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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.
Here's what sets us apart from the competition
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 revenue-first
Server-side tracking
Test with intent
Performance-driven SEO and CRO focus on measurable business outcomes rather than vanity metrics. These practices combine technical SEO, content strategy, site speed, and structured experimentation to increase qualified traffic and lift conversion value per visitor. For US-based Shopify, WooCommerce, and B2B sites this means optimising for lifetime value (LTV), reducing acquisition cost (CAC), and improving accurate attribution across ad platforms.
When SEO drives the right audience but the site fails to convert, revenue is left on the table. When CRO improves conversion but relies on unstable attribution, you can mis-reinvest ad spend. A performance-driven approach closes that loop by prioritising profitability, clean data pipelines, and systematic testing.
Organic / Paid Traffic --> Server-Side Tagging (GTM Server) --> GA4 + Clean Data Layer --> Funnel Attribution Model --> Revenue & Cohort Reporting
Implementing server-side tracking reduces data loss from browser restrictions and improves attribution accuracy for both SEO-driven sessions and paid campaigns. For an overview of Prebo Digital's services that combine analytics and growth systems, see Services Overview.
Use a clean data layer, deploy Google Tag Manager with a server-side container, and send canonical events to GA4 and your ad platforms. This reduces attribution discrepancies between platform-reported conversions and your backend revenue reporting. If you want a practical reference for how these pieces fit together, visit the Prebo Digital homepage for our agency approach: Prebo Digital.
Addressing these issues reduces friction for both search crawlers and users, improving the top-of-funnel quantity and quality. For teams building scalable stores on Shopify or WooCommerce, the Services Overview provides typical build and optimisation scopes that combine development and CRO.
Start with hypothesis-driven experiments on pages with the highest session volume and commercial intent: product pages, checkout steps, and pricing pages. Typical test types include copy variants, simplified checkout flows, urgency and trust signals, and price anchoring. Run A/B tests long enough to reach statistical confidence while prioritising minimum detectable effect sizes tied to $ metrics.
Use a prioritisation score combining estimated impact on revenue, confidence in the hypothesis, and implementation effort. Example framework: Impact x Confidence / Effort. Document expected $ upside (estimated, US dollar) before running the experiment to guide decision-making.
| KPI | Why it matters | Example US target |
|---|---|---|
| $ per session | Directly links traffic to revenue | Increase from $0.80 to $1.00 |
| Checkout conversion rate | Reduces CAC and improves MER | Raise from 1.8% to 2.2% |
Reconcile by ingesting server-side events into a central measurement layer and reconciling with payment provider records (Stripe, Shopify Payments). This approach clarifies MER and prevents overspending on channels that report inflated conversions. For a high-level explanation of our measurement-first methodology and team experience, see our team background at About Prebo Digital.
Design tracking to degrade gracefully: collect core revenue events server-side and use consented client signals for personalization. If you want to discuss implementation specifics, you can request an audit via our contact page: Contact page.
A mid-market Shopify store with $150k monthly revenue focused tests on product page layout and server-side purchase events. By prioritising $ per session and deploying a two-week A/B test, they improved $ per session from $0.75 to $0.95 (estimated) and reduced CAC by ~10% after accurate attribution reallocation. Results will vary; figures are presented as illustrative estimates for US stores.
Explore the framework: combine technical SEO health, server-side tracking, and a prioritised CRO backlog to shift investment toward profitable channels.
If you’re focused on sustainable revenue growth, document your measurement plan, map TOF → MOF → BOF funnels, and start a prioritised test roadmap. See a short services overview to understand how strategy, build, test, and scale form an ongoing cycle: Services Overview.
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