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AI use case
Pooldoktor (Linz, Austria) deployed Qualimero's AI product consultant Franz in Jan 2026 — A/B-tested over 6 months (90/10 split, 2,214 buyers), causally measured +18.75% increme…
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Title
Pooldoktor's AI Product Consultant Franz Lifts Revenue per Visitor 18.75% with 33× ROI
Content
Pooldoktor — a 10-person Austrian pool self-build specialist — deployed Qualimero's AI product consultant "Franz" in January 2026, and six months of A/B-tested live operation (90/10 split, 2,214 buyers, 136,183 treatment users) shows +18.75% incremental revenue per visitor vs. the control group, 33× return on investment, and an average webchat response time of 13 seconds around the clock. "Chat in sales is basically nonsense. It only works if you replace it with AI. That is exactly what Qualimero built," said Mario Wunder, Managing Director of Pooldoktor Handels GmbH. (Earlier on a prior failed chatbot: "It could not even read our domain.") Pooldoktor Handels GmbH, founded 2005 in Linz, Austria, runs a premium-positioned family business selling DIY pool self-build: basins in 5 construction types (styrofoam, steel wall, PP, stainless steel, wood), plus covers, liners, water care, saunas, and the own brand Poolripp. Three structural challenges drove the deployment: (1) ~1,200 monthly chats, 50% deep product consultation requiring decades of pool expertise — and went unanswered outside business hours; (2) a +117% seasonal demand peak (March–September) no qualified seasonal hire could scale to; (3) a previous chatbot that "could not even read our domain" and lacked safety-critical guardrails. Decision: December 2025. Go-live: January 2026, after 4.5 hours of client-side implementation effort. From day one, an A/B test with 90/10 control-group split ran, with monthly revenue impact reported against the control. Franz is trained exclusively on Pooldoktor sources (Shopware 6 catalog, build and care manuals) — not generic LLM data. Five design principles: (1) domain exclusivity — out-of-scope inquiries delimited, not answered; (2) liability-oriented guardrails — structural engineering, electrical work, damage analysis, and self-build suitability excluded from AI scope, referred to qualified professionals; (3) response speed — 13s avg, around the clock; (4) POS integration — natively embedded in webchat and WhatsApp, in the Pooldoktor brand; (5) causal impact measurement from day one — A/B test with control group. Languages: 100+. Six months causally measured: +18.75% incremental revenue per visitor (strongest single month +22%); 33× ROI (gross revenue treatment minus control, divided by Qualimero costs); 1,161 conversations/month (webchat + WhatsApp); 13 sec avg response time; 19% chat-to-checkout conversion; topic mix 50% product consultation / 22% application support / 8% sales; satisfaction 7.6/10. Pooldoktor: 10 employees, 13,500 pools since 2005, 38,600 visits/month, AOV €4–5k, markets AT 52% / DE 42% / CH 5%. The case study positions the deployment as complete and ongoing — the team focuses on closing and special cases while Franz handles consultation at expert level. The source's "Transferability" section frames the playbook for advice-intensive e-commerce broadly but does not announce a specific next-phase Pooldoktor roadmap.
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Linz
Company/Organization
Pooldoktor
Continent
Europe
Country
Austria
Category
Specialty Retail
Type
Deployment
Id
5265d988-4da8-40bc-9096-ff7543503b22
Created At
2026-06-28T21:47:30.739697+00:00