Staff Cost Reduction Ahead

The ongoing coronavirus crisis adds to deep dips in store traffic. As retailers look at staff cost reduction to minimise the negative effect on their bottom line, this case study provides insights beyond a simple staff cost vs. sales ratio.

Times are tough for retailers. Changing shopping behaviours, increasing online sales and escalating consumer expectations of assortment, store design, services and shopping experience make retailers’ life harder and chances of operating a profitable retail format less likely. On top of all those already challenging factors, retailers are now faced with a coronavirus outbreak that poses a threat to health and life of consumers and further reduces traffic in shopping malls and on high streets.

When consumers and turnover fail to materialise, retailers naturally look to cutting costs. Staff cost accounts for a substantial part of operating cost and is therefore often the first cost block retailers want to cut. And for chain stores, the staff cost ratio versus net sales seems to remain the holy grail of KPIs for determining staff cost reductions.

staff cost coronavirus

Relevant Retail KPIs for Executing Staff Cost Reductions (Graphic: Heike Blank)

I have experienced retailers reduce staffing to a minimum manning factor of 1.1 to 1.2. This means that during 10-20% of opening hours stores were operated by two members of staff, while for the remaining 80-90% of opening hours only one staff member operated the entire store.

The danger of using staff cost ratio as the sole KPI for executing staff cost reductions is that this is often the beginning of a self-fulfilling downward spiral. Turnover drops, staff hours are reduced. Part timers and temps that were hired to provide support for high traffic times are the first to go. The share of staff hours available for actively serving customers goes down, conversion rates and average tickets follow. This accelerates turnover erosion, which leads to further staff cost reduction and so on.

Case Study: Smarter Staff Cost Reduction

One approach that can help make smarter decisions when the coronavirus outbreak and other disruptions negatively affect store traffic, is to understand the format-specific correlation between Average Ticket Value (ATV) and Service Factor (number of visitors  each staff member has to serve on average per hour) as well as between Conversion Rate (CR) and Service Factor (SF).

ATV & Service Factor Brand B (Graphic: Heike Blank)

ATV & Service Factor Brand B (Graphic: Heike Blank)

Almost every retail format suffers in terms of ATV when the service factor is increasing, either through staff cost reduction beyond traffic losses or through increasing traffic with staff hours remaining unchanged. Higher service factor means less time per customer, hence lower UPT (unit per transaction) or lower priced items, both of which manifest in a lower ATV.

With the correlation between CR and SF the picture is not quite that clear. This correlation differs enormously from format to format. Self service formats (super markets, discount fashion retailers etc.) depend less on service factor than consulting-intensive formats (e.g. luxury brands, brands catering to elder consumers who need more support, car dealers etc.). Brands with high brand awareness and high desirability are less dependent on service factor than less desirable brands. Footwear retailers are more dependent on service factor than apparel retailers. Retailers catering to generations X, Y or Z are less dependent on service factor than retailers catering to baby boomers.

staff cost reduction coronavirus

Conversion Rate & Service Factor – Brand A (Graphic: Heike Blank)

Above graphic shows the correlation between conversion rate and service factor of a consulting-intensive brand retail portfolio that caters to silver agers and has been suffering from brand strength erosion for a couple of years.

The more visitors per hour each staff member has to serve, the lower the conversion rate. On average, each staff member has to serve 16 visitors per hour trying to turn them into buying customers. This means on average they would have 3 minutes 45 seconds for each person who enters the store. Achieving an average conversion rate of 11.2% they manage to ‘convert’ 1.8 of the 16 visitors into buying customers.

However, the store with lowest service factor (5.5) but a very high conversion rate (21.8 %) generates 1.19 transactions per staff hour and the one with the highest service factor (40.2) but quite a low conversion rate (6.3) accounts for 2.53 transactions per staff hour. Thus, part of the decreasing CR is outbalanced by an increasing number of transactions per staff hour. But, it is essential to know which staff hours are the efficient ones, and which ones can be cut without additionally stressing top line.

Conversion Rate & Service Factor - Brand B (Graphic: Heike Blank)

Conversion Rate & Service Factor – Brand B (Graphic: Heike Blank)

Brand B caters to a significantly younger audience, offers a high share of self-service products and suffers from low traffic in the majority of its locations. Brand B’s sales staff has to serve on average 12 visitors per hour which means they have on average 5 minutes available per visitor. With an average conversion rate of 8% they turn on average 1 out of 12 visitors into a buying customer.

The Empty-Store-Effect

Interestingly, Brand B is not that dependent on its service factor. Conversion rate shows the same decline as Brand A but only up to a SF of 10. After that, the CR remains almost stable for stores up to the highest SF of 23. The negative impact of staff cost reductions for Brand B will be significantly lower than for Brand A.

We call this stabilisation phase the ’empty-store-effect’. Stores lacking traffic suffer from 2 negative effects:

  1. Empty stores almost repel other customer from entering a store. Many consumers do not cope well with the undivided attention of the entire store staff.
  2. Staff working in a store with little traffic is less efficient than staff in high traffic stores. Operating an empty store is boring, staff feels useless but at the same time suffers from a high stress level, knowing that they will not be able to achieve the daily sales plan. This reduces motivation and confidence. Staff starts entertaining themselves. At first they look for other job tasks like merchandise maintenance, decoration, cleaning, reporting, stock control or stock taking. But once all this is done, they inevitably turn to other kinds of entertainment: chatting with colleagues, friends and family, managing family-related tasks etc. Once they settle into their way of entertaining themselves, they begin to see store visitors as a disruption.

But the empty-store-effect can be an advantage when you are forced to reduce staff cost. With increasing service factor (increasing number of visitors, each staff member has to serve) staff starts regaining motivation, improves customer service and sales performance. This effect takes place when traffic increases while staff hours remain unchanged. But to a certain extent this also works when staff hours are reduced to match lower traffic.

staff cost coronavirus

Footfall Utilization Index and Staff Cost Index (Graphic: Heike Blank)

Above graphic shows how the Footfall Utilization Index (FUI) develops in relation to the development of staff hours versus last year. The FUI shows how well a store team managed to make best use of a given traffic development. If turnover development versus last year exceeds traffic development, the FUI scores > 1. If turnover grew slower than traffic or if turnover lost more than traffic versus last year, the FUI scores < 1.

An effect we frequently observe is that the FUI increases even though staff hours were reduced. Vice versa, stores that benefit from increasing staff hours don’t manage to make use of the added manpower with same efficiency as the original team, hence the FUI drops.  In our example only two stores (store 11 and 12) managed to efficiently integrate new staff hours and thus outperformed the increase in staff hours by an even higher increase in FUI.

And there are more good news when looking into staff cost reductions. The effect already explained for brand A works in favor of most retailers we worked with in the past: The number of transactions per staff hour is increasing with increasing service factor. Thus, the more customers a staff member has to serve per hour, the larger the number of visitors they manage to turn into buying customers.

Transactions per Staff Hour & Service Factor (Graphic: Heike Blank)

Transactions per Staff Hour & Service Factor (Graphic: Heike Blank)

The store with lowest service factor (3.9) but a very high conversion rate (17.7 %) generates 0.65 transactions per staff hour and the one with the highest service factor (22.9) but quite a low conversion rate (7.7) accounts for 1.76 transactions per staff hour.

Cutting staff cost is neither enjoyable nor easy. Reducing overall staff hours without reducing staff hours at high traffic times is a heroic deed not many retailers are able to perform. And it works only if the stores are not already working at minimum staffing that just covers opening hours but does not allow leeway for extra hours at high traffic times.

If too undifferentiated or too frequently executed, it can become the starting point to the retailer’s doom, not only in times of coronavirus. Instead of cutting down staff cost in proportion to turnover, retailers should invest time and management attention into taking store-specific, differentiated decisions, taking KPIs such as traffic, conversion rate, average ticket value, service factor and staff productivity into account. And of course close monitoring of the effects on those KPIs is necessary to allow for timely corrections and adjustments when needed.


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-shops in 23 countries in Europe, the Middle East and Asia make her an expert in expansion and brand building. Read more of her work here and connect with her on LinkedIn.

Leave a Comment

Your email address will not be published. Required fields are marked *