Retail Portfolio Assessment: The Good Ones Into the Pot, the Bad Ones Into Your Crop

What tools are available to help brands choose their most promising retail locations, and identify the not so promising ones? Let’s mobilise some unusual KPIs!

Inspired online stores, a lack of innovation in brick & mortar retail, overpriced retail locations and double-digit traffic losses all contribute to diluting 4-Wall contributions of many brick & mortar stores. It’s therefore no big surprise that we are frequently asked to help brands to pick out the most promising retail locations, to identify locations to shut down and, even more importantly, to identify the ones with potential for store performance improvement.

Finding appropriate measures and actions to improve store performance forms part and parcel of any retail portfolio assessment. This article is about some retail KPIs that help brands to assess their retail portfolio and decide upon its future. I won’t waste  your time with KPIs you know and most likely already use – such as SPSQM (sales-per-sqm performance), 4WC (4-wall-contribution), GM (gross margin), conversion rates (CR), UPT (unit per transaction), ASP (average sales price), ATV (average ticket value), rent and staff cost ratios, sell-through rates (STHR), mark-down ratios (MDR) and this year vs. last year comparisons.

The Not so Commonly Used KPIs

Instead, I’ll focus on less well-known KPIs that don’t only help identify the good ones, the bad ones and the ones with potential, but also help understand what the problem of each store in your portfolio might be:

  1. FUI – footfall utilization index
  2. MF – manning factor
  3. FPH – footfall per hour
  4. SF – service factor
  5. SA – served area and
  6. TC – traffic cost

This post covers FUI and MF. A discussion of SF, SA and TC will follow in my next post.

Retail portfolio assessment and store performance improvementFUI – Footfall Utilization Index

The FUI (see also my previous article on FUI) measures store performance by comparing the footfall development with the turnover development over a given period of time. If the turnover development is higher than the footfall development the FUI is > 1, meaning that store staff did a very good job at utilising the traffic at hand. A store facing traffic and turnover losses can thus still be amongst the top performing points of sale (POS) in case staff manages to make up for those losses by increasing the store’s CR, UPT or ASP.  

Vice versa, stores that are top performers in terms of turnover or GM development could be underperforming when it comes to the FUI. If store traffic increased by 10% vs. last year while turnover only increased by 5%, for example, the FUI would be 0.95, meaning that the store didn’t utilise the given traffic sufficiently.

The FUI is the most objective KPI when it comes to retail portfolio assessment and staff assessment. No more discussions  about a location being better or worse than another, about  two vs. one floor stores, L-shape vs. square layouts, size and visibility of store front and windows, constructions going on in front of the store and other factors potentially affecting traffic etc.  Measured by its FUI, every store compares to itself alone.

However, there is one additional number you need to have a look at in order to understand and assess the FUI and any potential for store performance improvement, and that’s the development of the number of working hours in the same period. If a store didn’t manage to counteract footfall losses and thus shows an FUI below 1, the underlying reason might be that the staff cost budget was cut, which is a common reaction of retail management faced with turnover losses. While this can indeed balance the 4WC of a store in the short term, some retail organisations unfortunately see it as the holy grail of retail management and thus set themselves up for a downward spiral that inevitably leads to further reductions of CR, UPT, turnover and GM.

As wage reductions aren’t a realistic scenario, a store faced with staff cost budget cuts has to reduce the number of working hours of its staff. And this leads to less staff available to serve customers and maintain merchandise presentation, visual merchandising, stock intakes and maintenance and other retail operations. To evaluate whether lowering the staff cost budget is appropriate, we need clarity on what that would mean for the store in question by evaluating a few other KPIs. And one that helps with this assessment is the Manning Factor (MF). 

Retail portfolio assessment and store performance improvement

Manning Factor

The MF shows how many staff members are on the shop floor on average during opening hours. The MF is calculated by dividing the number of working hours spent, by the number of opening hours in a given period.

A store that is open on 308 days for 10 hours each has 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 store will need 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. If the same store spent a total 6,634 working hours, its MF would amount to 2.15 (= 6,634 / 3,080).

I have worked for a couple of brands that already take the MF into account, mainly to facilitate staff planning by defining the minimum staffing required per store. But unfortunately, I have yet to encounter a brand that properly works with the MF by integrating two additional pieces of information that are essential for an objective retail portfolio assessment: the net selling space and footfall per hour. Taking those into account alongside the MF helps understand how much retail space each member of staff needs to serve and maintain and how many customers each has to serve in order to generate the best possible turnover.

Thus, the MF is not only a crucial KPI when it comes to retail portfolio assessment. It is also an essential ingredient for calculating the next two KPIs on my list: the service factor (SF) and the served area (SA), both of which are useful when searching for evidence to support measures for store performance improvement. But this is a story for my next article!

 

All images are excerpts of an illustration by Alexander Zick (1845 – 1907) via Wikimedia Commons.


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 assessment and expansion. Get in touch with her via e-mail to discuss your own USP or read more from her here.

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