Rebike is Germany's leading platform for refurbished e-bikes. Since 2018, the startup has been buying used e-bikes and bikes – primarily from leasing companies – professionally refurbishing them, and selling them as certified refurbished bikes. With three sales channels (Rebike.com, marketplace presence, own Rebike stores and store-in-store concepts with Decathlon), Rebike reaches customers across 10 European countries.

But this very success brought a central challenge: How do you help customers find the right bike among 3,000–5,000 available e-bikes?

Roman Burdick, CMO at Rebike, describes the problem:

"We have many customers who come to our site through very unspecific search terms. They use keywords like 'e-bike', 'refurbished bike' or 'e-bike refurbished' and ultimately then have 3,000 to 5,000 bikes in front of them."

The challenges in detail:

• Choice overload: Customers arriving with generic keywords face a massive inventory

• Technical barriers: Classic filters like "motor power," "battery capacity," or "torque" require expert knowledge

• Fragmented customer journey: Customers research externally and may not return

• Different knowledge levels: While some customers know exactly what they're looking for, others are completely new to e-bikes

The classic on site search and navigation with technical filters only worked for knowledgeable customers. For everyone else, the risk was high that they would leave the site frustrated and land with the competition.