Hiring too much staff during peak periods can greatly impact your financial performance, while hiring the appropriate amount of staff to complete complex work will improve your operational efficiency. Most managers tend to hire too many people because they think of volume, especially high volume, as high urgency, but many customers utilize low-touch communications, making their volume lower than they anticipated. I once worked with a client who was going through a transition from using a static block-based rostering system to a more flexible one. The company was consistently over-staffing during slow periods and scrambling to staff for rapid spikes in volume. We implemented an artificial intelligence triage layer prior to a human receiving the first inquiry to automate repetitive inquiries before they got to the agent. By filtering on complexity rather than count of individuals, we were able to stabilize the workload. The conclusion was that refining what agents do based on the complexity of their inquiries rather than by just the number of workstations (agents) logged in would help to achieve service standards. Finding the right balance is an ongoing process requiring consistent analysis and adjustments. Part of this is to protect your team from the risk of burnout and the other part is protecting your company's bottom line.
We had a warehouse manager at my fulfillment company who wanted to staff our receiving dock identically Monday through Friday because "it looked more professional." I nearly went along with it until I pulled the data. Thursdays had 340% more inbound shipments than Mondays. We were burning money on idle workers early in the week while getting crushed on peak days. I built our first labor model using historical order volume by hour, not just by day. Turned out our highest pick volume hit between 10am-2pm when West Coast orders from the previous evening combined with early East Coast orders. We were staffing heaviest during first shift because that's what felt normal, but we actually needed bodies during that midday window. The decision I made was controversial at the time. I cut our Monday morning crew by 40% and added a floating mid-shift team that came in at 10am on our three heaviest days. My ops manager thought I was insane because "nobody wants to work 10-6." Wrong. Parents with school-age kids loved that shift. We filled those roles in a week. The outcome surprised me. Our labor cost per order dropped 18% in the first month, but here's what I didn't expect: employee satisfaction actually went up. Turns out people hate standing around with nothing to do as much as they hate being slammed. When your staffing matches your volume, everyone feels productive. Turnover dropped too. The lesson that stuck with me: your gut feel about staffing is almost always wrong because you remember the chaos of being understaffed way more vividly than the waste of being overstaffed. You need real data on volume patterns, and you need to staff in 2-4 hour blocks, not full shifts. I started tracking orders per labor hour as our north star metric. Anything below our target meant we were overstaffed. Anything above meant we were risking service failures. At Fulfill.com now, I tell brands to ask their 3PL what their orders-per-labor-hour metric is and whether they adjust staffing intraday. The best operators treat labor like a dimmer switch, not an on-off toggle.
In our case, demand isn't foot traffic but enquiry and project flow, which can also be uneven. We initially tried to maintain consistent staffing at all times, which led to underutilised time during slower periods. We adjusted by aligning staffing more closely with demand patterns, using a smaller core team and scaling up only when project volume required it. The biggest lesson was to base staffing decisions on actual data, not assumptions. Tracking enquiry volume and project timelines allowed us to stay responsive without carrying unnecessary overhead.
When I started supplying pharmacies, I saw how uneven foot traffic could be, especially around weekends and seasonal spikes. Early on, one store kept full staffing all day "just in case," and labour costs crept up without improving sales. What worked better was matching staff to real demand, not assumptions. We looked at sales patterns and identified short peak windows, then shifted to having more coverage during those times and lighter staffing in quieter periods. One simple change was ensuring a trained team member was always present during peak hours to handle blister advice and product questions properly. Sales improved without increasing hours. My view is you don't need more staff, you need the right staff at the right time. Start with your data, then protect the moments where service actually drives revenue.
At Doggie Park Near Me, our traffic patterns are extremely predictable but wildly uneven. Saturday mornings and weekday evenings after five are packed, while midday Tuesday through Thursday is practically empty. Early on, I made the mistake of staffing the same number of people every shift to be safe. We were hemorrhaging money on labor during slow periods while still being understaffed during peak times. The staffing decision that changed everything was creating a split-shift model with what I call flex staff. We have two full-time employees who cover our core hours, and then three part-time workers who only come in during peak windows. The part-timers are mostly dog lovers, college students and retirees, who are happy with short four-hour shifts on busy evenings and weekends. The biggest lesson was that overstaffing during slow hours doesn't improve service. It just creates idle employees who get bored and disengaged. During our slow midday hours, one person can handle the entire park because the dogs that come in are regulars who know the routine. But during Saturday morning rush, having four staff members on the ground is the difference between a safe, well-managed park and chaos. Match your staffing to your traffic patterns, not to your anxiety about what might happen.
Foot traffic needs to be managed by matching capacity with demand as opposed to simply filling shifts. The most expensive mistake you can make is to staff based on averages, as it causes teams to have too many people when they're slow and not enough people when they're busy. I've transitioned to an overlap model that uses leads. During the period of time when we have the most media attention, I use the best and brightest to work together. This will ensure consistent quality of service without having to carry the overhead of extra employees through quiet hours. I believe two extremely talented employees who work well together will always be better than four average employees. By utilizing your best employees during the highest volume times will create a strong brand reputation.
My equivalent of uneven store traffic is uneven category traffic on WhatAreTheBest.com. Server log analysis shows certain SaaS categories surge on weekday mornings — when buyers are actively researching tools — while others peak mid-week when teams are comparing options together. The staffing decision I made was allocating my own time to match traffic patterns: I prioritize rebuilding and updating the highest-traffic category pages early in the week when users are most active, and batch lower-priority administrative work — backlink vetting, database maintenance — for lower-traffic periods. The lesson: when you're a solo operator, you are the staff, and the most important scheduling decision is matching your highest-value work to your highest-traffic hours. Misaligning those two is how you waste your best inventory. Albert Richer, Founder, WhatAreTheBest.com