New Overtime Laws in Retail

New Overtime Rules in Retail
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NOTE: Congress voted this week to delay the start of this law to give small businesses more time to prepare. 

December is typically the most profitable season of the year for retailers. The holidays mean increased in-store traffic, sales, and opportunities to boost bottom lines before the year ends. However, the United States Department of Labor (DOL) is shaking things up for small businesses this December with the implementation of new overtime regulations that are expected to significantly raise labor costs.

Specifically, the DOL has raised the pay threshold of employees who receive overtime protections, meaning that retail managers and other staff members who were previously categorized as “exempt” are now entitled to overtime pay. This new law is estimated to extend overtime protections to more than four million employees, putting a big strain on businesses’ labor budgets.

Before the holidays get into full swing next month, it’s crucial that retailers begin implementing strategies to help minimize the impact of these new regulations on labor costs. Determining opportunities to accomplish this can be found by leveraging a retailer's data resources. By utilizing the same data-driven principles in place for supply chain optimization, retailers can avoid unnecessary and expensive labor increases by optimizing labor and staffing.

Correlating Data Pools to Help Anticipate In-Store Traffic

On a daily basis, retailers are actively collecting valuable information on sales, store traffic, and overall performance — and this information goes back to activity last month, last year, and even a decade ago.

By monitoring changes and fluctuations based on day and time of year, retailers can use this historical sales insight to anticipate store traffic in real-time.

This measurement can be made even more accurate by layering in accredited data from third parties. For example, taking into account information on weather can help retailers understand what shopping behavior is like just before a big blizzard.

Furthermore, using geospatial and census data can help retailers understand their customers by region, using this information to optimize their offerings, implement more targeted promotions, and therefore expect a boost in in-store traffic. All of these factors provide a deeper understanding of how a store performs under specific situations and during particular times of day and year. Ultimately, this helps the retail manager implement a more efficient staffing strategy, ensuring the store has enough (and not too many) bodies on the floor during expected busier times and that they’re not wasting money on labor during slower shopping periods.

Understanding Employee Performance Means Optimized Staffing

Drawing correlations between individual store data like foot traffic flow, volume, and revenue, with information from HR data like employee performance and payroll costs can help retail managers optimize who to schedule for different day parts. Decision-makers at the store level can utilize information on employee skill sets and costs per hour of labor against data revealing demand associated with each shift, allowing them to make more strategic decisions on who will be most valuable, effective, and efficient on specific days and during particular time periods.

Furthermore, this data can help retailers go a step further and optimize their teams and employee placement by department. By analyzing the information they already have on hand regarding in-store promotions and sales insight, managers can determine which employees will be most productive in each area of the store. Additionally, these managers can also determine which cohorts of employees complement each other’s skill sets, and therefore work well as a team. By this measure, the manager can ensure the most productive team is scheduled during the busiest or most demanding time of day. When looking to combat the costs associated with the DOL’s new overtime regulations, analyzing a retailer’s data can help increase productivity by strategically placing the best-fit employees in the areas where they will make the most significant contribution.

Optimizing Conversion Rates and Increasing Buyer Spend

All retailers understand how challenging it can be to turn in-store browsers into buyers — and it’s even more difficult to convince them to spend more money. Leveraging transactional data with in-store traffic information can help retailers effectively determine those areas in which they’re performing well, and those areas that can be improved upon. Based on this analysis, retailers can figure out which groups of items are most frequently purchased together and strategically place these products near the checkout lines to increase average buyer purchases.

By seeing a comprehensive picture of a store’s conversion rates, retail decision-makers can gain unique insight into how traffic, staffing, and profit are related — and how to improve upon this equation to offset additional labor costs from the new overtime regulations.

Beginning December 1st, 2016 retailers will be presented with the complicated challenge of dealing with new overtime regulations, that will likely result in higher labor costs. However, they can effectively navigate and combat these issues if they look at their data to better understand the product and staff performance, and use this information to continuously improve their processes.