high-performing channels. These figures are illustrative estimates and should be validated per store.
Turn learnings into a backlog: triage ideas (impact × confidence × effort), schedule sprinted experiments, and codify winners into templates. For teams without in-house capacity, a long-term retainer helps maintain momentum across strategy → build → test → scale → report cycles. To learn how retained programs are scoped, see our about page and the types of long-term partnerships we pursue.
When you have a validated winner, roll it out with analytics changes, update server-side events for accurate attribution, and document the change in your data catalog to keep reporting consistent. If you'd like a practical audit or a growth audit scoped to landing pages and tracking, our team can outline a custom plan-start by explaining your stack and objectives on the contact page.
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