Connected customer journeys are the foundation of growth. At Remarkable, channel optimisation means continuous experimentation — testing, measuring and refining every interaction across web, app, email, paid media and beyond.
We help organisations like yours unify data, content, and context to deliver the right experience to every user, wherever they are—web, app, email, paid media, and beyond. Our approach blends behavioural analytics, structured experimentation, and omnichannel personalisation to systematically increase engagement and conversion.
Experimentation is the engine. Every change we recommend starts as a hypothesis and is proved with an A/B or multivariate test, so decisions rest on measured uplift rather than opinion. Winning variants get rolled out; losing ones cost a test cycle instead of a quarter. Where traffic supports it we run continuous testing across the funnel — and where it doesn’t, qualitative diagnostics tell us which experiments are worth the volume.
AI-led orchestration ensures paid, owned, and earned media work in harmony, amplifying relevance and efficiency. With human insight guiding strategy, and AI refining execution, brands achieve up to 40% efficiency gains, and 25% less media waste, creating precision at scale.
If your channels are underperforming, siloed, or failing to deliver measurable outcomes, you’re not alone. Channel optimisation solutions from Remarkable transform fragmented paths into connected journeys that drive growth.
Here’s how our industry-leading channel optimisation experts can help:
Revolutions aren’t easy — if they were, they’d happen constantly. True progress happens when every channel starts working together, not in silos. Our channel optimisation experts help you turn fragmented pathways into connected, high‑performing journeys by enhancing how users discover, interact with, and move through your ecosystem.
Through funnel diagnostics, drop‑off analysis and a continuous cycle of A/B and multivariate experimentation, we tailor messaging and experiences to every moment. Machine‑learning personalisation engines adapt journeys across your CDP, CRM, and automation platforms, while advanced search and discovery intelligence strengthens visibility across SEO, AEO, and GEO environments. And with journey‑level automation and unified performance attribution, every channel becomes measurable, orchestrated, and accountable — creating clarity, efficiency, and momentum at scale.
Experimentation is the practice of testing changes to a digital experience against a control to see which performs better, before rolling anything out. It covers A/B tests, split tests and multivariate tests, and it replaces opinion-led decisions with measured evidence of what moves conversion.
An A/B test compares two versions of a page or journey to find the better performer. A multivariate test varies several elements at once and measures how the combinations interact. A/B tests need less traffic and answer one question cleanly; multivariate tests need far more traffic but show which combination of changes works hardest.
CRO is the continuous process of increasing the percentage of visitors who complete a valuable action — a purchase, an enquiry, a sign-up. It combines analytics and funnel diagnostics to find where people drop off, experimentation to test fixes, and personalisation to sustain the gain across audiences and channels.
Long enough to reach statistical significance and to cover full business cycles — usually a minimum of two full weeks, so weekday and weekend behaviour are both represented. Stopping a test early because it looks like it is winning is the most common way to record an uplift that never materialises in revenue.
Lower-traffic sites can still experiment; they just need bigger swings and longer run times. Where volume is genuinely too low for significance, we prioritise qualitative diagnostics, session analysis and sequential testing, and concentrate experiments on the highest-traffic steps of the funnel rather than spreading them thin.
We start with funnel diagnostics and drop-off analysis to find where the largest volume of value is being lost, then score candidate hypotheses on expected impact, confidence and effort. The first experiments target the biggest measurable losses, not the easiest changes.
A prioritised hypothesis backlog, a regular testing cadence with agreed guardrail metrics, and a documented read-out on every experiment — wins, losses and learnings. Results feed personalisation and channel orchestration, so proven uplifts compound across web, app, email and paid media rather than sitting in one channel.
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