Wie Blackroll 773.000 €/Monat aus komplexem Sortiment gewann.

Blackroll is a German recovery and wellness brand selling foam rollers, fascia tools, massage guns, pillows, blankets, and sleep products to customers across more than 60 countries.

As the brand scaled, its audience changed. The original athlete-heavy recovery audience was now joined by a large sleep audience looking for pain relief, better rest, and safer purchase decisions around premium products.

That expansion created a strategic conversion problem: the same store had to serve active recovery buyers and passive recovery buyers without flattening both into one generic experience.

Blackroll had tried A/B testing before, but the program was not producing enough insight. Tests treated the audience as one segment, even though sleep and fascia products triggered different motivations, objections, and decision paths.

The product catalogue was broad and complementary, but also cognitively demanding. Customers had to understand firmness, use case, sleep position, product compatibility, bundles, returns, and premium value before buying.

The challenge was amplified by mobile. Roughly 80% of traffic came from mobile devices, so small friction points in sticky bars, product cards, buy boxes, and PDP explanation modules had a direct revenue cost.

We started with predictive consumer research: review mining, survey analysis, Research Hub reports, heatmaps, session recordings, analytics, and product-level behavior analysis. The goal was to understand why people buy Blackroll, not just where they click.

Research split the roadmap into two core customer systems: sleep/passive recovery and fascia/active recovery. Sleep shoppers needed comfort, security, fit certainty, and risk reversal. Fascia shoppers needed product education, use-case clarity, and confidence that the tool would solve the right pain.

From there, we built persona-specific testing roadmaps, launched experiments across PDP, PLP, homepage, cart, checkout, and navigation, and kept reprioritizing based on revenue exposure, implementation cost, evidence strength, and live test learnings.