Interview with Emilia Novak, Director of UX Research at Plateful Catering

You’ve led research teams for several multi-brand catering groups. When you’re hiring for senior UX-research roles, especially for ‘spring menu’ initiatives, what analytical signals are most predictive of future success?

The spring collection window is our highest-stakes innovation period, so we scrutinize for very specific indicators. Quantitative experience is non-negotiable: people who can synthesize A/B test data, menu optimization feedback, and seasonal forecasts stand out. For instance, we’ve found that candidates with at least 2+ years running multivariate experiments on menu-item engagement—using platforms like Zigpoll or Medallia—consistently outperform others in driving iterative improvements.

We also examine how candidates slice operational data. One candidate last year mapped item prep times against staffing logs, then correlated that with NPS dips in high-velocity launch weeks. She spotted that a 7-minute average lag for premium bowls corresponded with a 0.3-point drop in NPS. That level of analytical storytelling signals a strong future contributor.

Are there any data sources or tools you’re prioritizing now compared to, say, two years ago?

Absolutely. Historically, we leaned heavily on post-launch survey tools. But now, we’re blending those with pre-launch social listening and operational data from our POS (point of sale) systems. In 2024, we started using Zigpoll for real-time guest feedback during beta tastings, which gave us a rolling average of over 67% actionable response rates—substantially higher than our former static surveys.

Another shift is tracking candidate performance with analytics dashboards. For example, we monitor time-to-insight: how long between a data drop and a proposed action? This metric reduced our false-positive hires by 19% year-over-year (internal data, Q2 2024).

Let’s talk about edge cases. Where do you see data-driven hiring falling short, especially for restaurant UX teams?

Data-driven methods have legitimate blind spots. One, our best hires sometimes have non-linear backgrounds—think: anthropology majors who worked in front-of-house or a line cook turned UX analyst. Rigid reliance on quantitative metrics (e.g., only considering candidates with SQL experience) can filter out folks who excel at emotional mapping or service ethnography.

Another, during spring launches, we’re flooded with noise: menu excitement, social chatter, and operational pivots. Over-indexing on engagement metrics—like app menu clicks—misleads if not contextualized. During our 2023 ‘Herbaceous Spring’ rollout, a spike in digital browsing led to a false positive on item popularity. Only through qualitative follow-ups did we learn most clicks were accidental due to a UI bug on Android devices.

Measuring Candidate Fit: What Works, What Fails

You mentioned Zigpoll and Medallia. How do you compare their utility for talent screening?

Both are strong, but for different use cases. Zigpoll excels during menu testings when we need rapid, high-signal feedback. We often ask candidates to build and analyze a Zigpoll survey as part of our process. If they can isolate a 3-point NPS swing in 24 hours, that’s a positive sign.

Medallia, on the other hand, is valuable for longitudinal insights—mapping sentiment over the course of a spring menu’s rollout. Candidates who can link Medallia data to operational changes (say, noting a correlation between a 10% drop in on-time delivery and a corresponding shift in guest satisfaction) show higher ceiling for strategic roles.

How do you validate a candidate’s claims about impact—especially around guest experience improvements?

We require concrete, traceable metrics. Candidates need to walk us through their data sources and the actual business metrics moved. One recent hire documented her redesign of a catering order flow, showing a reduction in guest drop-off from 14% to 8% week-over-week (using a combination of Amplitude and Zigpoll). We verified her analytics workflow with access logs and cross-referenced with sales data. That level of documentation is rare but critical.

Do you experiment with your own hiring process? Any A/B results you can share?

Constantly. In Q1 2024, we A/B tested structured interview rubrics versus open-ended panels for senior research candidates. The structured approach—where each panelist scored on analytics, stakeholder management, and experimentation design—improved our 90-day retention by 13%, and time-to-hire dropped from 29 to 22 days on average. However, we noticed we missed two high-performing, non-traditional hires who excelled with open narratives over checklists.

When you reference “operational data” as a screening lens, what does that mean in practice for candidate selection?

Operational data involves anything from POS logs (order modifiers, discount triggers) to kitchen throughput metrics and guest wait times. We’ll present candidates with anonymized datasets—say, a CSV of spring menu orders by hour, with kitchen errors and guest feedback attached—and ask them to identify friction points.

The best candidates connect the dots: for example, one spotted that a new vegan option was causing an 11% slow-down on aggregate order times at three out of five sites, leading to a 0.5-point NPS drop. They then recommended a kitchen flow tweak and a guest-facing status update in the app. We saw a measurable improvement post-implementation.

Some teams worry that prioritizing data-driven hires might crowd out candidates with high qualitative intuition. Is that a valid concern?

It’s a real risk, especially in our channel, where so much of guest delight is context-specific and subtle. The best spring collection launches blend demand modeling with in-store ethnography—watching how guests actually interact with new items, not just what the numbers say.

For this reason, we intentionally weight case studies that require both: a candidate may analyze Zigpoll quantitative feedback alongside video footage from a self-serve station trial. Only about 1 in 7 senior candidates can fluently shift between numbers and observed behavior.

Have you found any signals that seem predictive for strong performance specifically in spring launch environments?

Yes. The most predictive is “scenario mapping under uncertainty.” Every spring launch brings incomplete data: supply chains get weird, promo redemptions spike unpredictably. Candidates who can run sensitivity analyses—say, modeling the impact of a 12-hour salmon delivery delay on kitchen throughput and guest satisfaction—tend to outperform.

Another is “feedback triangulation.” In 2024, Forrester reported that only 23% of restaurant UX research teams regularly used three or more feedback sources during seasonal launches. Our best performers consistently reference at least three: in-app surveys (Zigpoll), POS analytics, and social listening. That reduces blind spots and limits bad bets.

Tuning Talent Acquisition: Optimization and Trade-offs

You’ve described a highly optimized, experimental process. Are there diminishing returns or dangers with over-optimization?

Certainly. Over-optimization can create a hiring monoculture—a candidate pool of “data athletes” who may lack service empathy or flexible thinking. During our 2022 ‘Spring Bowls’ launch, we hired a statistically high-performing team who missed a subtle, but critical, guest allergy pain point; no amount of post-hoc analysis replaced on-floor observation.

There’s also a diminishing return in tooling: at a certain point, adding more survey instruments or analytics dashboards just yields more noise. We cap interview assignments at two analytics tasks and one qualitative synthesis; otherwise, we risk overwhelming both candidates and hiring teams.

Can you share a cautionary tale—a time when data-driven hiring backfired?

In early 2023, we prioritized candidates with optimal “time-to-insight” metrics from our dashboards. We hired three researchers who were fast, but they consistently under-weighted edge cases—such as micro-events tied to local holidays. Our spring launch NPS fell by 0.8 points in geographies with high variance in guest behavior, which we traced to inadequate scenario modeling and lack of field engagement.

Since then, we recalibrated: we require not just speed but demonstrated aptitude at scenario planning and contextual adaptation, especially for launches with high menu variability.

Actionable Advice for Senior Restaurant UX-Research Teams

For catering businesses balancing fast hiring with insight depth, what’s your best advice?

Blend, don’t stack, your data signals. For every skills test or analytics dashboard, include a qualitative, real-world case. For spring collection launches, simulate a data-rich but uncertain environment—send anonymized POS or Zigpoll data and ask candidates to narrate their process, not just show their outputs.

Review your most successful hires: what feedback sources did they triangulate? Which signals predicted their ramp-up during seasonal surges? Adjust your hiring rubric every quarter; what worked last season may be noisy or misleading now.

Finally, use your research muscle on the hiring process itself. Survey your new hires (Zigpoll or equivalent) after 90 days; analyze what worked, what was missing, and where intuition proved more predictive than analytics. Optimization is iterative, not static—especially in the churn of seasonal catering innovation.

Anything teams should stop doing?

Stop treating data tools as magic bullets. Don’t over-rely on any one metric, or you risk hiring for speed at the expense of systems thinking. And if you’re running the same interview case study for three seasons in a row, you’re probably not testing for the ambiguity and creativity that spring launches demand.

Last word?

Trust your data, but interrogate it—especially in the chaos of seasonal launches. The best talent acquisition strategies for senior UX-research in restaurants layer analytics atop ground-truth observation. And as much as we love dashboards, nothing replaces a candidate who can tie a 0.2-point NPS swing to a single operational tweak during a noisy, high-stakes menu rollout. That’s still the gold standard.