Luke Harper — Senior Product Manager

Senior Product Manager with 10+ years across two-sided marketplaces — B2B, B2C, and C2C. I specialise in lead management and seller-to-buyer conversion: building the merchant tools and consumer experiences that drive platform growth and commercial outcomes. I have shipped AI products in production and am currently building in the AI space outside of work. I do my best work on 0 to 1 problems — turning ambiguous ideas into products that perform.

Contact: luke.harper90@outlook.com

LinkedIn: linkedin.com/in/luke-harper

Portfolio: luke-harper.com

Location: Berlin, Germany. Relocating to Barcelona in 2026.

Work authorisation: EU citizen, no sponsorship required.

Languages: English (native), German (B1), Spanish (learning)

Tools: Jira, Confluence, Coda, Notion, Figma, Looker, Google Analytics, Claude Code, Langdock

Sectors: Automotive, Classifieds, Education, Consumer Marketplaces

Notable employers: Volkswagen, mobile.de, IDP Connect

About

My career has spanned both sides of the marketplace — building consumer experiences that drive demand and merchant tools that improve supply quality, conversion, and seller performance.

In 2025 I built a Lead Management System from scratch, taking it from zero to a tightly scoped MVP and continuing to iterate since launch. The LMS unified fragmented seller inboxes, introduced lead status tracking — enabling dealers to mark whether a lead resulted in a sale — and established the platform's first ever Lead-to-Sale benchmark. It onboarded 25,000+ users within six months, tracked 14,000 sales representing an estimated 154 million euros in attributed platform transactions, and was a finalist for the company's most impactful project of the year. Most recently I shipped an AI-powered response feature within the LMS — using the Gemini API to generate personalised, context-aware replies for sellers, directly improving response rates and Lead-to-Sale performance.

Previously I held senior product roles at one of the UK's largest education marketplaces, building products used by hundreds of thousands of students across search, matching, applications, and AI-assisted discovery.

I have worked with brands including Volkswagen, mobile.de, and IDP Connect across automotive, education, and consumer marketplaces.

I am currently open to new opportunities and actively exploring my next role.

Case Study 1: Lead Management System — From Chaos to a Single Inbox

Role: Senior Product Manager, B2B Automotive Marketplace

Tags: New, B2B SaaS, Lead Management, 0 to 1

Pull quote: 154 million euros in estimated attributed sales tracked within six months of launch.

The challenge: Four disconnected inboxes, zero visibility of lead outcomes

What I did: Led 0 to 1 build of a unified Lead Management System with Lead-to-Sale tracking

Result: 25,000+ users onboarded, 154 million euros in attributed sales tracked within six months

Problem: Dealers were receiving buyer enquiries through four separate, disconnected inboxes — each built by a different team, in a different era, with a different design language. There was no unified view, no way to track what happened to a lead after it arrived, and no way for the business to know if its leads were actually resulting in sales. It was a fragmented, seller-hostile experience that nobody had properly named or owned.

Approach: I led the LMS from day zero: making the internal case, running discovery with dealers, scoping the MVP, and driving delivery across engineering, design, and commercial stakeholders. The brief was clear — put dealers first, consolidate the inboxes, and build the infrastructure to track lead outcomes. The most strategically significant decision was introducing Lead Status — a mechanism for dealers to mark whether a lead resulted in a sale. Simple in concept, transformational in practice. It established the business's first ever Lead-to-Sale benchmark, creating a new measurement layer and opening the door to outcome-based conversations with commercial partners. With four legacy inboxes to replace and stakeholders across five teams, scope control was critical. I made fast, decisive prioritisation calls throughout — constantly pressure-testing what was truly MVP and what could wait — managing competing stakeholder opinions to keep the project on track and on time.

Outcomes: 25,000+ dealer users onboarded within the first six months. 14,000 car sales tracked for the first time, establishing the L2S benchmark. Estimated 154 million euros in attributed platform transactions within six months. 5 teams aligned across product, engineering, design, commercial and ops. Finalist for most impactful project of the year, company-wide.

Key insight: With four legacy inboxes and five teams involved, scope could have ballooned fatally. Constant prioritisation discipline — and willingness to say no — was what made on-time delivery possible.

Case Study 2: AI-Generated Lead Responses for Professional Sellers

Role: Senior Product Manager, B2B Automotive Marketplace

Tags: New, Applied AI, GenAI, Gemini, Merchant Tooling

Pull quote: Within 30 days of launch, 20% of all written dealer replies in the LMS were AI-generated.

The challenge: Slow, generic dealer replies were killing lead conversion

What I did: Shipped a Gemini-powered AI response feature inside the LMS

Result: 20% of all dealer replies AI-generated within 30 days, response time down 34%

Problem: Speed of response is the single biggest predictor of a lead converting to a sale. Dealers are time-poor — managing a forecourt, walk-ins, and a lead inbox simultaneously. Many leads went unanswered, or received generic replies that failed to address the buyer's actual query. Both outcomes were damaging the Lead-to-Sale scores the LMS had made visible for the first time.

Approach: Using the Gemini API, we built an AI Response tool directly inside the LMS. When a dealer opens a lead, the feature generates a ready-to-send, personalised written response — informed by the full conversation history, the dealer's own inventory, and the buyer's behavioural signals from the platform. A deliberate design decision: the response is editable before sending, not a one-click auto-send. Dealers are professionals and trust in AI output had to be earned incrementally. The goal was to augment dealer judgement, not replace it. This was an intentionally constrained first release — the right scope for a new AI surface in a high-stakes professional tool.

Outcomes: 25,000 AI-generated messages sent within the first 30 days. 20% of all written replies in the LMS are now AI-generated. 52% acceptance rate — a strong quality signal for the model. 34% average decrease in lead response time.

Key insight: Sellers are professionals, not consumers. Making the AI response editable before sending was essential for trust and adoption. Augment judgement, do not replace it.

Case Study 3: Mobile App Launch — 70,000 Users, Top 5 Education App

Role: Head of Product, Education Marketplace

Tags: Consumer App, 0 to 1, Marketplace

Pull quote: 70,000 new student accounts and a 300% increase in returning users — all within the first year.

The challenge: Users found courses and left — no retention, no registration, poor lead quality

What I did: Led the company's first ever mobile app from 0 to 1, making registration central to the experience

Result: 70,000 new accounts and 300% returning users within the first year

Problem: The platform had a retention problem disguised as an engagement problem. Users would arrive, find a course, and leave — never to return. This was damaging the quality of referrals sent to university partners, because universities paying for leads want engaged, informed applicants — not one-and-done visitors. Without registration, there was also no way to track user behaviour meaningfully or pass valuable insights back to partners.

Approach: I led everything hands-on — from making the internal case for a mobile-first product to owning the vision, strategy, and delivery. This was the company's first ever mobile app, which brought significant organisational challenges alongside the usual product ones. Scope was the biggest risk. Without clear boundaries the project was heading towards a delayed launch — I stepped in, made the hard prioritisation calls, and cut what was not truly MVP. That decisiveness is what kept delivery on track and got the app shipped on time across both iOS and Android. A key strategic decision was making registration central to the experience — giving us the behavioural data to track user journeys and surface meaningful insights for university partners for the first time.

Outcomes: 70,000 new student user accounts created within the first year. 300% increase in returning users within the first year. Top 5 UK Education apps on the iOS App Store. Launched on iOS and Android on time.

Key insight: No user testing before launch meant post-launch fixes came at the cost of roadmap reprioritisation. Test with real users before you ship, even if scrappy.

Case Study 4: University Application System — A Better Way to Apply

Role: Senior Product Manager, Education Marketplace

Tags: B2B and B2C, 0 to 1, New Revenue

Pull quote: 730,000 pounds in net-new revenue in year one — on top of existing revenue streams, not cannibalising them.

The challenge: Students had to phone universities individually during a high-pressure one-day window

What I did: Identified the opportunity, built the case, and led delivery of a multi-application system against a hard deadline

Result: 2,000+ applications and 730,000 pounds in net-new revenue in year one

Problem: University Clearing is the UK's results day — the academic equivalent of Black Friday. For students who did not get into their first choice university, it is a last chance to secure a place before vacancies fill up within hours. Already dealing with the disappointment of missed grades, these students then had to phone each university individually, managing multiple stressful conversations simultaneously under extreme time pressure — all while terrified of missing out entirely. On the other side, universities were spending heavily staffing call centres to handle the surge — a costly, inefficient operation that still left students frustrated and underserved. Everyone was losing, and no digital alternative existed.

Approach: I identified the opportunity, made the case internally, and led the project end-to-end — working closely with a team across design, engineering, and commercial to take it from concept to launch. The commercial model was novel: institutions would pay per application received, creating a fresh revenue stream that did not cannibalise the existing referral business. Getting internal buy-in for this required as much stakeholder management as the product work itself. The hardest constraint was the deadline. Clearing happens on a single day each year — miss it and you wait another twelve months. Working with the team I kept scope tight and focus sharp, shipping a BETA first to run A/B tests and iron out issues before the high-stakes final launch. We hit the date.

Outcomes: 2,000+ university applications submitted in the first year. 730,000 pounds (approximately 870,000 euros) in net-new revenue generated in year one. BETA launch enabled A/B testing before the critical deadline. Delivered on time.

Key insight: The deadline was immovable. Clearing happens once a year. Keeping scope tight and the team focused was the only path to shipping.

Case Study 5: Course Search Chatbot — Conversation Over Configuration

Role: Senior Product Manager, Education Marketplace

Tags: Chatbot, Search Personalisation, Consumer UX

Pull quote: 80% of chatbot users completed a personalised, filtered search — reversing a platform-wide decline in search personalisation.

The challenge: Filter usage was declining despite users wanting personalised results

What I did: Led design and delivery of a conversational search chatbot, iterating post-launch based on direct user feedback

Result: 80% of users completed a personalised filtered search within six months

Problem: A counter-intuitive problem: users said in research they wanted personalised results, and the product had a sophisticated filter system to deliver them — but filter usage was falling. The interface was not the issue. The behaviour was. Filters require users to articulate their own preferences in the product's language — a significant cognitive ask for someone uncertain about what they want.

Approach: I led the project end-to-end, working closely with the user research team and engineering to take it from concept to launch across both the website and mobile app. The insight was simple: filters require users to already know what they want and express it in the product's language. A chatbot inverts that — it asks the right questions in plain language and does the translation automatically. Getting the logic right was the easy part. The harder challenge was tone. After launch we collected feedback directly in the product and ran dedicated user feedback sessions with the research team. Users were clear — the chatbot felt robotic, cold, and off-putting. We iterated fast: the language changed, the tone changed, and even the product name changed based on what users told us. The principle I took from it — that copy and personality are as product-critical as functionality — is one I have applied to every conversational and AI-facing product I have worked on since.

Outcomes: 30% average user session duration increase within 6 months of launch. 80%+ of chatbot users completed a personalised filtered search.

Key insight: Copy and personality are as product-critical as functionality. Users who do not trust the language will not trust the recommendations.