How To–Optimise Staff Profile

How To: Optimise Staff Profile

Ensuring that you have the right staff working at the right time will not only tighten up your staff costs, but it will also ensure that you maximise customer satisfaction.

Through understanding how your categories are performing throughout each trading day you will be able to plan your staff levels and capabilities for effective and efficient customer service. Floodlight makes it easy for you to achieve this through leveraging our Floodlight Focus advanced reporting suite.

This guide is designed to help you to download and prepare the report and to help you clearly understand your business operations.

Download and Prepare Report

  1. Download the ‘Optimise Staff Profile’ report from the ‘Reduce Staff Costs’ section of Floodlight Focus.
  • Navigate to ‘Floodlight Focus’ on the left sidebar
  • Scroll down to ‘Optimise Staff Profile’
  • Select required Start Date and End Date
  • Click ‘Download CSV’

2. Open the CSV file in your spreadsheet application of choice

The resulting report displays category performance by day of week, over the selected time period, further splitting out sales value horizontally by hour of day.

The power in this report come from understanding the data displayed in each of the columns:

The ‘categoryID’ and ‘categoryName’ columns correspond to the category IDs and category names that you have setup in Kounta.

The ‘dayOfWeek’ and ‘dayOfWeekNumber’ columns refer to the day of week and day of week number (Monday = 1 through to Sunday =7) for which sales have been recorded for each category:

The numbers to the right of the ‘dayOfWeekNumber’ column display the hours in which sales have been made for the respective category and day of week. Column names relate to the 24 hour time format, i.e. 1 – 1am through to 24 = midnight.

‘Accounting Average’ and ‘Trading Average’ display averages for the entire day’s trading for the respective category:

Accounting Average shows the total sales over the day divided by the number of hours in the day that the site was open. The Trading Average takes into account hours in the day when there was no trade, and adjusts accordingly, for example if a site was open on a Friday for 10 hours and sold $200 worth of the category “Bakery” then the Accounting average would be $20. If however all of these sales occurred in 2 hours on that day the Trading Average would be $100.

The “Datapoint” columns display how many data points are available for consideration in both the average calculations displayed and for further calculations. ‘Non-Zero Datapoints’ shows the number of hours within the day that there have been sales. ‘Zero Datapoints’ displays the number of hours within the day where there were no sales.

Whilst there is a lot of data displayed within this report the most effective way to utilise it is to simply look at the at the data in the columns up until the “Total”. Through looking at the sales by hour, by day, by category you can schedule the staff required to satisfy sales demand. You wouldn’t, for example, want 5 kitchen staff working for the hours of the days when you mostly make bar sales.

Please take some time to investigate your sales data using this report. If you have any further questions please reach out to one of the team, we are more than happy to work through your own data and help you optimise your staff profile accordingly.