Lucky Bike unites search, guided selling, and GenAI to turn undecided shoppers into confident buyers

About Lucky Bike

Lucky Bike is one of Germany’s largest bicycle retailers, with nearly 40 stores nationwide and a strong ecommerce presence. Known for expert in-store advice, quality products, and a customer-first ethos, the company set out to bring the same personal guidance to its online shoppers that they’d experience on the shop floor.

Helping customers find the right bike has always been at the heart of what we do. We wanted our website to give customers the same clarity and confidence they get in-store, when they just know they’ve found the right bike.

- Paul Heinemann, Ecommerce Project Manager at Lucky Bike

From complex selection to confident purchase decision

Buying a bike is rarely an impulse decision. It’s a high-consideration purchase where customers want to compare, touch, and test to be sure they’re making the right choice. In-store, Lucky Bike’s team walk shoppers through those decisions step by step. Online, the experience was different. Shoppers faced hundreds of models, multiple frame types, and technical specs they may have never heard of. Many didn’t know where to start, and without guidance, it was easy for shoppers to feel overwhelmed and abandon the site without buying.

But the challenge wasn’t only about guiding customers. The company also wrestled with a complex product data challenge. Product data came from multiple brands and suppliers, each with different formats and levels of detail. Important details like terrain suitability or e-bike systems were incomplete or inconsistent, making online guidance, search and filtering less effective and requiring heavy manual cleanup.

A guided start: The Bike Finder

Lucky Bike’s partnership with Zoovu began with the Bike Finder, a guided selling assistant built on Zoovu’s platform. In just a few questions, it matches customers to bikes that fit their needs, cutting through complexity in a way that felt intuitive and even fun.

Early results were compelling: strong engagement and completion rates, confirming the value of a guided, interactive process that was continuously refined and optimized through numerous A/B tests.

Adding Zoovu AI Search

Encouraged by the impact, Lucky Bike decided to go further and replaced their old search solution with Zoovu. bringing AI Search, guided selling, merchandising, and AI-driven data enrichment into one unified platform, removing the need for multiple disconnected tools.

The Bike Finder confirmed for us that online guidance works... It felt like fitting puzzle pieces together. With Zoovu, every part of discovery works together; Now, search, advice, merchandising, data works as one seamless customer journey.

- Paul Heinemann, Ecommerce Project Manager at Lucky Bike

What’s changed with Zoovu

Search isn’t just about keywords anymore, it understands intent and returns relevant results, even with vague or incomplete queries. Searching for “bike for city” instantly brings up urban commuter options, styled and ranked according to Lucky Bike’s merchandising strategy.

And it’s not just the search bar. The same ranking algorithm, dynamic filters, and merchandising power every category listing page. Wherever a customer starts browsing, they see fast, relevant results. The speed of page updates creates a smoother experience for customers, while the ecommerce team benefits from consistent, automated delivery across the entire site.

Product listings are no longer static grids; they adapt to highlight relevant features, with filters grouped in a way that feels intuitive. Using Zoovu AI Search as the foundation, the team can design and update PLPs directly in the platform, set merchandising rules with full styling and filter control, and push changes live in minutes instead of weeks.

We now have full backend control over search and configuration. Custom styling, filter grouping, things we couldn’t do before, are now in our hands. The easy integration and ability for our teams to manage configurations directly were key factors in choosing Zoovu.

— Robin Döberl, Engagement Manager, Lucky Bike

Because search experiences constantly evolve, Lucky Bike focuses on continuous optimization. With Zoovu’s platform tools, the team can fine-tune search on its own while working closely with Zoovu experts. Based on real usage data and regular tests, they can see what works, where adjustments make sense, and how to advance the search step by step.

Zoovu also integrates seamlessly with Lucky Bike’s OXID 7 web shop and backend systems, ensuring product availability, click-and-collect options, and store-specific filtering are always accurate without duplicating effort.

And product data? Zoovu’s AI-driven data enrichment and semantic mapping transforms supplier data into clear, customer-friendly attributes, ensuring attributes like “step-through frame” or “off-road capability” are consistent across the entire catalog. This not only improves filtering and search but AI-ready product data also unlocks new possibilities for future-oriented, AI-driven innovation.

Pioneering GenAI in ecommerce: AI Dynamic Questions

Always looking to push the online experience forward, Lucky Bike became one of the first major retailers to launch Dynamic Questions, Zoovu’s GenAI-powered capability that turns static product listings into interactive, contextual conversations.

When a shopper lands on a Search Results Page or Product Listing Page, Zoovu’s AI generates and dynamically serve questions like “Where will you mostly be riding?” or “Do you prefer a step-through frame?”. Instead of making customers hunt through filters and long lists of products, they get an experience that feels more like speaking with a store associate who knows what to ask and when.

We’re now advising customers in real time, right where they are... When customers interact with a question, they’re twice as likely to click through. They no longer get lost in long lists; they feel supported. And when they answer, the results instantly adapt.

— Robin Döberl, Engagement Manager, Lucky Bike

The questions are generated automatically in the Zoovu platform to help customers navigate the product range. The Lucky Bike team can tailor these AI-generated suggestions to its own requirements and decide which questions to display. Control stays with the team, while Zoovu’s AI dynamically manages the order and delivery based on user intent and context.

The outcome: Higher click-through rates—more customers move from search and listing pages to product detail pages:

• +77% higher CTR in search results
• +150% higher CTR on product listing pages

Results

Introducing Zoovu’s AI search and merchandising, product finder, and dynamic questions delivered measurable results across different stages of the customer journey.

In Zoovu-supported journeys, Lucky Bike increased the conversion rate by 47.7% year over year and revenue by 17.5%, thanks to continuous optimizations.

Customers engage more deeply with the offering: CTR is 43% higher (up 7 percentage points versus earlier benchmarks), and product finder see +33% more interaction.

The Bike Finder remains a top performer with an 89% completion rate. Dynamic Questions have proven especially impactful—search CTR rises by 77% as soon as customers interact with the AI-generated questions.

Zoovu is more than a vendor—they’re a partner... We collaborate closely on innovation, and the team actively looks for ways to optimize our results. From fast feedback loops to proactively highlighting new opportunities—they’re invested in our success.

- Sergej Kosyrev, Head of E-commerce | Customer Service

Benefits for stores, too

Brick-and-mortar teams also benefit: sales associates now use Zoovu’s search tools directly in-store to quickly find and present products—including images and specifications. This not only improves the in-store customer experience but also boosts internal efficiency.

Looking ahead

Lucky Bike plans to extend AI experiences to accessories like helmets and gear, embed AI assistants directly into search, and further refine cross-selling strategies. The goal remains the same: to make buying a bike online just as personal, guided, and enjoyable as in the store.