If you’ve gone shopping this holiday season, you may have had the following experience.
You go into a store looking for a gift but need help from a salesperson. Maybe you need more information on the product, or perhaps you need help finding the right color or size. You look around the store, but you can’t find anyone. Giving up, you leave the store without making a purchase.
If that sounds familiar, you aren’t alone. The proportion of customers who typically leave a store because of poor service is not negligible. Prior research shows that 33% of customers who experienced a problem were not able to locate sales help when they needed assistance, and 6% of all possible sales are lost because of lack of service.
Effective management of store labor is clearly important, as it impacts sales performance. However, labor-related expenses also constitute one of the largest components of retailers’ operating costs. As a result, there is a widespread tendency to understaff to save on those costs.
But what is the right number of employees? This is a complex question, as retail environments are characterized by volatile store traffic, making it hard to determine the correct staffing levels and often leading to inconsistent service.
The traditional method for determining staffing is sales-driven and depends on store budget allocation. A typical sales-based staffing rule is to match a constant ratio of expected store sales to the number of store associates. However, that rule ignores the fact that retail sales are also affected by store traffic and might result in labor-to-traffic mismatches, which can hurt sales revenue. Retailers can’t reach their full potential in sales if they follow that staffing practice.
Another problem is that shopper demand may be different from past sales, as past sales include only customers who purchased and not those who had an intention to purchase but left the store due to lack of service. As noted above, this is a fairly common scenario.
Matching staff to shoppers
To address this challenge, my colleagues and I developed a method to match store labor with incoming customer traffic in an efficient manner to improve sales performance. Our method is unique, as it goes beyond the focus on past sales at individual stores to leverage performance data across different stores within a retail chain. It enables retailers to derive aggregate labor requirements by using traffic data, point-of-sale data and labor data across stores with similar attributes like store format, product mix and market demographics. Read More