Retailers globally are struggling to staff their stores. Even with a 12.5% increase in starting hourly wages and lucrative $5,000 to $10,000 signing bonus, many stores still fail to offer a consistent shopping experience.
How is customer experience so inconsistent despite a 12.5% higher spend on staffing?
Consistent store experience can contribute to a revenue growth of over 10% according to 68% of businesses. In contrast, inconsistent experiences can lead 73% of consumers to switch brands. Rising labor costs make having an effective labor management solution even more important for retailers.
Labor management isn't just about scheduling staff
Many assume that labor management is all about scheduling the right amount of staff. It's only the first step. Effective management is about dynamically deploying associates to the right place.
POS data only captures successful sales, not those who abandon their carts
Relying solely on Point of Sale (POS) data for staffing decisions overlooks a crucial aspect: potential sales lost due to in-store issues. While POS data reflects successful transactions, it doesn't account for the frustrated shoppers who leave due to insufficient staff support, long queues, or empty shelves.
Key limitations of POS data for staffing include:
- Missed Opportunities: High sales days can still have countless unseen lost sales from unassisted customers or empty shelves.
- Reactive Approach: POS offers historical insights, making staffing always a step behind actual needs.
- Misinterpretation Risk: Drops in sales might result in reduced staff, compounding the very issues causing the sales dip.
For a holistic labor management strategy, retailers need more than just sales data; they need insights into the entire customer journey, including those who walk away unsatisfied.
Stores experience sudden changes
Static scheduling and task assignment makes it hard to respond to sudden changes in customer flow, product demand, and other unexpected needs. Traditional labor management often focuses on static scheduling, where staff is assigned to specific areas without considering real-time needs.
This can lead to overstaffing in some areas and understaffing in others, resulting in poor customer service and inefficient use of resources.
Without a dynamic system that considers real-time data, retail locations struggle to adapt to sudden changes in customer flow, product demand, or other immediate needs. This lack of flexibility can lead to missed sales opportunities and a diminished customer experience.
The bigger the stores, the harder to oversee what’s happening
In large retail spaces, effective oversight is tough. Even the most competent managers can't monitor every detail. Often, they depend on staff to spot and tackle in-store issues. Yet expecting this proactive approach, especially from minimum wage employees, can be unrealistic.
Hoping for customers to highlight concerns is also unreliable. Most issues go unnoticed and unreported. Consequently, managers and team leads find themselves frequently patrolling aisles, watching for everything from spills to potential thefts.
This manual supervision not only diverts attention from other tasks but also poses risks. Sole reliance on human monitoring can delay solutions, lead to inconsistencies in the shopping experience, and introduce potential errors or biases in staff assignments.
Why dynamic dispatch is currently impossible
1: Existing data are not directly actionable and requires extensive training
Managing and scheduling store staff is a daunting task for managers. Juggling the availability of 100~300 team members while ensuring they align with the store's needs is far from straightforward.
To avoid overtime pay, everyone should work at most 8 hours a day and 40 hours a week. Factor in 15-minute breaks and 30-minute lunch breaks, and managers must ensure staff coverage at all times.
Large retail stores often have 8 Human Resource(HR) personnels and team leads spending up to two weeks just to schedule their 300 staff members.
Data from beacons and lidar currently provides fragmented insights. Interpreting this information to determine staffing needs is time-consuming and complex. It's no surprise that many managers turn to POS data as a staffing guide. While not ideal, it's simple and allows them to focus on meeting KPIs.
2. Inconsistent data accuracy makes it hard to replace POS data
Even when store managers try to use data from legacy traffic counters, the accuracy is not reliable enough to plan out the scheduling of 100+ staff. Most legacy traffic counters fail to capture shoppers in the shadows, big crowds of shoppers, or when someone squats down to look at the bottom shelf.
All these situations frequently happen in retail. For example, a person was standing in the medicine aisle for 10 seconds and squatted down to read through descriptions of the bottom shelf products for 5 minutes. The store manager will end up disregarding this shopper due to lack of visibility.
To get the necessary accuracy, retailers would have to opt for the most advanced legacy traffic counters which can cost up to $1,000 per 20 square feet. These will give the accuracy in imperfect condition, but the intensity of CAPEX makes it hard to scale.
What’s worse, these custom hardwares require specific vendor technicians for installation, construction, and maintenance. Per their availability, retailers might end up waiting for weeks.
3. Most stereo solutions only tell how many people are in the store
Stereo vision solutions, often adopted for in-store analytics, provide a bird’s-eye count of visitors. However, mere numbers miss out on the nuanced dynamics of in-store experiences. Here are the challenges posed by such narrow data:
- Lack of Context: Knowing a store is busy doesn't necessarily inform about the specifics. Are customers clustering in a particular aisle? Is there a long queue forming at one of the counters?
- Missed Engagement Opportunities: Without insights into customer behavior, staff can't proactively assist those contemplating purchases or appearing lost.
- Unaddressed Frustrations: A crowd in an area may indicate a popular promotion, but it could also signal an out-of-stock product, a missing price tag, or other issues that need immediate attention.
A modern retail environment requires more detailed insights. To optimize staff interactions and rectify in-store issues, managers need to know more than just head counts; they need real-time, actionable data about customer behaviors and needs.
How AI ensures optimal store operations
There are many AI solutions available for retailers but not every solution can deliver results in optimizing store operations. Here are 3 things that make a successful AI solution.
Guarantee 24/7 staff performance oversight
At the end of the day, successful retail labor management is making sure the staff are performing the right duty to create delightful in-store experience. Retail industry leaders are using vision AI to oversee the entire store operations without a blindspot or disruptions, breaks, or PTOs.
Effective retail labor management is about ensuring staff perform the right tasks for a seamless in-store experience. Leading retailers use vision AI to monitor store operations, covering all areas without the constraints of breaks or personal time off.
Stores often see varying shopper numbers, leading to long queues. For instance, a checkout lane needs 7 staff for optimal service but only has 6. The AI manager can identify an idle staff member from a less busy bakery zone. It then alerts the idle bakery employee to assist at checkout. With AI's guidance, the store can redistribute staff to manage queues without negatively affecting operations or hiring extra help.
Advanced AI tools also recommend ideal staffing by analyzing hourly traffic, wait times, and the required staff numbers. Rather than managers sifting through fragmented data like heat maps, traffic counts, or PoS insights, AI pinpoints optimal staffing arrangements. This approach ensures top-tier checkout experiences without the need for hiring more staff.
Enable store managers focus on hitting KPIs
Store managers biggest and most impactful responsibility is hitting store-wide KPIs. Unfortunately, store managers are already stretched thin, juggling daily tasks and mentoring hundreds of staff. In some cases, brands faced fines of $200 million due to managers' unpaid overtime.
Overseeing 200,000 sq. ft. big-box stores leaves little room for adapting to new technologies.
Enter AI virtual managers. They handle routine operational tasks, swiftly address issues, and manage staff allocation. Advanced AI solutions can further learn to assist with strategic objectives, from smart loss prevention to demand-based HVAC adjustments. By leveraging AI, managers can focus on building a positive store culture, minimizing turnover, and meeting crucial KPIs.
Overcome labor challenge and ever-soaring staffing cost
Labor expenses typically consume 20% of total retail revenue, often outpacing inventory costs. As minimum wages rise, retailers grapple with balancing operational costs while maintaining excellent shopping experiences.
Enter the AI manager. It doesn't require bonuses, PTOs, 401Ks or other benefits, also eliminating concerns about labor union challenges. Remarkably, the cost of an AI manager is just half of a regular team member's wage. This is achievable as retailers can deploy using their existing camera systems, without onsite visits.
Companies like Zensors employ technologies that keep network bandwidth needs minimal, sidestepping new infrastructure expenses.
Bring labor spend under control with AI managers’ data-driven decision making today.
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