SERVICE · CONVERSION OPTIMIZATION
More results from the same traffic
Often the problem is not that there are too few visitors. The problem is that too few of them contact you, buy or move to the next step. We use data to find bottlenecks in your site, advertising and tracking, then fix them one at a time.
Paid traffic is expensive. Improving the page is cheap.
Paid traffic is expensive. Before increasing the budget, it is worth making sure the current traffic produces as well as possible. If 1.5 out of 100 visitors contact you now and we lift that to three, enquiries double without extra ad spend. This only works when changes are based on data, not opinions.
Opinion doesn't decide. Data decides.
Most site changes are made based on opinions: someone wants a new headline, another color or a shorter form. We do not work that way. We look at the data first, form a hypothesis and test the change with real users. Development is based on learning, not guessing.
- Statistical significance, not guessesWe don't declare a winner before the data is statistically robust. This prevents situations where a "winning" version is actually chance.
- Sites + advertising + tracking togetherConversion optimization works best when the site, advertising and tracking are with the same team. Then we see the whole path: where the visitor came from, what they did on the site and why they did or did not contact you.
- A learning library that grows every monthEvery test is documented and stored: wins and losses both. A year in you have hundreds of concrete lessons about what works for your audience.
Process
The cycle starts with data, ends with a better result
Conversion optimization is not a single trick, but a continuous development cycle. We collect data, identify bottlenecks, test changes and carry the learnings into the next improvement. That way the site improves month by month based on real users.- 01
Audit and data collection
We go over current visitor and conversion numbers, install heatmap tools and gather data for 2–4 weeks. Information drives action, not assumptions.
- 02
Hypotheses and prioritization
We identify problem areas and form testable hypotheses. We prioritize by which are likely to produce the biggest impact.
- 03
Tests live
We design and run A/B tests. One test typically takes 2–4 weeks until a statistically significant result is reached.
- 04
Implementation and the next cycle
Winning versions go live, losers are archived in the learning library. The cycle starts again at the next bottleneck.