A key use of workforce optimization in a retail setting is to align staffing with customer demand in order to effectively serve customers and maximize sales.
There’s a common perception in retail that workforce optimization is primarily about eliminating waste and enabling a store to achieve its goals with the least labor possible. Many also believe that workforce optimization exercises produce the greatest benefit when applied only in larger retail environments.
In fact, workforce optimization, when supported with the right data and analytics, can be a key strategy for increasing sales potential—and in retail environments both large and small. In this case, the focus is on aligning a retailer’s workforce with its store traffic, and particularly when that traffic includes the shoppers who are most likely to buy.
At a recent presentation at NRF, a Swarovski executive described his organization’s initiative to align its workforce with its sales traffic. The company found it was missing sales opportunities by not increasing staff on weekends, when sales and traffic are at their peak. By using available data and a series of studies, the organization adjusted its staffing and scheduling and generated a significant increase in sales as a result.
Aligning your workforce with your store traffic starts with a solid understanding of who is visiting your store—and not just the number of shoppers, but the number of shoppers in key segments. While many retailers identify customer segments specific to their store and brand, generally there are three baseline customer segments:
“Ready to buy” – These shoppers have come to the store with the intent to buy. They will likely linger the longest and push hardest for service. In fact, they may not leave unless the wait for service is too long. If you don’t have the staff in place ready to service everyone in this segment, you are losing sales.
“May buy” – Shoppers in this segment may not push for service, but they will talk to someone if they are approached. They may be reviewing prices, looking at the items and options available, and/or comparison-shopping—and the right level of interaction could push them into the “ready-to-buy” category. A store with staff available to answer questions and give feedback can convert some percentage of these shoppers to customers.
“Just looking” – Shoppers in this segment may be along for the ride with friends or family, looking around, thinking about future purchases, or getting an idea of what the store has in stock. They likely aren’t interested in spending much, if any, time with a salesperson.
While some take the view of attempting to convert all three baseline segments from ‘traffic’ to ‘customers’, your primary goal should be to convert all of the “ready to buy” and as many of the “maybe buying” as possible. One of the keys to doing so is making sure you have the right amount of staff and the right staff skills in your store when shoppers in those segments visit the store.
At a high level, here are some steps you might take to build an optimal staffing model that is well aligned with your store traffic patterns by leveraging an analytics strategy and platform.
Study customer traffic in your stores. In store sensors can measure traffic patterns and when analyzed with transactional data can be used to create analytical models to predict customer visits. These models can also use other data sources to increase accuracy of traffic predictions based promotions and even external factors like weather forecasts. In addition, ‘customer journey analysis’ can be done by visiting stores and shadowing customers to track their behavior – where they visit in the stores, length of customer service, time spent in fitting room – these, along with many other factors, can tell you volumes about what helps to convert traffic into customers, and typically provides insights into which customers fall into each baseline segment.
Build labor requirements based on activities and time required per customer segment and determine the typical amount of time spent servicing each type of customer. Don’t rely on transaction data alone, as many customers receive service but don’t end up purchasing anything. In-store observations or studies of store film can supplement transaction detail to help define the percentages of customers in each segment as well as the amount of time shoppers spend in the store and their tendency to seek service or interact with staff. An important distinction to note is that in-store studies of customers will only show you the actual amount of time spent servicing them – not the time that should be spent servicing them. The sampling helps you to determine the gap between the actual and desired customer experience, and the value of closing that gap. The time requirements for staffing, however, should be based on the activities that support the desired customer experience – ideally by segment.
Build a preliminary staffing schedule. Use the time requirements by segment, along with the number of customers you expect to visit the store in each segment, to provide enough capacity to convert all of the “ready to buy” shoppers. You can also factor in the buying patterns of your various customer segments to determine more accurate staffing.
Account for the “may buy” shoppers. Conduct a break-even analysis to determine the percentage of “may buy” shoppers you can convert, and adjust your staffing plan to provide service that is most likely to convert a percentage of this segment.
Build your ideal scheduling model by hour of day and day of week. Keep in mind that weekday shopping patterns will look much different than weekend shopping patterns and that seasonality should play a significant role in your long-term planning.
Align your workforce to this model. At this point, you also want to make sure your best and most experienced sales professionals are available at peak times for your “ready to buy” shoppers. This may require careful communication and significant change management (see below) if experienced or highly tenured salespersons must change their hours worked.
Test and learn. Designate a few test stores and experiment with staffing levels in those locations, while maintaining your “current state” in control stores. Be sure to measure the impact on sales, conversion, basket size, and wage costs of associates in the test stores. Adjust the model until your stores hit an optimal sales and profit gain relative to associate staffing cost.
Communicate, communicate, communicate. Provide transparency to store managers and staff of key indicators (e.g., customer conversion rates, sales to staffing cost ratios) through timely dashboards to understand the impacts of changes and focus teams on continuous improvement. Seek their input and involvement on what is working, and why.
Numbers (i.e., appropriate staffing levels) are an important outcome when trying to match staffing to peak traffic times, which may not coincide with “normal business hours.” While your traffic study will provide the necessary detail, it is easy to figure that in many scenarios, fewer people may be shopping at 10 a.m. on a Wednesday than at 1 p.m. on a Saturday. Given a choice, though, many retail workers may prefer regular Monday to Friday, 9 a.m. to 5 p.m. work schedules—which may not match peak traffic periods.
So how do you optimize staffing when peak traffic occurs in the evenings or on weekends? Many retailers lean on part-time workers—including college students and people working a second job—to cover “less desirable” night and weekend shifts. This category, however, tends to turn over faster, meaning that you may not be fielding your best staff when you have the greatest opportunity to convert traffic into customers and optimize sales.
When aligning your workforce with your store traffic to maximize sales, remember it isn’t only about the numbers. In many retail environments, skills and knowledge will play a key role as well—particularly when trying to convert “may buy” shoppers. Longer-tenured associates may tend to feel they have a “right” to better or more convenient shifts, but this can present a conflict when trying to position your best salespeople during peak traffic periods.
If your updated staffing model requires asking workers—particularly long-tenured workers—to begin picking up even a couple of evening or weekend shifts, you will want to give careful consideration to your change management approach. Additionally, if you must rely on part-time help during these critical periods, it is important to have appropriate training, job aids, and performance management in place so ‘newbies’ can quickly learn and supervisors can effectively coach.
You won’t convert every customer you talk to, but if you’ve taken the right steps to align your workforce with your store traffic—and particularly the traffic patterns of your “ready to buy” shoppers—then you are in a good position to increase your conversion rate and ultimately lift your sales.
While it is easy to make educated guesses about peak traffic, today’s analytics and contemporary workforce optimization methods can help increase the precision of your staffing model to a level that has a marked impact on sales—just as it did for Swarovski.
For more information on how to create a customer-centric data-driven staffing model for your retail stores, please contact us.
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