While excellent staff and service are what distinguishes brick and mortar from online retailers, staff performance is often overlooked in store evaluations. Two new KPIs help account for the human factor.
When evaluating a retail portfolio, very often mangers only look at the financial results of a store. But excellent service, competent and dedicated sales staff are a key differentiator of brick and mortar retailers. A fair and objective store staff performance evaluation is therefore increasingly important. This article introduces two new KPIs that help to fairly evaluate staff performance and to determine optimal staffing for each store in your portfolio.
After decades of focusing on store operations efficiency with the sole purpose of optimising staff cost, retailers are finally beginning to invest into their store staff to make a difference. According to a PWC consumer study, 75% of consumers appreciate attentive, kind, empathic and competent sales staff. Unfortunately, about 60% state that they had to actively look for sales staff in order to get help. And only 38% of sales staff approached were able to provide relevant information about a particular product.
Breuninger, for instance, is developing a mobile application that helps sales staff better provide appropriate information to customers, both regarding specific products and the availability of a product in their central DC or other Breuninger branches. Other retailers invest in better wages for top sales staff and a clear, performance-driven career path to motivate staff and incentivise excellent sales service.
Adding a high level of objectivity when evaluating performance of your store teams is a clear competitive advantage and helps to reduce staff turnover.
Store Staff Performance Evaluation
One of the key KPIs for evaluating performance of a store team is the Footfall Utilization Index (FUI). It tells you how well each store team made use of the existing traffic development. This is one of the most objective KPIs, as it compares each store with itself, measuring whether its turnover development was better or worse than its traffic development in any given period of time.
Another relevant KPI I’ve recently introduced is the Manning Factor (MF). The MF shows the average number of staff members on the shop floor during any opening hour. The MF is calculated by dividing the number of working hours spent, by the number of opening hours in a given period.
Let’s look at an example. A store that is open for 10 hours a day and 308 days per year needs to staff 3,080 opening hours. A typical full-time employee/equivalent (FTE) with 25 days vacation, a 40-hour work week and an absence rate of 10% brings 1,699 hours per year into the equation. This means this store will need minimum 1.81 FTE (= 3,080/1,699) just to cover the opening hours with 1 person on the shop floor at any given time and thus reaches an MF of 1.
But the MF is not only helpful when defining the minimum staffing requirement for your store. It’s also an essential ingredient for calculating two additional KPIs that help to evaluate staff performance and come in handy when identifying turnover potential through better staffing: the Service Factor (SF) and the Served Area (SA).
The Service Factor (SF)
The SF tells us how many customers each staff member has to serve on average per hour to generate the best possible turnover. It is calculated by dividing the average traffic per hour by the Manning Factor (the average number of store staff available at the sales floor during its opening hours).
The Served Area (SA)
The SA, on the other hand, helps understand how much retail space each member of staff needs to serve and maintain on average. This gives an indication of the ratio between productive and non-productive efforts of store staff (such as stock intake, pricing, merchandise presentation and its maintenance).
Most retailers compare conversion rates (CR), units per transaction (UPT) and/or average ticket value (ATV) and sales per hour (SPH) in order to evaluate staff performance. Don’t get me wrong! These are great KPIs but they do not take 2 of the major performance drivers of sales staff into account: Traffic and Net Selling Space. Analyzing Service Factor and Served Area when evaluating conversion rate and sales per hour, helps to truly understand staff performance background.
Service Factor and Served Area in Practice
We’ll use store A and store B to illustrate how to use these two KPIs in practice. Both stores are of similar size, approximately 1,600 sqm.
Unit Store A Store B Store B vs A Net Selling Space sqm 1,600 1,605 0.3% 4-Wall Contribution % 18.2% 17.9% -1.6% Traffic per hour (#) # 28.5 24.5 -14.1% Conversion rate % 28.9% 28.7% -0.8%
At first sight, we might conclude that store A is performing slightly better than store B. It generates a slightly better 4-Wall Contribution, attracts 14% more visitors per hour and manages to turn almost 1% more visitors into customers. And from a retail portfolio point of view that would be a correct conclusion.
But if we take the performance of the store teams into consideration by adding Service Factor and Served Area to the equation, we might arrive at a different outcome.
|Unit||Store A||Store B||Store A vs B|
|Net Selling Space||sqm||1,600||1,605||0.3%|
On average, store B has 4.5 staff members on the shop floor at any given time. With 5.8 staff members, Store A clocks around 23% more available staff hours. Considered alongside the 14% higher traffic of store A, this means that each team member of store A has to serve on average 4.9 customers per hour, while staff of store B has to serve 5.5 customers per hour. At the same time, staff of store B has to serve and maintain on average 360 sqm which is around 30% more than staff of store A needs to attend to.
Although the overall store performance of store A looks better than that of store B, an evaluation of store staff performance paints an entirely different picture. Team B is far busier and has to invest more effort to achieve results similar to team A. Team B is thus doing a far better job, each team member serving on average more consumers and maintaining significantly more sales floor while achieving similar results in terms of conversion rate and 4-Wall-Contribution.
Both KPIs, in combination with the FUI and the staff development index and more common KPIs like CR, ATV, UPT, ASP or staff cost – turnover ratio, help understand the true performance of your store teams and see the challenges they face. In times of double digit traffic losses, reduction of staff cost is often the only answer to shrinking turnover, which increases the workload for the remaining staff members and puts their motivation at risk.
Using a holistic set of KPIs will change your vantage point as a manager. You might discover hidden champions among your store teams; stores that face significant traffic losses compared to last year without loosing turnover. Or you might identify Stores that achieve lower conversion rates due to fewer staff hours compared to last year and a higher effort to maintain the sales floor.
Analyzing your portfolio by calculating, comparing and correlating these KPIs with one another offers new findings on how to improve store performance. They can also show what the optimum staffing level for each store is. How many customers per hour does the store get and how many customers can one staff member serve in order to achieve the best conversion rate? How much store space can one staff member serve without loosing too much time better spent helping customers? How can store operations be improved to create more productive time for staff rather than just to reduce cost?
Some stores will require an investment in coaching and encouraging teams, others need more control and more precise stipulations and a few stores will need a change in management. But you will also learn which stores might perform better with additional staff hours and which stores might need a better staff planning to match changing footfall patterns. And there will also be stores whose major factor of performance improvement will be to renegotiate rent and utility cost that are far too high for the given footfall potential. But that’s the subject of another article!
About the Author:
Heike Blank has worked for big organisations such as VF Europe and s.Oliver but also for niche brands such as Ecko Unltd. and Zoo York in top executive positions. Her extensive experience with opening and managing own retail, partner stores, concessions and shop-in-shop in 23 countries in Europe, the Middle East and Asia make her an expert in retail portfolio management and expansion. Get in touch with her via e-mail or read more from her here.