My Experience

IA & UX for eCommerce

Creating frictionless discovery experiences

Across my career I’ve had extensive experience researching and designing ecommerce experiences.

Within my earlier days in Advertising, I worked on a number of ecommerce experiences and designs, spanning Mizuno, Graco, Levelor, Sharpie, Chiquita, Huawei, Gatorade, Pepsi and more.

As I shifted into Product, I found myself focused more on software and app experiences - ranging from financial management, tech support, to even social media. The interesting thing is that a majority of the ecommerce patterns and funnels stuck with me in highly relevant ways.

For example, at Block.one, while leading experiences across Blockchain technology, the checkout and conversion paradigms I studied became even more important. With KYC and rigorous identity workflows, keeping the user engaged and motivated while jumping through arduous hurdles was critical.

Progressive Disclosure

Another facet of eCommerce UX patterns that has continued to resonate across my Product experience has been that of Progressive Disclosure. This is the concept of revealing more and more details and information as the user needs and requests it so as not to overwhelm them.

Just like most software, users typically have a goal or outcome in mind which is usually what business-driven, strategic designs are oriented around. Through user testing and validation, how users convert on those goals is ideally optimized. It’s always been something I’ve measured across my career - whether its conversion focused on checkout or any other observed success metric like Average Handle Time in Customer Support environments.

Here's a recent example of how I led a 400% increase in user conversion at Ozmo by experimenting with search and discovery patterns:

💼 Case:

At Ozmo, I led design across a family of products in the Tech Support space, one of which enabled Telecom and Broadband customers to self select their devices for troubleshooting support.

⚠️ The problem:

Self Support users had to leverage and manuever a static dismabiguation workflow, focused on finding their OS and device, before they could begin searching for content or answers that might solve their issue. From a journey perspective, users generally approach problem solving with their issue as their starting point.

🛠️ Process:

Across years of experimentation and validation, my team and I iterated and evolved the Self Support experience.

Various patterns were explored and refined to hone discovery and conversion - through collaborative partnerships with engineering and customer success, I measured several success factors including how many answers were delivered and how helpful they were.

✅ Results:

Data driven patterns and featured solutions led to ptimized conversion funnels - helping users find the information they need more efficiently. Additionally, introducing relational patterns ensured users' discovery journeys never hit dead ends and supported organic search poathways.

Ultimately, I led the introduction of a conversational search experience that, for the first time at Ozmo, flipped the user journey to something more natural for what we heard users express - they wanted to start with their problem. The intelligent conversational experience helped them disambiguate from problem to potential answers, leading to an increase of 400% of answers delivered.

View a detailed look at the Conversational Support Assistant work ⇨

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