Case studies
A high-end drinks ecommerce platform wanted an intelligent wine and spirits search built on AI, a delivery partner it could rely on, and someone to manage the build through to launch. The search was designed to work from taste profile, price range and region rather than keyword matching. Alongside it I ran the vendor evaluation and contracting for the delivery partner, and managed the project through a soft launch before the full one.
A high-end drinks ecommerce platform wanted to improve the experience of finding a bottle and to tidy up its operations at the same time. Two distinct problems sat underneath that.
Product discovery on a drinks site is genuinely hard. A customer rarely knows the name of what they want; they know roughly what they like, roughly what they want to spend, and sometimes a region. Keyword search serves that badly.
The second was delivery. A premium drinks purchase is judged partly on how it arrives, and the platform needed a partner whose reliability and coverage matched the positioning.
Market research. Consumer preferences, industry trends and competitor strategies in the high-end drinks market, alongside customer demographics and purchasing behaviour for wine and spirits.
The AI search. Working with AI specialists on a search facility designed to understand user preferences and offer personalised recommendations, working from taste profile, price range, region and other parameters rather than from matching words in a product name.
User experience. Optimising the interface for product discovery: intuitive filtering, layouts that suit browsing, and product descriptions that inform rather than list. Mobile was prioritised, with responsive design and accelerated mobile pages for load speed.
Content. A strategy to carry the brand's story, educate customers about different wine and spirits varieties and highlight exclusive offerings, with images, video and editorial to support it.
The delivery partner. Candidates identified on reputation, reliability, geographical coverage, delivery speed and cost, then evaluated through background checks, client references and performance assessment. I negotiated terms with the selected partner, covering pricing, delivery options, service level agreements and quality standards, and drafted the contracts setting out roles, responsibilities, timelines and dispute resolution. Their logistics infrastructure was then integrated with the platform and tested against real delivery workflows to find bottlenecks before customers did.
Running the project. A cross-functional team of developers, designers, AI specialists, content creators, logistics experts and marketers, worked in an agile cycle with sprint planning and regular review. Quality assurance ran across multiple devices, browsers and operating systems, and a soft launch to a limited audience gathered feedback through surveys, user interviews and analytics before the full launch.
The platform launched with the AI search, the delivery partner integrated and tested, and a soft launch behind it.
Beyond that I have nothing measured to report. The original version of this study listed enhanced user experience, increased sales, improved brand perception and operational efficiency as outcomes, but the text describing them was written in the future tense, before any of it had happened. They were expectations, and presenting them as results would be dishonest.
Search is the product on a specialist ecommerce site. A customer who cannot describe what they want in the words the catalogue uses will leave, and matching on taste profile, price and region rather than on product names is what closes that gap.
The delivery partner deserves the same scrutiny as the platform. On a premium purchase the courier is part of the product, and evaluating candidates on references and tested performance rather than on price is the difference between a good site and a good experience.
Work of this shape is digital strategy. On AI doing the personalisation work, see how AI can help personalise content on your website.
Because customers rarely know the product name. Filters help someone who already knows they want a Rioja under £40; a search working from taste profile, price range and region helps the much larger group who know what they like but not what it is called.
On evidence rather than price. Candidates were assessed on reputation, reliability, geographical coverage and speed, then checked through references and performance assessment, with the service levels written into the contract and the logistics integration tested against real workflows before launch.
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