Key takeaways
- Headcount is a proxy. Score the people actually present, by hour, and the hours when they have no other food option.
- Estimate transactions as people present × capture rate × purchases per buyer, then pick the format that demand supports.
- The breakroom trash can, the lobby delivery shelf and an existing machine's empty spirals are better market research than a payroll number.
- Placement can halve a good site. Score the exact spot, not the building.
- Check every score against actual sales at 30, 60 and 90 days, and recalibrate the model with what you learn.
Spend a day on any trade show floor and you’ll hear some version of the rule: a micro market needs 300 employees, with smaller thresholds for coolers and machines. The exact numbers depend on who’s talking. The logic doesn’t, and the logic is the problem. Headcount is a stand-in for demand. When the stand-in is accurate, the rule works. When it isn’t, operators turn down great sites and sign bad ones with equal confidence.
This guide replaces the rule with a model you can fill in on a site visit, shows it working on two real-world site types, and maps the result to a format.
Why headcount fails
Three hundred names on a payroll can mean very different things in a breakroom.
- Presence. A hybrid office with 300 employees may have 90 people on site on a Friday and 200 on a Tuesday. A 120-person plant running full shifts has all 120 there on every scheduled day.
- Hours. Nine-to-five sites squeeze demand into lunch and an afternoon slump. Three-shift sites spread it across 24 hours, including hours when nothing else is open.
- Alternatives. A subsidized cafeteria, a lobby café and four delivery apps all compete for the same lunch. A distribution center four miles from the nearest food has no competition at 2 a.m.
- Dwell and break structure. Thirty-minute fixed breaks with a long walk to the parking lot make an on-site option the only practical one.
Our field results point the same way. At a Southeast distribution center with 900 workers across three shifts, breakroom spend per worker rose 38% in the first six months after fresh food was added. That’s what serving the hours nobody else serves looks like (case study).
The model: from presence to transactions
Start with a demand estimate, then adjust it with a fit score.
Step 1: daily transactions. Daily transactions = people present per day × capture rate × purchases per buyer.
- People present per day comes from badge data, shift rosters or a door count, never from headcount alone.
- Capture rate is the share of present people who buy on a given day. It rises with long shifts, missing alternatives and good placement, and falls with a cafeteria down the hall.
- Purchases per buyer rises on long shifts and at sites where the store is the only food.
Step 2: daily revenue. Multiply transactions by the average basket.
Step 3: fit score. Adjust confidence, flag risks and choose the format (more on this below).
Worked example: headquarters vs. distribution center
Two prospective sites. The assumptions are illustrative, but each one is something you can observe on a visit.
| Site A: regional headquarters | Site B: distribution center | |
|---|---|---|
| Headcount | 320 | 140 |
| People present per day | 140 (hybrid, about 44%) | 120 (three shifts, seven-day operation) |
| Food alternatives | Subsidized cafeteria 7 a.m.–2 p.m., lobby café, delivery | No cafeteria; nothing within 4 miles |
| Hours without a food option | Evenings, with few people present | About 16 of 24 |
| Capture rate | 15% | 60% |
| Purchases per buyer | 1.0 | 1.25 |
| Daily transactions | 21 | 90 |
| Average basket | $5.00 | $6.50 |
| Daily revenue | $105 | $585 |
Site B has less than half the headcount and more than five times the revenue. The 300-employee rule would have chased Site A and walked past Site B.
Neither number is a forecast to bank. It’s how you reason before you have sales data. What matters is the structure: every input can be measured on a site visit, and every one can be checked after launch.
Step 3: the fit score
Revenue isn’t everything. Score each site from 1 to 5 on the factors that change risk and format choice, then weight them.
| Factor | Weight | A 1 looks like | A 5 looks like |
|---|---|---|---|
| Presence (people on site, by hour) | 25% | Sparse, unpredictable attendance | Dense and scheduled, every day |
| Food gap (hours without alternatives) | 20% | Cafeteria and café open all day | No other option most hours |
| Dwell and breaks | 15% | People pass through in seconds | Long shifts with fixed on-site breaks |
| Placement and flow | 15% | Two floors from where people gather | On the path to the break area |
| Access and shrink exposure | 10% | Public and unmonitored | Badge-controlled, staffed nearby |
| Host support | 10% | No power, network or subsidy | Dedicated circuit, network, subsidy budget |
| Seasonality | 5% | Empty for months, like a summer campus | Steady all year |
Site A might score 3, 1, 2, 4, 4, 4 and 4 down that list, which weights to about 2.9. Site B might score 5, 5, 5, 3, 4, 3 and 5, which weights to 4.4. Use the composite to rank your pipeline and flag risks; use the transaction estimate to choose the format and size the assortment. High demand with a weak placement score means negotiate a better spot before you sign. A strong fit with modest demand points to a smaller format, not a rejection.
Turning demand into a format
The useful question isn’t whether a site is big enough. It’s which format the site is big enough for. Our format decision guide covers the trade-offs in depth; here’s the short version.
| Site profile | Likely starting format |
|---|---|
| Modest demand, packaged snacks and drinks, mixed access | Smart spiral vending machine |
| Steady demand for fresh food, badge-controlled, limited space | One or two smart coolers |
| High demand, long dwell, appetite for variety | Micro market: kiosk, coolers and open shelving |
| Very high demand, with room outdoors or in a loading area | Walk-in autonomous micro-store |
| Hot meals or orders that need pickup | Smart lockers |
| No grid power: trailheads, job sites, EV plazas | Weatherized, solar-ready cooler |
Then sanity-check against break-even. Take a single smart cooler on Vending-as-a-Service at $189 a month, with an illustrative 45% contribution margin after product and payment costs and $290 a month of restocking labor. It needs about $1,064 a month in sales, or roughly $35 a day, to cover itself. Site A’s $105 a day clears that comfortably. Site B could support a full micro market.
So the 300-employee rule would have put a micro market in Site A and turned Site B away. The model puts a cooler in A and a micro market in B.
What to measure on a site visit
- Badge or door counts by hour for a typical week (anonymized totals are enough)
- Shift schedules and break times, including weekends
- Cafeteria hours, prices and subsidies; nearest outside food and how long it takes to reach
- Delivery volume: count the bags on the lobby shelf at lunch
- The breakroom trash can: wrappers and cups from outside vendors are unmet demand with a brand name on it
- Existing machines: DEX history if available, and which spirals are empty at the end of the day
- The exact placement: power, network signal, floor space, the path from entrance to break area
- Access control: badge-only or public, monitored or not
- The host’s appetite for subsidies or payroll deduct
- Seasonal patterns: shutdowns, summer breaks, peak seasons
The trash can deserves its line. Ten minutes with a breakroom bin tells you what people buy, where they buy it and roughly what they’re willing to pay, and no survey will be as honest.
Common scoring mistakes
- Averaging away the peaks. A shift change that sends 80 people to the breakroom in 15 minutes needs capacity and fast checkout, not just a healthy daily total.
- Scoring the building instead of the spot. The same cooler on the path to the break area and in a corner two floors up are different businesses.
- Trusting the host’s headcount. It’s usually the payroll number, not the presence number.
- Ignoring shrink exposure. A public lobby with no badge control scores lower on access and needs a locking format. Our shrink taxonomy explains why.
- Never checking the score. A model that isn’t compared with actual results at 30, 60 and 90 days is a guess with a spreadsheet attached.
How 8Kava approaches this
Site Intelligence is the first step of every 8Kava deployment. We model foot traffic, shift patterns, dwell time and competing options to choose the format, the placement and the opening assortment, then compare the estimate with real sales once the device is live. When the numbers say a site isn’t ready, we say so.
See how it works on Site Intelligence, compare formats on hardware, or run your own numbers in the ROI calculator.