Conversion rate optimisation services, judged on evidence

Agencies with a verified published price

How to buy conversion rate optimisation

1. Check you have the volume for a test. Count conversions per week on the page you want to improve, not sessions. If a realistic improvement would take months to detect at that volume, buy research and redesign work instead and treat testing as a later purchase.
2. Agree the primary metric before anything is built. One primary metric per test, chosen in advance, plus a small set of guardrail metrics. Deciding afterwards which number to report is how a losing test becomes a case study, and it is the most common integrity failure in the category.
3. Require a stopping rule in writing. Fix the sample size or the run length before the test starts and do not stop early because the result looks good. Peeking at a running test and calling it when it is ahead manufactures wins that do not replicate in the following quarter.
4. Separate research from build. Analytics review, session replay, user testing and survey work produce the hypotheses. Building and shipping variants is engineering. Price them separately so a thin research phase cannot hide inside a busy build schedule.

Do you have enough traffic to test?

The arithmetic is unforgiving and it is worth doing before any provider is contacted. Detecting a small relative improvement reliably needs a large number of conversions in each variant, and that number rises sharply as the effect you are trying to detect gets smaller. A checkout with thousands of orders a month can test a small change and read the answer within weeks. A business-to-business site with twenty demo requests a month cannot, and running a test there anyway produces a result that is mostly noise wearing a confidence interval.

The honest response to low volume is not to abandon improvement, it is to change the method. Qualitative research, usability testing, expert review, analytics diagnosis of where people drop out, and simply fixing the obviously broken things all work at any volume. They just do not come with a statistical claim attached. A provider who proposes an experimentation retainer for a site that cannot support one is either not doing the arithmetic or is hoping you will not.

What a real engagement contains

Research first: analytics diagnosis to find where value is being lost, session replay and form analytics to see how, and direct user input through testing or surveys to understand why. Then a prioritised hypothesis backlog, each item stating what is believed, what will change, what metric should move and by how much for the test to be worth running. Then build, run, analyse and, importantly, document, including the losers, because a documented loser is knowledge and an undocumented one is a repeated mistake.

The deliverable that distinguishes a serious provider is the test archive: every test run, its hypothesis, its result and what was learned, kept where your team can read it. Ask to see a redacted example. Providers who only present winners have either been extraordinarily lucky or are not showing you everything, and in a discipline where a large share of tests are expected to fail or be inconclusive, a perfect record is a warning rather than a credential.

Fee models and red flags

Most engagements are monthly retainers scoped by test throughput, which is reasonable provided throughput is defined honestly and the traffic supports it. Performance-linked fees sound attractive and are difficult to implement fairly, because the uplift has to be measured against a counterfactual that no longer exists once the change ships, and because seasonality will do some of the work either way. Where a performance component is used, tie it to results measured inside the test rather than to overall revenue after it.

Three red flags recur. A guaranteed uplift, which cannot be honestly promised before anyone has seen your data. A proposal that leads with a list of tactics, button colours, urgency timers, popups, rather than with research, since a tactic list is not a hypothesis. And a case study quoting an improvement with no baseline, no sample size and no time period, which describes a shape rather than a result and would not survive a single question.

Questions people actually ask

How much traffic do we need for A/B testing?

Enough conversions, not enough visitors, and the threshold depends on how large an effect you want to detect. As a practical screen: if a page produces fewer than a few hundred conversions a month, most realistic improvements will take an impractically long time to prove. Below that, buy research and fixes rather than an experimentation programme.

Can CRO work without testing at all?

Yes, and for low-traffic sites it is the right approach. Usability testing, analytics diagnosis, expert review and fixing broken or confusing steps all improve outcomes. What you lose is the ability to attribute the improvement precisely, so change fewer things at once and keep a clear record of when each change shipped.

Should the agency implement the changes or just recommend them?

Whichever gets changes shipped. The usual failure is a beautiful research deck that nobody has capacity to implement, so the programme stalls after the first month. If your engineering team has no room, buy implementation. If it does, buy research and hypotheses and keep the build in house where it will be maintained.

What is a realistic result from a CRO programme?

A minority of tests win, some are inconclusive and some lose, and the value accumulates from compounding the winners over quarters rather than from any single dramatic result. Anyone describing consistent large uplifts test after test is either working on a badly broken starting point or is not counting the tests that failed.

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