Simona Piftor

Bringing back
the thrill of the auction

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Overview

At the end of 2024, Catawiki made a deliberate strategic reset. A prior push into buy-it-now and fixed-price listings hadn't delivered. Those objects sold at a lower rate than auctioned ones in almost every category, and buyers were visiting and bidding less often than before. Research pointed to the reason: when buyers described what they loved about Catawiki, it wasn't the objects. It was bidding itself, the competition, the suspense, the moment of winning. Catawiki recommitted to the auction as its core experience.

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Role & Approach

My role had two dimensions.

Alignment and discovery

• Executive and commercial alignment: ran discovery workshops with the executive team and 15 experts and category leads from the commercial department, combining their input with voice-of-the-customer research into a single strategic framework the whole program worked from.
• Cross-functional buy-in: got leadership and commercial stakeholders to a shared understanding of what "the thrill of the auction" meant in practice, which made every later conversation about priorities and trade-offs across teams noticeably smoother.

Creative direction and execution

• Mobile-first strategy: directed a north star vision focused on mobile, since 70-75% of bidding happened there, while shaping tailored additions for web and the app, like live auction updates.
• Hands-on co-creation: ran design jams with PMs and designers to slice that vision into testable, iterable releases. I wasn't just facilitating, I proposed ideas and stepped into the design work myself when it was needed.
• Rigorous validation: combined unmoderated Maze testing with live A/B experiments on high-friction surfaces like the timer and bidding panel, since not every idea that looked good on paper held up with real bidders.

Success was measured primarily by winning and bidding rate, with bids per user and return rate as the next layer of signal.

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Key Decisions

Buyer priorities were explicit: better auction discovery, more desirable objects, the thrill of the auction itself, and reasons to come back. Mobile was the bar every decision had to clear, since 70-75% of users bid there, even when a design that worked on desktop didn't hold up on mobile, or the reverse.

The bidding section of the page, price, timer, bid history, button, was where things worked against themselves the most. The timer looked the same whether an auction closed in five days or five minutes. Pricing was scattered. Bidders in the bid history read like placeholder data.

Here's how each of those got solved.

1. Making the timer reflect the auction.

The timer read "Closes in 4h 39m 43s," styled identically no matter how much time was left. It gave buyers no reason to feel urgency, and no reason to come back once they'd moved on.

The fix made the timer's appearance track the auction's actual state. It grew larger and more insistent as the close approached. Colour and animation shifted in the final hour. Outbid signals made a lost lead as visible as the time running out. The same escalation logic carried through to a lock screen live activity, so the countdown stayed visible even after someone closed the app, most critically in the final four hours before close.

Result: the timer became one of the clearest urgency signals in the redesigned experience.

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2. Trusting the data over the instinct.

Bigger, more immersive photos seemed like an obvious confidence builder. We redesigned the gallery: larger images, thumb-swipe, a progress bar. On desktop it worked as expected. On mobile it didn't. The number of bidders and bids per user both dropped, across iOS and Android, even though people engaged with the new gallery more and scrolled further into it than before.

The instinct wasn't wrong, exactly. It just wasn't converting into more bidding, and we couldn't fully explain why gallery engagement rose while bidding fell. Rather than over-invest in diagnosing the gap, we deprioritized the redesign and flagged it for a later return.

Result: first version reverted on mobile. A clear example of testing catching what instinct alone wouldn't have.

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3. Toning down social proof to make it credible.

Bid activity, how many people were bidding, how many were watching, needed to move from lower on the page, where it read as background information, up next to the buttons, where a buyer was actually deciding whether to act. But moving it wasn't what made it work.

The first version pushed hard: bold red copy on a coloured background, constant and insistent. It read as aggressive rather than exciting, and bidding dropped. The fix scaled it back to a plain count of active bids and followers, with the full bid history expanding only when someone tapped to place a bid. Less visual pressure, not more, is what made buyers trust the signal.

Result: GMV won per participant on web rose over 6%. The louder version had the opposite of its intended effect; the quieter one delivered it.

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4. Holding the quality bar on AI-generated content.

Two new pieces of the page ran on AI-generated content: a one-sentence object summary, and a generated expert bio meant to reinforce trust in the object's selection.

The summary worked from the start. Bidders rose over 1.6%, and a later iteration on the model and prompts pushed it further still.

The expert bio didn't. Digging into why revealed the generated content itself was subpar. It went through prompt improvements and changes to the underlying content logic, then relaunched successfully.

Result: one AI feature succeeded immediately, the other needed a diagnose-and-fix cycle before it did. Both shipped working.

A larger set of changes shipped alongside these four: trust signals like free shipping and no-reserve-price labels, a redesigned seller block, human seller names replacing anonymized ones, quick bid buttons, outbid notifications and CRM improvements, and a "Stay in the Game" section on Home.

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Outcome & Impact

What shipped

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Reflection

The discovery workshops proved something at a scale I hadn't seen before: shared language is foundational work. Getting agreement on what "the thrill of the auction" actually meant made every later conversation about roadmap and trade-offs smoother, well beyond product and design.

Designing with AI-generated content taught me something more specific. You can't fully judge it before it ships. What a model produces at scale, across thousands of different objects, isn't fully predictable in advance. Testing, catching quality issues early, and being willing to revise after launch has to be part of the process from the start, not a sign that something went wrong.

If I did this again, I'd isolate one variable per test. The gallery redesign bundled a larger gallery, a smaller title, and new button placement into a single test. That made it harder, and slower, to tell which change was actually hurting bidding on mobile. A narrower test first, then added complexity once I knew what was doing the work, would have gotten a clearer answer faster.

Frontline AI

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Overview

By early 2025, Catawiki had a visible customer satisfaction problem. Buyers and sellers were publicly expressing frustration on Trustpilot: slow responses, unresolved cases, feeling abandoned by a platform they'd trusted. Support hadn't scaled with the business, and retention was suffering for it. The solution Catawiki needed wasn't just more agents; it was a fundamentally different approach to first-contact support.

The answer was Catawiki's first production AI experience: an AI assistant as the first point of contact for all support queries, for both buyers and sellers. It shipped in two phases, each expanding what it could actually do.

The project was one of Catawiki's highest priorities for 2025 and remains ongoing.

I led this project as Head of Design, coordinating a team of three designers across content, interaction, and design systems, and representing design at the leadership table alongside CS, product, engineering, data science, policy, and trust and safety.

Role & Approach

My role had two dimensions: keeping the customer at the centre of a room full of competing priorities, and helping define what design even means when nobody, including me, had done this kind of work before.

Keeping the user at the centre

• Cross-functional representation: sat at a leadership table with CS, product, engineering, data science, policy, and trust and safety, on a project where engineering and data science carried most of the technical weight. Everyone came in with a different priority; my job was making sure the customer stayed at the centre of decisions that could easily have been made on purely technical or operational logic.
• User advocacy: pushed back when a technically convenient answer would have made the experience worse, not just when it clearly broke something.
• Coordinating the design team: led three designers across content, interaction, and design systems through both build phases.

Designing in unfamiliar territory

• North star vision: helped the team define what great AI-powered support could look like, then stayed on as a thought partner on key decisions as phase one and phase two took shape.
• Reframing the design problem: introduced the wave pool model to reset how the team thought about the work, not fixed flows, but unpredictable, nonlinear conversations where every user takes a different path. Design's job became setting boundaries and managing expectations, not defining a single happy flow.
• Keeping a way out: made sure users could always reach a human agent, a constraint that was easy to lose sight of the more the assistant took on.

Alex Klein, founder of Team Human, described this shift well: traditional design is like building a waterslide, minimising friction and keeping everyone on one fixed path, the happy flow. AI-driven experiences are more like a wave pool, no clear path, and every user finds their own way through.

Success was measured primarily by contact deflection and human-handled contact volume, with customer satisfaction as the check that automation wasn't coming at the user's expense.

Key Decisions

This was a technology-led project more than a design-led one. What the model could reliably do, and what engineering could safely connect it to, set the boundaries most decisions had to work within. Design's job was to make sure the user didn't feel those boundaries as friction, and to push back when a technically convenient answer would have made the experience worse.

1. Standardizing responses to control quality, not just consistency.

When a model update started adding headers to responses and broke the tone of voice completely, having standardized response templates meant we could catch and correct it quickly rather than letting the design quietly unravel. The real value of standardization wasn't uniformity for its own sake: it was reducing our dependence on the behaviour of the underlying model.

2. Building QA as an ongoing practice, not a launch gate.

With a fixed product, you test the happy flow and ship. With AI, the model's behaviour shifts with updates, and conversations go in directions you never anticipated. Part of that meant building an automated evaluation harness, benchmarked against a golden dataset, to catch drift before users did. The decision to build prompt refinement and sentiment monitoring into the team's regular rhythm, permanently, was a cultural shift as much as a process one.

3. Rolling out gradually to preserve the ability to course-correct.

A full launch would have scaled problems before we could identify them. Incremental exposure gave us room to observe, learn, and adjust at each stage before going further.

Outcome & Impact

Reflection

This project changed how I think about what design leadership means in an AI context. On most projects, the design team's job is to define the experience and then hand it over. Here, shipping was the beginning of the work, not the end.

What I'm most proud of is establishing a way of working that the organisation could carry forward. The structured experimentation, the prompt refinement cycles, the content standardisation approach: these weren't just solutions to this project's problems, they became patterns for how Catawiki thinks about AI product work more broadly.

The thing I'd do differently is push for design to be involved earlier in the technical scoping. There were moments where architectural decisions had already been made that constrained the experience in ways that were hard to unpick later.

Object-Based Submission

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Overview

Catawiki's original submission flow asked sellers to choose an auction before describing their object. Which auction is right for a diamond engagement ring? Exclusive Certified Jewellery. Fine Certified Jewellery. Gold Jewellery. Antique & Vintage Jewellery. All plausible, and a new seller had no way to know which one mattered. Most sellers guessed. Neither did the flow, really; it just asked them to pick. When experts corrected the auction choice afterwards, all the structured data the seller had submitted was lost, tied to the auction rather than the object, and had to be reworked by hand. The questions sellers faced were generic, not specific to what they were actually selling. Data quality suffered on both sides of the marketplace.

The new flow flipped the model entirely. Start with the object, not the auction. "What can we help you sell?" became the opening question, followed by cascading, object-specific questions that built structured data tied to the object itself.

I joined Catawiki in May 2021 as Lead Product Designer, responsible for the seller experience across the board. OBS quickly became the centre of that work, growing from a hands-on design effort into leading three multidisciplinary teams through to full launch in 2023. A year and a half in, I stepped into management, becoming Group Design Manager, and stayed at the helm through the incremental migration that followed: piece by piece, always testing, always releasing. It's the most structurally significant project I've worked on at Catawiki.

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Role & Approach

My role changed as the project did. I joined Catawiki in May 2021 as Lead Product Designer, responsible for the seller experience across the board. OBS, the infrastructure overhaul already underway, became the centre of that work: one of the largest projects Catawiki had undertaken in a decade, with no design vision yet for what sellers would actually experience. I worked as the main designer on the seller experience, collaborating with another designer who focused on the expert side of the flow.

Defining the vision

• Running design sprints: ran multiple design sprints with product designers, our UX writer, and the design systems designer to define what sellers would actually experience.
• Designing hands-on: started out designing the seller experience hands-on, then saw the team grow to three product designers, working alongside our UX writer and design systems designer as scope increased. The build was desktop-first, since that's where sellers overwhelmingly worked, then extended to mobile, with a transition designed for the moment someone moved between the two mid-flow.
• Validating with real sellers and experts: tested the vision through multiple rounds of moderated user testing with real sellers, alongside the experts who would later review their submissions and organise the auctions, before parts of the flow were rolled out and A/B tested.

In November 2022, I stepped into management, becoming Group Design Manager and continuing to lead the initiative while coordinating three multidisciplinary product teams within the Supply vertical. I directly managed three product designers and coordinated a design systems designer and a UX writer.

Leading the migration

• Running design jams and workshops: ran weekly alignment meetings, design critiques, and design jams across teams to keep design language, tone of voice, and UX consistent as the work scaled.
• Releasing continuously: shipped piece by piece rather than in one block, always user testing, always A/B testing, always incorporating feedback from sellers and from the experts reviewing their submissions.
• Running the incremental migration: worked alongside a Group Product Manager and Engineering Manager as product and engineering counterparts to run the migration in stages, often maintaining two or even three submission flows in parallel as sellers moved across.
• Aligning stakeholders: kept account managers, customer support, engineers, experts, and Catawiki's executive team aligned on what was changing and why, given how far the infrastructure shift reached across the business.

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Key Decisions

1. One UI for two very different users.

Pro sellers were a tiny fraction of the seller base but drove nearly half of all submissions. Rather than building separate experiences, I designed a single submission UI that adapted to returning sellers, saving them real time and effort:

• Resuming drafts: the flow opened with any drafts still in progress, surfaced in a lane above the search field. Once that shipped, draft usage went up, and more drafts made it all the way to submission.
• One-click object selection: below the search box, we surfaced the object types a seller typically listed, so most of the time a single tap replaced typing.
• Duplicating past submissions: once an object was selected, we showed other objects the seller had listed or sold before, ready to duplicate. For sellers listing near-identical items, this alone saved a lot of repeat work.

The same journey served beginners and experts, just at different points along the same path.

2. The first release: from auction to object.

The first major release changed how object selection worked:
from "which auction would you like to sell in?" to "what can we help you sell?"

3. Adding CES alongside the business metrics.

Submission rate and sell rate stayed the primary business metrics, but they tended to stay flat during transitions, giving us little signal on how the experience itself was landing. I introduced Customer Effort Score to fill that gap, running a survey widget inside each variant of every A/B test. After roughly 1,500 responses per variant, results were segmented by new versus existing sellers and pro versus private sellers, giving us clear visibility into who was being affected, and how, alongside the business numbers.

4. Solving for the most vocal sellers first.

A couple of years before OBS, the submission flow had already been redesigned once, with private sellers in mind: more steps, more structure. When it launched, around 800 of Catawiki's heaviest sellers hated it enough to threaten leaving the platform. Catawiki let them stay on the original flow, the "old-old" one, while everyone else moved to the redesigned version. By the time OBS started, there were two live flows to migrate, not one, and these were exactly the sellers who'd want reassurance first.

While backend engineers were occupied elsewhere, I noticed some frontend capacity sitting free. Overseeing the whole Supply vertical made it easier to spot that kind of gap, so I used it to get moving on the old-old flow. We tested it against the new interface in isolation, UI changes only, no infrastructure yet, to keep the variable clean. The results held steady on the business side, and CES went up for this particularly picky, but very vocal, group of sellers. That gave us the confidence to move on to the second flow, the private-seller redesign, this time testing UI and infrastructure changes together.

5. A cautious, phased rollout, and the title generation fix.

Once the new submission UI and the new object-tied details were already in place, we hit a big bump in the road: title generation broke down. A lamp in a Lighting auction was simply called "lamp." We paused, rolled back, and dug in, auditing title templates for the most affected object types and rewriting question sets, before re-releasing. A taxonomist who'd joined the company a few months earlier turned out to be instrumental here, her work on the underlying structure was what cleared the path to a full release.

All metrics recovered, and the Customer Effort Score for pro sellers climbed to around 70% once the fix landed.

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Outcome & Impact

What shipped

• Full rollout and growth: OBS reached 100% of sellers, with the original category-based flow now fully deprecated. Weekly submissions grew from roughly 80,000 objects at rollout to 100,000, then 130,000, as the new flow made experts' work considerably faster, curating objects, organising auctions, and later organising collections.
• Stable core metrics: submission rate, approval rate, and sell rate all held on par with the old flow throughout the rollout.
• Gains for new sellers: CES, submission rate, and approval rate all improved for sellers submitting for the first time.
• Pro seller CES: climbed to around 70%.
• Better data, better buyer experience: richer, object-tied data translated directly into a better experience for buyers too, sharper search, more accurate filters, and stronger recommendations, built on roughly 3,500 object types instead of a fixed set of categories.

Leadership impact

• Bringing experts into user testing: personally drove the inclusion of Catawiki's internal experts in the testing process, strengthening the working relationship between tech and commercial and contributing to the formation of a new Commercial Operations team.
• A cross-functional template: established a way of working that other teams in the Supply vertical could reuse for projects affecting expert workflows.
• Foundations for what came next: laid the groundwork for price prediction, AI-assisted submission with image recognition, and a seamless desktop-to-mobile handover.

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Reflection

What I'm most proud of is the scale of what this flow serves: around 200,000 sellers, across vastly different levels of tech-savviness, and an enormous diversity of object types. Submitting a car, a Rolex, a limited-edition Lego set, a designer armchair, the experience is the same. What changes is the questions asked and the photos requested, not the UI itself. Getting that balance right, across three years and a complete infrastructure overhaul, is the thing I'd point to first.

Two things I'd do differently: involve internal experts much earlier, and push for working prototypes sooner across the board. Experts struggled to engage with static mockups, but the moment we put working prototypes in front of them the feedback was immediate and rich.

UX Competencies Framework

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Overview

When I joined Catawiki's design team, the competency framework wasn't working. It didn't reflect how the team had matured, wasn't useful for hiring or promotions, and created constant misalignment between managers and designers. People didn't know what good looked like at each level, and growth conversations felt arbitrary as a result.

The problems this caused were concrete: performance ambiguity, career frustration, and inefficient hiring. I led the complete redesign of the framework in early 2025, creating a unified structure covering product designers, design systems designers, and content designers, built around five core competencies and five proficiency levels.

I owned this end-to-end, from gap analysis and framework design through to stakeholder alignment and team rollout.

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Role & Approach

I worked with HR partners, hiring managers, and senior individual contributors across the design team to make sure the framework worked for everyone it would affect. My process started with understanding where things weren't working: conversations with designers and managers surfaced where the existing framework was causing ambiguity or frustration. I then benchmarked against industry standards and Catawiki's engineering competency framework, which gave me both a quality bar and a structural reference point for consistency across the company.

From there I defined a core set of competencies with clear proficiency levels, created discipline-specific descriptions where the expectations for designers and writers genuinely diverged, and aligned the whole thing with HR's company-wide competency structure. I validated with individual contributors and managers before finalizing, then rolled it out with team presentations.

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Key Decisions

1. Proficiency levels, not checklists.

The shift was from "what you do" to "how well you do it." Each level describes a degree of mastery, not a checklist, giving people a clear target and growth conversations something concrete to point to.

2. Simplicity over comprehensiveness.

Each competency could have had ten subcategories, but that would have killed the usefulness. I landed on five: Design Vision, Business Acumen, Craft Execution, Stakeholder Management, and Leadership, clarity over covering every edge case. Some of these aren't unique to design either: Business Acumen is also expected of product managers and engineers, just expressed differently. Same competency, same structure, different content depending on the discipline.

3. Shared structure, tailored details.

Writers and designers needed different content, not different structures. Same five competencies, same proficiency model for both, respecting that craft execution looks different for a writer than a designer.

4. Special treatment for design systems designers.

Rather than a separate track, I added targeted notes under each competency for how expectations shift for that role, simple, without ignoring their different scope. Two examples:

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Outcome & Impact

What shipped

• A complete competency framework with five competencies and five proficiency levels.
• Separate but aligned versions for product designers and content designers, with targeted notes for design systems designers.
• Clear proficiency mapping for each role level, from associate through to senior principal.
• Integrated into hiring, with each interview round mapped to one or more competencies, and into performance reviews.

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Leadership impact

• Created a shared language for growth and expectations across the UX organisation, replacing arbitrary conversations with a clear, consistent structure.
• Managers have concrete language for coaching; designers have a real roadmap for progression.
• The framework is actively used for hiring, performance reviews, and promotion decisions.

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Reflection

The hardest part of this project was resisting the urge to make it comprehensive. Every time I added a nuance, I was making it less useful. The discipline of simplicity, choosing clarity over coverage, is something I'd apply more consciously from the start on any future framework work.