Reducing Wait Times
at Solid State Coffee
I utilized a mixed methods approach — quantitative data to source baselines and qualitative data to inform insights — which led to a full service design undertaking spanning end-to-end research, design, and implementation.
Role
Timeline
Methods
Deliverables
01 · The Challenge
A reputation problem
hiding in plain sight
13:37
Average weekend wait — vs. 5 min Starbucks benchmark
Solid State is a specialty coffee shop and café on the Upper West Side. Founded in 2021, the shop had built a loyal following through exceptional coffee and a full kitchen program. By 2025, staff had noticed customers glancing at the line and walking away — but how often this was happening, and why, remained unknown. No baseline data existed.
The bar manager approached me as a friend and asked if I had any ideas for changes. Before jumping to solutions I wanted to have better clarity on what exactly was happening in the service flow and knew that research would be a necessary first step.
02 · Research Approach
Three simultaneous studies
Quantitative Study
Wait Time Analysis
Ten volunteer secret shoppers collected ~60 data points over one month during peak hours between 9am and noon. Each tracked total wait time, time to the register, and time from ordering to drink delivery — providing the first factual baseline for the cafe.
Qualitative Study
Contextual Inquiry
Approximately 20 sessions of naturalistic observation and 10 interview-plus-immersion sessions working behind the counter alongside baristas. I watched how each workstation fit into the service flow, how equipment positioning shaped behavior, and followed up on observations with staff interviews in real time.
Qualitative Study
Semi-Structured Interviews
Ten semi-structured interviews with participants recruited from around the neighborhood at other coffee shops — not inside Solid State — to avoid sampling bias toward high-affinity customers. Probed for how people experienced and responded to wait times and what drove decisions about where to go for coffee.
In the field
Working behind the counter, not just watching from it
The contextual inquiry involved working alongside baristas during peak hours — not as an outside observer but as someone embedded in the flow. That proximity surfaced details that observation alone would have missed: where staff hesitated, which handoffs created friction, and how the physical layout shaped decisions in real time.
03 · Key Findings
A single overtasked register was
the bottleneck for the entire experience.
Findings, in priority order
01
The register was the primary bottleneck
The register workstation had accumulated 6+ distinct task types simultaneously during peak hours — order taking, payment, menu questions, food delivery, drink service, and pickup management — because it was never redistributed as staff and customer volume expanded. At peak, a single complex order could hold up a queue of 10 to 15 people behind it.
02
Wait times were long enough to pose a reputational risk
Weekday waits averaged 8:13 and weekends 13:37 — well outside Starbucks' reported 5-minute benchmark. A customer who recommends Solid State almost always adds "but be prepared to wait awhile." That caveat, repeated enough times, becomes the shop's identity.
"It's the best coffee on the Upper West Side. I just don't go on weekends anymore."
— Neighborhood Interview Participant
"All ten participants mentioned the line in their interview — with five of the ten saying they would never visit Solid State on weekends because of it. Four of the ten stated it was great coffee, if you can wait for it."
— Neighborhood interview synthesis
03
Two customer types were moving through a flow designed for neither
To-go orders typically cleared the register in under 2 minutes. Dine-in orders with food regularly took 2 to 6 minutes, often involving allergy questions, ingredient specifics, and multi-item specifications. Both moved through the same line and the same register, with no mechanism to distinguish them — meaning a single complex order stalled every quick transaction behind it.
Order time per customer
Flow A
To-go customers
< 2 min
Single drink, fast transaction, in-and-out. Cleared the register quickly when the queue allowed.
Flow B
Dine-in customers
2–6 min
Multi-item food orders, allergy and ingredient questions, menu specifications.
Recommendations · prioritized by effort to impact
Two high-impact interventions emerged directly from the findings
01 · High Impact
Strip the register back to a single function
Redistribute non-ordering tasks to other workstations, simplifying the register's responsibilities to order taking and menu discussion. Create an additional workstation to absorb pickup and delivery, with a second supporting role between the order queue and dine-in service.
02 · High Impact
Create separate entry points
Build a true split between to-go and dine-in customers — either through QR ordering for dine-in only with the register reserved for to-go, or by adding a second physical register to create two parallel entry points. Either path moves complex, multi-item orders out of the main queue entirely.
Service flow — before & after redesign
Current flow — before redesign
This diagram maps every step a customer and staff member move through under the original service model. All interactions — order taking, payment, menu questions, food delivery, and pickup — funnel through a single register workstation. At peak hours, one complex dine-in order can stall the entire queue behind it, causing the 13-minute average wait times captured in the quantitative study.
Revised weekday flow — after redesign
The revised weekday flow separates to-go and dine-in pathways at the point of entry. Tasks previously stacked at the register are redistributed across dedicated workstations, and complex multi-item orders move through a parallel track rather than blocking the main queue. The result is a significantly shorter and more legible flow for both customers and staff.
04 · Impact & Outcomes
From cost-cutting
to evidence-led decisions
Before the research, operational decisions at Solid State were driven primarily by a lean cost-reduction mindset — staffing and inventory kept as tight as possible. The café had outgrown that framework, but the service flow had never been adapted to reflect the increase in staff or customers.
Organizational Action
Findings translated into role shifts before redesign
After the research surfaced several insights about the service design, the team recognized that they didn't have a structured system to receive feedback or catalog changes. They also recognized that certain team members were responsible for tasks that were not best suited for their skills and preferences. So, a re-assignment of responsibilities happened within the organization based on the research insights and my influence through asking pointed questions through the contextual inquiry.
Cultural Shift
From change-resistant to design-curious
With the new infrastructure in place, the impact on the team went beyond physical changes. Staff members who had previously been resistant to change began making design recommendations unprompted, and on multiple occasions asked me to either run additional research on something they had observed or to redesign other aspects of the café.
Ongoing · Measurement
Orders taken per hour
Implementation is ongoing. The primary metric I'm tracking is orders taken per hour during peak hours. Once throughput data is in, I'll pair it with customer satisfaction data to get a complete picture of whether dual entry point system is working as intended.
Research artifacts — service blueprints
05 · Reflection
What I would
do differently
"Stakeholder management isn't adjacent to research work — it is research work."
If I could go back, I would shift my initial strategy to include more quantitative methods to better assess the baseline. Methods like neighborhood-focused surveys and competitive analysis of nearby coffee shops might have provided more relevant benchmarks to compare against. When I started this project I was very focused on practicing qualitative skills; more recently, I've taken a quantitative methods course to better utilize quantitative data in the future.
Further, I realized the hardest part wasn't the research itself — it was navigating a team cautious about change. Learning to roll with resistance, understand what each person's goals were within the organization, and earn trust before pushing for change: all of these are motivational interviewing concepts I thought I had left behind in mental health counseling, but it turns out they are equally relevant in stakeholder management. I now understand that stakeholder management isn't adjacent to research work; it is research work.
I started this project underselling my own contributions, but what I learned is that this kind of work — surfacing what leadership couldn't see, reframing a general "how do we go faster" question into a specific structural redesign — can change teams, systems, and culture in ways that go well beyond the original brief. That has fundamentally changed how I see what research can do.
Contact Information
Want to talk? Good news:there's no co-pay anymore.
thornton.coen@gmail.com