Planning shifts around footfall: notes from a fashion chain

Shift planning around footfall means placing staff hours according to how visitors are spread across the day. In many stores the shift plan still rests on the same week last year or the store manager’s intuition. Hourly visitor data makes that plan measurable. In this post we share notes from the retail team of a fashion chain that rebuilt its shift plan around hourly entry data, and how you can apply the same method in your own store.

The problem: a plan built on intuition

With a plan built on intuition, some hours see a busy store and too few staff, while in others the team stands idle, and there is no data to show it. Sales reports show the people who reach the till; they do not show the visitors who came in, found no one to help and left without buying.

The starting point: the hourly entry curve

The first step was to chart the average visitor curve for each store, for every day and hour of the week. The curve revealed two clear peaks: around midday and after 6 p.m. To chart the same curve for your own store: entries are counted anonymously by the Sayiyo PEOPLE people counter above the door; export a few weeks of hourly data from the Sayiyo visitor analytics dashboard as Excel/CSV and average it by day and hour.

The method: visitors per staff member per hour

A simple way to link the curve to the staff plan is to look at this ratio for every hour: average entries in that hour ÷ number of staff on the floor in that hour. It roughly shows how many visitors one employee faces in that hour. The aim is not to make the ratio flat all day, but to stop it spiking at peak hours and dropping needlessly in quiet ones. An illustrative example: with an average of 120 entries and 3 staff between 6 and 7 p.m., the ratio is 40; with 30 entries and 3 staff between 10 and 11 a.m., it is 10. The gap suggests that one of the morning staff could move to the evening.

• Build a day × hour table of average entries for each store.
• Take the number of staff on the floor per hour from the current shift plan.
• Divide one by the other to get visitors per staff member per hour.
• Mark the hours where the ratio is highest and lowest.
• Draft scenarios that redistribute hours without increasing total staff hours.

The change

• One person was cut from the morning opening and shifted to the 5:30–8:30 p.m. window.
• A flexible half-shift was added for weekend checkout queues.
• Store managers began tracking the daily curve from Sayiyo Mobile.

The team made the decisions itself; Sayiyo offers no staffing suggestions or forecasts, it provides the data for planning. In Sayiyo Mobile, store managers see today’s entries, store ranking and a weekly summary, and can receive rule-based alerts when hourly entries cross a threshold they set.

Follow-up: did the change work?

Results varied from store to store, but the team set out to even out the number of visitors per staff member during peak hours without adding any staff hours. A practical way to follow such a change is to set up a weekly scheduled report in the dashboard, receive it by email and compare the peak-hour curve with the period before the change. If you have sales integration, look at conversion rate for the same hours too: hours with high entries but low conversion often signal that there aren’t enough staff to help.

Common challenges

Teams that build shifts around data tend to run into three issues. The first is data completeness: in a store with a side entrance, the curve stays incomplete unless that door is counted too. The second is special days: campaign weeks pull the average up and should be assessed separately. The third is habit: store managers come to trust the data after comparing the curve with their own observations for a while. Looking at store ranking and peak hours together in the dashboard during a short weekly review makes this easier.

Checklist for applying this in your store

• Are all visitor entrances being counted?
• Have you collected at least a few weeks of hourly data?
• Have holidays, campaigns and special days been separated from the average?
• Have you calculated visitors per staff member per hour?
• Is a scheduled report set up to compare before and after the change?
• Can store managers see the data every day?

A plan should not be set once and left alone. When the season changes, a new store opens nearby or opening hours change, the entry curve shifts too. Looking at the last four weeks’ curve once a month and updating the shift template keeps the plan in line with the data. We cover the peak-hour rhythm of mall stores in how to manage peak hours in a mall store.

Frequently asked questions

Does Sayiyo build the shift plan automatically?

No. Sayiyo offers no forecasts or staffing suggestions; it provides hourly entries, peak hours and comparison data, and your team builds the plan from that data. If you want to send the data to your own workforce or HR system via API or webhook, see the integration options.

How much data is enough?

Deciding on the basis of a single week can mislead. Averaging days and hours over several weeks and looking at special days separately gives a sounder basis.

How often is the data updated?

Entries reach the dashboard about every 10 minutes; store managers can follow the day’s curve both in the dashboard and in the mobile app.