Searchlab Conversion Optimization
Getting more traffic is one lever. Converting more of the traffic you already pay for is usually the cheaper one. We run a research first CRO programme: find the friction, write a testable hypothesis, run it to a valid sample, then keep what wins and document what did not.
Research comes before any test idea. On the quantitative side we build funnel reports in GA4, segment by device and channel, find the steps with the steepest drop off, and look at form field abandonment. On the qualitative side we use heatmaps, session recordings, exit intent polls, support ticket themes and short moderated tests with five to eight people from the target audience. Five users find most usability problems, which is why small tests are worth running early.
Findings become hypotheses in a fixed format: because we observed X, we believe changing Y for audience Z will improve metric M. That format forces evidence and makes results interpretable either way. The backlog is scored, commonly with ICE or PIE, weighing potential impact, confidence in the evidence and effort to build. High effort tests need strong evidence behind them, not enthusiasm.
Statistics decide when a test can stop. Before launching we calculate sample size from the current conversion rate, the minimum detectable effect worth acting on and standard thresholds, typically 95 percent confidence with 80 percent power. Small lifts need large samples, so a two percent baseline and a modest target lift can require tens of thousands of visitors per variant. Tests run for full weeks to cover weekday and weekend behaviour, usually two to four weeks, and we do not stop early because an early lead looked good. Peeking at results is the most common way teams fool themselves.
Low traffic sites are handled differently, and honestly. Below a few hundred conversions a month, split testing cannot resolve anything useful in reasonable time. There we fix known usability failures directly, use before and after measurement with seasonality accounted for, and test at the level of whole page changes rather than button colours. On the SEO side, testing is safe when done to Google guidance: keep both versions accessible to crawlers, use canonical tags for split URL tests, prefer temporary 302 redirects, and remove the test once a winner is live.
Everything in Conversion Optimization
Conversion research report
Combined quantitative and qualitative findings with the specific friction points named, evidenced by recordings, funnel data and user feedback.
Funnel and event tracking
GA4 configured with the events that matter, form step tracking, and cross device attribution, so the numbers you optimise against are trustworthy.
Usability audit
A heuristic review of key templates covering clarity, friction, trust signals, form design and mobile behaviour, with issues ranked by severity.
Prioritised experiment backlog
Scored hypotheses with expected impact, evidence strength and build effort, so the sequence of tests is defensible rather than driven by opinion.
Test design and sample size plan
Per experiment: hypothesis, primary and guardrail metrics, minimum detectable effect, required sample and planned duration agreed before launch.
Variant build and QA
Variants built and checked across browsers and devices, with flicker control, correct traffic allocation and tracking verified before traffic is split.
Results readout
Each test reported with the numbers, the confidence level, segment breakdowns and a plain conclusion on whether to ship, iterate or drop it.
Learning repository
A running record of every test, win or loss, so insights compound and the same idea is not retested in a different form next year.
The process
Research
Funnel analysis, heatmaps, session recordings, on site polls and short user tests. We finish with a ranked list of friction points backed by evidence, not opinions.
Hypothesise and prioritise
Turn findings into structured hypotheses, score them on impact, confidence and effort, and agree the running order and the primary metric for each test.
Test
Build and QA variants, calculate the required sample, run for full business cycles without early stopping, and monitor guardrail metrics for side effects.
Ship and learn
Deploy winners permanently, document losers with the reasoning, and feed every result back into the next round of hypotheses and the site design system.
Questions about Conversion Optimization
How much traffic do we need for A/B testing?
As a working rule, meaningful split tests need a few hundred conversions per variant per month. Below that, results stay inconclusive for months. If your volume is lower we still run CRO, just with usability research, direct fixes, sequential before and after measurement and bolder full page changes rather than incremental splits.
How long should a test run?
Until it reaches the pre calculated sample size, and always in whole weeks so weekday and weekend behaviour are both included. Most tests land between two and four weeks. We do not stop the moment a variant looks ahead, because early leads reverse often, and repeatedly checking for significance inflates false positives.
Does A/B testing hurt SEO?
Not when it follows Google guidance. Show the same content to crawlers and users, avoid cloaking, use rel canonical to the original for split URL tests, use temporary 302 redirects rather than 301s, and take the test down once a winner is implemented. Long running tests left in place are the actual risk.
What if most of our tests lose?
That is normal and expected across the industry. A losing test still buys information, because it rules out an assumption and often reveals something in the segment data. The programme is judged over a quarter of tests, not on any single result. What matters is that each test was well designed enough for the answer to mean something.
Ready to talk about Conversion Optimization?
Tell us where you are stuck. We reply within the hour on WhatsApp, usually sooner.
Or email Searchlabtools@gmail.com