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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
Outcome-first audits
Measurement-first testing
Funnel-aligned roadmap
Implementing website optimization strategies requires more than visual fixes or traffic volume goals. For US-based founders and growth teams, optimization should be a structured system that increases revenue, lowers CAC, and improves accurate attribution across channels. This guide walks through a technical-first, test-driven approach you can apply to Shopify, WooCommerce, and B2B platforms.
Start by mapping business outcomes: incremental revenue ($), reduced CAC, improved LTV, or better lead quality. Translate those into measurable site KPIs such as conversion rate, average order value (AOV), and contribution margin. For example, a $120 AOV store that lifts conversion from 1.2% to 1.5% yields measurable revenue gains - use dollar estimates when prioritizing experiments.
A robust audit covers page performance, tracking fidelity, and funnel leaks. Key checks:
A technical audit should tie directly to your measurement plan and be documented so experiments can rely on clean data. If you want a centralized view of capabilities, see our services overview for how teams typically structure analytics and tracking builds.
| Touch Source | Client-Side Event | Server-Side Receipt | Revenue Attribution |
|---|---|---|---|
| Google/Meta Ads | purchase event (browser) | server-side event collector (order ID) | order-level attribution in data warehouse |
| Email (Klaviyo) | click→session | webhook order sync | channel credit via unified ETL |
Break experiments into strategic funnel layers so tests align with intent and CAC expectations:
Each layer requires different success metrics and sample size planning. For BOF changes (checkout flow), prioritize attribution clarity and server-side reconciliation to avoid overcounting conversions from ad platform pixels.
If you want a framework that combines analytics and CRO execution, review our homepage approach to performance-driven work at Prebo Digital for examples of how teams sequence audit → build → test.
Improve server response, compress images, and defer non-critical scripts. For Shopify stores, small improvements to theme code and app-load order can reduce bounce and improve conversion. Track gains against baseline revenue and be explicit about expected $ impact (example: a 0.2s TTFB improvement may increase conversions by a small percent - always validate with experiments).
Run prioritized A/B tests that tie directly to revenue. Example experiments:
Use server-side or GTM server containers for test tracking when possible-this improves attribution accuracy and reduces lost events from browser restrictions.
Build a single source of truth using GA4 events, server-side tracking, and an ETL to the warehouse. Reconcile platform-reported conversions with back-end order data to calculate true MER (marketing efficiency ratio). For teams scaling ad spend, clean attribution prevents wasted budget and helps lower CAC.
Hypothesis: Adding a $10 cross-sell at checkout will increase AOV and net revenue after accounting for expected cannibalization.
Metric: net revenue per visitor (NRPV) and conversion rate. Estimated sample: 40,000 sessions. Measurement: server-side order sync and GA4 validation.
Be mindful of cookie consent rules and CCPA/CPRA requirements when implementing tracking. Consent banners that block scripts can break attribution - adopt consent-aware server-side tagging and document how denied consent affects reporting. For US GDPR-like scenarios or state privacy laws, maintain clear documentation of which signals are used for modeling.
A repeatable cadence includes weekly analytics checks, biweekly experiment reviews, and monthly revenue impact reporting. Roles often include a growth lead, CRO specialist, developer, and an analytics engineer to maintain tracking and ETL pipelines. For an overview of how agencies structure long-term partnerships, read about our approach on the About page.
If you lack reliable order-level attribution, consistent GA4 event naming, or server-side tracking, consider bringing in technical support. A short audit often reveals high-impact fixes that are built to scale. For engagement models and typical scopes, see our contact page to understand how teams start discovery calls.
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