CRO and experimentation for category leader Blinds2Go
Overview
CRO and experimentation for Blinds2Go, an industry-leading online retailer for blinds, curtains, and cushions, aimed at improving its competitive standing through a structured, data-driven testing programme.
The challenge
Blinds2Go, a leading online retailer for blinds, curtains, and cushions, brought us in with three clear goals: increase conversion rate, reduce basket abandonment, and increase average order value. The catch: the client's tech team was mid-replatform and stretched thin, with limited appetite to take on new work.
The approach
Working from a prior Experience Review, we benchmarked existing performance and ran a gap analysis to identify where the biggest opportunities sat. From there, we built a full pipeline of experiment opportunities, from small visual tweaks to large transformational changes, each one following the same rigour: define the problem, form a hypothesis, brainstorm solutions, design the experiment, and define what success would look like before building anything. Just as important as the design process was the stakeholder one: despite the tech team's focus being elsewhere, we got them genuinely engaged and excited about the experimentation roadmap we'd built for them.
The outcome
We handed over a complete, prioritised set of data-driven experiments, fully designed and ready to test, work the client was extremely pleased with. Development constraints tied to the ongoing replatform meant we weren't able to see these through to production, so we don't have live performance data to share here. What we can stand behind is the rigour of the process: every experiment was hypothesis-led, benchmarked, and built to be measurable from day one.
Reflection
The biggest lesson from this project wasn't about the experiments themselves. It was about timing. Good experimentation design means nothing without a development pipeline that can ship it. On a project like this again, I'd push earlier to align the experimentation roadmap with the client's technical capacity, so validated ideas don't stall before they can prove themselves.
Various Experience Review artefacts that helped drive data-driven decisions.
Experiment design suggestion [L] and suggestion documentation [R].
Experimentation designs aimed at targeting 3 key goals.
