Staff Roster Planner
Designed for Marketing Managers
everaging the footfall data and predictive analytics, it helps operation managers with smart staff allocations based on golden ratio of footfall-to-staff.
Challenges
“Without the use of footfall data, staff planning for a shopping mall can be inefficient and lead to compromised customer service or unnecessary labour costs.”
How Does the App Work?
Step 1: Configure the staff data
Import or configure a comprehensive list of employees, which includes their permitted working hours and corresponding wages, for the purpose of staff planning. This allows the app to effectively generate staff schedules based on the available workforce.
Step 2: Identify the optimal footfall-to-staff ratio
By using data on foot traffic patterns and peak periods, mall managers can identify the optimal footfall-to-staff ratio and adjust staffing levels and allocate resources more effectively to improve operational efficiency.
Step 3: Plan for the staff scheduling using predictive analytics
The app features built-in Artificial Intelligence (AI) that can intelligently plan staff rosters based on staff profiles, forecasted footfall, and sales data, to ensure a satisfactory shopping experience. Manual adjustments to the roster can be made for any functions at any time period.
Step 4: Review Staffing Level
Constantly reviewing staffing levels can help identify potential issues such as understaffing, which could result in missed sales opportunities due to inadequate customer service, or overstaffing, which can lead to unnecessary allocation of resources and increased labour costs.
Features
Staff Roster Planner
Staffing Level Review
Staff Data Configuration
Staff Level Review
For Management Review
The review of staffing levels in a shopping mall is essential in ensuring that there is an optimal balance between customer service and labour cost to achieve optimised shopping experience.
Staff Data Configuration
Getting the Staff Data Ready
A workspace for operation managers to import staff data and ensure the data readiness for staff planning purpose.
Case Studies
See all Case Studies in United StatesThe cleaning labour cost has been reduced by an average of 12%.
“The system can assist in optimising staff scheduling through footfall-based cleaning scheduling, which allows for effective reduction of operational costs.”
A Shopping Mall in Argentina
