Conversion multiplies the return on every pound already spent on acquisition, which is why it comes before buying more traffic. We quantify where visitors leave, rank the fixes by what they are worth, and size each test before running it — so a quarter is not spent proving something the sample was never going to show.
A programme, not a list of test ideas
(a ranked backlog, a testing cadence and a decision rule agreed before the first result arrives)
Tests sized to reach a conclusion
(most stores cannot detect a small lift at their traffic level — we say so before running the test, not after)
Measured in revenue per visitor, not rate
(conversion rate rises when average order value falls; only one of those is worth having)
Tell us your current challenges — we'll reply within 24 hours.
30 minutes • no obligations
By submitting this form or contacting us by phone or email you accept the data processing described in our Privacy Policy. Controller: Space Ads Sp. z o.o. We process your data only to respond to your inquiry.
The failures are structural rather than creative: tests too small to conclude, a backlog ordered by convenience, and a metric that can improve while the business gets worse.
Conversion multiplies, traffic only adds
A change that lifts conversion improves the return on every pound already being spent on acquisition. Buying more traffic into an unfixed funnel purchases the same loss at greater scale.
Most tests never reach significance
Detecting a modest lift requires far more conversions than most stores generate in a sensible timeframe. Running underpowered tests produces confident answers that are noise.
The biggest wins are rarely a button colour
Shipping cost revealed late, a checkout requiring account creation, product content that fails to answer an obvious question — these move numbers that no amount of headline testing will.
Which is why the first deliverable is a number, not a hypothesis: what each identified leak is costing, so the order of work stops being a matter of taste.
From quantified drop-off through checkout and product content to experiment design — and the measurement that makes any of it trustworthy.
Funnel and drop-off analysis
Where people leave, on which device and at which step, quantified in lost revenue rather than percentages — so the backlog is ranked by money instead of by how easy something is to change.
Checkout and cart
The single highest-value surface in most stores. Unexpected costs, forced account creation, payment method gaps and validation friction — the failures that abandon otherwise-willing buyers.
Product and category pages
Whether the page answers the questions a buyer actually has: sizing, materials, delivery, returns, and what makes this the right choice. Missing answers become abandoned sessions.
Experiment design and analysis
Hypothesis, sample size calculated upfront, one decision rule, and an honest read at the end — including calling a test inconclusive when it is, which is more often than most reporting admits.
Speed and accessibility
Field performance from real visitors rather than a lab score, plus the accessibility failures that quietly exclude buyers and carry regulatory exposure in several markets.
Measurement you can act on
Event tracking that matches how the store actually works, so a drop-off number means what it appears to mean and a test result can be trusted.
We tell you when not to test
If traffic cannot support detecting the effect you care about, running the test wastes a quarter. In those cases the honest route is a considered change plus before-and-after monitoring, stated as such.
Ranked by revenue, not by ease
The backlog is ordered by the money each fix is worth. Quick wins get done because they are cheap, not because they are pretending to be important.
CRO, analytics and implementation in one loop
Findings do not die in a backlog. Analytics, UX judgement and implementation work together, with revenue and contribution per visitor as the metric — so the win is commercial, not cosmetic.
It pays when there is traffic worth improving and an ability to actually ship the changes the analysis identifies.
It makes sense when
Not yet, when
The ranking step is what separates a programme from a list — every item carries what it is worth and whether your traffic can prove it.
Quantify the leaks
Analytics, funnel drop-off by step and device, session review and heuristic analysis. Every finding is expressed in revenue at stake so the list can be ranked by something other than opinion.
Ranked backlog with expected value
Each item carries an estimate of what it is worth, what it costs to implement and whether it can be tested at your traffic level. That last column is what turns a wish list into a plan.
Ship, measure, repeat
A steady cadence of implementation and testing, with results read against the rule agreed at the start — including inconclusive outcomes reported as inconclusive.
Test history and documentation are yours, and nothing is priced on a percentage nobody can promise honestly.
Findings are yours to keep
Documentation, test history and analysis stay with you. The learning does not leave when the engagement does.
Priced on the programme, not on uplift
We do not sell a guaranteed percentage. Anyone who does is either capping the ambition or hiding the sample-size problem.
Implementation can be in scope
Where the constraint is that nothing gets shipped, we can do the implementation rather than hand over a backlog that waits.
A cross-section of work across premium e-commerce, marketplaces, local lead generation and ticketed experiences. Each story shows how the right strategy, capabilities and operating cadence come together around the business model.
Verified reviews on Clutch — the global B2B review platform. Our reviews come from real clients who delivered measurable results with us.
“With Space Ads' support our client saw great month-over-month results in online sales and traffic. The team was responsive, available and budget-aware. They also adapted strategies and tactics to deliver even better outcomes.”Read all reviews
The way we work is consistent across every scope: the business objective comes first, each capability has a defined role, and decisions are made against real business numbers.
Direct strategist contact
You work with the person responsible for direction, priorities and delivery — not a layer of account managers.
Infrastructure on your side
Accounts, analytics, automations, creative and data stay yours from day one — no dependency on the agency.
A daily decision rhythm
We connect marketing and analytics data in one operational view, so evidence turns into priorities and action quickly.
No long lock-ins
A 14-day rolling notice, a clear billing model and a clean handover if you decide to stop.
Conversion rate optimisation is the systematic practice of increasing the share of visitors who complete a valuable action, using analysis of where people drop off and controlled experiments to verify that changes actually help. It is distinct from redesign in that each change is tied to an identified problem and, where traffic allows, measured against a control rather than assumed to work.
An audit is a one-off diagnostic: it examines the experience and returns a prioritised list of problems. A CRO programme is ongoing — it takes that list, implements against it, tests where the traffic supports testing, and keeps a cadence running. Many engagements start with the audit and continue into the programme; buying the audit repeatedly without implementing it is a common and expensive pattern.
It depends on your current conversion rate and the size of the effect worth detecting, and the requirement is higher than most people expect — detecting a small relative lift can require tens of thousands of conversions per variant to be reliable. We calculate this before proposing a test. Below that threshold, the honest approach is a considered change with before-and-after monitoring, described as exactly that rather than dressed up as an experiment.
Published averages are close to useless for judging an individual store, because conversion rate is dominated by traffic mix. A store with a high share of returning and brand traffic will look excellent against any benchmark while acquiring poorly, and a store investing in top-of-funnel reach will look weak while growing. The useful comparison is against your own trend, segmented by traffic source and device.
The first quantified findings arrive within weeks, because analysis of existing data does not require new traffic. Implemented changes show up as soon as they ship. Tested results depend on how long the sample takes to accumulate at your volume — which is the number we calculate at the start so the timeline is known rather than discovered.
No, and we would be cautious of anyone who does. What can be committed to is the process: quantified findings, a backlog ranked by revenue at stake, tests sized properly, and honest reporting including inconclusive results. A guaranteed percentage either assumes an outcome nobody can know in advance or quietly sets the bar low enough to be safe.
Tell us about the site, monthly sessions and order volume. We'll come back on where the largest drop-offs are, what they are worth, and whether your traffic can support testing them.
